Journal of Engineering

Journal of Engineering / 2020 / Article

Review Article | Open Access

Volume 2020 |Article ID 8090521 | https://doi.org/10.1155/2020/8090521

Ocident Bongomin, Aregawi Yemane, Brendah Kembabazi, Clement Malanda, Mwewa Chikonkolo Mwape, Nonsikelelo Sheron Mpofu, Dan Tigalana, "Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art", Journal of Engineering, vol. 2020, Article ID 8090521, 45 pages, 2020. https://doi.org/10.1155/2020/8090521

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

Academic Editor: Jong M. Park
Received03 Jun 2020
Revised11 Aug 2020
Accepted25 Aug 2020
Published10 Oct 2020

Abstract

Very well into the dawn of the fourth industrial revolution (industry 4.0), humankind can hardly distinguish between what is artificial and what is natural (e.g., man-made virus and natural virus). Thus, the level of discombobulation among people, companies, or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption into the industrial sectors remains ill-defined in both academic and nonacademic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technology convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in the education industry (education 4.0), energy industry (energy 4.0), agriculture industry (agriculture 4.0), healthcare industry (healthcare 4.0), and logistics industry (logistics 4.0) was described. The convergence of 12 disruptive technologies including 3D printing, artificial intelligence, augmented reality, big data, blockchain, cloud computing, drones, Internet of Things, nanotechnology, robotics, simulation, and synthetic biology in agriculture, healthcare, and logistics industries was illustrated. The study divulged the need for extensive research to expand the application areas of the disruptive technologies in the industrial sectors.

1. Introduction

In the second decade of the twenty-first century, the world stands on the cusp of industry 4.0 paradigm which has remarkably become global emergence with a core of industrial transformation, revitalization, and development [1]. Simply put, industry 4.0 is the integration of cyber and physical worlds through introduction of new technologies in the industrial fields [2, 3]. In other words, it is a technological revolution in every production system including operator and maintenance [4], which is quite unique from the previous revolutions as shown in Table 1 [59]. Industry 4.0 is the digitization of the industrial value chain which has become unexampled for economic and social development in the recent years [1012]. On the one hand, it allows high-wage countries to maintain their business responsiveness and competitiveness [13]. On the other hand, research and development units are organizationally, personally, and methodically being aligned for innovation competitiveness [14, 15].


TransitionIndustryOperatorMaintenance

Level 1Industry 1.0Mechanical production, rail road, steam powerOperator 1.0
Manual and dexterous work
Machine tools
Maintenance 1.0Visual inspection

Level 2Industry 2.0
Mass production
Assembly line
Electrical power
Operator 2.0
Assisted work
Numerical control
Maintenance 2.0
Instrument inspection

Level 3Industry 3.0
Automated production
Electronics, computers, and IT
First PLC system
Operator 3.0
Cooperative work
Industrial robots
Maintenance 3.0
Real-time condition monitoring

Level 4Industry 4.0
Fusion of virtual, physical, digital, and biological sphere (CPPS)
Convergence of technologies: AI, IoT, VR/AR, big data, etc.
Operator 4.0
Work-aided human-CPS
Maintenance 4.0Predictive maintenance
Use of big data
Statistical analysis
Smart sensors and IoT
Use of digital twins

IT: information technology, PLC: programmable logic controller, CPPS: cyber-physical production system, AI: artificial intelligence, IoT: Internet of Things, VR: virtual reality, AR: augmented reality, and CPS: cyber-physical system.

Industry 4.0 is a data-driven production system which is progressing exponentially while reshaping the way individuals live and work essentially. Therefore, the public remains optimistic regarding the opportunities it may offer for sustainability and the future of quality work in the global digital economy [1620]. Actually, industry 4.0 is increasingly being promoted as the key for improving productivity, promoting economic growth, and ensuring the sustainability of manufacturing companies [2123]. Moreover, it aims to improve the flexibility, adaptability, and resilience of the industrial systems [24, 25].

Industry 4.0 has been considered a new industrial stage in which several emerging or disruptive technologies including Internet of Things (IoT), artificial intelligence (AI), 3D printing, and big data are converging to provide digital solutions [26, 27]. Industry 4.0 is characterized by the mass employment of smart objects in highly reconfigurable and thoroughly connected industrial product-service systems [28]. In this respect, industry 4.0 phenomenon is bringing unprecedented disruptions for all traditional production/service systems and business models (value chains) and hotfooting the need for redesign and digitization of activities [2932]. Tout ensemble is retrofitting and/or redefining the patterns of value creation and annexations, production networks, and supplier base and customer interfaces [3335].

The concept of industry 4.0 is greatly linked to other concepts such as servitization [36, 37], crowdsourcing [38], circular economy (sharing economy) [3944], green economy, and bioeconomy [45]. Besides being complemental to a vast number of existing concepts, the main strength of industry 4.0 is the promises for shorter delivery time, more efficient and automated processes, higher quality, agility in production, and profitable and customized products [46, 47]. Furthermore, it is expected to create extra values as the world is massively experiencing digital transformation [48]. In this regard, industry 4.0 has not only opened windows of opportunity for emerging economies but also brought its own bureaucracy in terms of the main challenges that these changes pose to firms, industrial systems, and policy approaches [49].

The curiosity and the need to contemplate the meaning and concept of industry 4.0 have been ubiquitous among academic and business communities and thus make industry 4.0 to be one of the most important topics in the modern world as a result of digital milestones in the innovation area [50]. So far so good, there are several ambiguities with almost 100 definitions and related concepts of industry 4.0 already in existence among academic and nonacademic literature [51]. In the academic community, engineering has incredibly gained more attention to the industry 4.0 topic than other subject areas including computer science, chemistry, and energy (Figure 1) [52].

The rapid and fascinating adoption of the industry 4.0 topic among academic and business entities has led to the massive use of icon or neologism “4.0” to depict industry 4.0 disruption in the systems, processes, activities, or even industrial sectors. However, the collective numbers and names of the existing industry 4.0 neologisms have remained unclear [53]. In addition, industry 4.0 disruption through the convergence of its technologies has been ill-defined among previous researchers [26]. To this end, the outstanding contributions of the present study are trifold: (1) identify industry 4.0 neologisms used among the academic and business communities, (2) clearly understand industry 4.0 disruption in education industry (education 4.0), energy industry (energy 4.0), agriculture industry (agriculture 4.0), healthcare industry (healthcare 4.0), and logistics industry 4.0 (logistics 4.0), and (3) illustrate the convergence of industry 4.0 technologies in the agriculture, healthcare, and logistics industries.

2. Methodology

A comprehensive literature search was conducted in electronic databases Google Scholar, ScienceDirect, Taylor & Francis, Springer, and Emerald Insight from January 2020 to May 2020 following procedures employed in previous studies [27, 54]. The search was performed independently in all the databases and then combined with “and” operators. The multidisciplinary databases included peer-reviewed journal articles, conference papers, books, theses, working papers, white papers, discussion papers, patents, and reports published between 2015 and 2020. Thus, articles in the returned results were assessed concerning their inclusion in this study, and further searches were carried out at the Google search engine.

The literature search from the databases was done using the following search terms: “Agriculture 4.0,” “Education 4.0,” “Energy 4.0,” “Healthcare 4.0,” and “Logistics 4.0,” On the contrary, the search on the Google search engine was accomplished with the following search terms: “3D printing and Agriculture,” “Artificial intelligence and Agriculture,” “Augmented reality and Agriculture,” “Big data and Agriculture,” “Blockchain and Agriculture,” “Cloud computing and Agriculture,” “Drones and Agriculture,” “Internet of things and Agriculture,” “Nanotechnology and Agriculture,” “Robotics and Agriculture,” “Simulation and Agriculture,” “Synthetic biology and Agriculture,” “3D printing and Healthcare,” “Artificial intelligence and Healthcare,” “Augmented reality and Healthcare,” “Big data and Healthcare,” “Blockchain and Healthcare,” “Cloud computing and Healthcare,” “Drones and Healthcare,” “Internet of things and Healthcare,” “Nanotechnology and Healthcare,” “Robotics and Healthcare,” “Simulation and Healthcare,” “Synthetic biology and Healthcare,” “3D printing and Logistics,” “Artificial intelligence and Logistics,” “Augmented reality and Logistics,” “Big data and Logistics,” “Blockchain and Logistics,” “Cloud computing and Logistics,” “Drones and Logistics,” “Internet of things and Logistics,” “Nanotechnology and Logistics,” “Robotic and Logistics,” “Simulation and Logistics,” and “Synthetic biology and Logistics.”

All the relevant literature studies were downloaded (PDF files) and saved on the computer, but only important literature studies that meet the scope of the present study were considered for in-depth literature study. The first screening was done through evaluation of the title and abstract (TA) followed by full-text (FT) screening for inclusion in the study in terms of availability of requisite information for the present study (Figure 2). The last search was done on 20th May 2020. The search outputs were saved on databases, and the authors received notification of any new searches meeting the search criteria (from ScienceDirect, Taylor & Francis, Emerald Insight, and Google Scholar).

3. Industry 4.0 Neologisms

The concept of industry 4.0 originated from the manufacturing industry purposely to improve the engineering excellence from machine building to informatization [55]. However, nowadays, the concept of industry 4.0 has expanded enormously, and its definition spans beyond engineering, smart and connected machines, and systems and has become a more general concept with mainstream appeal and applicability [56]. This can be evidenced by a multitude of neologisms such as fashion 4.0 and care 4.0. Interestingly, the icon “4.0” and beyond (e.g., “5.0”) have spread exceedingly as witnessed by the fact that the combination of a noun and the icon “4.0” is used to signal and usher in discussions about the future of business and society [53]. In this study, 99 industry 4.0 neologisms were identified and categorized into 6 areas based on the subject of the published literature as depicted in Table 2. However, the previous study done by Madsen [53] reported only 37 neologisms. This alone can divulge that there is an increasing disruptive landscape of industry 4.0 in business, society, service, and industry sectors.


S/NCategoryNeologismReference

1Process, operation, quality, materials, machine, methods, maintenanceSix Sigma 4.0[57]
Service 4.0[58, 59]
Excellence 4.0[60]
Workstation Interaction 4.0, OWI 4.0[61]
Operator 4.0, O4.0[6, 62, 63]
Machine Tool 4.0[64]
Forming 4.0[65]
Robotics 4.0[66]
Lean 4.0[67, 68]
Quality 4.0[69, 70]
Machine Shop 4.0[71]
Value Stream Method 4.0[72]
Maintenance 4.0[8]
Assembly 4.0[73, 74]
Material 4.0[75]
Paint Shop 4.0[76]
Industrial Maintenance 4.0[77]

2Industry (oil and gas, manufacturing, agriculture, engineering and technology, construction, pharmaceutical, textiles and apparel, energy, and web)Fashion 4.0[78, 79]
Airport 4.0[58]
Industrial 4.0[80]
Agriculture 4.0[81]
Farming 4.0[82, 83]
Landwirtschaft 4.0[82]
Pharma 4.0[84]
Industrial Revolution 4.0, IR4.0[8589]
Apparel 4.0[90]
Technology 4.0[91]
Service Engineering 4.0[92]
Construction 4.0[93]
Oil and Gas 4.0[94]
Agri-Food 4.0[9598]
Energy 4.0[99, 100]
Web 4.0[101]
Energy Cloud 4.0[102]
Energy System 4.0[103]
Manufacturing 4.0[104]

3Education and trainingEducation 4.0[105]
Teaching Factory 4.0[106]
Literacy 4.0[107]
Learning 4.0[108]
Teaching I4.0[109]
Academic Course 4.0[110]
University 4.0[111]
University in the Form 4.0[112]
ECLECTIC 4.0[113]
Human Capital 4.0[114]
Engineering Education 4.0[115]
iNduce 4.0[116]

4Logistics, supply chain, services and financial inclusion, healthcareHealthcare Logistic 4.0[117, 118]
Logistics 4.0[119]
Healthcare 4.0, H4.0[120]
Health 4.0[121]
Hospital 4.0[122, 123]
Electric Utility 4.0[124]
Logistics Center 4.0[125]
Market 4.0[126]
Marketing 4.0[127, 128]
Maritime 4.0[129]
Shipping 4.0[130]
Enterprise 4.0[131]
Supply Chain 4.0[132, 133]
Care 4.0[134]
Retail 4.0[135, 136]
Post 4.0[137]
Distribution 4.0[138]
Warehousing 4.0[138]
Warehouse 4.0[139, 140]
Delivery Process Maturity Model 4.0, DPMM 4.0[141]
Procurement 4.0[142]
Customer 4.0[127]
Consumer 4.0[143]
Finance 4.0[144]
Bank 4.0[145]

5Society, government, economy, human resource, workforce, management, leadership, globalizationSmart HR 4.0, SHR 4.0[146]
Knowledge Management 4.0, KM 4.0[147, 148]
Leadership 4.0[149, 150]
Building Management 4.0[151]
Neighborhood 4.0[152]
Arbeit 4.0[153]
Work 4.0[53, 154]
HR 4.0[155]
HRM 4.0[156]
Controlling 4.0[157]
Globalization 4.0[158]
Society 4.0[159]
Supply Chain Management 4.0[160]
Inventory Management 4.0[138]
Order Management 4.0[138]
E-government 4.0 or e-Government 4.0[161]
Development 4.0[162]
Skills 4.0[163]
Professional Competence 4.0[164]

6Others and beyond “4.0”Thailand 4.0[165, 166]
Generation 4.0[167]
Revolution 4.0[168]
Digital 4.0[142]
Quality 5.0[70]
Society 5.0[169, 170]
Agriculture 5.0[171, 172]
Industry 5.0[173175]

4. Major Industrial Sectors

4.1. Education 4.0
4.1.1. Overview of Education 4.0

The disruptive landscape of industry 4.0 is so strong that change is inevitable, including within the education industry [176], making education 4.0 the illustrious cant among educationists today [177, 178]. Education 4.0 is an advanced education and networked ecosystem capable of developing skills and building competences for the new era of manufacturing [106]. In other words, education 4.0 is advanced engineering education for industry 4.0 [179, 180]. Furthermore, it is termed as higher education in the fourth industrial revolution [181184]. Moreover, education 4.0 can be defined in terms of OECD future education and skills 2030 [185] and PhD program in the era of industry 4.0 [186]. Simply put, education 4.0 is creativity-focused technology education in the age of industry 4.0 [187].

Generally, education 4.0 came forth in response to industry 4.0 which is a technology- and data-fueled world [188]. It has similar remarkable trends of (r)evolution just as industry 4.0. Table 3 shows the characteristics of each education evolution [189195]. Education 4.0 is the most complex system as compared to the previous evolutions. This is derived from the fact that industry 4.0 disruption is introducing rapid and unbelievable changes and challenges including the issue of skills and job profiles [196]. Therefore, it poses the question of how to educate and prepare new logical innovations and to develop not only left-brain skills but also right-brain skills [197]. As a result, education 4.0 topic has attracted a number of researchers in the recent year. Lately, the World Economic Forum developed education 4.0 framework that can be easily adopted and implemented by any institution, government, or university as presented in Table 4 [198].


S/NCharacteristicsEducation 1.0Education 2.0Education 3.0Education 4.0

1Students’ behaviourLargely passivePassive to activeActive, enthusiastic, string and confidenceIndependent, active, innovative, and self-directed learning style

2Primary roles for teacher/professorAuthoritarian and source of knowledgeGuide and source of knowledgeFacilitator of collaborative knowledge creationMonitor and observer of learning

3Teacher/professor source of contentTraditional books and copyright handoutsCopyright and free educational materials for studentsE-books and educational websitesTechnology-based dynamic and 3D materials

4Institutional arrangementCampus-based with fixed boundary institution involving traditional paragraphs, test assignments, and, sometimes, group classroomIncreasing collaboration between universities but one-to-one between students and universitiesOpen, collaborative, and creative activities with loose international affiliations and relationCreative, skillful innovative, and dynamic activities are performed; universities are boundaryless

5MethodsDictation and direct transfer of information
Guru-shishya method of teaching
Progressivism and openness to internetKnowledge production and co-constructivismInnovation production and classroom replacement

6TechnologyE-learning through electronic management within an institutionE-learning and collaboration involving other universitiesE-learning driven from the point of view of personal independent learning environments, use of computers and internetE-learning is totally based on new innovative technology tools, high-speed internet, mobile technology, social media platforms, virtual reality, etc.

7Location of the institutionSpecific building; mortar and brickSpecific building plus online; brick and clickEverywhere in a creative societyGlobally networked human body; anytime, anywhere, any device, and any platform


CategoryCritical characteristicsDescription

Learning content (built-in mechanism for skill adaptation)Global citizenship skillsInclude content that focuses on building awareness about the wider world, sustainability, and playing an active role in the global community
Innovation and creativity skillsInclude content that fosters skills required for innovation, including complex problem-solving, analytical thinking, creativity, and system analysis
Interpersonal skillsInclude content that focuses on interpersonal emotional intelligence, including empathy, cooperation, negotiation, leadership, and social awareness
Technology skillsInclude content that focuses on interpersonal emotional intelligence, including empathy, cooperation, negotiation, leadership, and social awareness

Experiences (leveraging innovative pedagogies)Personalized and self-paced learningMove from a system where learning is standardized to the one based on the diverse individual needs of each learner and flexible enough to enable each learner to progress at their own pace
Accessible and inclusive learningMove from a system where learning is confined to those with access to school buildings to the one in which everyone has access to learning and is therefore inclusive
Problem-based and collaborative learningMove from process-based to project- and problem-based content delivery, requiring peer collaboration and more closely mirroring the future of work
Lifelong and student-driven learningMove from a system where learning and skilling decrease over one’s lifespan to the one where everyone continuously improves on existing skills and acquires new ones based on their individual needs

4.1.2. Learning Factory

As far as education 4.0 is concerned, adequate and innovative manufacturing education and training are required in order to prepare employees for changes in their working environment related to quickly advancing digitalization. Most importantly, theoretical knowledge and practical skills regarding data acquisition, processing, visualization, and interpretation are needed to exploit the full potential of digitalization [199]. Consequently, the concepts of learning factory (LF)/teaching factory and innovation laboratory have egressed in the recent epoch as the lucrative approaches for qualification of participants from the field of engineering, especially industrial and mechanical engineering [200204].

LFs offer a suitable environment to combine theoretical learning and practical application and are therefore predestined to impart industry 4.0 knowledge and skills [205]. LFs are employed to teach students how the methods and concepts are learned in theory work in a hands-on and industry-related environment [200]. Elaborately, LFs are a platform created to provide an effective learning environment that will bring about human capacity development in a bid to bridge the gap between learning and practice (i.e., the gap between academia and industry) [206, 207]. The promising strength of LFs is the ability to solve problems in a structured way which is an essential competence of people in a factory, from the shop floor operator to the management level factory [208]. Furthermore, LFs are an effective solution to deal with new technologies, concepts, and methods [209]. Generally, LFs develop a uniform, unambiguous concept of competence that can be applied to production technology in the engineering community [210]. However, the requirements for the planning, implementation, and operation of an academic LF vary depending on the specific area of the respective institution [211]. For instance, the use of LFs differs for education in maintenance, manufacturing, production design, and technology adoption [212]. To this end, several learning factory concepts have been developed including game-based learning or gamification for manufacturing education [213] and Internet of Things laboratory (IoT lab) [214]. Table 5 outlines some examples of the learning factories launched majorly by institutions.


S/NExampleDescriptionReferences

1LEAD FactoryIt focuses on lean, energy efficiency, agility, and digitalization and deals with production-relevant process.[215220]
2Schumpeter Laboratory for Innovation (SLFI)It is an academic makerspace with the focus on product and service development.[215]
3Tiphys projectIt aims to build an open-networked platform for learning of industry 4.0 themes.[221]
4SEPT Learning FactoryW Booth School of Engineering Practice and Technology (SEPT) is an educational unit in the faculty of engineering at McMaster University focusing at developing talents for a workforce that has industry 4.0 foundational education and skills.[203, 222224]
5ELLI projectExcellent Teaching and Learning in Engineering Science (ELLI) aims to develop, introduce, and evaluate several kinds of remote and virtual laboratories into higher engineering education.[115]
6Tampere RoboLabA new learning concept and environment focusing on robotics formal and nonformal education.[225]
7Virtual FMS engineering education environmentIt focuses on planning, operation, and analysis of flexible manufacturing systems (FMS). The aim is to allow the students to achieve the intended learning outcomes mostly with learning by doing.[226]
8Automated class roomThis is an industrial cyber-physical system (ICPS) demonstration platform. It is used as a practical testbed, where students from different departments can learn together on how to implement the industry 4.0 concept and technologies.[227]
9Industrie 4.0 learning factoryIt aims to support “made in China 2025” strategy with necessary qualification of employees in Chinese production companies.[228]
10Training Factory Stator ProductionIt aims at providing small- and medium-sized companies, particularly those affected by change, with the opportunity to train their employees.[229]
11MTA SZAKI learning factoryIt aims at providing infrastructure, learning content, and opportunities for future production engineers, with a strong emphasis on automation and human-robot collaboration.[230]
12Chair of production system (LPS)It aims at teaching industry 4.0 requirements in application and development.[231, 232]
13LogCentre learning factoryIt aims at availing a low-cost environment for German Kazakh University in Almaty, Kazakhstan, to learn how state-of-the-art concepts and technologies are applied in logistics systems, e.g., RFID.[233]
14Learning Factory advanced Industrial Engineering (LF aIE)LF aIE is a model factory at the Institute of Industrial Manufacturing and Management (IFF) of University of Stuttgart designed for training on methods of production optimization.[201, 234]
15AAU Smart Production LabThis is the Aalborg University (AAU) learning factory. It has implemented industry 4.0 nine core technologies including collaborative robots, virtual environments, horizontal and vertical system integration, industrial Internet of Things, cyber security, use of cloud service, additive manufacturing, and big data and analytics for training purposes.[235]
16TU Wien Pilot Factory Industry 4.0 (TUPF)It is a pilot, demonstration, and learning factory, aiming to provide companies a fundamental insight into industry 4.0 techniques, applications, and associated challenges through exemplary implementation of a digitized production environment as well as subsequent research, workshops, and presentation.[236]

4.2. Energy 4.0
4.2.1. Overview of Energy 4.0

Despite the tremendous improvement in the industrial systems brought about by industry 4.0 in terms of the rudimentary achievements on higher level of operational efficiency, productivity, and automatization, it brought bureaucracy as huge amount of energy and materials is demanded and extremely large amount of solid, liquid, and gaseous wastes or greenhouse gases is generated from these complex industrial systems [237, 238]. Therefore, smart factories need to be sustainable and renewable in terms of energy pattern (electric system industry) [124, 239241]. Furthermore, the United Nations Industrial Development Organization (UNIDO) has set the relevancy of industry 4.0 and sustainability in the global Sustainable Development Goals (SDG 7 and 9) that digital industrial development should support the growth of industrial sustainable energy [242]. This has pointed towards the evolution of new energy concept known as energy 4.0.

Energy 4.0 is a digital revolution in the energy sector and also known as smart energy or green energy [99]. It presents opportunities for companies to establish new business models and sustainable strategies of producing and delivering energy [99]. Moreover, the idea of energy 4.0 is based on accelerating clean energy through adoption of the industry 4.0 concept in the energy sector [243]. The energy transition from 1.0 to 4.0 can be traced back in a similar manner to that of the web system as illustrated in Figure 3 [99, 101, 102].

4.2.2. The Drivers of Energy 4.0

The concept of energy 4.0 is nascent, and therefore, no clear information on its concept exists so far in the literature [99]. Nevertheless, renewable energy is fundamental to the energy 4.0 epoch. However, the transition to intermittent energy production from renewable energy sources increases the complexity of providing reliable energy supply. This has been handled with the introduction of digital or smart energy systems [244]. The truth is that smart renewable energy ware systems lie at the core of industry 4.0, and a number of recent advanced technologies and approaches play pivotal roles by exploiting innovative technologies and optimization methods [241]. For instance, the production of crude biofuels obtained from biomass and renewable energy sources is unheard-of. The biomass crude oil generation technology is currently up-to-date in terms of reducing dangerous emissions into the environment [245]. In addition, offshore and onshore wind energy harvesting has become the driving force towards the realization of energy 4.0 in most developed and developing countries [100].

Another key driver of energy 4.0 is how to reduce energy consumption whilst maintaining or increasing profits and productivity. The fact that energy requirements have grown due to automation of industrial systems makes energy optimization central in energy 4.0. Thus, a number of sophisticated energy-efficient mechanisms and software have been developed including real-time embedded systems [246] and computational modeling (e.g., energy efficiency analysis modeling system) [247249].

Additionally, the advancement in power distribution is another driver of energy 4.0. This is accomplished through the integration of conventional power grid system with industry 4.0 technologies including IoT, big data, and AI. The combination of these technologies and power grid has been cited as smart grid [99].

Furthermore, the advancement in the energy storage system which employed nanotechnology as one of the core technologies for its development is emblematic to energy 4.0. Currently, the next-generation lithium-ion batteries are under rapid development using various nanostructured materials including silicon nanowires and silicon nanotubes which are two promising anode materials due to their high specific capacities [250].

4.3. Agriculture 4.0
4.3.1. Overview of Agriculture 4.0

The disruptive waves of industry 4.0 in agriculture and food systems (agri-food) can be witnessed from the digital transformation of the production infrastructures such as connected farms, new farm equipment, and connected tractors and machines [251, 252]. The driving force behind this is the need to increase efficiency, productivity, and quality in agri-food systems and environmental protection (reduce global warming) [253, 254], that is, the sustainability of agricultural systems which is paramount important for the survival and wellbeing of humans worldwide [255]. In fact, agriculture plays a great role in providing human food security and sustainability in any country [256]. Therefore, to meet this ever-increasing food demand in the epoch of industry 4.0, the new concept “agriculture 4.0” was born [82].

Agriculture 4.0 is known with several names including data-driven and automated agriculture [172, 257], intelligence agriculture [258], smart agriculture [259], digital agriculture [260], digital farming [261], smart farming [262], and farming 4.0 [83]. Agriculture 4.0 emerged when telematics and data management were combined to the already known concept of precision agriculture (improving the accuracy of operations) [172]. Agriculture 4.0 can further be defined as farming in the era of industry 4.0 through digitalization [263]. Moreover, it is the future of farming technology which is based on the emergence of smart technology including smart devices (sensors and actuators) and communication technology [263, 264]. Simply put, agriculture 4.0 is the fourth evolution in the farming technology which is unparalleled to the previous (r)evolutions (Figure 4) [168, 172, 265]. Some authors have argued that it should be called “agriculture 5.0” [171], but it is not yet common among the academic and nonacademic literature.

Similar to industry 4.0, agriculture 4.0 is universally complemental to a number of concepts including vertical farming and food systems, bioeconomy, circular agriculture, and aquaponics [266]. Agriculture 4.0 is composed of existing or developing technologies such as robotics, nanotechnology, synthetic protein, cellular agriculture, gene editing technology, AI, blockchain, and cloud computing [266].

Importantly, food and farming systems must reconcile the need to produce enough healthy and affordable food with the equally important motive of ensuring that humans do not degrade the ecosystems on which they entirely depend for sustenance [171]. On the one hand, agriculture industry is critical to sustainable development, and agricultural production by smallholders in lower-income countries contributes substantially to the food security of both rural and urban populations [267]. On the other hand, food industry is a key issue in the economic structure due to both weight and position of this industry in the economy and its advantages and potential [268]. In order to harness and control both agriculture and food industries, a complex industry (agri-food) has emerged, better known as “agri-food 4.0,” in the era of the fourth industrial revolution [96, 98, 269]. In fact, the term agri-food 4.0 is an analogy to the term industry 4.0, coming from the concept of agriculture 4.0 [95]. In this regard, agriculture 4.0 was adopted in this study to cover all the aspects of food and agricultural industries.

4.3.2. Convergence of Disruptive Technologies in Food and Agriculture

More like in the manufacturing system, industry 4.0 is disrupting agricultural and food systems through the convergence of its technologies [26]. In order to understand and illustrate this fact, a massive exploratory literature search was conducted to identify the mentioned use cases or applications of industry 4.0 technologies (disruptive technologies) in the published literature. Disruptive technologies are the technologies that create disruption on the status quo as they produce unique sets of values. In this respect, 12 disruptive technologies were considered [27], and these included 3D printing (3DP), AI, augmented reality (AR), big data, blockchain (BC), cloud computing (cloud), drones, IoT, nanotechnology (nanotech), robotics (robots), simulation (Sim), and synthetic biology (Syn-Bio). The identified applications were categorized into 10 major application areas in agriculture and food systems, namely, food processing and management (FPM), farm equipment and facility maintenance (FEFM), agriculture machinery automation (AMA), general agri-food planning and operation management (GAFPOM), yield prediction and precision farming (YPPF), weather and environment management (WEM), land preparation and planting optimization (LPPO), crop and livestock growth, improvement and protection (CLGIP), food packaging and storage (FPS), and irrigation and water management (IWM) as shown in Table 6. These application areas were derived just from the mentioned applications in the collected relevant publications. So, by mapping the application areas with the disruptive technologies, the convergence of these technologies is clearly visible as illustrated in Figure 5. The quantitative analysis involved counting the converging technologies in each application area and calculating the percentage convergence as shown in Table 7. The result demonstrates that the application areas GAFPOM and YPPF were the dominant with 17% technology convergence followed by WEM and CLGIP with 15% and then LPPO (11%) (Figure 6). However, each application area has totally different sets of technologies in convergence. For instance, the technologies converging in the GAFPOM application area include AI, AR, big data, blockchain, cloud computing, drones, IoT, robotics, and simulation, whilst the technologies converging in YPPF include AI, AR, big data, cloud computing, drones, IoT, nanotechnology, robotics, and synthetic biology. These differences were also observed in the other application areas.


S/NTechnologyApplicationsReferences

13D printingFPM: 3D food printing (sugar, chocolate, pureed food, flat food such as pasta, pizza, and crackers, and snack from waste food)[270277]
FEFM: on-site farm tools and equipment making[276, 278]

2AIAMA: automation of farming and computer vision[279281]
IWM: automated irrigation[279, 280]
GAFPOM: digital twin, real-time data analysis, predictive analytics, recommendation systems (decision-making)[282285]
YPPF: crop, soil, and livestock monitoring, yield management[280, 282, 284287]
FPM: food (supply chain) traceability and safety[282, 283]

3ARGAFPOM: optimizing feed and cultivation management, boardroom farm planning, and remote expert assistance (training of farmers)[288290]
YPPF: precision farming and livestock (virtual fencing)[289]
WEM: agricultural health and safety (emergency response)[291]
LPPO: soil sampling[292]

4Big dataYPPF: intelligence agriculture, remote sensing, crop yield prediction, and crop selection[258, 293295]
GAFPOM: crop or farm planning and management, agricultural policy and trade, farm-to-fork traceability, and agri-food by-product supply chain management[97, 294, 296299]
CLGIP: crop disease prediction, weed detection, and plant breeding[295, 297, 300, 301]
WEM: weather forecasting[295, 302]
LPPO: estimation of soil components, temperature, and soil moisture content[295]

5BlockchainGAFPOM: food and agricultural traceability, smart contract and crop insurance, food trade, land governance and registries, financial services in agriculture, transport and agrologistics, and agricultural supply chain supervision and management (informative)[303316]
FPM: food integrity and food safety[309, 317]
WEM: waste reduction and environmental awareness[317, 318]

6Cloud computingGAFPOM: farm management and quality traceability, mobile agriculture services (M-Agric services), agri-info (delivering agriculture as a service), farm documents, and video dissemination[319322]
CLGIP: weed detection (cloud farming)[323]
YPPF: smart tunnel farming[324]

7DronesYPPF: supervision or precision agriculture, crop monitoring, harvest prediction or estimation and optimization, yield forecast and management, vegetable index extraction, and variable rate prescriptions in agriculture[46, 325335]
CLGIP: crop spraying or sprinkling (fertilizers, pesticides, and herbicides), efficient scarecrow for birds and insects, disease detection or health assessment and control, pollination, and 3D crop modeling[325, 326, 328, 330332, 334, 336340]
GAFPOM: planning, production, and disaster management, insurance (agriculture claims management)[327, 333, 338, 340]
LPPO: analysis (soil profiles, field, weed presence, nutrient profile, moisture, plant health, fungal abundance, and drainage), ariel planting or seed sowing, field-level phenotyping[328, 330334, 339, 340]
IWM: drones for crop irrigation[330, 332, 333]
WEM: frost protection[337]

8IoTYPPF: monitoring of crop, soil, irrigation, weather, remote sensing, machinery, farm facilities, and field or environment, livestock, dairy, greenhouse condition and water quality, yield forecasting and prediction, and animal husbandry (smart cow farm and smart chick farm)[334, 341348]
GAFPOM: documentation and traceability, agri-supply chain management and security, and agricultural education[341344, 349]
CLGIP: crop disease and pest management, fertilization, fertigation and chemigation, crop spraying, intrusion detection in agriculture fields[343, 346, 347, 350, 351]
AMA: IoT-based agricultural machinery[342, 352]
IWM: IoT-based irrigation control systems[341, 344, 346, 347, 350, 351]
LPPO: soil sampling and mapping[346]
WEM: weather prediction (predicting the rainfall)[350]

9NanotechnologyYPPF: pathogen monitoring, pesticide detection (nanosensors, diagnostic devices, and nanobarcodes), Internet of nano-Things, and nanobisensors[353361]
CLGIP: plant bleeding (plant genetic modification), nanobiotechnology, nanofertilizers, nanobiofertilizers, nanoelements, nanoscale carrier, nanocoating, nanoencapsulation, crop production (plant protection products), nanobionics and photosynthesis, pest, weeds and disease control (nanopesticides, nanoherbicides, antimicrobial nanoparticles, nanoengineered metabolites, nanofungicides, and nanoinsecticides), hydroponics, and nanoparticles from plants for controlling plant virus[353, 355369]
IWM: water purification and pollution remediation (heavy metal removal), irrigation (nanobubbles for biofouling mitigation)[353, 354, 356, 357, 362, 364, 370]
LPPO: soil improvement (water/liquid retention), soil remediation (heavy metal removal)[353, 356, 358]
FPS: safety and labelling, package material with nanosensors, nanoparticles, smart/intelligence packaging, nanoadditives, control and nutraceutical delivery, nanocoding of plastics and paper materials, nanoencapsulation and target delivery, nanocomposites, nanoplates, nanoclay, nanolaminates, edible film/coating, and pesticide, pathogen, and toxin detection[355, 359, 361, 363, 365, 366, 371]
FPM: food security; nanoresearch (nanodevices and nanobiotechnology), nanoscale agroproducts (nanocellulose), nanocomposites, nanofood, color additives, additives or polymer aids, preservatives, flavor carrier, marking fruits and vegetables, anticaking, and nutritional dietary supplements[354, 361, 364, 368, 371]
WEM: agrowaste reduction and high-value product (biofuel), biochar nanoparticle[354, 357, 369]

10RoboticsCLGIP: weed detection and control, target spraying, pest and disease monitoring and control, pruning, thinning, mowing, pollination, and fertilization[372379]
YPPF: harvesting (picking of fruits), crop status monitoring, counting crops, and classification of plant species[372, 373, 375, 376, 378, 380, 381]
LPPO: seeding, sowing, and transplanting, phenotyping, land tilling (plowing, harrowing, rototilling, and cultivating), and soil and field analysis[373, 378, 379, 381]
AMA: autonomous navigation (field layout planning, vehicle route, and motion planning), computer vision, and remote-control systems[375, 378, 379, 381]
IWM: irrigation robots[378]
GAFPOM: livestock management (dairy cattle, pigs, and chickens), milking animals, removing waste from animal cubicle pens, carrying and moving feedstuffs, manipulators, and transportation[379, 382]
FPS: labelling and tracking of food products[382]

11SimulationCLGIP: development of process-based biophysical models of crops and livestock, crop growth simulation model[383386]
GAFPOM: statistical models based on historical observations and economic optimization, simulation models at household and regional to global scales, simulation of farm machinery operation (optimization of tillage and sowing operations), multiagent modeling and simulation of farmland use[383, 387, 388]
WEM: interdisciplinary climate change impact assessment on agriculture, water resources, forestry, and economy through simulations[389]

12Synthetic biologyCLGIP: synthetic photorespiratory pathway, modifying and creating new systems, advancing pest control (engineered insects), precise antimicrobials (Eligobiotics), designing crops for fuel production, plant breeding, synthetic genomics, metabolites in microorganisms (vitamins, nutraceuticals, and probiotics), pest and disease control (control of viral, bacterial, and fungal pathogens, parasitic weeds, and insect vectors of plant pathogens. Synthetic chloroplast genome, a synplastome for pest resistance), cellular agriculture (plant cells, animal cells, and microbial cells), and nonfertilization (synthetic nitrogen-fixing bacteria)[390396]
FPM: food processing monitors, biosafety, and biosecurity[391]
WEM: bioremediation (waste and pollution control)[393, 397]
YPPF: biosensors and molecular circuitry[393, 396]

FPM: food processing and management, FEFM: farm equipment and facility maintenance, AMA: agriculture machinery automation, GAFPOM: general agri-food planning and operation management, YPPF: yield prediction and precision farming, WEM: weather and environment management, LPPO: land preparation and planting optimization, CLGIP: crop and livestock growth, improvement and protection, FPS: food packaging and storage, and IWM: irrigation and water management.

Application areaFPMFEFMAMAIWMGAFPOMYPPFFPSWEMCLGIPLPPO

Number of technologies4134992886
Convergence (%)725717174151511

4.4. Healthcare 4.0
4.4.1. Overview of Healthcare 4.0

The disruptive and transformative waves of industry 4.0 which are incredibly retrofitting many industries have also paved its way into the healthcare industry or medical fields including orthopaedics and dentistry. As the result of the tremendous disruption into the healthcare system, a new concept termed as “healthcare 4.0” has evolved [398401]. Although the implementation of the healthcare 4.0 concept has been characterized as being highly complex and costly and requires a more skilled labor force, a number of hospitals in the advanced countries are already embracing it [402, 403]. The driving force behind this healthcare (r)evolution is the need to deploy industry 4.0 technologies to deliver more effective and efficient healthcare services including high security and privacy on the patients’ data electronic health record while allowing remote and real-time access and diagnosis by the doctors or healthcare personnel [404407].

Healthcare 4.0 is also known as hospital 4.0 [123]. It is a term that has egressed recently and derived from industry 4.0. Simply put, healthcare 4.0 is digital health or the use of digital technologies for health. The term digital health is rooted in electronic health (eHealth). eHealth is defined as the use of ICT in support of health and health-related fields, while mobile health (mHealth), which is a subset of eHealth, entails the use of mobile wireless technologies for health. On the contrary, healthcare 4.0 germinated as a broad umbrella term encompassing eHealth (which includes mHealth), as well as emerging areas, such as the use of industry 4.0 technologies including IoT, big data, 5G, AI, computing (cloud, fog, and edge), and blockchain [408414]. Holistically, the World Health Organization (WHO) [408] reiterated the term healthcare 4.0 as a discrete functionality of digital technology that is applied to achieve health objectives and is implemented within digital health applications and ICT systems, including communication channels such as text messages. In a similar manner to industry 4.0, the healthcare industry has revolutionized from 1.0 to 4.0 as illustrated in Figure 7 [415417]. Besides the implementation of industry 4.0 technologies in the healthcare system, there are ongoing studies in the development of healthcare services including the Social Cooperation for Integrated Assisted Living (SOCIAL) [418], OpenEHR [419], GraphQL, and HL7 FHIR [420]. This is because healthcare service and management plays an essential role in the human society [421, 422]. These are also contributing a lot to shaping the journey of healthcare 4.0.

One of the factors that is boosting the adoption of healthcare 4.0 is the concept of smart city. Smart healthcare is an essential part of creating a smart city because anyone can go to the hospital for treatment [423]. To this far, some of the major players in healthcare 4.0 include Abbott Laboratories, Philips Healthcare, Life Watch, GE Healthcare, Omron Healthcare, Siemens Healthcare, and Honeywell International Inc. [424]. Nevertheless, the healthcare industry lags behind other industries in protecting its data from cyber attacks [425]. The strength and the benefit of healthcare 4.0 adoption have been witnessed in the fight of the novel coronavirus 2019 (COVID-19) pandemic [426]. Coronavirus is one of the viral respiratory illnesses and can be fatal to some immunocompromised patients [427]. However, combating this pandemic has become a global hurdle. As a lifesaving strategy, a number of healthcare facilities have devoted to using 3D-printed patient respiratory ventilators and breathing equipment to sustain the life of patients [428].

4.4.2. Convergence of Disruptive Technologies in Healthcare

As with agriculture 4.0, the convergence of industry 4.0 technologies in healthcare has been demonstrated. Here, the analysis was based on 10 application areas which included medical education, research and training (MERT), medical devices and equipment (MDE), pharmaceuticals, drug delivery and discovery (PDDD), detection, diagnosis, prediction, prognosis, prevention and treatment (DDPPPT), telemedicine and medical record (TMR), healthcare facility management and process optimization (HFMPO), surgery, medical imaging, monitoring, and dentistry as shown in Table 8. The convergence of the disruptive technologies in these application areas is illustrated in Figure 8 and quantified as shown in Table 9. The result depicts that convergence of the disruptive technologies was the highest in both DDPPPT and MERT with 13.5% followed by TMR and monitoring with 12% (Figure 9). The technology convergence in DDPPPT, for example, includes synthetic biology, robotics, IoT, drones, cloud computing, blockchain, AI, and big data, while for MERT, it includes 3D printing, AI, AR, big data, blockchain, drones, simulation, and robotics. However, dentistry has received only one technology (3D printing). This could be because of limited studies on the technology’s application in the field of dentistry.


S/NTechnologyApplicationsReferences

13D printingSurgery: surgical marking guide, implant placement guide, radiation shield, and surgical saw guide[429434]
MERT: patient education[429, 430, 432, 435]
MDE: implants (metallic implants, tracheal splint; cranial implants), tissue and organ manufacturing (organ on chips), scaffold manufacturing, respiratory apparatus (ventilators), PPE (face mask and shield), prosthetics and orthotics (e.g., knee replacement; nasal stent; hearing aid cases), and active and wearable devices (wearable sensors, lab on a chip, and microfluidics)[428432, 434, 436440]
Medical imaging: anatomical modeling, organoids, e.g., 3D-printed model of coronavirus[428, 430, 431, 433, 434, 437, 439]
PDDD: construction of oral dosage medications, pills or drug printing, tables, drug-delivery implants, and transdermal delivery[430432, 437, 439, 440]
Dentistry[431, 432, 437, 439]

2AIDDPPPT: prediction and treatment of diseases such as stroke and cancer[122, 441446]
Medical imaging[442, 443, 447]
Monitoring: patient care, diabetes care, eye care, and adult care or wellbeing[442444, 446, 448]
Surgery[442]
MERT: virtual assistant for patients[442, 443, 448]
MDE: AI-based wearables[442]
PDDD: for discovery of a new class of diagnostics and treatment[442, 443, 445, 446, 448]

3ARMERT: medical education and training[449455]
Monitoring: wellness (adult care)[453, 455458]
DDPPPT: rehabilitation, diagnosis, and prediction[458, 459]
TMR: information (telemedicine)[449451, 453, 459]
Surgery: surgical planning, surgical navigation, and surgical rehearsal[450, 451, 453, 454, 457, 458, 460]
Medical imaging: anatomical imaging[451]

4Big dataMedical imaging[461, 462]
Monitoring: real-time monitoring[461463]
DDPPPT: treatment (precision medicine)[122, 461469]
TMR: patient care (patient drug history, clinical trials, and medical records) and medical data management[461, 463465, 468]
MERT: clinical research[465]
PDDD: drug discovery and design[463, 464]
HFMPO: fraud detection in healthcare facilities and workflow process optimization[463, 465]

5BlockchainDDPPPT: medical data privacy and security, medical fraud detection, diagnosis, and prescription tracking[470473]
TMR: electronic health record (EHR) modification, medical data management (patient-centred), personal health records (PHRs), and medication regimen[403, 470480]
HFMPO: independent medical reviews, claim and billing management, control of contracts for healthcare service, healthcare delivery, drug tracing, tracking and verification, drug supply chain management[471473, 478, 481, 482]
MERT: clinical and neuroscience research, education of medical staff[471, 478, 481]
Monitoring: blockchain for 5G-enabled IoT[483]

6Cloud computingTMR: teleconsultation, EHRs, PHRs, patient-centred[484487]
Monitoring: fitness and wellness monitoring[488]
HFMPO: patient assignment scheduling, clinical operation, and workflow optimization[488, 489]
DDPPPT: treatment of disease (stroke), therapy[487, 490]
Medical imaging[487, 488]

7DronesHFMPO: transportation of medical goods (medications, vaccines, biological samples, medical devices, tissue, patient), healthcare delivery and pick-up services, emergency response (transport of blood and plasma), deployment of networks for data harvesting in unconnected areas[491496]
DDPPPT: disease prevention (sterile mosquito release for vector control), public health disaster relief (disaster prediction and management, detection of harmful substances)[492, 496]
TMR: telemedicine[492]
MERT: health research[496]

8IoTMonitoring: homecare (IoT-based information system), caring and monitoring of patients, Internet of Health Things (IoHT), wearable Internet of things (WIoT)[422, 424, 497499]
MDE: Internet of Medical Things (IoMT) (IoT in implantable and wearable devices)[422, 500]
DDPPPT: Internet of nano-Things (IoNT) (IoT in nanomedicine for diagnostics, treatment, preventive health, chronic care disease management, and follow-up care)[422]
TMR: Internet of mobile-health Things (m-IoT) (remote monitoring of patients), wearable Internet of Things (WIoT)[414, 422, 501, 502]

9NanotechnologyMDE: biodegradable and bioactive sutures and dressings, drug-eluting stents and scaffolds, cell production, nanobiosensors[503505]
DDPPPT: gene therapy, diagnosis, cancer treatments[503506]
Surgery[505]
PDDD: drug delivery, drug coating, and encapsulation[503]

10RoboticsTMR: tele-healthcare[507]
Surgery[508511]
Monitoring: care and wellness[511]
DDPPPT: diagnosis and rehabilitation[508, 511]
MERT: training[511]

11SimulationHFMPO: operation process improvement and optimization, resource planning, emergency room efficiency improvement[512517]
MERT: clinical or midwifery education and training, clinical research[515, 518523]

12Synthetic biologyPDDD: drug development, vaccine, biopolymer vaccines, antimalaria, antibiotics, drug delivery (caveospheres), antimicrobial agents: engineered phages (viruses)[524528]
DDPPPT: gene therapy (bacteria cells), e.g., chromosome-free cell or SimCells, DNA assembly, transplantation, and recombination, genome editing, diagnostics, biologics and biodetection, metabolomics[524, 527532]
MDE: implant (wound care), new biosensors and smart microdevices and nanodevices, re-engineered antibodies and cellular therapeutics for cancer, T-cells, CAR (chimeric antigen receptor) technology, theragnostic cell lines, artificial enzymes, making neurons, regenerative medicine, cybernetic systems[524, 526528, 531533]
Medical imaging: biomarker[528, 532]

MERT: medical education, research and training, MDE: medical devices and equipment, PDDD: pharmaceuticals, drug delivery and discovery, DDPPPT: detection, diagnosis, prediction, prognosis, prevention and treatment, TMR: telemedicine and medical record, and HFMPO: healthcare facility management and process optimization.

Application areasSurgeryMERTMedical imagingPDDDMDEDentistryMonitoringHEMPODDPPPTTMR

Number of technologies5866517587
Convergence (%)913.510109212913.512

4.5. Logistics 4.0
4.5.1. Overview of Logistics 4.0

The intricacy of the disruptive and transformative powers of industry 4.0 including the process of globalization of the world economy is a prerequisite for the successful operation and disruption of logistics which is well-known today as logistics 4.0 [534, 535]. In fact, the formation of logistics 4.0 banks in particular on cutting-edge technologies and the digitalization of business processes [534]. In addition, logistics 4.0 concept emerged purposely to overcome the growing uncertainty and dissatisfaction in implementing industry 4.0, new methods, and tools that specifically address dedicated companies’ areas, such as logistics or reverse logistics, supply chain management, and manufacturing processes [536, 537]. For the case of supply chain management, industry 4.0 with its associated technological advances is increasing supply chain resilience or lean supply chain management which is highly linked to the general operation and performance of the logistics industry [538540].

Generally, logistics 4.0 refers to the combination of using logistics with the innovations and applications added by the cyber-physical system. However, so many related concepts and definitions of logistic 4.0 exist today including smart services and products [541], green logistics [542], smart logistics or intelligent logistics, and smart warehouses [543547]. Furthermore, logistics 4.0 reflects logistics innovation [548], digitization in maritime logistics [549], digital supply chain [550, 551], smart ships, smart containers (container 42), and autonomous vessels [130].

Logistics 4.0 is a new paradigm in the logistics industry that focuses on the description of the newest technologies in contemporary supply chain applications [552, 553]. The concept of logistics 4.0 was created as a consequence of industry 4.0 and emergence of new and intelligent technological solutions in logistics including blockchain, IoT, AI, and big data [554560]. The term logistics 4.0 first appeared in 2011 as a response and support to industry 4.0, but today, the terms such as supply chain 4.0, procurement 4.0, marketing 4.0, customer 4.0, consumer 4.0, distribution 4.0, warehousing 4.0, inventory management 4.0, order management 4.0, finance 4.0, maritime 4.0, bank 4.0, globalization 4.0, leadership 4.0, and society 4.0 can be seen. These represent the response of the logistic field to the development and requirements of industry 4.0 [129, 138, 561]. Just as industry 4.0, the generations from 1.0 to 4.0 have been traced for both logistics, supply chain, marketing, and customer as shown in Table 10 [562568].


GenerationLogisticsSupply chainMarketingCustomer

Level 1Logistics 1.0
Mechanization of transport
There is no concept in this levelMarketing 1.0
Product-centric approach
User needs
Customer 1.0
Passive consumer
A recipient of advertising messages

Level 2Logistics 2.0
Automation of handling system
Supply chain 2.0
Mainly paper-based
Marketing 2.0
Customer-centric approach
User wants
Customer 2.0
Active consumer
Expressing own opinion

Level 3Logistics 3.0
System of logistic management
Supply chain 3.0
Integration between two channels
Basic digital components in place
Marketing 3.0
Human-centric approach
User anxieties, desires, creativity, values
Customer 3.0
Cocreating consumer
Cooperating cocreator

Level 4Logistics 4.0
Intelligent transportation system
Real-time location and tracking system
Supply chain 4.0
Total network integration
Leveraging all data available
Marketing 4.0
Content-centric approach: brand integrity, identity, image, and interaction
User participates and validates
Customer 4.0
Involved advocate
A prosumer promoting the brand

Globalization, finance, governance, leadership, and society at large play an astonishing role in enhancement of the general performance and development of the logistics industry as well as economic growth in any country [569573]. Most importantly, delivering on digitalization for large multinational business, in the contemporary context of global operations and real-time delivery, is a significant opportunity to the logistic industry [574, 575]. In globalization, all the three modes including trade, financial, and technological globalizations are now practiced everywhere in the world as an important and economic reason for company improvement [576578]. However, globalization in the era of industry 4.0 has taken a quantum leap into a new concept known as “globalization 4.0” which is among the main drivers of logistic 4.0 [579]. One of the key countries behind globalization 4.0 is China. China’s Belt and Road Initiative is an important vector for globalization 4.0 as it helps to bring its enabling infrastructure and technologies to all corners of the globe [580]. Table 11 depicts the transition or (r) evolution from 1.0 to 4.0 for globalization, leadership, and society [581585].


TransitionGlobalizationLeadershipSociety

Level 1Globalization 1.0
Free country-to-country movement without passports
Immigration policy free from governmental limitation
Existence of international economic agreements and institutions, e.g., the International Telegraph Union and Universal Postal Union
Leadership 1.0
Charismatic
Society 1.0
Seeker gatherer

Level 2Globalization 2.0
Modern international economic enabling architecture
Multinational corporations, policy liberalization, and improved communications, cross-border integration
Leadership 2.0
Directive
Society 2.0
Peaceful agrarian

Level 3Globalization 3.0
The advent of the internet, the establishment of the World Trade Organization (WTO) and the formal entry of China into the trading system
Leadership 3.0
Relational
Society 3.0
Modern social order

Level 4Globalization 4.0
Immigration policy, data privacy, and security, China’s Belt and Road Initiative, multispeed European integration
Leadership 4.0
Responsive
Society 4.0
Data social order

Similarly, finance sectors in the logistic industry are leapfrogging as disruptive technologies paved their ways into services and financial inclusion. In most organizations today, finance professionals are being asked to learn new skills, often related to such technologies, because work is morphing into more project-oriented opportunities. For instance, the major challenges that chief finance officers of the logistics industry are facing include handling massively big data, liquidity and cash flow, complicated cash lifecycle finding, and retaining good talent [586]. In order to overcome these hurdles in finance systems for the logistics industry, a new concept of finance called “finance 4.0” was born, which is driven by digital transformation in the finance and banking system [587589]. Figure 10 elaborates three generations of finance and banking systems from 2.0 to 4.0 [145, 586588, 590].

4.5.2. Convergence of Disruptive Technologies in Logistics

In this section, 10 application areas in logistics were derived and defined majorly based on the selected studies [591, 592]. These include warehouse capacity optimization and automation (WCOA), logistics assets and facility maintenance (LAFM), delivery and distribution (DD), customer order picking (COP), forecasting, planning and reporting (FPR), dynamic route optimization (DRO), procurement and financial management (PFM), threat and fraud detection and prevention (TFDP), monitoring, tracking and traceability (MTT), and environment monitoring and management (EMM) as shown in Table 12. The mapping of the technologies in these application areas was conducted as demonstrated in Figure 11. The technology convergence in the application areas was calculated as presented in Table 13. The result shows LAFM and DD were the dominant with 15% convergence followed by WCOA and FPR with 13% as illustrated in Figure 12. The technologies converging in LAFM include 3D printing, AI, AR, big data, drones, IoT, nanotechnology, and robotics, while for DD, they include 3D printing, AR, blockchain, drones, IoT, robotics, simulation, and synthetic biology. In WCOA, the converging disruptive technologies are 3D printing, AI, AR, drones, IoT, robotics, and simulation. In the same way, the technologies converging in FPR and the rest of application areas were elaborated.


S/NTechnologyApplicationsReferences

13D printingWCOA: mass customization (individualized direct product manufacturing), localized manufacturing and delivery, mass individualization and personalization, decentralized manufacturing.[436, 593599]
LAFM: on-demand spare parts’ making, end-of-runway service.[595, 596, 599601]
DD: 3D print shops for business and consumers, decentralized production of parts (regional warehouses, delivery depot of logistics service providers).[595, 602]

2AIWCOA: smart warehousing environment, back-office automation, predicting inbound logistics, intelligent logistics assets (seeing, speaking, and thinking logistics assets), and recognition of reverse logistics.[537, 596, 603609]
COP: new customer experience models (seamless, voice-enabled customer interactions). AI-powered customer experience.[596, 605]
FPR: simulation and optimization of supply chain operations (eliminating bottleneck), supply chain management decision support, resilience supplier selection, and decision-making.[606, 608615]
LAFM: predictive maintenance to prescriptive maintenance of logistics equipment, trucks, buildings, and machines.[609, 616]

3ARWCOA: AR-powered warehouse operations (product routing, picking, packing, labelling, sorting, and even assembling).[596, 617619]
FPR: facility planning (displays task information, reads barcodes, and supports indoor navigation and can be integrated into warehouse management systems for real-time operations).[596, 619, 620]
DRO: safer and smarter driving (next generation of navigation and driver-assistance systems).[596, 618, 620]
PFM: procurement.[621]
DD: intelligent last-mile operations (AR can help in last-meter navigation to correctly locate entrances), freight/container loading (conduct completeness checks of each shipment using object-recognition technology, utilized to virtually highlight inside a vehicle to display the optimal internal loading sequence of each shipment (taking into account route, weight, fragility, etc.)).[596, 619621]
COP: creating a new standard of order picking (picking optimization).[596, 618620, 622]
LAFM: predictive and prescriptive maintenance of warehousing robots, delivery truck, cargo aircraft, and other equipment.[618, 621]

4Big dataDRO: dynamic, real-time route optimization, optimization of material and product transportation routing.[46, 596]
FPR: smarter forecasting of demand, capacity, and labor. Anticipatory shipping (to predict an order before it occurs), inventory control and logistic planning, supply chain statistics, supply chain simulation, supply chain forecasting, logistics optimization, supply chain network design, learning from customer assessment, decision on the supply chain infrastructure, and product recovery decisions.[596, 623629]
EMM: end-to-end supply chain risk management (detecting, evaluating, and alerting all potential disruptions on key trade lanes, caused by unexpected events such as growing port congestion or high flood risks).[596]
PFM: procurement management.[625]
LAFM: utility and maintenance aspects.[628]
TFDP: fraud detection, smart contracts.[630]

5BlockchainMTT: end-to-end status tracking (orders, receipts, invoices, payments, and any other official document), track digital assets (such as warranties, certifications, copyrights, licenses, serial numbers, and bar codes) in a unified way and in parallel with physical assets, and freight tracking.[596, 631650]
TFDP: smart contract for automating commercial processes or supply chain orchestration, immutability (ensures the records’ originality and authenticity), anticorruption and humanitarian operations, trust, security, trust and fraud detection, trusting load board.[596, 630, 634636, 640, 641, 643, 644, 647, 649651]
PFM: finance (remittances and online payments), serve as a base for bitcoin cryptocurrency, invoice and payment management (transaction automatization), smart billing, decentralized transaction, trade finance.[630, 638, 641, 642, 644648, 651, 652]
DD: last-mile delivery by connectivity with drones, fresh food delivery.[630, 653]
FPR: demand forecasting, supply chain visibility, supply chain visualization and tokenization.[644, 645, 648, 650, 651, 653]

6Cloud computingMTT: logistics tracking information management system to support whole-ranged and real-time logistics tracking services.[596, 654, 655]
FPR: 360-degree management dashboards (coordination and orchestration of logistic information into one integrated view), port logistics service and supply chain optimization, internet-based supply chain forecasting and planning, supplier network logistics planning and manufacturing service composition (configured cloud entropy of logistics and operation suppliers).[596, 655661]
DRO: cloud-powered global supply chains virtualize information and material flows by moving all supply chain processes into the cloud[596, 656]
PFM: cloud-based procurement (sourcing and procurement).[655, 660662]

7DronesWCOA: warehouse inventory checks, fully autonomous indoor cycle counting with drones, inventory counts (audits), and real-time inventory management.[139, 140, 596, 663, 664]
DD: intraplant transport and urgent supplier-to-plant spare parts’ delivery as well as to ferry products from back rooms to the sales floor, last-mile deliveries, remote delivery and disaster response, deliver small packages between warehouses.[140, 596, 665673]
LAFM: surveillance of infrastructure (check the condition of industrial buildings and inspect trade lines for damage or the need for maintenance work. Additionally, assets can be monitored for theft prevention at warehouses and yards).[140, 596, 664, 674]
DRO: analysis of the traffic parameter.[675]

8IoTMTT: intelligent identification, monitoring and management of the intelligent network system, cold chain traceability, tracking and remote monitoring of equipment, identifying and locating critical pieces of cargo at each stage in an operation, smart cargo solutions, and asset tracking, tracking and monitoring of stock level.[92, 596, 676682]
DRO: intelligent transportation solutions (in-vehicle telematics)[596]
DD: connected consumer and the proliferation of smart homes (e.g., smart locks) (secured in-home delivery services).[596]
FPR: IoT-enabled logistics and supply chain management (IoT-based laundry services for real-time scheduling), supply chain (end-to-end) visibility, managing supply chain risk, optimization, and prediction.[680688]
EMM: IoT-enabled smart indoor parking system for industrial hazardous chemical vehicles, IoT-enabled solutions monitor perishable cargo for temperature, humidity, and other environmental factors, and humanitarian assistance disaster response scenario.[680, 688690]
WCOA: warehouse and yard management system (IoT-controlled safe area), inventory management.[677, 691]
COP: IoT-based safety interaction mechanisms for storage and picking.[678, 680]
LAFM: condition-based maintenance of equipment (fleet management).[680]
TFDP: theft prevention, after-sale service, and warranty validation.[692]

9NanotechnologyMTT: nanochip RFID labels for tracking.[693, 694]
LAFM: nano-based coatings to handle biofouling and corrosion, nano-based materials for the enhancement of strength of marine vehicles, and efficient and durable nano-based tires for trucks.[54]

10RoboticsWCOA: flexible automation in warehousing and fulfillment (picking, packing, palletizing, and sorting), stationary-mobile piece-picking robots, receiving, replenishment, shipping, robots for autonomously supply workstations, keep control over inventory.[46, 596, 695702]
DD: transportation and loading tasks, autonomous kitting, trailer and container unloading robots (equipped with powerful sensors and grippers to locate single parcels, analyze their size and shape, and determine the optimal unloading sequence), assistance robots for local or home delivery (follow delivery personnel to transport heavy items, presort shipments inside delivery vehicles, and autonomously deliver shipments to dedicated collection points), last-mile delivery, and distribution centres.[596, 697, 699, 702]
LAFM: perform maintenance.[699]
COP: innovation in order fulfillment with human-robot collaboration.[596]

11SimulationDRO: evaluation and assessment of road transport.[703]
FPR: analysis of supply chain activities, supply chain management optimization and logistics cost control, design and implementation of reverse logistics networks, planning and monitoring of fourth-party logistic (4PL) process.[598, 704710]
DD: define an optimal distribution cost for products shipped to wholesale customers.[711, 712]
WCOA: flow- oriented models of inventory control systems.[713]

12Synthetic biologyDD: biofuels for trucks and ships’ vessel.[714, 715]
MTT: biosensors, biosafety, and biosecurity.[716, 717]

WCOA: warehouse capacity optimization and automation, LAFM: logistics assets and facility maintenance, DD: delivery and distribution, COP: customer order picking, FPR: forecasting, planning and reporting, DRO: dynamic route optimization, PFM: procurement and financial management, TFDP: threat and fraud detection and prevention, MTT: monitoring, tracking and traceability, EMM: environment monitoring and management, and RFID: radio frequency identification.

Application areaWCOALAFMDDCOPFPRDROEMMPFMMTTTFDP

Number of technologies7884762453
Convergence (%)131515713114796

5. Conclusions

Just like the light set-up in the morning, the disruptive landscape of industry 4.0 in the industrial sectors is unlimited. This can be evidenced from the explosive use of neologism “4.0” among the academic and business communities. The convergence of disruptive technologies is incredible, and this is the remarkable power of industry 4.0 disruption in any industrial sector. Adoption of education 4.0 through implementation of the learning factory is paramount important for building the requisite skills into the workforce for industry 4.0. The concept of energy 4.0 is still nascent, and thus, much research is required to ensure its success because the amount of energy requirement has reached unprecedented level due to the automation of the industrial plants to make them industry 4.0-compliant. The present study demonstrated the convergence of disruptive technologies in agriculture, healthcare, and logistics industries. This might not depict the real-life situation because the study was solely based on the published literature and therefore limited by the availability of information. Nonetheless, it provides an insight into the convergence of industry 4.0 technologies in the industrial sectors. A number of disruptive technologies, however, have received very few applications in these selected industries. This points out the need for more research to increase the application areas of these technologies. More especially, application of synthetic biology in logistics needs to be investigated. Additionally, application of disruptive technologies in the field of dentistry should be expanded. The convergence of disruptive technologies in education and energy sectors should also be investigated.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

Authors OB, CM, MCM, NSM, and DT are grateful to the World Bank and the Inter-University Council of East Africa (IUCEA) for the scholarships awarded to them through the Africa Centre of Excellence II in Phytochemicals, Textiles and Renewable Energy (ACE II-PTRE) at Moi University. Author BK is thankful to the Centre of Excellence in Sustainable Agriculture and Agribusiness Management (CESAAM) at Egerton University for the scholarship award.

References

  1. C.-C. Kuo, J. Z. Shyu, and K. Ding, “Industrial revitalization via industry 4.0-a comparative policy analysis among China, Germany and the USA,” Global Transitions, vol. 1, pp. 3–14, 2019. View at: Publisher Site | Google Scholar
  2. M. Sony and S. Naik, “Industry 4.0 integration with socio-technical systems theory: a systematic review and proposed theoretical model,” Technology in Society, vol. 1, 2020. View at: Google Scholar
  3. T. Lins and R. A. R. Oliveira, “Cyber-physical production systems retrofitting in context of industry 4.0,” Computers & Industrial Engineering, vol. 139, pp. 1–13, 2020. View at: Publisher Site | Google Scholar
  4. F. Galati and B. Bigliardi, “Industry 4.0: emerging themes and future research avenues using a text mining approach,” Computers in Industry, vol. 109, pp. 100–113, 2019. View at: Publisher Site | Google Scholar
  5. T. Ruppert, S. Jaskó, T. Holczinger, and J. Abonyi, “Enabling technologies for operator 4.0: a survey,” Applied Sciences, vol. 8, no. 1650, p. 1, 2018. View at: Google Scholar
  6. I. Zolotová, P. Papcun, E. Kajáti, M. Miškuf, and J. Mocnej, “Smart and cognitive solutions for operator 4.0: laboratory H-CPPS case studies,” Computers & Industrial Engineering, vol. 8, 2018. View at: Google Scholar
  7. D. Romero, J. Stahre, and M. Taisch, “The Operator 4.0: towards socially sustainable factories of the future,” Computers & Industrial Engineering, vol. 139, pp. 1–5, 2019. View at: Google Scholar
  8. M. Jasiulewicz and A. Gola, “Maintenance 4.0 technologies for sustainable manufacturing-an overview,” IFAC-PapersOnLine, vol. 52, no. 10, pp. 91–96, 2019. View at: Publisher Site | Google Scholar
  9. M. Haarman, M. Mulders, and C. Vassiliadis, “Predictive maintenance 4.0: predict the unpredictable,” 2017. View at: Google Scholar
  10. H. Ç. Erkan and Ç. Erkan, “Industry 4.0 and competitiveness,” Procedia Computer Science, vol. 158, pp. 625–631, 2019. View at: Publisher Site | Google Scholar
  11. G. Beier, A. Ullrich, S. Niehoff, M. Reißig, and M. Habich, “Industry 4.0: how it is defined from a sociotechnical perspective and how much sustainability it includes-a literature review,” Journal of Cleaner Production, vol. 259, pp. 1–12, 2020. View at: Publisher Site | Google Scholar
  12. B. Eynard and Z. Cherfi, “Digital and organizational transformation of industrial systems,” Computers & Industrial Engineering, vol. 139, no. 1, 2020. View at: Publisher Site | Google Scholar
  13. U. Meyer, “The emergence of an envisioned future. Sensemaking in the case of “Industrie 4.0” in Germany,” Futures, vol. 109, pp. 130–141, 2019. View at: Publisher Site | Google Scholar
  14. W. Bauer, S. Schuler, T. Hornung, and J. Decker, “Development of a procedure model for human-centered industry 4.0 projects,” Procedia Manufacturing, vol. 39, pp. 877–885, 2019. View at: Publisher Site | Google Scholar
  15. O. Bongomin, E. O. Nganyi, M. R. Abswaidi, E. Hitiyise, and G. Tumusiime, “Sustainable and dynamic competitiveness towards technological leadership of industry 4.0: implications for East african community,” Journal of Engineering, vol. 2020, pp. 1–22, 2020. View at: Publisher Site | Google Scholar
  16. A. Rainnie and M. Dean, “Industry 4.0 and the future of quality work in the global digital economy,” Labour & Industry: A Journal of the Social and Economic Relations of Work, vol. 30, no. 1, pp. 16–33, 2020. View at: Publisher Site | Google Scholar
  17. M. Ghobakhloo, “Industry 4.0, digitization, and opportunities for sustainability,” Journal of Cleaner Production, vol. 30, 2019. View at: Google Scholar
  18. T. Yıldız, “Examining the concept of industry 4.0 studies using text mining and scientific mapping method,” Procedia Computer Science, vol. 158, pp. 498–507, 2019. View at: Google Scholar
  19. C. O. Klingenberg, M. Antônio, and V. Borges, “Industry 4.0 as a data-driven paradigm: a systematic literature review on technologies,” Journal of Manufacturing Technology Management, vol. 30, 2019. View at: Google Scholar
  20. G. Büchi, M. Cugno, and R. Castagnoli, “Smart factory performance and Industry 4.0,” Technological Forecasting & Social Change, vol. 150, pp. 1–10, 2020. View at: Google Scholar
  21. F. Rosin, P. Forget, S. Lamouri, and R. Pellerin, “Impacts of industry 4.0 technologies on lean principles,” International Journal of Production Research, vol. 58, no. 6, pp. 1644–1661, 2020. View at: Publisher Site | Google Scholar
  22. S. Luthra, A. Kumar, E. K. Zavadskas, S. K. Mangla, and J. A. Garza-reyes, “Industry 4.0 as an enabler of sustainability diffusion in supply chain: an analysis of influential strength of drivers in an emerging economy,” International Journal of Production Research, vol. 58, no. 5, pp. 1505–1521, 2020. View at: Publisher Site | Google Scholar
  23. C. G. Machado and E. H. D. Ribeiro da Silva, “Sustainable manufacturing in Industry 4.0: an emerging research agenda,” International Journal of Production Research, vol. 58, no. 5, pp. 1462–1484, 2020. View at: Publisher Site | Google Scholar
  24. B. Winroth, B. Bettayeb, M. H. Sahnoun, and F. Duval, “Towards predicting system disruption in industry 4.0: machine learning-based approach,” Procedia Computer Science, vol. 151, pp. 667–674, 2019. View at: Publisher Site | Google Scholar
  25. D. Mourtzis, S. Fotia, N. Boli, and E. Vlachou, “Modelling and quantification of industry 4.0 manufacturing complexity based on information theory: a robotics case study,” International Journal of Production Research, vol. 57, no. 22, pp. 6908–6921, 2019. View at: Publisher Site | Google Scholar
  26. A. G. Frank, L. S. Dalenogare, and N. F. Ayala, “Industry 4.0 technologies: implementation patterns in manufacturing companies,” International Journal of Production Economics, vol. 210, pp. 15–26, 2019. View at: Publisher Site | Google Scholar
  27. O. Bongomin, G. Gilibrays Ocen, E. Oyondi Nganyi, A. Musinguzi, and T. Omara, “Exponential disruptive technologies and the required skills of industry 4.0,” Journal of Engineering, vol. 2020, pp. 1–17, 2020. View at: Publisher Site | Google Scholar
  28. N. Dragicevic, A. Ullrich, E. Tsui, and N. Gronau, “A conceptual model of knowledge dynamics in the industry 4.0 smart grid scenario,” Knowledge Management Research & Practice, vol. 20, pp. 1–15, 2019. View at: Google Scholar
  29. M. M. Queiroz, S. C. F. Pereira, and M. C. Machado, “Industry 4.0 and digital supply chain capabilities A framework for understanding digitalisation challenges and opportunities,” Benchmarking: An International Journal, vol. 20, 2019. View at: Google Scholar
  30. M. Mariani and M. Borghi, “Industry 4.0: a bibliometric review of its managerial intellectual structure and potential evolution in the service industries,” Technological Forecasting & Social Change, vol. 149, pp. 1–24, 2019. View at: Publisher Site | Google Scholar
  31. M. Asif, “Are qm models aligned with industry 4.0? A perspective on current practices,” Journal of Cleaner Production, vol. 20, pp. 1–26, 2020. View at: Google Scholar
  32. A. Bruzzone, M. Massei, and K. Sinelshnkov, “Enabling strategic decisions for the industry of tomorrow,” Procedia Manufacturing, vol. 42, pp. 548–553, 2020. View at: Google Scholar
  33. A. Primi and M. Toselli, “A global perspective on industry 4.0 and development: new gaps or opportunities to leapfrog?” Journal of Economic Policy Reform, vol. 20, pp. 1–19, 2020. View at: Publisher Site | Google Scholar
  34. G. Culot, G. Nassimbeni, G. Orzes, M. Sartor, and M. Sartor, “The future of manufacturing: a delphi-based scenario analysis on industry 4.0,” Technological Forecasting & Social Change, vol. 20, 2020. View at: Google Scholar
  35. J. Weking, M. Stocker, M. Kowalkiewicz, M. Bohm, and H. Krcmar, “Leveraging industry 4.0-a business model pattern framework,” International Journal of Production Economics, vol. 225, pp. 1–17, 2020. View at: Publisher Site | Google Scholar
  36. A. G. Frank, G. H. S. Mendes, N. F. Ayala, and A. Ghezzi, “Servitization and Industry 4.0 convergence in the digital transformation of product firms: a business model innovation perspective,” Technological Forecasting and Social Change, vol. 141, pp. 341–351, 2019. View at: Publisher Site | Google Scholar
  37. M.-L. Martín-Peña, J.-M. Sánchez-Lopez, and E. Díaz-Garrido, “Servitization and digitalization in manufacturing: the in fluence on firm performance,” Journal of Business & Industrial Marketing, vol. 35, no. 3, pp. 564–574, 2020. View at: Google Scholar
  38. F. R. P. M. Vianna, A. R. Graeml, and J. Peinado, “The role of crowdsourcing in industry 4.0: a systematic literature review,” International Journal of Computer Integrated Manufacturing, vol. 33, no. 4, pp. 411–427, 2020. View at: Publisher Site | Google Scholar
  39. S. Rajput and S. P. Singh, “Connecting circular economy and industry 4.0,” International Journal of Information Management, vol. 49, pp. 98–113, 2019. View at: Publisher Site | Google Scholar
  40. G. Piscitelli, A. Ferazzoli, A. Petrillo, R. Cioffi, A. Parmentola, and M. Travaglioni, “Circular economy models in the industry 4.0 era: a review of the last decade,” Procedia Manufacturing, vol. 42, pp. 227–234, 2020. View at: Publisher Site | Google Scholar
  41. G. Yadav, S. Luthra, S. Jakhar, S. K. Mangla, and D. P. Rai, “A framework to overcome sustainable supply chain challenges through solution measures of industry 4.0 and circular economy: an automotive case,” Journal of Cleaner Production, vol. 20, 2020. View at: Google Scholar
  42. C. J. C. Jabbour, “First-mover firms in the transition towards the sharing economy in metallic natural resource-intensive industries: implications for the circular economy and emerging industry 4.0 technologies,” Resources Policy, vol. 66, pp. 1–13, 2020. View at: Google Scholar
  43. N. K. Dev, R. Shankar, and F. H. Qaiser, “Industry 4.0 and circular economy: operational excellence for sustainable reverse supply chain performance,” Resources, Conservation & Recycling, vol. 153, pp. 1–15, 2020. View at: Publisher Site | Google Scholar
  44. C. Chauhan, A. Sharma, and A. Singh, “A SAP-LAP linkages framework for integrating Industry 4.0 and circular economy,” Benchmarking: An International Journal, vol. 15, 2019. View at: Google Scholar
  45. J. Ordieres-meré, T. P. Remón, and J. Rubio, “Digitalization: an opportunity for contributing to sustainability from knowledge creation,” Sustainability, vol. 12, no. 1460, p. 1, 2020. View at: Publisher Site | Google Scholar
  46. B. Bigliardi, E. Bottani, and G. Casella, “Enabling technologies, application areas and impact of industry 4.0: a bibliographic analysis,” Procedia Manufacturing, vol. 42, pp. 322–326, 2020. View at: Google Scholar
  47. L. Fratini, I. Ragai, and L. Wang, “New trends in manufacturing systems research 2020,” Journal of Manufacturing Systems, vol. 15, pp. 1–3, 2020. View at: Google Scholar
  48. S. Aheleroff, “IoT-enabled smart appliances under industry 4.0: a case study,” Advanced Engineering Informatics, vol. 43, pp. 1–14, 2020. View at: Publisher Site | Google Scholar
  49. K. Lee, F. Malerba, and A. Primi, “The fourth industrial revolution, changing global value chains and industrial upgrading in emerging economies,” Journal of Economic Policy Reform, vol. 15, pp. 1–12, 2020. View at: Publisher Site | Google Scholar
  50. M. D. Anuşlu and S. Ü. Fırat, “Clustering analysis application on Industry 4.0-driven global indexes,” Procedia Computer Science, vol. 158, pp. 145–152, 2019. View at: Google Scholar
  51. G. Culot, “Behind the definition of industry 4.0: analysis and open questions,” International Journal of Production Economics, vol. 15, pp. 1–47, 2020. View at: Google Scholar
  52. F. Chiarello, L. Trivelli, A. Bonaccorsi, and G. Fantoni, “Extracting and mapping industry 4.0 technologies using wikipedia,” Computers in Industry, vol. 100, pp. 244–257, 2018. View at: Publisher Site | Google Scholar
  53. D. Ø. Madsen, “The emergence and rise of industry 4.0 viewed through the lens of management fashion theory,” Administrative Science, vol. 9, no. 71, p. 1, 2019. View at: Publisher Site | Google Scholar
  54. M. Shafique and X. Luo, “Nanotechnology in transportation vehicles: an overview of its applications, environmental, health and safety concerns,” Materials, vol. 12, no. 2493, p. 1, 2019. View at: Publisher Site | Google Scholar
  55. S. H. Moon, “Industry 4.0 for advanced manufacturing and its implementation,” Eurasian Journal of Analytical Chemistry, vol. 13, no. 6, pp. 491–497, 2018. View at: Google Scholar
  56. V. Alcácer and V. Cruz-machado, “Scanning the industry 4.0: a literature review on technologies for manufacturing systems,” Engineering Science and Technology, an International Journal, vol. 22, no. 3, pp. 899–919, 2019. View at: Publisher Site | Google Scholar
  57. J. Franke, A. Mayr, F. Shepherd, and C. Zeiselmair, “Six Sigma 4.0: data mining as supporting technology in zero error management,” Economic Factory Journal, vol. 114, no. 3, pp. 140–144, 2019. View at: Google Scholar
  58. F. Koenig, P. A. Found, and M. Kumar, “Innovative airport 4.0 condition-based maintenance system for baggage handling DCV systems,” International Journal of Productivity and Performance Management, vol. 68, no. 3, pp. 561–577, 2019. View at: Publisher Site | Google Scholar
  59. T. Paschou, F. Adrodegari, M. Rapaccini, N. Saccani, and M. Perona, “Towards service 4.0 : a new framework and research priorities,” Procedia CIRP, vol. 68, pp. 1–7, 2018. View at: Google Scholar
  60. A. M. Carvalho, P. Sampaio, E. Rebentisch, and P. Saraiva, “35 years of excellence, and perspectives ahead for excellence 4.0,” Total Quality Management, vol. 6, pp. 1–34, 2019. View at: Google Scholar
  61. M. Golan, Y. Cohen, and G. Singer, “A framework for operator-workstation interaction in Industry 4.0,” International Journal of Production Research, vol. 58, no. 8, pp. 2421–2432, 2020. View at: Publisher Site | Google Scholar
  62. Á. Segura, “Visual computing technologies to support the Operator 4.0,” Computers & Industrial Engineering, vol. 139, pp. 1–8, 2020. View at: Publisher Site | Google Scholar
  63. L. Gazzaneo, A. Padovano, and S. Umbrello, “Designing smart operator 4.0 for human values: a value sensitive design approach,” Procedia Manufacturing, vol. 42, pp. 219–226, 2020. View at: Google Scholar
  64. C. Liu and X. Xu, “Cyber-physical machine tool-the Era of machine tool 4.0,” Procedia CIRP, vol. 63, pp. 70–75, 2017. View at: Publisher Site | Google Scholar
  65. R. Kurth and T. Päßler, “Forming 4.0: smart machine components applied as a hybrid plain bearing and a tool clamping system,” Procedia Manufacturing, vol. 27, pp. 65–71, 2019. View at: Publisher Site | Google Scholar
  66. Z. Tehel, T. Wanyama, I. Singh, A. Gadhrri, and R. Schmidt, “From industry 4.0 to robotics 4.0-a conceptual framework for collaborative and intelligent robotic systems,” Procedia Manufacturing, vol. 46, pp. 591–599, 2020. View at: Publisher Site | Google Scholar
  67. A. Mayr and A. Kühl, “Lean 4.0-a conceptual conjunction of lean management and Industry 4.0,” Procedia CIRP, vol. 72, pp. 622–628, 2018. View at: Publisher Site | Google Scholar
  68. M. Weigelt, M. Hofbauer, and B. Mandl, “Integration of IT into A lean basic training: target group-specific insights and recommendations,” Procedia Manufacturing, vol. 31, pp. 52–59, 2019. View at: Publisher Site | Google Scholar
  69. A. Zonnenshain, R. S. Kenett, A. Zonnenshain, and R. S. Kenett, “Quality 4.0-the challenging future of quality engineering,” Quality Engineering, vol. 31, pp. 1–13, 2020. View at: Google Scholar
  70. S. Arsovski, “Social oriented quality: from quality 4.0 towards quailty 5.0,” in Proceedings of the 13th International Quality Conference, pp. 397–404, New York, NY, USA, 2019. View at: Google Scholar
  71. D. Mourtzis, N. Milas, and N. Athinaios, “Towards machine shop 4.0: a general machine model for CNC machine-tools through OPC-UA,” Procedia CIRP, vol. 78, pp. 301–306, 2018. View at: Publisher Site | Google Scholar
  72. L. Hartmann, T. Meudt, S. Seifermann, and J. Metternich, “Value stream method 4.0: holistic method to analyse and design value streams in the digital age,” Procedia CIRP, vol. 78, pp. 249–254, 2018. View at: Publisher Site | Google Scholar
  73. Y. Cohen, M. Faccio, and A. Elaluf, “Hierarchy of smart awareness in assembly 4.0 systems,” IFAC-PapersOnLine, vol. 52, no. 13, pp. 1508–1512, 2019. View at: Publisher Site | Google Scholar
  74. D. Guo, R. Y. Zhong, S. Ling, Y. Rong, and G. Q. Huang, “A roadmap for Assembly 4.0: self-configuration of fixed-position assembly islands under Graduation Intelligent Manufacturing System,” International Journal of Production Research, vol. 5, pp. 1–16, 2020. View at: Google Scholar
  75. R. Jose and S. Ramakrishna, “Materials 4.0: materials big data enabled materials discovery,” Applied Materials Today, vol. 10, pp. 127–132, 2018. View at: Publisher Site | Google Scholar
  76. S. Bysko, J. Krystek, and S. Bysko, “Automotive paint shop 4.0,” Computers & Industrial Engineering, vol. 10, pp. 1–13, 2018. View at: Google Scholar
  77. E. Lima, E. Gorski, E. F. R. Loures, E. A. P. Santos, and F. Deschamps, “Applying machine learning to AHP multicriteria decision making method to assets prioritization in the context of industrial maintenance 4.0,” IFAC-PapersOnLine, vol. 52, no. 13, pp. 2152–2157, 2019. View at: Publisher Site | Google Scholar
  78. P. Bertola and J. Teunissen, “Fashion 4.0. Innovating fashion industry through digital transformation,” Research Journal of Textile and Apparel, vol. 22, no. 4, pp. 352–369, 2018. View at: Publisher Site | Google Scholar
  79. O. Behr, “Fashion 4.0-digital innovation in the fashion industry,” Journal of Technology and Innovation Management, vol. 2, no. 1, pp. 1–9, 2018. View at: Google Scholar
  80. V. Luiz, J. L. Kovaleski, and R. N. Pagani, “Technology transfer in the supply chain oriented to industry 4.0 : a literature review,” Technology Analysis & Strategic Management, vol. 31, no. 5, pp. 546–562, 2019. View at: Google Scholar
  81. M. A. Rapela, Fostering Innovation for Agricultre 4.0: A Comprehensive Plant Germplasm System, Springer Nature Switzerland AG, Cham, Switzerland, 2019.
  82. A.-T. Braun, E. Colangelo, and T. Steckel, “Farming in the era of industrie 4.0,” Procedia CIRP, vol. 72, pp. 979–984, 2018. View at: Publisher Site | Google Scholar
  83. R. Berger, Farming 4.0: How Precision Agriculture Might Save the World: Precision Farming Improves Farmer Livelihoods and Ensures Sustainable Food Production, Springer, Munich, Germany, 2019.
  84. I. C. Reinhardt, C. O. Jorge, and D. T. Ring, “Current perspectives on the development of industry 4.0 in the pharmaceutical sector,” Journal of Industrial Information Integration, vol. 72, 2020. View at: Google Scholar
  85. W. S. Alaloul, M. S. Liew, N. A. W. A. Zawawi, I. B. Kennedy, and I. B. Kennedy, “Industrial Revolution 4.0 in the construction industry: challenges and opportunities for stakeholders,” Ain Shams Engineering Journal, vol. 11, no. 1, pp. 225–230, 2020. View at: Publisher Site | Google Scholar
  86. R. M. Ellahi, M. U. Ali Khan, and A. Shah, “Redesigning curriculum in line with industry 4.0,” Procedia Computer Science, vol. 151, pp. 699–708, 2019. View at: Publisher Site | Google Scholar
  87. R. Butt, H. Siddiqui, R. A. Soomro, and M. M. Asad, “Integration of industrial revolution 4.0 and iots in academia: a state-of-the-art review on the concept of education 4.0 in pakistan,” Interactive Technology and Smart Education, vol. 11, 2020. View at: Google Scholar
  88. B. K. M. Wong and S. A. S. Hazley, “The future of health tourism in the industrial revolution 4.0 era,” Journal of Tourism Futures, vol. 11, 2020. View at: Google Scholar
  89. A. Hussain, “Industrial revolution 4.0: implication to libraries and librarians,” Library Hi Tech News, vol. 37, no. 1, p. 1, 2020. View at: Google Scholar
  90. E. Gökalp, M. O. Gökalp, and P. E. Eren, “Industry 4.0 revolution in clothing and apparel factories: apparel 4.0,” Industry 4.0 From The Management Information Systems Perspectives, vol. 37, pp. 169–184, 2018. View at: Google Scholar
  91. M. C. Annosi, F. Brunetta, A. Monti, and F. Nati, “Is the trend your friend? An analysis of technology 4.0 investment decisions in agricultural SMEs,” Computers in Industry, vol. 109, pp. 59–71, 2019. View at: Publisher Site | Google Scholar
  92. M. Romer and S. Meißner, “Data-based services for smart load carriers: functional design and requirements analysis for internet of things technologies,” IFAC-PapersOnLine, vol. 52, no. 13, pp. 2098–2103, 2019. View at: Publisher Site | Google Scholar
  93. B. G. De Soto, I. Agustí-juan, S. Joss, and J. Hunhevicz, “Implications of Construction 4.0 to the workforce and organizational structures,” International Journal of Construction Management, vol. 5, pp. 1–13, 2019. View at: Google Scholar
  94. H. Lu, L. Guo, M. Azimi, and K. Huang, “Oil and Gas 4.0 era: a systematic review and outlook,” Computers in Industry, vol. 111, pp. 68–90, 2019. View at: Publisher Site | Google Scholar
  95. M. Lezoche, J. E. Hernandez, M. D. M. E. A. Diaz, H. Panetto, and J. Kacprzyk, “Agri-food 4.0: a survey of the supply chains and technologies for the future agriculture,” Computers in Industry, vol. 117, pp. 1–13, 2020. View at: Publisher Site | Google Scholar
  96. H. Panettoa, M. Lezochea, J. E. H. Hormazabal, M. D. M. E. A. Diaz, and J. Kacprzyk, “Special issue on Agri-Food 4.0 and digitalization in agriculture supply chains-new directions, challenges and applications,” Computers in Industry, vol. 116, pp. 4–6, 2020. View at: Publisher Site | Google Scholar
  97. J.-P. Belaud, N. Prioux, C. Vialle, and C. Sablayrolles, “Big data for agri-food 4.0: application to sustainability management for by-products supply chain,” Computers in Industry, vol. 111, pp. 41–50, 2019. View at: Publisher Site | Google Scholar
  98. J. Miranda, P. Ponce, A. Molina, and P. Wright, “Sensing, smart and sustainable technologies for Agri-Food 4.0,” Computers in Industry, vol. 108, pp. 21–36, 2019. View at: Publisher Site | Google Scholar
  99. B. Satuyeva, C. Sauranbayev, I. A. Ukaegbu, and H. S. V. S. K. Nunna, “Energy 4.0: towards IoT applications in Kazakhstan,” Procedia Computer Science, vol. 151, pp. 909–915, 2019. View at: Publisher Site | Google Scholar
  100. M. Seixas, R. Melicio, and V. Mendes, “Comparison of offshore and onshore wind systems with MPC five-level converter under energy 4.0,” Electric Power Components and Systems, vol. 46, no. 13, pp. 1399–1415, 2018. View at: Publisher Site | Google Scholar
  101. H. Kurgun, O. A. Kurgun, and E. Aktaş, “What does web 4.0 promise for tourism ecosystem? A qualitative research on tourism ecosystem stakeholders’ awareness,” Journal of Tourism and Hospitality Management, vol. 6, no. 1, pp. 55–65, 2018. View at: Publisher Site | Google Scholar
  102. Guidehouse, Energy Cloud 4.0: Capturing Business Value through Disruptive Energy Platforms, Guidehouse, Washington, DC, USA, 2020.
  103. P. Vingerhoets, M. Chebbo, and N. Hatziargyriou, “The digital energy system 4.0,” 2016. View at: Google Scholar
  104. Development Dimensions International, “A leader’s guide to manufacturing 4.0: four talent strategies to transform your organization for the future,” 2017. View at: Google Scholar
  105. T. J. Lopez-Garcia, J. A. Alvarez-Cedillo, T. A. Sanchez, and C. M. Vicario-Solorzano, “Review of trends in the educational model of distance education in Mexico, towards an education 4.0,” Computer Reviews Journal, vol. 3, pp. 111–121, 2019. View at: Google Scholar
  106. D. Mourtzis, E. Vlachou, G. Dimitrakopoulos, and V. Zogopoulos, “Cyber-physical systems and education 4.0-the teaching factory 4.0 concept,” Procedia Manufacturing, vol. 23, pp. 129–134, 2018. View at: Publisher Site | Google Scholar
  107. L. Farrell, T. Newman, and C. Corbel, “Literacy and the workplace revolution: a social view of literate work practices in Industry 4.0,” Discourse: Studies in the Cultural Politics of Education, vol. 1–15, 2020. View at: Google Scholar
  108. D. Janssen, C. Tummel, A. Richert, and I. Isenhardt, “Virtual environments in higher education-immersion as a key construct for learning 4.0,” International Journal of Advanced Corporate Learning, vol. 23, pp. 1–7, 2016. View at: Google Scholar
  109. M. Bartelt, J. Stecken, and B. Kuhlenkötter, “Automated production of individualized products for teaching I4.0 concepts,” Procedia Manufacturing, vol. 45, pp. 337–342, 2020. View at: Publisher Site | Google Scholar
  110. L. Angrisani, P. Arpaia, F. Bonavolontá, and N. Moccaldi, “A learning small enterprise networked with a FabLab: an academic course 4.0 in instrumentation and measurement,” Measurement, vol. 150, pp. 1–8, 2020. View at: Publisher Site | Google Scholar
  111. A. V. Laptevа and V. S. Efimov, “New generation of universities,” Journal of Siberian Federal University, vol. 11, no. 9, pp. 2681–2696, 2016. View at: Google Scholar
  112. K. Vodenko, “Science and education in the form 4.0: public policy and organization based on human and artificial intellectual capital,” Journal of Intellectual Capita, vol. 15, 2020. View at: Google Scholar
  113. S. Sathya, “ECLECTIC 4.0: the new learning model for business schools,” Higher Education, Skills and Work-Based Learning, vol. 10, no. 3, pp. 581–590, 2020. View at: Publisher Site | Google Scholar
  114. E. Flores, X. Xu, and Y. Lu, “Human Capital 4.0: a workforce competence typology for Industry 4.0,” Journal of Manufacturing Technology Management, vol. 31, no. 4, pp. 687–703, 2020. View at: Publisher Site | Google Scholar
  115. J. Grodotzki, T. R. Ortelt, and A. E. Tekkaya, “Remote and virtual labs for engineering education 4.0,” Procedia Manufacturing, vol. 26, pp. 1349–1360, 2018. View at: Google Scholar
  116. L. Moldovan, “State-of-the-art analysis on the knowledge and skills gaps on the topic of industry 4.0 and the requirements for work-based learning,” Procedia Manufacturing, vol. 32, pp. 294–301, 2019. View at: Publisher Site | Google Scholar
  117. K. Jordon, P. Dossou, and J. C. Junior, “Using lean manufacturing and machine learning for improving medicines procurement and dispatching in a hospital,” Procedia Manufacturing, vol. 38, pp. 1034–1041, 2020. View at: Google Scholar
  118. J. C. Pinheiro, P.-E. Dossou, and J. C. Junior, “Methods and concepts for elaborating a decision aided tool for optimizing healthcare medicines dispatching flows,” Procedia Manufacturing, vol. 38, pp. 209–216, 2020. View at: Google Scholar
  119. S. Winkelhaus and E. H. Grosse, “Logistics 4.0: a systematic review towards a new logistics system,” International Journal of Production Research, vol. 58, no. 1, pp. 18–43, 2020. View at: Publisher Site | Google Scholar
  120. G. L. Tortorella, F. S. Fogliatto, A. M. C. Vergara, R. Vassolo, and R. Sawhney, “Healthcare 4.0: trends, challenges and research directions,” Production Planning & Control, vol. 38, pp. 1–16, 2019. View at: Google Scholar
  121. A. C. B. Monteiro, R. P. França, V. V. Estrela, Y. Iano, A. Khelassi, and N. Razmjooy, “Health 4.0: applications, management, technologies and review,” Medical Technologies Journal, vol. 2, no. 4, pp. 262–276, 2019. View at: Google Scholar
  122. J. Lopes, T. Guimarães, and M. F. Santos, “Predictive and prescriptive analytics in healthcare: a survey,” Procedia Computer Science, vol. 170, pp. 1029–1034, 2020. View at: Publisher Site | Google Scholar
  123. A. Moreira and M. F. Santos, “Multichannel interaction for healthcare intelligent decision support,” Procedia Computer Science, vol. 170, pp. 1053–1058, 2020. View at: Publisher Site | Google Scholar
  124. L. B. Liboni, L. H. B. Liboni, and L. O. Cezarino, “Electric utility 4.0: trends and challenges towards process safety and environmental protection,” Process Safety and Environmental Protection, vol. 117, pp. 593–605, 2018. View at: Publisher Site | Google Scholar
  125. V. Yavas and Y. D. Ozkan-ozen, “Logistics centers in the new industrial era: a proposed framework for logistics center 4.0,” Transportation Research Part E, vol. 135, pp. 1–18, 2020. View at: Publisher Site | Google Scholar
  126. D. Mourtzis, V. Siatras, J. Angelopoulos, and N. Panopoulos, “An augmented reality collaborative product design cloud-based platform in the context of learning factory,” Procedia Manufacturing, vol. 45, pp. 546–551, 2020. View at: Publisher Site | Google Scholar
  127. W. Wereda and J. Wo´zniak, “Building relationships with customer 4.0 in the era of marketing 4.0: the case study of innovative enterprises in Poland,” Social Sciences, vol. 8, no. 177, p. 1, 2019. View at: Publisher Site | Google Scholar
  128. A. U. Rahayu, I. Herawaty, N. R. S, A. S. Prafitriyani, A. P. Afini, and A. P. Kautsar, “Marketing 4.0: a digital transformation in pharmaceutical industry to reach customer brand experience,” Farmaka, vol. 16, no. 1, p. 80, 2018. View at: Google Scholar
  129. B. P. Sullivan, S. Desai, J. Sole, M. Rossi, L. Ramundo, and S. Terzi, “Maritime 4.0-opportunities in digitalization and advanced manufacturing for vessel development,” Procedia Manufacturing, vol. 42, pp. 246–253, 2020. View at: Publisher Site | Google Scholar
  130. G. Aiello, A. Giallanza, and G. Mascarella, “Towards Shipping 4.0. A preliminary gap analysis,” Procedia Manufacturing, vol. 42, pp. 24–29, 2020. View at: Publisher Site | Google Scholar
  131. J. Wullbrandt, J. Pontevedra, and S. Fochler, “Center of excellence for lean enterprise 4.0,” Procedia Manufacturing, vol. 31, pp. 66–71, 2019. View at: Google Scholar
  132. P.-E. Dossou, “Impact of Sustainability on the supply chain 4.0 performance,” Procedia Manufacturing, vol. 17, pp. 452–459, 2018. View at: Publisher Site | Google Scholar
  133. D. Makris, Z. N. L. Hansen, and O. Khan, “Adapting to supply chain 4.0: an explorative study of multinational companies,” Supply Chain Forum: An International Journal, vol. 20, no. 2, pp. 116–131, 2019. View at: Publisher Site | Google Scholar
  134. C. Chute and T. French, “Introducing care 4.0: an integrated care paradigm built on industry 4.0 capabilities,” International Journal of Environmental Research and Public Health, vol. 16, no. 2247, p. 1, 2019. View at: Publisher Site | Google Scholar
  135. S. A. Gawankar, A. Gunasekaran, and S. Kamble, “A study on investments in the big data-driven supply chain, performance measures and organisational performance in Indian retail 4.0 context,” International Journal of Production Research, vol. 58, no. 5, pp. 1574–1593, 2020. View at: Publisher Site | Google Scholar
  136. C. K. H. Lee, “A GA-based optimisation model for big data analytics supporting anticipatory shipping in Retail 4.0,” International Journal of Production Research, vol. 55, no. 2, pp. 593–605, 2017. View at: Publisher Site | Google Scholar
  137. O. Kunze, “Replicators, ground drones and crowd logistics a vision of urban logistics in the year 2030,” Transportation Research Procedia, vol. 19, pp. 286–299, 2016. View at: Google Scholar
  138. N. Karunarathna, R. Wickramarachchi, and K. Vidanagamachchi, “A study of the implications of logistics 4.0 in future warehousing: a Sri Lankan perspective,” in Proceedings of the International Conference on Industrial Engineering and Operations Management, pp. 1024–1035, New York, NY, USA, 2019. View at: Google Scholar
  139. FlytBase, “Drone automation for warehouse 4.0,” 2019. View at: Google Scholar
  140. L. Wawrla, O. Maghazei, and T. Netland, “Applications of drones in warehouse operations,” 2019. View at: Google Scholar
  141. B. Asdecker and V. Felch, “Development of an Industry 4.0 maturity model for the delivery process in supply chains,” Journal of Modelling in Management, vol. 13, no. 4, pp. 840–883, 2018. View at: Publisher Site | Google Scholar
  142. S. Bag, L. C. Wood, S. K. Mangla, and S. Luthra, “Procurement 4.0 and its implications on business process performance in a circular economy,” Resources, Conservation & Recycling, vol. 152, pp. 1-2, 2020. View at: Publisher Site | Google Scholar
  143. J. J. Blazquez-resino, S. Gutiérrez-broncano, and P. Ruiz-palomino, “Dealing with human resources in the age of consumer 4.0: aiming to improve service delivery,” Frontiers in Psychology, vol. 0, no. 3058, 2020. View at: Google Scholar
  144. B. Nicoletti, “Fintech and procurement finance 4.0,” Palgrave Studies in Financial Services Technology, vol. 1, pp. 155–248, 2018. View at: Google Scholar
  145. B. King, Bank 4.0: Banking Everywhere, Never at a Bank, Marshall Cavendish Business, Singapore, 2018.
  146. B. Sivathanu and R. Pillai, Smart HR 4.0-How Industry 4.0 Is Disrupting HR, Human Resource Management International Digest, Singapore, 2018.
  147. F. Ansari, “Knowledge management 4.0: theoretical and practical considerations in cyber physical production systems,” IFAC-PapersOnLine, vol. 52, no. 13, pp. 1597–1602, 2019. View at: Publisher Site | Google Scholar
  148. E. Tom, G. Neumann, and J. Majewska, Theory and Applications in the Knowledge Economy, Marshall Cavendish Business, Poznan, Poland, 2018.
  149. R. Kelly, Constructing Leadership 4.0: Swarm Leadership and the Fourth Industrial Revolution, Springer Nature Switzerland AG, Kent, UK, 2019.
  150. S. Helming, F. Ungermann, N. Hierath, N. Stricker, and G. Lanza, “Development of a training concept for leadership 4.0 in production environments,” Procedia Manufacturing, vol. 31, pp. 38–44, 2019. View at: Publisher Site | Google Scholar
  151. D. Rogers, “Building management 4.0: smart technology and the great American retrofit,” Construction Research and Innovation, vol. 9, no. 1, pp. 21–25, 2018. View at: Publisher Site | Google Scholar
  152. A. Cooper and N. Sebake, “Neighbourhood 4.0: a response to urban futures,” in Proceedings of the Out-of-the Box 2018 Conference, New York, NY, USA, 2019. View at: Google Scholar
  153. G. Reischauer, “Industry 4.0 as policy-driven discourse to institutionalize innovation systems in manufacturing,” Technological Forecasting & Social Change, vol. 23, pp. 1–8, 2018. View at: Google Scholar
  154. H. Fischer, M. Engler, and S. Sauer, “A human-centered perspective on software quality: acceptance criteria for work 4.0,” Lecture Notes in Computer Science, vol. 2017, 2017. View at: Google Scholar
  155. World Economic Forum, HR 4.0 : Shaping People Strategies in the Fourth Industrial Revolution, Cologny, Geneva, Switzerland, 2019.
  156. L. B. Liboni, L. O. Cezarino, C. J. C. Jabbour, B. G. Oliveira, and N. O. Stefanelli, “Smart industry and the pathways to HRM 4.0: implications for SCM,” Supply Chain Management: An International Journal, vol. 24, no. 1, pp. 124–146, 2019. View at: Publisher Site | Google Scholar
  157. U. Schäffer and J. Weber, “Controlling 4.0,” Controlling & Management Review, vol. 60, no. 6, p. 3, 2016. View at: Publisher Site | Google Scholar
  158. World Economic Forum, Globalization 4.0: Shaping a New Global Architecture in the Age of the Fourth Industrial Revolution, Cologny, Geneva, Switzerland, 2019.
  159. M. E. Gladden, “Who will Be the members of society 5.0? Towards an anthropology of technologically posthumanized future societies,” Social Sciences, vol. 8, no. 148, p. 1, 2019. View at: Publisher Site | Google Scholar
  160. E. M. Frazzon, C. M. T. Rodriguez, M. M. Pereira, M. C. Pires, and I. Uhlmann, “Towards supply chain management 4.0,” Brazilian Journal of Operations & Production Management, vol. 16, no. 2, pp. 180–191, 2019. View at: Publisher Site | Google Scholar
  161. W. Cho and E. M. Berman, “E-government 4.0 in Thailand : the role of central agencies,” Information Polity, vol. 23, pp. 343–353, 2018. View at: Google Scholar
  162. The United Nations Development Programme, “Development 4.0: opportunities and challenges for accelerating progress towards the sustainable development goals in asia and the pacific,” 2018. View at: Google Scholar
  163. Skills Development Scotland, “Skills 4.0: a skills model to drive scotland’s future,” 2018. View at: Google Scholar
  164. J. I. T. Goena, Á. L. de Nalda, E. V. Díez, and J. S. García, “Professional competences 4.0,” 2018. View at: Google Scholar
  165. P. Buasuwan, “Rethinking Thai higher education for Thailand 4.0,” Asian Education and Development Studies, vol. 7, no. 2, pp. 157–173, 2018. View at: Publisher Site | Google Scholar
  166. P. Chiengkul, “Uneven development, inequality and concentration of power: a critique of Thailand 4.0,” Third World Quarterly, vol. 40, no. 9, pp. 1689–1707, 2019. View at: Publisher Site | Google Scholar
  167. D. Mourtzis, J. Angelopoulos, G. Dimitrakopoulos, and J. Angelopoulos, “Design and development of a flexible manufacturing cell in the concept of learning factory paradigm for the education of generation 4.0 engineers,” Procedia Manufacturing, vol. 45, pp. 361–366, 2020. View at: Publisher Site | Google Scholar
  168. I. Zambon, M. Cecchini, G. Egidi, M. G. Saporito, and A. Colantoni, “Revolution 4.0 : industry vs agriculture in a future development for SMEs,” Processes, vol. 7, no. 36, p. 1, 2019. View at: Publisher Site | Google Scholar
  169. O. Onday, “Japan’s society 5.0: going beyond industry 4.0,” Business and Economics Journal, vol. 10, no. 2, pp. 2–7, 2019. View at: Google Scholar
  170. T. Salimova, N. Guskova, I. Krakovskaya, and E. Sirota, “From industry 4.0 to Society 5.0: challenges for sustainable competitiveness of Russian industry,” IOP Conference Series: Materials Science and Engineering, vol. 497, 2019. View at: Publisher Site | Google Scholar
  171. E. D. G. Fraser and M. Campbell, “Agriculture 5.0: reconciling production with planetary health,” One Earth, vol. 1, no. 3, pp. 278–280, 2019. View at: Publisher Site | Google Scholar
  172. V. Saiz-rubio, “From smart farming towards agriculture 5.0 : a review on crop data management,” Agronomy, vol. 10, 2020. View at: Google Scholar
  173. K. A. Demir, G. Döven, and B. Sezen, “Industry 5.0 and human-robot Co-working,” Procedia Computer Science, vol. 158, pp. 688–695, 2019. View at: Publisher Site | Google Scholar
  174. F. Aslam, W. Aimin, M. Li, and K. U. Rehman, “Innovation in the era of IoT and industry 5.0: absolute innovation management (AIM) framework,” Information, vol. 11, no. 124, 2020. View at: Publisher Site | Google Scholar
  175. S. Nahavandi, “Industry 5.0-a human-centric solution,” Sustainability, vol. 11, no. 4371, 2019. View at: Publisher Site | Google Scholar
  176. G. F. Mukwawaya and B. Emwanu, ““Assessing the readiness of South Africa for Industry 4.0-analysis of government policy, skills and education,” in Proceedings of the International Conference on Industrial Engineering and Operations Management, pp. 1587–1604, New York, NY, USA, 2018. View at: Google Scholar
  177. A. A. Hussin, “Education 4.0 made Simple: ideas for teaching,” International Journal of Education & Literacy Studies, vol. 6, no. 3, pp. 92–98, 2018. View at: Google Scholar
  178. B. Andrea and T. Jiří, “Requirements for education and qualification of people in industry 4.0,” Procedia Manufacturing, vol. 11, pp. 2195–2202, 2017. View at: Google Scholar
  179. C. Selim, K. Yasanur, and G. Eray, “Adapting engineering education to industry 4.0 vision,” Technologies, vol. 10, no. 7, pp. 1–13, 2019. View at: Google Scholar
  180. D. Mourtzis, D. Tsakalos, F. Xanthi, and V. Zogopoulos, “Optimization of highly automated production line: an advanced engineering educational approach,” Procedia Manufacturing, vol. 31, pp. 45–51, 2019. View at: Publisher Site | Google Scholar
  181. A. A. Shahroom and N. Hussin, “Industrial revolution 4.0 and education,” International Journal of Academic Research in Business and Social Sciences, vol. 8, no. 9, pp. 314–319, 2018. View at: Publisher Site | Google Scholar
  182. C. C. Chea, J. Tan, and J. Huan, “Higher education 4.0: the possibilities and challenges,” Journal of Social Sciences and Humanities, vol. 5, no. 2, pp. 81–85, 2019. View at: Google Scholar
  183. M. A. Peters, “Technological unemployment: educating for the fourth industrial revolution,” Educational Philosophy and Theory, vol. 49, no. 1, pp. 1–6, 2017. View at: Publisher Site | Google Scholar
  184. R. Jamaludin, G. Town, and E. Mckay, “Are we ready for Education 4.0 within ASEAN higher education institutions? Thriving for knowledge, industry and humanity in a dynamic higher education ecosystem?” Journal of Applied Research in Higher Education, vol. 49, pp. 1–13, 2019. View at: Google Scholar
  185. OECD, “OCED future of education and skills 2030: project background,” 2019. View at: Google Scholar
  186. T. Molla and D. Cuthbert, “Calibrating the PhD for Industry 4.0: global concerns, national agendas and Australian institutional responses,” Policy Reviews in Higher Education, vol. 3, no. 2, pp. 167–188, 2019. View at: Publisher Site | Google Scholar
  187. A. Cropley, “Creativity-focused technology education in the age of industry 4.0,” Creativity Research Journal, vol. 49, pp. 1–8, 2020. View at: Google Scholar
  188. S. EMokhtar, J. A. Q. Alshboul, and G. O. A. Shahin, “Towards data-driven education with learning analytics for educator 4.0,” Journal of Physics: Conference Series, vol. 1339, 2019. View at: Publisher Site | Google Scholar
  189. A. Hariharasudan and S. Kot, “A scoping review on digital english and education 4.0 for industry 4.0,” Social Sciences, vol. 7, p. 227, 2018. View at: Google Scholar
  190. M. Ciolacu, P. M. Svasta, W. Berg, and H. Popp, “Education 4.0 for tall thin engineer in a data driven society,” in Proceedings of the 2017 IEEE 23rd International Symposium for Design and Technology in Electronic Packaging, London, UK, 2018. View at: Google Scholar
  191. S. H. Halili, “Technological advancemnts in education 4.0,” The Online Journal of Distance Education and E-Learning, vol. 7, no. 1, pp. 63–69, 2019. View at: Google Scholar
  192. H. K. Pangandaman, N. D. Ali, J. H. C. Lambayong, and M. L. G. Ergas, “Philippine higher education vis-à-vis education 4.0: a scoping review,” International Journal of Advanced Research and Publications ISSN, vol. 3, no. 3, pp. 65–69, 2019. View at: Google Scholar
  193. M. Maria, F. Shahbodin, and N. C. Pee, “Malaysian higher education system towards industry 4.0-current trends overview,” in Proceedings of the 3rd International Conference on Applied Science and Technology (ICAST’18), London, UK, 2018. View at: Google Scholar
  194. FICCI-EY, Leapfrogging to Education 4.0: Student at the Core, Springer, New Delhi, India, 2017.
  195. N. Songkram, S. Chootongchai, J. Khlaisang, and P. Koraneeki, “Education 3.0 system to enhance twenty-first century skills for higher education learners in thailand,” Interactive Learning Environments, vol. 3, pp. 1–17, 2019. View at: Google Scholar
  196. S. Fareri, G. Fantoni, F. Chiarello, E. Coli, and A. Binda, “Computers in Industry Estimating Industry 4.0 impact on job profiles and skills using text mining,” Computers in Industry, vol. 118, pp. 1–19, 2020. View at: Publisher Site | Google Scholar
  197. C. Catal and B. Tekinerdogan, “Aligning education for the life sciences domain to support digitalization and industry 4.0,” Procedia Computer Science, vol. 158, pp. 99–106, 2019. View at: Publisher Site | Google Scholar
  198. World Economic Forum, Schools of the Future: Defining New Models of Education for the Fourth Industrial Revolution, Cologny, Geneva, Switzerland, 2020.
  199. D. Mourtzis and G. Chryssolouris, “Editorial,” Procedia Manufacturing, vol. 23, pp. 7-8, 2018. View at: Publisher Site | Google Scholar
  200. E. Rauch, F. Morandell, and D. T. Matt, “AD design guidelines for implementing I4.0 learning factories,” Procedia Manufacturing, vol. 31, pp. 239–244, 2019. View at: Google Scholar
  201. T. Rossmeissl, E. Groß, M. Tzempetonidou, and J. Siegert, “Living learning environments,” Procedia Manufacturing, vol. 31, pp. 20–25, 2019. View at: Publisher Site | Google Scholar
  202. F. Baena, A. Guarin, J. Mora, J. Sauza, and S. Retat, “Learning factory: the path to industry 4.0,” Procedia Manufacturing, vol. 9, pp. 73–80, 2017. View at: Google Scholar
  203. E. Mo, D. Centea, I. Singh, and T. Wanyama, “SEPT learning factory for industry 4.0 education and applied research,” Procedia Manufacturing, vol. 23, pp. 249–254, 2018. View at: Google Scholar
  204. B. Salah, M. H. Abidi, S. H. Mian, M. Krid, H. Alkhalefah, and A. Abdo, “Virtual reality-based engineering education to enhance manufacturing sustainability in industry 4.0,” Sustainability, vol. 11, p. 1477, 2019. View at: Publisher Site | Google Scholar
  205. L. Büth, S. Blume, G. Posselt, and C. Herrmann, “Training concept for and with digitalization in learning factories: an energy efficiency training case,” Procedia Manufacturing, vol. 23, pp. 171–176, 2018. View at: Publisher Site | Google Scholar
  206. S. Imran, E. Szczerbicki, and C. Sanin, “Propostion of the methodology for data acquisitoion, analysis and visualization in support of industry 4.0,” Procedia Computer Science, vol. 159, p. 1976, 2019. View at: Google Scholar
  207. I. Daniyan, K. Mpofu, M. Oyesola, B. Ramatsetse, and A. Adeodu, “Artificial intelligence for predictive maintenance in the railcar learning factories,” Procedia Manufacturing, vol. 45, pp. 13–18, 2020. View at: Publisher Site | Google Scholar
  208. F. Sieckmann, N. Petrusch, and H. Kohl, “Effectivity of Learning Factories to convey problem solving competencies,” Procedia Manufacturing, vol. 45, pp. 228–233, 2020. View at: Publisher Site | Google Scholar
  209. L. F. C. S. Durão, M. O. Guimarães, M. S. Salerno, and E. Zancul, “Uncertainty management in advanced manufacturing implementation: the case for learning factories,” Procedia Manufacturing, vol. 31, pp. 213–218, 2019. View at: Publisher Site | Google Scholar
  210. J. Siegert, T. Schlegel, and T. Bauernhansl, “Verifiable competencies for production technology,” Procedia Manufacturing, vol. 45, pp. 466–472, 2020. View at: Google Scholar
  211. B. J. Ralph, A. Schwarz, and M. Stockinger, “An implementation approach for an academic learning factory for the metal forming industry with special focus on digital twins and finite elements analysis,” Procedia Manufacturing, vol. 45, pp. 253–258, 2020. View at: Google Scholar
  212. J. L. Jooste and K. Leipzig, “Teaching maintenance plan development in a learning factory environment,” Procedia Manufacturing, vol. 45, pp. 379–385, 2020. View at: Publisher Site | Google Scholar
  213. E. Louw, O. C. Chaim, D. Braatz, B. Muschard, and H. Rozenfeld, “Exploring gamification to support manufacturing education on industry 4.0 as an enabler for innovation and sustainability,” Procedia Manufacturing, vol. 21, pp. 438–445, 2018. View at: Publisher Site | Google Scholar
  214. K. Lensing and J. Friedhoff, “Designing a curriculum for the Internet-of-Things-Laboratory to foster creativity and a maker mindset within varying target groups,” Procedia Manufacturing, vol. 23, pp. 231–236, 2018. View at: Publisher Site | Google Scholar
  215. P. Herstätter, T. Wildbolz, M. Hulla, and C. Ramsauer, “Data acquisition to enable research, education and training in learning factories and makerspaces,” Procedia Manufacturing, vol. 45, pp. 289–294, 2020. View at: Publisher Site | Google Scholar
  216. E. A. Sadaj, M. Hulla, and C. Ramsauer, “Design approach for a learning factory to train services,” Procedia Manufacturing, vol. 45, pp. 60–65, 2020. View at: Publisher Site | Google Scholar
  217. A. Kohlweiss, E. Auberger, A. Ketenci, and C. Ramsauer, “Integration of a teardown approach at graz university of Technology´s LEAD factory,” Procedia Manufacturing, vol. 45, pp. 240–245, 2020. View at: Publisher Site | Google Scholar
  218. M. Eder, A. Ketenci, E. Auberger, M. Gotthard, and C. Ramsauer, “Integration of low-cost digital energy meters in learning factory assembly lines,” Procedia Manufacturing, vol. 45, pp. 202–207, 2020. View at: Publisher Site | Google Scholar
  219. A. Santana, P. Afonso, A. Zanin, and R. Wernke, “Learn how to cope with volatility in operations at Graz University of Technology’s LEAD Factory,” Procedia Manufacturing, vol. 23, pp. 15–20, 2018. View at: Google Scholar
  220. M. Wolf, P. Herstätter, and C. Ramsauer, “Using the IIM LEAD factory to identify countermeasures for the demographic challenge,” Procedia Manufacturing, vol. 31, pp. 123–128, 2019. View at: Publisher Site | Google Scholar
  221. D. Antonelli, “Tiphys: an open networked platform for higher education on industry 4.0,” Procedia CIRP, vol. 79, pp. 706–711, 2018. View at: Google Scholar
  222. D. Centea, I. Singh, T. Wanyama, M. Magolon, J. Boer, and M. Elbestawi, “Using the SEPT learning factory for the implementation of industry 4.0: case of SMEs,” Procedia Manufacturing, vol. 45, pp. 102–107, 2020. View at: Google Scholar
  223. D. Centea, I. Singh, M. Yakout, J. Boer, and M. Elbestawi, “Opportunities and challenges in integrating additive manufacturing in the SEPT learning factory,” Procedia Manufacturing, vol. 45, pp. 108–113, 2020. View at: Publisher Site | Google Scholar
  224. D. Centea, I. Singh, and M. Elbestawi, “SEPT approaches for education and training using a learning factory,” Procedia Manufacturing, vol. 31, pp. 109–115, 2019. View at: Publisher Site | Google Scholar
  225. M. Lanz, R. Pieters, and R. Ghabcheloo, “Learning environment for robotics education and industry-academia collaboration,” Procedia Manufacturing, vol. 31, pp. 79–84, 2019. View at: Publisher Site | Google Scholar
  226. H. Nylund, V. Valjus, V. Toivonen, M. Lanz, and H. Nieminen, “The virtual FMS-an engineering education environment,” Procedia Manufacturing, vol. 31, pp. 251–257, 2019. View at: Publisher Site | Google Scholar
  227. J. Wermann, A. W. Colombo, A. Pechmann, and M. Zarte, “Using an interdisciplinary demonstration platform for teaching Industry 4.0,” Procedia Manufacturing, vol. 31, pp. 302–308, 2019. View at: Google Scholar
  228. J.-A. Scholz, F. Sieckmann, and H. Kohl, “Implementation with agile project management approaches: case study of an industrie 4.0 learning factory in China,” Procedia Manufacturing, vol. 45, pp. 234–239, 2020. View at: Publisher Site | Google Scholar
  229. L. Hausmann, F. Wirth, M. O. Flammer, J. Hofmann, and J. Fleischer, “Aligning vocational training to the electromobile transformation by establishing the “Training Factory Stator Production”-a methodical deficit analysis with derivation of measures,” Procedia Manufacturing, vol. 45, pp. 448–453, 2020. View at: Publisher Site | Google Scholar
  230. Z. Kemény, R. Beregi, J. Nacsa, C. Kardos, and D. Horváth, “Example of a problem-to-course life cycle in layout and process planning at the MTA SZTAKI learning factories,” Procedia Manufacturing, vol. 31, pp. 206–212, 2019. View at: Publisher Site | Google Scholar
  231. C. Block, D. Kreimeier, and B. Kuhlenkötter, “Holistic approach for teaching IT skills in a production environment,” Procedia Manufacturing, vol. 23, pp. 57–62, 2018. View at: Publisher Site | Google Scholar
  232. A. Conrad, H. Oberc, and M. Wannöffel, “Co-determination-an interdisciplinary concept to train PhD students from different disciplines,” Procedia Manufacturing, vol. 31, pp. 129–135, 2019. View at: Publisher Site | Google Scholar
  233. V. D. Kuhlenkötter, S. Lang, and T. Reggelin, “Integration of LiFi technology in an industry 4.0 learning factory,” Procedia Manufacturing, vol. 31, pp. 232–238, 2019. View at: Publisher Site | Google Scholar
  234. J. Siegert, L. Zarco, T. Schlegel, and T. Bauernhansl, “Software control system requirements for ultra-flexible learning factories,” Procedia Manufacturing, vol. 45, pp. 442–447, 2020. View at: Publisher Site | Google Scholar
  235. S. T. Mortensen, K. K. Nygaard, and O. Madsen, “Outline of an industry 4.0 awareness game,” Procedia Manufacturing, vol. 31, pp. 309–315, 2019. View at: Publisher Site | Google Scholar
  236. M. Hennig, G. Reisinger, T. Trautner, P. Hold, D. Gerhard, and A. Mazak, “TU wien pilot factory industry 4.0,” Procedia Manufacturing, vol. 31, pp. 200–205, 2019. View at: Publisher Site | Google Scholar
  237. L. Zhao-hui, Z. Wei-min, X. Zhong-yue, S. Jia-bin, and L. Dongdong, “Research on extended carbon emissions accounting method and its application in sustainable manufacturing,” Procedia Manufacturing, vol. 43, pp. 175–182, 2020. View at: Publisher Site | Google Scholar
  238. T. Zaimovic, “Setting speed-limit on Industry 4.0-an outlook of power-mix and grid capacity challenge,” Procedia Computer Science, vol. 158, pp. 107–115, 2019. View at: Publisher Site | Google Scholar
  239. Z. Huang, H. Yu, Z. Peng, and Y. Feng, “Planning community energy system in the industry 4.0 era: achievements, challenges and a potential solution,” Renewable and Sustainable Energy Reviews, vol. 78, pp. 710–721, 2017. View at: Publisher Site | Google Scholar
  240. F. Schafer, C. Andr, C. David, P. Crovato, and R. Righi, “Looking at energy through the lens of industry 4.0: a systematic literature review of concerns and challenges,” Computers & Industrial Engineering, vol. 78, pp. 1–57, 2020. View at: Google Scholar
  241. R. Ferrero, M. Collotta, M. V. Bueno-delgado, and H. Chen, “Smart management energy systems in industry 4.0,” Energies, vol. 13, no. 382, 2020. View at: Google Scholar
  242. A. Hidayatno, A. R. Destyanto, and C. A. Hulu, “Industry 4.0 technology implementation impact to industrial sustainable energy in Indonesia: a model conceptualization,” Energy Procedia, vol. 156, pp. 227–233, 2019. View at: Publisher Site | Google Scholar
  243. UNIDO, Accelerating Clean Energy through Industry 4.0 Manufacturing the Next Revolution, Springer, Vienna, Austria, 2017.
  244. S. Scharl and A. Praktiknjo, “The role of a digital industry 4.0 in a renewable energy system,” International Journal of Energy Research, vol. 43, no. 8, pp. 3891–3904, 2019. View at: Publisher Site | Google Scholar
  245. C. Koyunoğlu and H. Karaca, “Application of Industry 4.0 on biomass liquefaction study: a case study,” Procedia Computer Science, vol. 158, pp. 401–406, 2019. View at: Google Scholar
  246. A. K. Shukla, R. Nath, P. K. Muhuri, and Q. M. D. Lohani, “Energy efficient multi-objective scheduling of tasks with interval type-2 fuzzy timing constraints in an Industry 4.0 ecosystem,” Engineering Applications of Artificial Intelligence, vol. 87, pp. 1–18, 2020. View at: Publisher Site | Google Scholar
  247. V. J. Mawson and B. R. Hughes, “The development of modelling tools to improve energy efficiency in manufacturing processes and systems,” Journal of Manufacturing Systems, vol. 51, pp. 95–105, 2019. View at: Publisher Site | Google Scholar
  248. R. Rentsch and B. Karpuschewski, “Energy and Resource efficiency analysis of manufacturing chains by modular process models and simulation,” Procedia Manufacturing, vol. 43, pp. 159–166, 2020. View at: Publisher Site | Google Scholar
  249. G. Thiele, O. Heimann, K. Grabowski, and J. Krüger, “Framework for energy efficiency optimization of industrial systems based on the Control Layer Model,” Procedia Manufacturing, vol. 33, pp. 414–421, 2019. View at: Publisher Site | Google Scholar
  250. F. Wang, L. Ma, and C. Yuan, “Experimental methods to study environmental sustainability of silicon-based lithium ion battery manufacturing,” Procedia Manufacturing, vol. 33, pp. 501–507, 2019. View at: Publisher Site | Google Scholar
  251. S. Fielke, B. Taylor, and E. Jakku, “Digitalisation of agricultural knowledge and advice networks: a state-of-the- art review,” Agricultural Systems, vol. 180, pp. 1–11, 2020. View at: Publisher Site | Google Scholar
  252. L. Klerkx, “Advisory services and transformation, plurality and disruption of agriculture and food systems: towards a new research agenda for agricultural education and extension studies,” The Journal of Agricultural Education and Extension, vol. 26, no. 2, pp. 131–140, 2020. View at: Publisher Site | Google Scholar
  253. V. Bonneau, B. Copigneaux, L. Probst, and B. Pedersen, “Industry 4.0 in agriculture: focus on iot aspects,” 2017. View at: Google Scholar
  254. N. M. Trendov, S. Varas, and M. Zeng, Digital Technologies in Agriculture and Rural Areas, Briefing Paper, Rome, 2019.
  255. B. Talukder, A. Blay-palmer, W. Gary, and K. W. Hipel, “Towards complexity of agricultural sustainability assessment: main issues and concerns,” Environmental and Sustainability Indicators, vol. 26, pp. 1–11, 2020. View at: Google Scholar
  256. M. Anshari, M. N. Almunawar, M. Masri, and M. Hamdan, “Digital marketplace and FinTech to support agriculture sustainability,” Energy Procedia, vol. 156, pp. 234–238, 2019. View at: Publisher Site | Google Scholar
  257. I. Charania and X. Li, “Smart farming: agriculture’s shift from a labor intensive to technology native industry,” Internet of Things, vol. 1–22, 2019. View at: Google Scholar
  258. J. Huang and L. Zhang, “The big data processing platform for intelligent agriculture,” AIP Conference Proceedings, vol. 1864, 2017. View at: Google Scholar
  259. F. Bu and X. Wang, “A smart agriculture IoT system based on deep reinforcement learning,” Future Generation Computer Systems, vol. 99, pp. 500–507, 2019. View at: Publisher Site | Google Scholar
  260. J. M. S. Garcia and D. P. Jerez, “Agro-food projects: analysis of procedures within digital revolution projects,” Nternational Journal of Managing Projects in Business, vol. 9, 2019. View at: Google Scholar
  261. L. Klerkx, E. Jakku, and P. Labarthe, “A review of social science on digital agriculture, smart farming and agriculture 4.0: new contributions and a future research agenda,” NJAS-Wageningen Journal of Life Sciences, vol. 90, 2019. View at: Google Scholar
  262. P. O. Skobelev, Е. V. Simonova, S. V. Smirnov, D. S. Budaeve, G. Y. Voshchuke, and A. L. Morokovd, “Development of a kmowledge base in the smart farming system for agricultural enterprise management,” Procedia Computer Science, vol. 150, pp. 154–161, 2019. View at: Google Scholar
  263. I. Kovács and I. Husti, “The role of digitalization in the agricultural 4.0-how to connect the industry 4.0 to agriculture?” Hungarian Agricultural Engineering, vol. 7410, no. 33, pp. 38–42, 2018. View at: Publisher Site | Google Scholar
  264. M. De Clercq, A. Vats, and A. Biel, “Agriculture 4.0: the future of farming technology,” 2018. View at: Google Scholar
  265. Z. Zhai, J. F. Martínez, V. Beltran, and N. L. Martínez, “Decision support systems for agriculture 4.0: survey and challenges,” Computers and Electronics in Agriculture, vol. 170, pp. 1–16, 2020. View at: Publisher Site | Google Scholar
  266. L. Klerkx and D. Rose, “Dealing with the game-changing technologies of Agriculture 4.0: how do we manage diversity and responsibility in food system transition pathways?” Global Food Security, vol. 24, pp. 1–7, 2020. View at: Publisher Site | Google Scholar
  267. P. K. Thornton, P. Kristjanson, W. Förch, C. Barahona, L. Cramer, and S. Pradhan, “Is agricultural adaptation to global change in lower-income countries on track to meet the future food production challenge?” Global Environmental Change, vol. 52, pp. 37–48, 2018. View at: Publisher Site | Google Scholar
  268. A. Luque, M. E. Peralta, A. de las Heras, and A. Córdoba, “State of the industry 4.0 in the andalusian food sector,” Procedia Manufacturing, vol. 13, pp. 1199–1205, 2017. View at: Publisher Site | Google Scholar
  269. S. Saetta and V. Caldarelli, “How to increase the sustainability of the agri-food supply chain through innovations in 4.0 perspective: a first case study analysis,” Procedia Manufacturing, vol. 42, pp. 333–336, 2020. View at: Publisher Site | Google Scholar
  270. N. Shahrubudina, T. C. Leea, and R. Ramlana, “An overview on 3D printing technology: technological, materials, and technology: applications,” Procedia Manufacturing, vol. 35, pp. 1286–1296, 2019. View at: Google Scholar
  271. C. He, M. Zhang, and Z. Fang, “3D printing of food: pretreatment and post- treatment of materials,” Critical Reviews in Food Science and Nutrition, vol. 35, pp. 1–14, 2019. View at: Google Scholar
  272. J. L. Tran, “3D-Printed food,” Minnesota Journal of Law, Science & Technology, vol. 17, no. 2, pp. 1–27, 2016. View at: Google Scholar
  273. Rural Industries Research & Development Corporation, “3D printing: a fact sheet series on new and emerging transformative technologies in australian agriculture,” 2016. View at: Google Scholar
  274. DeltaHedron, The Impact of Emerging Tecnologies on Agriculture: Recent Trends, Hull HU1 1UU, London, UK, 2019.
  275. F. C. Godoi, S. Prakash, and P. B. R. Bhandari, “3d printing technologies applied for food design: status and prospects,” Journal of Food Engineering, vol. 17, pp. 1–27, 2016. View at: Google Scholar
  276. M. Javaid and A. Haleem, “Using additive manufacturing applications for design and development of food and agricultural equipments,” International Journal of Materials and Product Technology, vol. 58, no. 2/3, pp. 225–238, 2019. View at: Publisher Site | Google Scholar
  277. P. Phupattanasilp and S. Tong, “Augmented reality in the integrative internet of things (AR-IoT): application for precision farming,” Sustainability, vol. 11, no. 2658, 2019. View at: Publisher Site | Google Scholar
  278. J. M. Pearce, “Applications of open source 3-D printing on small farms,” Organic Farming, vol. 1, no. 1, pp. 19–35, 2015. View at: Publisher Site | Google Scholar
  279. K. Jha, A. Doshi, P. Patel, and M. Shah, “A comprehensive review on automation in agriculture using artificial intelligence,” Artificial Intelligence in Agriculture, vol. 2, pp. 1–12, 2019. View at: Publisher Site | Google Scholar
  280. Deloitte, “Transforming agriculture through digital technologies,” 2020. View at: Google Scholar
  281. H. Tian, T. Wang, Y. Liu, X. Qiao, and Y. Li, “Computer vision technology in agricultural automation-a review,” Information Processing in Agriculture, vol. 7, no. 1, pp. 1–19, 2020. View at: Publisher Site | Google Scholar
  282. AI Forum of New Zealand and AsureQuality, “Artificial intelligence for agriculture in New Zealand,” 2019. View at: Google Scholar
  283. M. J. Smith, “Getting value from artificial intelligence in agriculture,” Animal Production Science, vol. 60, no. 1, pp. 46–54, 2020. View at: Publisher Site | Google Scholar
  284. OECD, Artificial Intelligence in Society, Springer, Paris, France, 2019.
  285. A. Gurumurthy and D. Bharthur, Taking Stock of Artificial Intelligence in Indian Agriculture, Springer, New Delhi, India, 2019.
  286. S. G. Salcedo, Artificial Intelligence in Digital Agriculture Towards in Field Grapevine Monitoring Using Non-invasive Sensors, University of La Rioja, New Delhi, India, 2019.
  287. V. Dharmaraj and C. Vijayanand, “Artificial intelligence (AI) in agriculture,” International Journal of Current Microbiology and Applied Sciences, vol. 7, no. 12, pp. 2122–2128, 2018. View at: Publisher Site | Google Scholar
  288. M. Xi, M. Adcock, and O. McCulloch, “Future agriculture farm management using augmented reality,” in Proceedings of the IEEE Workshop On Virtual And Augmented Realities For Good, pp. 1–3, New York, NY, USA, 2018. View at: Google Scholar
  289. M. Caria, G. Sara, G. Todde, M. Polese, and A. Pazzona, “Exploring smart glasses for augmented reality: a valuable and integrative tool in precision livestock farming,” Animals, vol. 9, 2019. View at: Google Scholar
  290. A. Katsaros, E. Keramopoulos, and M. Salampasis, “A prototype Application for cultivation optimization using augmented reality,” in Proceedings of the 8th International Conference on Information and Communication Technologies in Agriculture, pp. 805–811, London, UK, 2017. View at: Google Scholar
  291. B. Weichelt, A. Yoder, C. Bendixsen, M. Pilz, G. Minor, and M. Keifer, “Augmented reality farm MAPPER development: lessons learned from an app designed to improve rural emergency response,” Journal of Agromedicine, vol. 23, no. 3, pp. 284–296, 2018. View at: Publisher Site | Google Scholar
  292. J. Huuskonen and T. Oksanen, “Soil sampling with drones and augmented reality in precision agriculture,” Computers and Electronics in Agriculture, vol. 154, pp. 25–35, 2018. View at: Publisher Site | Google Scholar
  293. Y. Huang, Z.-X. Chen, T. Yu, X.-Z. Huang, and X.-F. Gu, “Agricultural remote sensing big data: management and applications,” Journal of Integrative Agriculture, vol. 17, no. 9, pp. 1915–1931, 2018. View at: Publisher Site | Google Scholar
  294. K. Prasad, “Big data in the bigger world of agriculture today,” IEEE India Info, vol. 14, no. 3, pp. 154–157, 2019. View at: Google Scholar
  295. N. Tantalaki, S. Souravlas, and M. Roumeliotis, “Data-Driven decision making in precision agriculture: the rise of big data in agricultural systems data-driven decision making in precision Agriculture,” Journal of Agricultural & Food Information, vol. 14, pp. 1–37, 2019. View at: Google Scholar
  296. C. C. Sekhar, J. U. Kumar, B. K. Kumar, and C. Sekhar, “Effective use of big data analytics in crop planning to increase agriculture production in India,” International Journal of Advanced Science and Technology, vol. 113, pp. 31–40, 2018. View at: Publisher Site | Google Scholar
  297. T. Guo and Y. Wang, “Big data application issues in the agricultural modernization of China,” Ekoloji, vol. 28, no. 107, pp. 3677–3688, 2019. View at: Google Scholar
  298. J. A. Delgado, D. P. Roberts, and B. Vandenberg, “Big data analysis for sustainable agriculture on a geospatial cloud framework,” Frontiers in Sustainable Food Systems, vol. 3, no. 54, pp. 1–13, 2019. View at: Publisher Site | Google Scholar
  299. K. H. Coble, A. K. Mishra, and S. Ferrell, “Big data in agriculture: a challenge for the future,” Applied Economic Perspectives and Policy, vol. 40, no. 1, pp. 79–96, 2018. View at: Publisher Site | Google Scholar
  300. FAO and ITU, E-Agriculture in Action: Big Data for Agriculture, Springer, Bangkok, Thailand, 2019.
  301. N. Griffin, D. Northrup, S. Murray, and T. C. Mockler, “Big data driven agriculture: big data analytics in plant breeding, genomics, and the use of remote sensing technologies to advance crop productivity,” The Plant Phenome Journal, vol. 2, 2019. View at: Google Scholar
  302. C. Zhang and Z. Liu, “Application of big data technology in agricultural Internet of Things,” International Journal ofDistributed Sensor Networks, vol. 15, no. 10, pp. 1–11, 2019. View at: Publisher Site | Google Scholar
  303. V. S. Yadav and A. R. Singh, “A systematic literature review of blockchain technology in agriculture,” in Proceedings of the International Conference on Industrial Engineering and Operations Management, pp. 973–981, New York, NY, USA, 2019. View at: Google Scholar
  304. V. S. Yadav and A. R. Singh, “Use of blockchain to solve select issues of Indian farmers,” AIP Conference Proceedings, vol. 2148, 2019. View at: Google Scholar
  305. H. Xiong, T. Dalhaus, P. Wang, and J. Huang, “Blockchain technology for agriculture: applications and rationale,” Frontiers in Blockchain, vol. 3, no. 7, 2020. View at: Publisher Site | Google Scholar
  306. A. D. Nazarov, V. V Shvedov, and V. V. S. Ural, “Blockchain technology and smart contracts in the agro-industrial complex of Russia Blockchain technology and smart contracts in the agro- industrial complex of Russia,” IOP Conference Series: Earth and Environmental Science, vol. 315, 2019. View at: Publisher Site | Google Scholar
  307. M. Mattern, “Exploring blockchain applications to agricultural finance,” 2018. View at: Google Scholar
  308. G. Sylvester, “E-agriculture in Action: Blockchain for Agriculture, Opportunities and Challenges,” 2019. View at: Google Scholar
  309. T. Surasak, N. Wattanavichean, C. Preuksakarn, and S. C. Huang, “Thai agriculture products traceability system using blockchain and internet of things,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 9, pp. 578–583, 2019. View at: Publisher Site | Google Scholar
  310. J. Lin, A. Zhang, Z. Shen, and Y. Chai, “Blockchain and IoT based food traceability for smart agriculture,” in Proceedings of the 3rd International Conference on Crowd Science And Engineering, pp. 1–6, Singapore, 2018. View at: Google Scholar
  311. G. Mirabelli and V. Solina, “Blockchain and agricultural supply chains traceability: research trends and future challenges,” Procedia Manufacturing, vol. 42, pp. 414–421, 2020. View at: Publisher Site | Google Scholar
  312. S. S. Kamble, A. Gunasekaran, and R. Sharma, “Modeling the blockchain enabled traceability in agriculture supply chain,” International Journal of Information Management, vol. 42, pp. 1–16, 2019. View at: Google Scholar
  313. C. Addison, I. Boto, T. Heinen, and K. Lohento, “Opportunities of blockchain for agriculture of blockchain for agriculture,” 2019. View at: Google Scholar
  314. M. Tripoli and J. Schmidhuber, “Emerging opportunities for the application of blockchain in the agri-food industry,” 2018. View at: Google Scholar
  315. D. Mao, Z. Hao, F. Wang, and H. Li, “Innovative blockchain-based approach for sustainable and credible environment in food trade: a case study in shandong province, China,” Sustainability, vol. 10, p. 3149, 2018. View at: Publisher Site | Google Scholar
  316. V. Micale and H. Van Caenegem, “Blockchain climate risk crop insurance,” 2019. View at: Google Scholar
  317. A. Kamilaris, A. Fonts, and F. X. Prenafeta-Boldύ, “The rise of blockchain technology in agriculture and food supply chains,” Trends in Food Science & Technology, vol. 91, pp. 640–652, 2019. View at: Publisher Site | Google Scholar
  318. D. Zhang, “Application of blockchain technology in incentivizing efficient use of rural wastes: a case study on yitong system,” Energy Procedia, vol. 158, pp. 6707–6714, 2019. View at: Publisher Site | Google Scholar
  319. F. Yang, K. Wang, Y. Han, and Z. Qiao, “A cloud-based digital farm management system for vegetable production process management and quality traceability,” Sustainability, vol. 10, 2018. View at: Google Scholar
  320. E. M. Emeana, L. Trenchard, and K. Dehnen-schmutz, “The revolution of mobile phone-enabled services for agricultural development (m-Agri services) in Africa: the challenges for sustainability,” Sustainability, vol. 12, no. 485, 2020. View at: Publisher Site | Google Scholar
  321. S. Singh, I. Chana, and R. Buyya, “Agri-info: cloud based autonomic system for delivering agriculture as a service,” Internet of Things, vol. 1–18, 2019. View at: Google Scholar
  322. V. Anandhi and J. P. Venkatapirabu, “Cloud computing-an innovative initiative for technology transfer,” International Journal of Current Microbiology and Applied Sciences, vol. 9, no. 3, pp. 645–647, 2020. View at: Publisher Site | Google Scholar
  323. J. Joshi, “Machine learning based cloud integrated farming,” ICMLSC ’17, vol. 13-16, 2017. View at: Google Scholar
  324. S. M. Alam, A. Hasssan, A. Bashir, and M. Iqbal, “Smart tunnel farming model: an inculcation of cloud computing with cortex for reliable agricultural production,” International Journal of Sensor Networks and Data Communications, vol. 7, no. 4, pp. 1–11, 2018. View at: Google Scholar
  325. U. R. Mogili and B. B. V. L. Deepak, “Review on application of drone systems in precision agriculture,” Procedia Computer Science, vol. 133, pp. 502–509, 2018. View at: Publisher Site | Google Scholar
  326. P. Diamantoulakis, “Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVS) in smart farming: a comprehensive review,” Internet of Things, vol. 1–43, 2020. View at: Google Scholar
  327. J. V. N. Nakshmi, K. S. Hemanth, and J. Bharath, “Optimizing quality and outputs by improving variable rate prescriptions in agriculture using UAVs,” Procedia Computer Science, vol. 167, pp. 1981–1990, 2020. View at: Publisher Site | Google Scholar
  328. PwC Belgium and Agoria, “A drone’s eye view: overview of the belgian uav ecosystem & the development of commercial drone application in Belgium,” 2018. View at: Google Scholar
  329. A. Brown, “Agri-drones in China: market status and growth potential,” 2017. View at: Google Scholar
  330. PwC, “Clarity from above: PwC global report on the commercial applications of drone technology,” 2016. View at: Google Scholar
  331. P. Daponte, “A review on the use of drones for precision agriculture A review on the use of drones for precision agriculture,” IOP Conference Series: Earth and Environmental Science, vol. 275, 2019. View at: Publisher Site | Google Scholar
  332. L. Probst, B. Pedersen, and L. Dakkak-Arnoux, “Digital transfermation monitor: drones in agriculture,” 2018. View at: Google Scholar
  333. D. C. Tsouros, S. Bibi, and P. G. Sarigiannidis, “A review on UAV-based applications for precision agriculture,” Information, vol. 10, no. 349, 2019. View at: Publisher Site | Google Scholar
  334. S. Ahirwar, R. Swarnkar, S. Bhukya, and G. Namwade, “Application of drone in agriculture,” International Journal of Current Microbiology and Applied Sciences, vol. 8, no. 1, pp. 2500–2505, 2019. View at: Publisher Site | Google Scholar
  335. K. R. Krishna, Agricultural Drones: A Peaceful Pursuit, Apple Academic Press, Oakville, OA, USA, 2018.
  336. I. A. Joiner, “Drones : agriculture’s new best friend!,” Modern Concepts & Developments in Agronomy, vol. 1, no. 4, pp. 62-63, 2018. View at: Google Scholar
  337. P. Frankelius, C. Norrman, and K. Johansen, “Agricultural innovation and the role of institutions: lessons from the game of drones,” Journal of Agricultural and Environmental Ethics, vol. 1, pp. 1–27, 2017. View at: Google Scholar
  338. D. K. Giles and R. C. Billing, “Deployment and performance of a UAV for crop spraying,” Chemical Engineering Transactions, vol. 44, pp. 307–312, 2015. View at: Google Scholar
  339. S. Kanase, S. Patwegar, P. Patil, A. Pore, and Y. Kadam, “Agriculture drone sprayer,” International Journal of Recent Trends in Engineering & Research, vol. 4, no. 3, pp. 181–185, 2018. View at: Google Scholar
  340. G. Sylvester, “E-agriculture in action: drones for agriculture,” 2018. View at: Google Scholar
  341. A. Villa-Henriksen, G. T. C. Edwards, L. A. Pesonen, and O. Green, “Internet of Things in arable farming: implementation, applications, challenges and potential,” Biosystems Engineering, vol. 191, pp. 60–84, 2020. View at: Publisher Site | Google Scholar
  342. O. Sørensen, T. A. Rahman, I. Orikumhi, C. Y. Leow, and M. H. D. N. Hindia, “An overview of internet of things (IoT) and data analytics in agriculture: benefits and challenges,” IEEE Internet of Things Journal, vol. 5, no. 5, pp. 3758–3773, 2018. View at: Publisher Site | Google Scholar
  343. P. P. Ray, “Internet of things for smart agriculture: technologies, practices and future direction,” Journal of Ambient Intelligence and Smart Environments, vol. 9, no. 4, pp. 395–420, 2017. View at: Publisher Site | Google Scholar
  344. A. Srilakshmi, J. Rakkini, K. R. Sekar, and R. Manikandan, “A comparative study on internet of things (IoT) and its applications in smart agriculture,” Pharmacognosy Journal, vol. 10, no. 2, pp. 260–264, 2018. View at: Publisher Site | Google Scholar
  345. A. Pathak, M. Amazuddin, M. J. Abedin, K. Andersson, R. Mustafa, and M. S. Hossain, “IoT based smart system to support agricultural parameters: a case study,” Procedia Computer Science, vol. 155, pp. 648–653, 2019. View at: Publisher Site | Google Scholar
  346. M. Ayaz, S. Member, and M. A. S. Member, “Internet-of-Things (IoT) based smart Agriculture: towards making the fields talk,” IEEE Access Date, vol. xx, pp. 1–34, 2019. View at: Google Scholar
  347. L. Zhang and I. K. Dabipi, “Internet of Things Applications for Agriculture,” John Wiley & Sons, London, UK, 2018. View at: Google Scholar
  348. R. Dhall and H. Agrawal, “An improved energy efficient duty cycling algorithm for IoT based precision agriculture,” Procedia Computer Science, vol. 141, pp. 135–142, 2018. View at: Publisher Site | Google Scholar
  349. K. Gunasekera, A. N. Borrero, F. Vasuian, and K. P. Bryceson, “Experiences in building an IoT infrastructure for agriculture education,” Procedia Computer Science, vol. 135, pp. 155–162, 2018. View at: Publisher Site | Google Scholar
  350. S. K. Roy and D. De, “Genetic algorithm based internet of precision agricultural things (IoPAT) for agriculture 4.0,” Internet of Things, vol. 1–19, 2020. View at: Google Scholar
  351. D. Thakur, Y. Kumar, and S. Vijendra, “Smart irrigation and intrusions detection in agricultural field using IoT,” Procedia Computer Science, vol. 167, pp. 154–162, 2020. View at: Google Scholar
  352. R. Zhang, F. Hao, and X. Sun, “The design of agricultural machinery service management system based on internet of things,” Procedia Computer Science, vol. 107, pp. 53–57, 2017. View at: Publisher Site | Google Scholar
  353. C. Parisi, M. Vigani, and E. Rodríguez-Cerezo, “Agricultural Nanotechnologies: what are the current possibilities?” Nano Today, vol. 10, no. 2, pp. 124–127, 2015. View at: Publisher Site | Google Scholar
  354. H. N. Cheng, K. T. Klasson, T. Asakura, and Q. Wu, “Nanotechnology in Agriculture,” American Chemical Society, Washington, DC, USA, 2016. View at: Google Scholar
  355. R. Prasad, A. Bhattacharyya, and Q. D. Nguyen, “Nanotechnology in sustainable agriculture: recent developments, challenges, and perspectives,” Frontiers in Microbiology, vol. 8, no. 1014, 2017. View at: Publisher Site | Google Scholar
  356. A. Elizabath, M. Babychan, A. M. Mathew, and G. M. Syriac, “Application of nanotechnology in agriculture,” International Journal of Pure & Applied Bioscience, vol. 7, no. 2, pp. 131–139, 2019. View at: Publisher Site | Google Scholar
  357. D.-Y. Kim, A. Kadam, S. Shinde, R. G. Saratale, and J. Patra, “Recent developments in nanotechnology transforming the agricultural sector: a transition replete with opportunities,” Journal of the Science of Food and Agriculture, vol. 98, no. 3, pp. 849–864, 2018. View at: Publisher Site | Google Scholar
  358. Y. Ghodake, K. Hasan, G. J. Ahammed, M. Li, and H. Yin, “Applications of nanotechnology in plant growth and crop protection: a review,” Molecules, vol. 24, no. 2558, 2019. View at: Publisher Site | Google Scholar
  359. N. Ndlovu, T. Mayaya, C. Muitire, and N. Munyengwa, “Nanotechnology applications in crop production and food systems,” International Journal of Plant Breeding and Crop Science, vol. 7, no. 1, pp. 624–634, 2020. View at: Google Scholar
  360. L. Marchiol, “Nanotechnology in agriculture: new opportunities and perspectives,” IntechOpen, London, UK, 2018. View at: Google Scholar
  361. X. He, H. Deng, and H. Hwang, “The current application of nanotechnology in food and agriculture,” Journal of Food and Drug Analysis, vol. 27, pp. 1–21, 2018. View at: Google Scholar
  362. R. A. Taha, “Nanotechnology and its application in agriculture,” Advances in Plants & Agriculture Research, vol. 3, no. 2, p. 15406, 2016. View at: Publisher Site | Google Scholar
  363. S. Thakur, S. Thakur, and R. Kumar, “Bio-nanotechnology and its role in agriculture and food industry,” Journal of Molecular and Genetic Medicine, vol. 12, no. 1, pp. 1–5, 2018. View at: Publisher Site | Google Scholar
  364. I. Iavicoli, V. Leso, D. H. Beezhold, and A. A. Shvedova, “Nanotechnology in agriculture: opportunities, toxicological implications, and occupational risks,” Toxicol Appl Pharmacol, vol. 329, pp. 96–111, 2019. View at: Google Scholar
  365. H. Joshi, P. Choudhary, and S. L. Mundra, “Future prospects of nanotechnology in agriculture,” International Journal of Chemical Studies, vol. 7, no. 2, pp. 957–963, 2019. View at: Google Scholar
  366. A. Y. Ghidan and T. M. Al Antary, “Applications of nanotechnology in agriculture,” IntechOpen, London, UK, 2019. View at: Google Scholar
  367. S. Mishra, C. Keswani, P. C. Abhilash, L. F. Fraceto, and H. B. Singh, “Integrated approach of agri-nanotechnology: challenges and future trends,” Frontiers in Plant Science, vol. 8, no. 471, 2017. View at: Publisher Site | Google Scholar
  368. D. G. Panpatte, Nanotechnology for Agriculture: Crop Production & Protection, Springer Nature Singapore Pte Ltd, Singapore, 2019.
  369. S. Ali, O. Shafique, T. Mahmood, M. A. Hanif, I. Ahmed, and B. A. Khan, “A review about perspectives of nanotechnology in agriculture,” Pakistan Journal of Agricultural Research, vol. 30, no. 2, pp. 116–121, 2018. View at: Google Scholar
  370. Y. Xiao, S. C. Jiang, X. Wang, T. Muhammad, P. Song, and B. Zhou, “Mitigation of biofouling in agricultural water distribution systems with nanobubbles,” Environment International, vol. 141, pp. 1–12, 2020. View at: Google Scholar
  371. I. Chung, G. Rajakumar, T. Gomathi, S. Kim, and M. Thiruvengadam, “Nanotechnology for human food: advances and perspective,” Frontiers in Life Science, vol. 3769, no. 10, pp. 63–72, 2017. View at: Publisher Site | Google Scholar
  372. R. R. Shamshiri, “Research and development in agricultural robotics: a perspective of digital farming,” International Journal of Agricultural and Biological Engineering, vol. 11, no. 4, pp. 1–14, 2018. View at: Google Scholar
  373. K. R. Aravind, P. Raja, and M. Pérez-ruiz, “Task-based agricultural mobile robots in arable farming: a review,” Spanish Journal of Agricultural Research, vol. 15, no. 1, pp. 1–16, 2017. View at: Publisher Site | Google Scholar
  374. D. Zhang, “Robotics and mechatronics for agriculture,” CRC Press Taylor & Francis Group, Boca Raton, MA, USA, 2018. View at: Google Scholar
  375. S. G. Vougioukas, “Agricultural robotics,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 2, no. 1, pp. 365–392, 2019. View at: Publisher Site | Google Scholar
  376. M. Mitra, “Robotic farmers in agriculture,” Advances in Robotics & Mechanical Engineering, vol. 1, no. 5, pp. 91–93, 2019. View at: Publisher Site | Google Scholar
  377. B. L. Steward, J. Gai, and L. Tang, “The use of agricultural robots in weed management and control,” Robotics and automation for improving agriculture, vol. 44, pp. 1–25, 2019. View at: Google Scholar
  378. D. Albiero, “Agricultural robotics: a promising challenge,” Current Agriculture Research Journal, vol. 7, no. 1, 2019. View at: Google Scholar
  379. A. Bechar and C. Vigneault, “Agricultural robots for field operations: concepts and components,” Biosystems Engineering, vol. 149, pp. 94–111, 2016. View at: Publisher Site | Google Scholar
  380. M. G. Lampridi, “A case-based economic assessment of robotics employment in precision arable farming,” Agronomy, vol. 9, no. 175, 2019. View at: Google Scholar
  381. A. Proskokov, M. Momot, and D. N. Nesteruk, “Prospects and features of robotics in Russian crop farming,” Journal of Physics: Conference Series, vol. 803, 2017. View at: Google Scholar
  382. UK-RAS Network, “Agricultural robotics: the future of robotic agriculture,” 2018. View at: Google Scholar
  383. J. W. Jones, “Brief history of agricultural systems modeling,” Agricultural Systems, vol. 155, pp. 240–254, 2016. View at: Google Scholar
  384. K. O. Rauff and R. Bello, “A review of crop growth simulation models as tools for agricultural meteorology,” Agricultural Sciences, vol. 6, no. 9, pp. 1098–1105, 2015. View at: Publisher Site | Google Scholar
  385. M. I. Khan and D. Walker, “Application of crop growth simulation models in agriculture with special reference to water management planning,” International Journal Of Core Engineering & Management, vol. 2, no. 5, pp. 113–130, 2015. View at: Google Scholar
  386. J. Van Wart, P. Grassini, H. Yang, L. Claessens, A. Jarvis, and K. G. Cassman, “Creating long-term weather data from thin air for crop simulation modeling,” Agricultural and Forest Meteorology, vol. 209-210, pp. 49–58, 2015. View at: Publisher Site | Google Scholar
  387. A. K. Moghaddam, H. Sadrnia, H. Aghel, and M. Bannayan, “Optimization of tillage and sowing operations using discrete event simulation,” Research in Agricultural Engineering, vol. 64, no. 4, pp. 187–194, 2018. View at: Google Scholar
  388. X. Bai, H. Yan, L. Pan, and H. Huang, “Multi-agent modeling and simulation of farmland use change in a farming-pastoral zone: a case study of qianjingou town in inner Mongolia, China,” Sustainability, vol. 7, no. 11, pp. 14802–14833, 2015. View at: Publisher Site | Google Scholar
  389. FAO, “A modelling system for the assessment of the agricultural impacts of climate change,” 2015. View at: Google Scholar
  390. E. T. Wurtzel, “Revolutionizing agriculture with synthetic biology,” Nature Plants, vol. 7, pp. 1–15, 2019. View at: Google Scholar
  391. Rural Industries Research & Development Corporation, Synthetic Biology: Transformative Technologies, Wagga Wagga NSW, New York, NY, USA, 2016.
  392. E. W. Welch, M. Bagley, and T. Kuiken, “Potential implications of new synthetic biology and genomic research trajectories on the international treaty for plant genetic resources for food and agriculture (ITPGRFA or treaty),” 2017. View at: Google Scholar
  393. L. J. Frewer, D. Coles, A. M. Dijkstra, S. Kuznesof, H. Kendall, and G. Kaptan, “Synthetic biology applied in the agrifood sector: societal priorities and pitfalls,” Applied Studies in Agribusiness and Commerce, vol. 10, no. 2-3, pp. 89–95, 2016. View at: Publisher Site | Google Scholar
  394. K. V. Pixley and K. E. Giller, “Genome editing, gene drives, and synthetic biology: will they contribute to disease-resistant crops, and who will benefit?” Annual Review of Phytopathology, vol. 57, no. 1, pp. 165–188, 2019. View at: Publisher Site | Google Scholar
  395. H. Falck-Zepeda, G. R. Szilvay, and K.-M. Oksman-Caldentey, “Cellular agriculture-industrial biotechnology for food and materials,” Current Opinion in Biotechnology, vol. 61, pp. 128–134, 2020. View at: Publisher Site | Google Scholar
  396. H. D. Goold, P. Wright, and D. Hailstones, “Emerging opportunities for synthetic biology in agriculture,” Genes, vol. 341, no. 9, pp. 1–17, 2018. View at: Google Scholar
  397. V. De Lorenzo, “The power of synthetic biology for biotechnology on a global scale,” EMBO Reports, vol. 19, 2018. View at: Publisher Site | Google Scholar
  398. M. U. Rehman, A. E. Andargoli, and H. Pousti, “Healthcare 4 .0: Trends, Challenges and Benefits,” Australasian Conference on Information Systems, vol. 19, pp. 556–564, 2019. View at: Google Scholar
  399. X. Larrucea, M. Moff, S. Asaf, and I. Santamaria, “Towards a GDPR compliant way to secure European cross border Healthcare,” Computer Standards & Interfaces, vol. 69, pp. 1–7, 2020. View at: Publisher Site | Google Scholar
  400. M. Javaid and A. Haleem, “Industry 4.0 applications in medical field: a brief review,” Current Medicine Research and Practice, vol. 9, no. 3, pp. 102–109, 2019. View at: Publisher Site | Google Scholar
  401. M. Javaid and A. Haleem, “Impact of industry 4.0 to create advancements in orthopaedics,” Journal of Clinical Orthopaedics and Trauma, vol. 19, pp. 1–8, 2020. View at: Google Scholar
  402. G. L. Tortorella, “Effects of contingencies on healthcare 4.0 technologies adoption and barriers in emerging economies,” Technological Forecasting & Social Change, vol. 156, pp. 1–11, 2020. View at: Publisher Site | Google Scholar
  403. S. Tanwar, K. Parekh, and R. Evans, “Blockchain-based electronic healthcare record system for healthcare 4.0 applications,” Journal of Information Security and Applications, vol. 50, pp. 1–13, 2020. View at: Publisher Site | Google Scholar
  404. P. P. Jayaraman, A. R. M. Forkan, A. Morshed, P. D. Haghighi, and Y.-B. Kang, “Healthcare 4.0: A Review of Frontiers in Digital Health,” WIREs Data Mining Knowledge Discovery, vol. 19, 2019. View at: Google Scholar
  405. J. J. Hathaliya, S. Tanwar, S. Tyagi, and N. Kumar, “Securing electronics healthcare records in Healthcare 4.0: a biometric-based approach,” Computers & Electrical Engineering, vol. 76, pp. 398–410, 2019. View at: Publisher Site | Google Scholar
  406. I. Albarki, M. Rasslan, A. M. Bahaa-eldin, and M. Sobh, “Robust hybrid-security protocol for HealthCare systems,” Procedia Computer Science, vol. 160, pp. 843–848, 2019. View at: Publisher Site | Google Scholar
  407. M. Haddara and A. Staaby, “RFID applications and adoptions in healthcare: a review on patient safety,” Procedia Computer Science, vol. 138, pp. 80–88, 2018. View at: Publisher Site | Google Scholar
  408. World Health Organization, “WHO Guideline: Recommendations for Health System Interventions on Digital Strengthening,” 2019. View at: Google Scholar
  409. A. Mavrogiorgou, A. Kiourtis, K. Perakis, D. Miltiadou, S. Pitsios, and D. Kyriazis, “Computer Methods and Programs in Biomedicine,” Computer Methods and Programs in Biomedicine, vol. 19, pp. 1–10, 2019. View at: Google Scholar
  410. A. Abugabah, N. Nizamuddin, and A. Abuqabbeh, “A review of challenges and barriers implementing RFID technology in the Healthcare sector,” Procedia Computer Science, vol. 170, pp. 1003–1010, 2020. View at: Publisher Site | Google Scholar
  411. K. Tiwari, S. Kumar, and R. K. Tiwari, “FOG assisted healthcare architecture for pre-operative support to reduce latency,” Procedia Computer Science, vol. 167, pp. 1312–1324, 2020. View at: Publisher Site | Google Scholar
  412. A. A. Mutlag, M. Khanapi, A. Ghani, N. Arunkumar, and M. A. Mohamed, “Enabling Technologies for Fog Computing in Healthcare IoT Systems,” Future Generation Computer Systems, vol. 19, pp. 1–38, 2018. View at: Google Scholar
  413. P. P. Ray, D. Dash, and D. De, “Edge computing for Internet of Things: a survey, e-healthcare case study and future direction,” Journal of Network and Computer Applications, vol. 140, pp. 1–22, 2019. View at: Publisher Site | Google Scholar
  414. T. Aladwani, “Scheduling IoT healthcare tasks in fog computing based on their importance,” Procedia Computer Science, vol. 163, pp. 560–569, 2019. View at: Publisher Site | Google Scholar
  415. J. J. Hathaliya and S. Tanwar, “An exhaustive survey on security and privacy issues in Healthcare 4.0,” Computer Communications, vol. 153, pp. 311–335, 2020. View at: Publisher Site | Google Scholar
  416. A. Kumari, S. Tanwar, S. Tyagi, and N. Kumar, “Fog computing for Healthcare 4.0 environment: opportunities and challenges,” Computers & Electrical Engineering, vol. 72, pp. 1–13, 2018. View at: Publisher Site | Google Scholar
  417. C. Chen, E. Loh, K. N. Kuo, and K. Tam, “The times they are a-changin ’-healthcare 4.0 is coming!,” Journal of Medical Systems, vol. 44, no. 40, 2020. View at: Publisher Site | Google Scholar
  418. M. Rosa, C. Faria, A. M. Barbosa, H. Caravau, A. F. Rosa, and N. P. Rocha, “A fast healthcare interoperability resources (FHIR) implementation integrating complex security mechanisms,” Procedia Computer Science, vol. 164, pp. 524–531, 2019. View at: Publisher Site | Google Scholar
  419. F. Hak, D. Oliveira, N. Abreu, P. Leuschner, A. Abelha, and M. Santos, “An OpenEHR adoption in a Portuguese healthcare facility,” Procedia Computer Science, vol. 170, pp. 1047–1052, 2020. View at: Publisher Site | Google Scholar
  420. S. K. Mukhiya, F. Rabbi, V. K. I Pun, A. Rutle, and Y. Lamo, “A GraphQL approach to healthcare information exchange with HL7 FHIR,” Procedia Computer Science, vol. 160, pp. 338–345, 2019. View at: Publisher Site | Google Scholar
  421. J. Wu, Y. Wang, L. Tao, and J. Peng, “Stakeholders in the healthcare service ecosystem,” Procedia CIRP, vol. 83, pp. 375–379, 2019. View at: Publisher Site | Google Scholar
  422. G. Aceto, V. Persico, and A. Pescap, “Industry 4.0 and health: internet of things, big data, and cloud computing for healthcare 4.0,” Journal of Industrial Information Integration, vol. 19, pp. 1–14, 2020. View at: Google Scholar
  423. B. Feng, P. He, P. Li, H. Yao, Y. Ji, and J. He, “Developing a smart healthcare framework with an Aboriginal lens,” Procedia Computer Science, vol. 162, pp. 347–354, 2019. View at: Publisher Site | Google Scholar
  424. A. Papa, M. Mital, P. Pisano, and M. Del Giudice, “E-health and wellbeing monitoring using smart healthcare devices: an empirical investigation,” Technological Forecasting & Social Change, vol. 19, pp. 1–10, 2018. View at: Google Scholar
  425. M. Barad, “Linking cyber security improvement actions in healthcare systems to their strategic improvement needs,” Procedia Manufacturing, vol. 39, pp. 279–286, 2019. View at: Publisher Site | Google Scholar
  426. N. W. S. Chew, “A multinational, multicentre study on the psychological outcomes and associated physical symptoms amongst healthcare workers during COVID-19 outbreak,” Brain Behavior and Immunity, vol. 19, pp. 1–7, 2020. View at: Google Scholar
  427. S. Buckrell and S. A. McNeil, “Sources of viral respiratory infections in Canadian acute care hospital healthcare personnel,” Journal of Hospital Infection, vol. 104, no. 4, pp. 513–521, 2020. View at: Publisher Site | Google Scholar
  428. R. Coleman, “COVID-19 and the role of 3D printing in medicine,” 3D Printing in Medicine, vol. 6, no. 11, pp. 1–8, 2020. View at: Publisher Site | Google Scholar
  429. Q. Yan and J. Su, “A review of 3D printing technology for medical applications,” Engineering, vol. 4, no. 5, pp. 729–742, 2018. View at: Publisher Site | Google Scholar
  430. E. J. Dong, “3D printing in healthcare: emerging applications,” Journal of Hospital Librarianship, vol. 16, no. 3, pp. 255–267, 2016. View at: Publisher Site | Google Scholar
  431. Medical Manufacturing Innovations, “Medical Additive Manufacturing/3D Printing: Annual Report,” 2018. View at: Google Scholar
  432. H. Dodziuk, “Applications of 3D printing in healthcare,” Polish Journal of Cardio-Thoracic Surgery, vol. 3, no. 3, pp. 283–293, 2016. View at: Publisher Site | Google Scholar
  433. A. Christensen and F. J. Rybicki, “Maintaining safety and efficacy for 3D printing in medicine,” 3D Printing in Medicine, vol. 3, no. 1, pp. 1–10, 2017. View at: Publisher Site | Google Scholar
  434. L. E. Diment, M. S. Thompson, and J. H. M. Bergmann, “Clinical efficacy and effectiveness of 3D printing: a systematic review,” BMJ Open, vol. 7, 2017. View at: Publisher Site | Google Scholar
  435. A. Aimar, A. Palermo, and B. Innocenti, “The role of 3D printing in medical applications: a state of the art,” Journal of Healthcare Engineering, vol. 2019, 2019. View at: Publisher Site | Google Scholar
  436. World Economic Forum, 3D Printing: A Guide for Decision-Makers, Cologny, Geneva, Switzerland, 2020.
  437. P. Ahangar, M. E. Cooke, M. H. Weber, and D. H. Rosenzweig, “Current biomedical applications of 3D printing and additive manufacturing,” Applied Sciences, vol. 9, 2019. View at: Publisher Site | Google Scholar
  438. S. Sharma and S. A. Goel, “Three-dimensional printing and its future in medical world,” Journal of Medical Research and Innovation, vol. 3, no. 1, pp. 1–8, 2019. View at: Google Scholar
  439. S. J. Trenfield, “Shaping the future: recent advances of 3D printing in drug delivery and healthcare,” Expert Opinion on Drug Delivery, vol. 3, pp. 1–14, 2019. View at: Google Scholar
  440. P. Ravi, “Understanding the relationship between slicing and measured fill density in material extrusion 3D printing towards precision porosity constructs for biomedical and pharmaceutical applications,” 3D Printing in Medicine, vol. 6, no. 10, pp. 1–10, 2020. View at: Publisher Site | Google Scholar
  441. F. Jiang, H. Zhi et al., “Artificial intelligence in healthcare: past, present and future,” Stroke and Vascular Neurology, vol. 2, no. 4, pp. 230–243, 2017. View at: Publisher Site | Google Scholar
  442. F. Jiang, “Artificial Intelligence in Healthcare Table of Contents Abbreviations,” 2018. View at: Google Scholar
  443. A. J. Mason, A. Morrison, and S. Visintini, An Overview of Clinical Applications of Artificial Intelligence, CADTH, Ottawa, OA, USA, 2018.
  444. T. Davenport and R. Kalakota, “The potential for artificial intelligence in healthcare,” Future Healthcare Journal, vol. 6, no. 2, pp. 94–98, 2019. View at: Publisher Site | Google Scholar
  445. N. Noorbakhsh-sabet, R. Zand, Y. Zhang, and V. Abedi, “Artificial intelligence transforms the future of health care,” The American Journal of Medicine, vol. 132, no. 7, pp. 795–801, 2019. View at: Publisher Site | Google Scholar
  446. N. Deloitte, “The future of artificial intelligence in health care: how ai will impact patients, clinicians, and the pharmaceutical industry,” 2019. View at: Google Scholar
  447. Pure Storage, “AI in Healthcare: Building the Foundation,” 2019. View at: Google Scholar
  448. Academy of Medical Royal Colleges, “Artificial Intelligence in Healthcare,” 2019. View at: Google Scholar
  449. M. Bonham, “Augmented reality simulation toward improving therapeutic healthcare communication techniques,” in Proceedings of the 24th International Conference on Intelligent User Interfaces (IUI’ 19 Companion), pp. 19-20, Marina del Rey, CA, USA, 2019. View at: Google Scholar
  450. J. Gerup, C. B. Soerensen, and P. Dieckmann, “Augmented reality and mixed reality for healthcare education beyond surgery: an integrative review,” International Journal of Medical Education, vol. 11, no. 1, pp. 1–18, 2020. View at: Publisher Site | Google Scholar
  451. D. S. Lopes and J. A. Jorge, “Extending medical interfaces towards virtual reality and augmented reality,” Annals of Medicine, vol. 51, no. 1, p. 29, 2019. View at: Publisher Site | Google Scholar
  452. J. Herron, “Augmented reality in medical education and training,” Journal of Electronic Resources in Medical Libraries, vol. 13, no. 2, pp. 51–55, 2016. View at: Publisher Site | Google Scholar
  453. M. Eckert, J. S. Volmerg, C. M. Friedrich, and C. M. Friedrich, “Augmented reality in medicine: systematic and bibliographic review,” JMIR Mhealth Uhealth, vol. 7, no. 4, pp. 1–17, 2019. View at: Publisher Site | Google Scholar
  454. N. Wake, “Patient-specific 3D printed and augmented reality kidney and prostate cancer models: impact on patient education,” 3D Printing in Medicine, vol. 5, no. 4, pp. 1–8, 2019. View at: Publisher Site | Google Scholar
  455. P. Katkin, K. H. Onkka, T. Moyer, and D. P. Goel, “Virtual and augmented reality best practices for healthcare,” 2018. View at: Google Scholar
  456. L. N. Lee, M. J. Kim, and W. J. Hwang, “Potential of augmented reality and virtual reality technologies to promote wellbeing in older adults,” Applied Sciences, vol. 9, no. 3556, pp. 1–17, 2019. View at: Publisher Site | Google Scholar
  457. V. Ferrari, G. Klinker, and F. Cutolo, “Augmented reality in healthcare,” Journal of Healthcare Engineering, vol. 2019, 2019. View at: Publisher Site | Google Scholar
  458. CAICT, Virtual Reality/Augmented Reality White Paper, Huawei Technologies Co., Ltd, China, 2017.
  459. R. H. Brown, Augmenting the Reality of Everything Everything, Teaneck, London, UK, 2017.
  460. F. Salehahmadi and F. Hajialiasgari, “Grand adventure of augmented reality in landscape of surgery,” World Journal of Plastic Surgery, vol. 8, no. 2, pp. 135–145, 2019. View at: Publisher Site | Google Scholar
  461. P. Kaur, M. Sharma, and M. Mittal, “Big data and machine learning based secure healthcare framework,” Procedia Computer Science, vol. 132, pp. 1049–1059, 2018. View at: Publisher Site | Google Scholar
  462. L. Hong, M. Luo, R. Wang, P. Lu, W. Lu, and L. Lu, “Big data in health care: what is so different about was ist so anders am neuroenhancement?” Data and Information Management, vol. 1, no. 2, pp. 122–135, 2018. View at: Google Scholar
  463. P. K. D. Pramanik, S. Pal, and M. Mukhopadhyay, “Healthcare Big Data: A Comprehensive Overview,” IGI Global, vol. 1, pp. 72–100, 2018. View at: Google Scholar
  464. S. Sa, B. K. Rai, A. A. Meshram, A. Gunasekaran, and S. Chandrakumarmangalam, “Big data in healthcare management: a review of literature,” American Journal of Theoretical and Applied Business, vol. 4, no. 2, pp. 57–69, 2018. View at: Publisher Site | Google Scholar
  465. Big Data Value Association, “Big data technologies in healthcare: needs, opportunities and challenges,” 2016. View at: Google Scholar
  466. R. Pastorino, “Benefits and challenges of Big Data in healthcare: an overview of the European initiatives,” European Journal of Public Health, vol. 29, no. 3, pp. 23–27, 2019. View at: Publisher Site | Google Scholar
  467. R. M. Visconti and D. Morea, “Big data for the sustainability of healthcare project financing,” Sustainability, vol. 11, no. 3748, 2019. View at: Google Scholar
  468. Q. K. Fatt and A. Ramadas, “The usefulness and challenges of big data in healthcare,” Journal of Healthcare Communications, vol. 3, no. 2, pp. 1–4, 2018. View at: Google Scholar
  469. H. Khaloufi, K. Abouelmehdi, A. Beni-hssane, and M. Saadi, “Security model for big healthcare data lifecycle,” Procedia Computer Science, vol. 141, pp. 294–301, 2018. View at: Publisher Site | Google Scholar
  470. M. Prokofieva and S. J. Miah, “Blockchain in healthcare Blockchain in healthcare,” Australasian Journal of Information Systems, vol. 23, pp. 1–22, 2019. View at: Publisher Site | Google Scholar
  471. A. A. Siyal, A. Z. Junejo, M. Zawish, K. Ahmed, A. Khalil, and G. Soursou, “Applications of blockchain technology in medicine and healthcare: challenges and future perspectives,” Cryptography, vol. 3, no. 3, pp. 1–16, 2019. View at: Publisher Site | Google Scholar
  472. Advanced Medical Reviews, “Blockchain in healthcare: how it could transform ehrs, drug safety and medical reviews,” 2017. View at: Google Scholar
  473. P. Zhang, D. C. Schmidt, J. White, and G. Lenz, Blockchain Technology use Cases in Healthcare, Elsevier, Berlin, Germany, 2018.
  474. S. Demarinis, “US Health Care Companies Exploring Blockchain Technologies,” 2018. View at: Google Scholar
  475. S. Dash, A. Majumdar, and P. Gunjikar, “Blockchain : A Healthcare Industry View,” 2017. View at: Google Scholar
  476. H. S. Chen, J. T. Jarrell, K. A. Carpenter, D. S. Cohen, and X. Huang, “Blockchain in healthcare: a patient-centered model,” Biomedical Journal of Scientific & Technical Research, vol. 20, no. 3, pp. 15017–15022, 2019. View at: Google Scholar
  477. S. Khezr, M. Moniruzzaman, A. Yassine, and R. Benlamri, “Blockchain Technology in Healthcare: A Comprehensive Review and Directions for Future Research,” Applied Sciences, vol. 9, 2019. View at: Google Scholar
  478. C. C. Agbo, Q. H. Mahmoud, and J. M. Eklund, “Blockchain technology in healthcare: a systematic review,” Healthcare, vol. 7, no. 56, pp. 1–30, 2019. View at: Publisher Site | Google Scholar
  479. T. Mackey, H. Bekki, T. Matsuzaki, and H. Mizushima, “Examining the potential of blockchain technology to meet the needs of 21st-century Japanese health care: viewpoint on use cases and policy,” Journal of Medical Internet Research, vol. 22, no. 1, pp. 1–12, 2020. View at: Publisher Site | Google Scholar
  480. S. Badr, I. Gomaa, and E. Abd-elrahman, “Multi-tier blockchain framework for IoT-EHRs systems,” Procedia Computer Science, vol. 141, pp. 159–166, 2018. View at: Publisher Site | Google Scholar
  481. K. A. Koshechkin, G. S. Klimenko, I. V. Ryabkov, and P. B. Kozhin, “Scope for the application of blockchain in the public healthcare of the Russian federation,” Procedia Computer Science, vol. 126, pp. 1323–1328, 2018. View at: Publisher Site | Google Scholar
  482. W. Chien, “The last mile: DSCSA solution through blockchain technology: drug tracking, tracing, and verification at the last mile of the pharmaceutical supply chain with BRUINchain,” Blockchain in healthcare TodayTM, vol. 12, pp. 1–28, 2020. View at: Google Scholar
  483. I. Mistry, S. Tanwar, S. Tyagi, and N. Kumar, “Blockchain for 5G-enabled IoT for industrial automation: a systematic review, solutions, and challenges,” Mechanical Systems and Signal Processing, vol. 135, pp. 1–20, 2020. View at: Publisher Site | Google Scholar
  484. I. Mistry, “Cloud computing and healthcare services,” Journal of Biosensors & Bioelectronics, vol. 7, no. 3, pp. 1–4, 2017. View at: Google Scholar
  485. Cloud Standards Customer Council, “Impact of Cloud Computing on Healthcare: Version 2.0,” 2017. View at: Google Scholar
  486. R. Ganiga, R. M. Pai, and M. Pai, “Private cloud solution for securing and managing patient data in rural healthcare system,” Procedia Computer Science, vol. 135, pp. 688–699, 2018. View at: Publisher Site | Google Scholar
  487. L. Griebel, “A scoping review of cloud computing in healthcare,” BMC Medical Informatics and Decision Making, vol. 15, no. 17, pp. 1–16, 2015. View at: Publisher Site | Google Scholar
  488. L. M. Dang, J. Piran, D. Han, K. Min, and H. Moon, “A survey on internet of things and cloud computing for healthcare,” Electronics, vol. 8, no. 768, 2019. View at: Publisher Site | Google Scholar
  489. Y. Li, H. Wang, Y. Li, and L. Li, “Patient assignment scheduling in a cloud healthcare system based on petri net and greedy-based heuristic,” Enterprise Information Systems, vol. 13, no. 4, pp. 515–533, 2019. View at: Publisher Site | Google Scholar
  490. Y. Karaca, M. Moonis, Y. Zhang, and C. Gezgez, “Mobile cloud computing based stroke healthcare system,” International Journal of Information Management, vol. 13, pp. 1–12, 2018. View at: Google Scholar
  491. M. Eichleay, E. Evens, K. Stankevitz, and C. Parker, “Using the unmanned aerial vehicle delivery decision tool to consider transporting medical supplies via drone,” Global Health: Science and Practice, vol. 7, no. 4, pp. 500–506, 2019. View at: Publisher Site | Google Scholar
  492. J. C. Rosser, V. Vignesh, B. A. Terwilliger, and B. C. Parker, “Surgical and medical applications of drones: a comprehensive review,” JSLS, vol. 22, no. 3, pp. 1–9, 2018. View at: Publisher Site | Google Scholar
  493. M. Balasingam, ““Drones in medicine-the rise of the machines,” The International Journal of Clinical Practice, vol. 71, 2017. View at: Publisher Site | Google Scholar
  494. S. J. Kim, G. J. Lim, J. Cho, and M. J. Cote, “Drone-Aided healthcare services for patients with chronic diseases in rural areas,” Journal of Intelligent Robotic System, vol. 3, pp. 1–18, 2017. View at: Google Scholar
  495. M. S. Y. Hii, P. Courtney, and P. G. Royall, “An evaluation of the delivery of medicines using drones,” Drones, vol. 3, no. 52, pp. 1–20, 2019. View at: Google Scholar
  496. A. M. Knoblauch, “Bi-directional drones to strengthen healthcare provision: experiences and lessons from Madagascar, Malawi and Senegal,” BMJ Global Health, vol. 4, 2019. View at: Publisher Site | Google Scholar
  497. D. Dziak, B. Jachimczyk, and W. J. Kulesza, “IoT-based information system for healthcare application: design methodology approach,” Applied Sciences, vol. 7, no. 596, pp. 1–26, 2017. View at: Publisher Site | Google Scholar
  498. P. A. Laplante, M. Kassab, N. L. Laplante, and J. M. Voas, “Building caring healthcare systems in the internet of things,” IEEE System Journal, vol. 12, no. 3, pp. 1–19, 2018. View at: Publisher Site | Google Scholar
  499. N. Mani, A. Singh, and S. L. Nimmagadda, “An IoT guided healthcare monitoring system for managing real-time notifications by fog computing services,” Procedia Computer Science, vol. 167, pp. 850–859, 2020. View at: Publisher Site | Google Scholar
  500. H. Zakaria, N. A. Abu Bakar, N. H. Hassan, and S. Yaacob, “IoT security risk management model for secured practice in healthcare environment,” Procedia Computer Science, vol. 161, pp. 1241–1248, 2019. View at: Publisher Site | Google Scholar
  501. Z. Lou, L. Wang, K. Jiang, Z. Wei, and G. Shen, “Reviews of wearable healthcare systems: materials, devices and system integration,” Materials Science & Engineering R, vol. 140, pp. 1–43, 2020. View at: Publisher Site | Google Scholar
  502. J.-C. Ni, C.-S. Yang, J.-K. Huang, and L. C. Shiu, “Combining non-invasive wearable device and intelligent terminal in HealthCare IoT,” Procedia Computer Science, vol. 154, pp. 161–166, 2019. View at: Publisher Site | Google Scholar
  503. A. Mohammad, A. Moshed, M. Khairul, I. Sarkar, and A. Khaleque, “The application of nanotechnology in medical Sciences : new horizon of treatment,” American Journal of Biomedical Sciences, vol. 9, no. 1, pp. 1–14, 2017. View at: Google Scholar
  504. V. Bhardwaj and A. Kaushik, “Biomedical applications of nanotechnology and nanomaterials,” Micromachines, vol. 8, no. 298, 2017. View at: Publisher Site | Google Scholar
  505. F. A. Radwan, “Nanotechnology and medicine,” Material Science and Nanotechnology, vol. 2, no. 2, pp. 7-8, 2018. View at: Google Scholar
  506. P. Fletcher and A. Holian, “Nanotechnology: the risks and benefits for medical diagnosis and treatment,” Journal of Nanomedicine & Nanotechnology, vol. 7, no. 4, pp. 1-2, 2016. View at: Publisher Site | Google Scholar
  507. A. Joseph, B. Christian, A. A. Abiodun, and F. Oyawale, “A review on humanoid robotics in healthcare,” MATEC Web of Conferences, vol. 153, 2018. View at: Google Scholar
  508. E. D. Oña, J. M. Garcia-haro, A. Jardón, and C. Balaguer, “Robotics in Health Care: Perspectives of Robot-Aided Interventions in Clinical Practice for Rehabilitation of Upper Limbs,” Applied Sciences, vol. 9, no. 2586, 2019. View at: Google Scholar
  509. R. Wason, V. Jain, G. S. Narula, A. Balyan, and M. Kaur, “Smart Robotics for Smart Healthcare,” Advances In Robotics & Mechanical Engineering, vol. 1, no. 5, pp. 73-74, 2019. View at: Google Scholar
  510. M. Vatandsoost and S. Litkouhi, “The future of healthcare facilities: how technology and medical advances may shape hospitals of the future,” Hospital Practices and Research, vol. 4, no. 1, pp. 1–11, 2019. View at: Publisher Site | Google Scholar
  511. Z. Dolic, R. Castro, and A. Moarcas, “Robots in Healthcare: A Solution or a Problem,” 2019. View at: Google Scholar
  512. M. K. Traoré, G. Zacharewicz, R. Duboz, and B. Zeigler, “Modeling and simulation framework for value-based healthcare systems,” Simulation, vol. 95, no. 6, pp. 481–497, 2019. View at: Publisher Site | Google Scholar
  513. K. Steins, Towards Increased Use of Discrete-Event Simulation for Hospital Resource Planning, Linkoping University, London, UK, 2017.
  514. S. N. Roy, “Healthcare Resource Planning: A Simulation Approach,” Indian Institute of Management, vol. 13, 2018. View at: Google Scholar
  515. N. Harder, “The value of simulation in health care: the obvious, the tangential, and the obscure,” Clinical Simulation in Nursing, vol. 15, pp. 73-74, 2018. View at: Publisher Site | Google Scholar
  516. A. Atalan and C. C. Donmez, “Employment of emergency advanced nurses of Turkey: a discrete-event simulation application,” Processes, vol. 7, no. 48, pp. 1–18, 2019. View at: Publisher Site | Google Scholar
  517. Z. Liu, “Modeling and simulation for healthcare operations management using high performance computing and agent-based model,” Universitat Autònoma de Barcelona, vol. 13, 2016. View at: Google Scholar
  518. H. Y. So, P. P. Chen, G. K. C. Wong, and T. T. N. Chan, “Simulation in medical education,” Journal of the Royal College of Physicians of Edinburgh, vol. 49, no. 1, pp. 52–57, 2019. View at: Publisher Site | Google Scholar
  519. D. S. Dîrzu, ““Medical simulation-a costly but essential teaching tool,” Romanian Journal of Anaesthesia and Intensive Care, vol. 24, no. 1, pp. 5-6, 2017. View at: Google Scholar
  520. J. C. A. Martins, R. C. N. Baptista, V. R. D. Coutinho, M. I. D. Fernandes, and A. M. Fernandes, Simulation in Nursing and Midwifery Education Simulation, WHO regional office, Copenhagen, Denmark, 2018.
  521. A. Pavlovic, N. Kalezic, S. Trpkovic, N. Videnovic, and L. Sulovic, “The application of simulation in medical education-our experience from improvisation to simulation,” Srpski Arhiv Za Celokupno Lekarstvo, vol. 146, no. 5-6, pp. 338–344, 2018. View at: Publisher Site | Google Scholar
  522. L. F. N. D. Carramate, “Simulation of image-guided intervention in medical imaging education,” Journal of Medical Imaging and Radiation Sciences, vol. 13, pp. 1–6, 2019. View at: Google Scholar
  523. E. J. Yeuna and M. Y. Chona, “Perceptions of video-facilitated debriefing in simulation education among nursing students: findings from a Q-methodology study,” Journal of Professional Nursing, vol. 13, pp. 1–8, 2019. View at: Google Scholar
  524. V. Raper, “Synthetic biology seizes new ground in healthcare,” Genetic Engineering & Biotechnology News, vol. 39, no. 11, pp. 40–43, 2019. View at: Google Scholar
  525. L. Clarke and R. Kitney, “Developing synthetic biology for industrial biotechnology applications,” Biochemical Society Transactions, vol. 48, no. 1, pp. 113–122, 2020. View at: Publisher Site | Google Scholar
  526. V. B. Reddy, “Current synthetic and systems biology synthetic biology : a good choice for medicinal advances,” Current Synthetic and Systems Biology, vol. 3, no. 2, pp. 2-3, 2015. View at: Google Scholar
  527. H. Van Mierlo, Tackling Antimicrobal Resistance: The Role of Synthetic Biology, University of Groningen, Melbourne Victoria, Australia, 2016.
  528. P. Gray, “Synthetic biology in australia: an outlook to 2030,” 2018. View at: Google Scholar
  529. C. Fan and R. Habgood, “Chromosome-free bacterial cells are safe and programmable platforms for synthetic biology,” Proceedings of the National Academy of Sciences, vol. 117, no. 12, pp. 6752–6761, 2020. View at: Publisher Site | Google Scholar
  530. T. A. D. O. Davison, F. Castiglione, P. Tieri, and L. Felicori, “Systems and Synthetic Biology Applied to Health,” Current Developments in Biotechnology and Bioengineering: Human and Animal Health Applications, vol. 11, pp. 183–213, 2017. View at: Google Scholar
  531. B. J. Kinder and M. Robbins, “The Present and Future State of Synthetic Biology in Canada,” 2018. View at: Google Scholar
  532. M. El Karoui, M. Hoyos-flight, and L. Fletcher, “Future trends in synthetic biology-a report,” Frontiers in Bioengineering and Biotechnology, vol. 7, 2019. View at: Publisher Site | Google Scholar
  533. UNCTAD, “Synthetic Biology and its Potential Implications for Biotrade and Access and Benefit-Sharing,” 2019. View at: Google Scholar
  534. M. Igor, B. Sergiy, S. Tatyana, and G. Larissa, “Logistics and transport in industry 4.0: perspective for Ukraine,” SHS Web of Conferences, vol. 67, 2019. View at: Google Scholar
  535. A. Galkin, L. Obolentseva, I. Balandina, E. Kush, V. Karpenko, and P. Bajdor, “Last-Mile delivery for consumer driven logistics,” Transportation Research Procedia, vol. 39, pp. 74–83, 2019. View at: Publisher Site | Google Scholar
  536. L. Ranieri, A. Urbinati, F. Facchini, and J. Ole, “A maturity model for logistics 4.0: an empirical analysis and a roadmap for future research,” Sustainability, vol. 12, no. 86, pp. 1–18, 2019. View at: Google Scholar
  537. C. Briese, M. Schlüter, J. Lehr, K. Maurer, and J. Krüger, “Towards deep learning in industrial applications taking advantage of service-oriented architectures,” Procedia Manufacturing, vol. 43, pp. 503–510, 2020. View at: Publisher Site | Google Scholar
  538. P. Ralston and J. Blackhurst, “Industry 4.0 and resilience in the supply chain: a driver of capability enhancement or capability loss,” International Journal of Production Research, vol. 11, pp. 1–14, 2020. View at: Google Scholar
  539. M. Núñez-merino, J. M. Maqueira-marín, J. Moyano-fuentes, and P. J. Martínez-jurado, “Information and digital technologies of Industry 4.0 and Lean supply chain management: a systematic literature review,” International Journal of Production Research, vol. 11, pp. 1–27, 2020. View at: Google Scholar
  540. H. Fatorachian and H. Kazemi, “The Management of Operations Impact of Industry 4.0 on Supply Chain Performance,” Production Planning & Control, vol. 11, pp. 1–19, 2020. View at: Google Scholar
  541. L. Barreto, A. Amaral, and T. Pereira, “Industry 4.0 implications in logistics: an overview,” Procedia Manufacturing, vol. 13, pp. 1245–1252, 2017. View at: Publisher Site | Google Scholar
  542. O. Seroka-stolka and A. Ociepa-kubicka, “Green logistics and circular economy,” Transportation Research Procedia, vol. 39, pp. 471–479, 2019. View at: Publisher Site | Google Scholar
  543. J. Korczak and K. Kijewska, “Smart Logistics in the development of Smart Cities,” Transportation Research Procedia, vol. 39, pp. 201–211, 2019. View at: Google Scholar
  544. C. Cimini, A. Lagorio, F. Pirola, and R. Pinto, “Exploring human factors in Logistics 4.0: empirical evidence from a case study,” IFAC-PapersOnLine, vol. 52, no. 13, pp. 2183–2188, 2019. View at: Publisher Site | Google Scholar
  545. C. K. M. Lee, Y. Lv, K. K. H. Ng, W. Ho, and K. L. Choy, “Design and application of Internet of things-based warehouse management system for smart logistics,” International Journal of Production Research, vol. 56, no. 8, pp. 2753–2768, 2018. View at: Publisher Site | Google Scholar
  546. A. J. C. Trappey, C. V. Trappey, C.-Y. Fan, A. P. T. Hsu, X.-K. Li, and I. J. Y. Lee, “IoT patent roadmap for smart logistic service provision in the context of Industry 4.0,” Journal of the Chinese Institute of Engineers, vol. 40, no. 7, pp. 593–602, 2017. View at: Publisher Site | Google Scholar
  547. W. Liu, J. Zhang, S. Wei, and D. Wang, “Factors influencing organisational efficiency in a smart-logistics ecological chain under e-commerce platform leadership,” International Journal of Logistics Research and Applications, vol. 11, pp. 1–28, 2020. View at: Publisher Site | Google Scholar
  548. M. Wang, S. Asian, L. C. Wood, and B. Wang, “Logistics innovation capability and its impacts on the supply chain risks in the Industry 4.0 era,” Modern Supply Chain Research and Applications, vol. 2, no. 2, pp. 83–98, 2020. View at: Publisher Site | Google Scholar
  549. M. Fruth and F. Teuteberg, “Digitization in maritime logistics-what is there and what is missing,” Cogent Business & Management, vol. 17, no. 1, pp. 1–40, 2017. View at: Google Scholar
  550. S. Gupta, S. Modgil, A. Gunasekaran, and S. Bag, “Dynamic capabilities and institutional theories for Industry 4.0 and digital supply chain,” Supply Chain Forum: An International Journal, vol. 11, 2020. View at: Google Scholar
  551. C. L. Garay-rondero, J. L. Martinez-flores, S. O. C. Morales, N. R. Smith, and A. Aldrette-malacara, “Digital Supply Chain Model in Industry 4.0,” Journal of Manufacturing Technology Management, vol. 11, pp. 1–47, 2019. View at: Google Scholar
  552. O. Szymańska, M. Adamczak, and P. Cyplik, “Logistics 4.0-a new paradigm or set of known solutions,” Research in Logistics & Production, vol. 7, no. 4, pp. 299–310, 2017. View at: Google Scholar
  553. M. Kostrzewski, M. Kosacka-Olejnik, and K. Werner-Lewandowska, “Assessment of innovativeness level for chosen solutions related to Logistics 4.0,” Procedia Manufacturing, vol. 38, pp. 621–628, 2019. View at: Publisher Site | Google Scholar
  554. W. Kersten and T. Blecker, “digitalization in supply chain management and logistics: smart and digital solutions for an industry 4.0 environment,” 2017. View at: Google Scholar
  555. G. Radivojević and L. Milosavljević, “The Concept of Logistics 4.0,” 2019. View at: Google Scholar
  556. P.-E. Dossou, “Using industry 4.0 concepts and theory of systems for improving company supply chain: the example of a joinery,” Procedia Manufacturing, vol. 38, pp. 1750–1757, 2019. View at: Publisher Site | Google Scholar
  557. M. Kosacka-olejnik and K. Werner-lewandowska, “Logistics 4.0 Maturing in Service Industry: Emprirical Research Reasults,” Procedia Manufacturing, vol. 38, pp. 1058–1065, 2019. View at: Google Scholar
  558. W. Torbacki and K. Kijewska, “Identifying Key Performance Indicators to be used Logistics 4.0 and Industry 4.0 for the needs of sustainable municipal logistics by means of the DEMATEL method,” Transportation Research Procedia, vol. 39, pp. 534–543, 2019. View at: Google Scholar
  559. S. Kauf, “Smart logistics as a basis for the development of the smart city,” Transportation Research Procedia, vol. 39, pp. 143–149, 2019. View at: Publisher Site | Google Scholar
  560. G. Li, H. Wang, and W. Hardjawana, “New advancement in information technologies for industry 4.0,” Enterprise Information Systems, vol. 14, no. 4, pp. 402–405, 2020. View at: Publisher Site | Google Scholar
  561. T.-M. Choi, “Facing market disruptions: values of elastic logistics in service supply chains,” International Journal of Production Research, vol. 14, pp. 1–15, 2020. View at: Publisher Site | Google Scholar
  562. M. Szymczak, “Digital Smart Logistics. Managing Supply Chain 4.0: Concepts, Components and Strategic Perspective,” 2019. View at: Google Scholar
  563. V. Bamberger, F. Nansé, B. Schreiber, and M. Zintel, “Logistics 4.0-Facing Digitalization-Driven Disruption,” 2017. View at: Google Scholar
  564. M. J. Ferrantino and E. E. Koten, “Understanding Supply Chain 4.0 and its Potential Impact on Global Value Chains,” World Bank Group, vol. 14, pp. 103–119, 2019. View at: Google Scholar
  565. G. F. Frederico, J. A. Garza-reyes, and A. Anosike, “Supply Chain 4.0: concepts , maturity and research agenda,” Supply Chain Management: An International Journal, vol. 25, no. 2, pp. 262–282, 2020. View at: Google Scholar
  566. R. Fioravanti, S. Kraiselburd, and L. M. Laporte, “Monitoring and Assesing the Impact of Supply Chain 4.0,” 2019. View at: Google Scholar
  567. World Economic Forum, Impact of the Fourth Industrial Revolution on Supply Chains, Cologny, Geneva, Switzerland, 2017.
  568. K. Alicke, J. Rachor, and A. Seyfert, Supply Chain 4.0-the Next-Generation Digital Supply Chain, McKinsey & Company, Geneva, Switzerland, 2016.
  569. A. Benešová, M. Hirman, F. Steiner, and J. Tupa, “Determination of changes in process management within industry 4.0,” Procedia Manufacturing, vol. 38, pp. 1691–1696, 2020. View at: Google Scholar
  570. A. Ancarani, C. Di Mauro, and F. Mascali, “Backshoring strategy and the adoption of industry 4.0: evidence from europe,” Journal of World Business, vol. 54, no. 4, pp. 360–371, 2019. View at: Publisher Site | Google Scholar
  571. J. Zheng and C. Shen, “Domestic demand-based economic globalization and inclusive growth,” China Political Economy, vol. 2, no. 1, pp. 136–156, 2019. View at: Google Scholar
  572. S. A. Asongu, U. Efobi, and V. S. Tchamyou, “Globalisation and governance in Africa: a critical contribution to the empirics,” International Journal of Development Issues, vol. 17, no. 1, pp. 2–27, 2018. View at: Publisher Site | Google Scholar
  573. W. O. Shittu, H. A. Yusuf, A. E. M. El Houssein, and S. Hassan, “The impacts of foreign direct investment and globalisation on economic growth in West Africa: examining the role of political governance,” Journal of Economic Studies, vol. 2, pp. 1–23, 2020. View at: Google Scholar
  574. A. Telukdarie, E. A. Buhulaiga, S. Bag, S. Gupta, and Z. Luo, “Industry 4.0 Implementation for Multinationals,” Process Safety and Environmental Protection, vol. 2, pp. 1–40, 2018. View at: Google Scholar
  575. P. Gimenez-escalante and S. Rahimifard, “Metrics for identifying the most suitable strategy for distributed localised food manufacturing,” Procedia Manufacturing, vol. 33, pp. 586–593, 2019. View at: Publisher Site | Google Scholar
  576. N. P. Petersson, S. Tenold, and N. J. White, Shipping and Globalization in the Post-War Era: Contexts, Companies, Connections, Palgrave Studies in Maritime Economics, Cham, Switzerland, 2019.
  577. K. Munir and M. Bukhari, “Impact of globalization on income inequality in Asian emerging economies,” International Journal of Sociology and Social Policy, vol. 40, no. 1/2, pp. 44–57, 2020. View at: Google Scholar
  578. W. Khlif, S. El Omari, and H. Hammami, “Challenging the meaning of globalisation in Tunisian context,” Society and Business Review, vol. 14, no. 4, pp. 320–337, 2019. View at: Publisher Site | Google Scholar
  579. A. Daribay, A. Serikova, and I. A. Ukaegbu, “Industry 4.0: kazakhstani industrialization needs a global perspective,” Procedia Computer Science, vol. 151, pp. 903–908, 2019. View at: Publisher Site | Google Scholar
  580. H. Wang, “China and globalization: 40 years of Reform and Opening-up and globalization 4.0,” Journal of Chinese Economic and Business Studies, vol. 17, no. 3, pp. 215–220, 2019. View at: Publisher Site | Google Scholar
  581. M. J. Sousa, V. Santos, A. Sacavém, I. Pinto, and M. C. Sampaio, “4.0 leadership skills in hospitality sector,” Journal of Reviews on Global Economics, vol. 7, pp. 1–13, 2018. View at: Google Scholar
  582. K. A. Prince, “Industrie 4.0 and leadership,” 2017. View at: Google Scholar
  583. C. Promsri, “Training program analysis for leadership 4.0 in fourth industrial revolution,” East African Scholars Journal of Economics, Business and Management, vol. 2, no. 9, pp. 591–595, 2019. View at: Google Scholar
  584. F. Herder-Wynne, R. Amato, and F. Uit de Weerd, Leadership 4.0: A Review of the Thinking, Oxford Leadership, Oxford, UK, 2017.
  585. C. Deloitte, “Success personified in the fourth industrial revolution: four leadership personas for an era of change and uncertainity,” 2019. View at: Google Scholar
  586. Axiom Groupe, “Finance 4.0: bringing financial functions into 4th Revolution,” 2019. View at: Google Scholar
  587. Siemens Financial Services, “The finance factor: the role of integrated finance in enabling digital transformation for manufacturers and technology providers,” 2019. View at: Google Scholar
  588. UniCredit, “Trade Finance 4.0: a world of new opportunities,” 2015. View at: Google Scholar
  589. T. LynnJ. G. Mooney, P. Rosati, and M. Cummins, “Disrupting Finance: FinTech and Strategy In the 21st Century,” 2019. View at: Google Scholar
  590. M. M. Dapp, S. Klauser, and M. Ballandies, “Finance 4.0 Concept-Technical Report-WP3 Interim Report (M12) for futurICT2 Project,” 2018. View at: Google Scholar
  591. M. Lacey, H. Lisachuk, A. Giannopoulos, and A. Ogura, “Shipping Smarter: IoT Opportunities in Transport and Logistics,” 2015. View at: Google Scholar
  592. J. Fitzgerald and E. Quasney, “Using Autonomous Robots to Drive Supply Chain Innovation: A Series Exploring Industry 4.0 Technologies and Their Potential Impact for Enabling Digital Supply Networks in Manufacturing,” 2017. View at: Google Scholar
  593. H. K. Chan, J. Griffin, J. J. Lim, F. Zeng, and A. S. F. Chiu, “The impact of 3D Printing Technology on the supply chain: manufacturing and legal perspectives,” International Journal of Production Economics, vol. 205, pp. 156–162, 2018. View at: Publisher Site | Google Scholar
  594. A. Wieczorek, “Impact of 3D printing on logistics,” Research in Logistics and Production, vol. 7, no. 5, pp. 443–450, 2017. View at: Publisher Site | Google Scholar
  595. DHL, 3D Printing and the Future of Supply Chains: A DHL Perspective on the State of 3D Printing and Implications for Logistics, Troisdorf, Germany, 2016.
  596. G. Chung, B. Gesing, K. Chaturvedi, and P. Bodenbenner, Logistics Trend Radar: Delivering Insight Today, Creating Value Tomorrow, Troisdorf, Germany, 2018.
  597. W. Boon and B. Van Wee, “Influence of 3D printing on transport: a theory and experts judgment based conceptual model,” Transport Reviews, vol. 38, no. 5, pp. 556–575, 2018. View at: Publisher Site | Google Scholar
  598. E. Özceylan, C. Çetinkaya, N. Demirel, and O. Sabırlıoglu, “Impacts of additive manufacturing on,” Supply Chain Flow : A Simulation Approach in Healthcare Industry, vol. 2, no. 1, pp. 1–20, 2018. View at: Google Scholar
  599. L. Kubáč and O. Kodym, “The Impact of 3D Printing Technology on Supply Chain,” MATEC Web of Conferences, vol. 134, 2017. View at: Google Scholar
  600. N. Vasileios, The Impact of 3D Printing on the Traditional Supply Chain: A Quantitative Analysis, Aristotle University of Thessaloniki, London, UK, 2018.
  601. R. Geissbauer, J. Wunderlin, and J. Lehr, “The Future of Spare Parts Is 3D: A Look at the Challenges and Opportunities of 3D Printing,” 2017. View at: Google Scholar
  602. J. R. Daduna, “Disruptive effects on logistics processes by additive manufacturing,” IFAC-PapersOnLine, vol. 52, no. 13, pp. 2770–2775, 2019. View at: Publisher Site | Google Scholar
  603. D. A. P. Pandian, “Artificial intelligence application in smart warehousing environment for automated logistics,” IFAC-PapersOnLine, vol. 2019, no. 2, pp. 63–72, 2019. View at: Publisher Site | Google Scholar
  604. Big Innovation Centre, Future Supply Chains with Artificial Intelligence: Achieving AI Success-Building Capacity & Understanding Complexities, Innovate UK, London, UK, 2018.
  605. B. Gesing, S. J. Peterson, and D. Michelsen, Artificial Intelligence in Logistics: A Collaborative Report by DHL and IBM on Implications and Use Cases for the Logistics Industry, Troisdorf, Germany, 2018.
  606. C. Zhou, A. Stephen, X. Cao, and S. Wang, “A data-driven business intelligence system for large-scale semi-automated logistics facilities,” International Journal of Production Research, vol. 5, pp. 1–19, 2020. View at: Publisher Site | Google Scholar
  607. D. Knoll, M. Prüglmeier, and G. Reinhart, “Predicting future inbound logistics processes using machine learning,” Procedia CIRP, vol. 52, pp. 145–150, 2016. View at: Publisher Site | Google Scholar
  608. S. Soleimani, “A perfect triangle with: artificial intelligence,” Supply Chain Management, and Financial Technology, vol. 6, no. 11, pp. 85–94, 2018. View at: Publisher Site | Google Scholar
  609. SSI Schafer IT Solutions GmbH, Artificial Intelligence in Logistics: Terms, Applications and Perspectives, Friesach, Germany, 2018.
  610. M. M. Hasan, D. Jiang, A. M. M. S. Ullah, and M. Noor-E-Alam, “Resilient supplier selection in logistics 4.0 with heterogeneous information,” Expert Systems With Applications, vol. 139, pp. 1–24, 2020. View at: Publisher Site | Google Scholar
  611. R. Domański, J. Oleśków-szłapka, H. Wojciechowski, R. Domański, and G. Pawlowski, “Logistics 4.0 maturity levels assessed based on GDM (grey decision model) and artificial intelligence in logistics 4.0- trends and future perspective,” Procedia Manufacturing, vol. 39, pp. 1734–1742, 2019. View at: Google Scholar
  612. H. C. W. Lau, L. Zhao, and D. Nakandala, “An intelligent approach for optimizing supply chain operations,” Journal of Economics, Business and Management, vol. 3, no. 6, pp. 571–575, 2015. View at: Publisher Site | Google Scholar
  613. S. Danielsson and E. Ekström, Improving the Supply Chain Using Artificial Intelligence, Lund University, London, UK, 2018.
  614. W. Kersten, T. Blecker, and C. M. Ringle, “Artificial intelligence and digital transformation in supply chain management: innovative approaches for supply chains,” 2019. View at: Google Scholar
  615. Y. Li, M. K. Lim, and M.-L. Tseng, “A green vehicle routing model based on modified particle swarm optimization for cold chain logistics,” Industrial Management & Data Systems, vol. 119, no. 3, pp. 473–494, 2019. View at: Publisher Site | Google Scholar
  616. C.-F. Chien, S. Dauzère-Pérès, W. T. Huh, Y. J. Jang, and J. R. Morrison, “Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies,” International Journal of Production Research, vol. 58, no. 9, pp. 2730-2731, 2020. View at: Publisher Site | Google Scholar
  617. M.-H. Stoltz, V. Giannikas, D. McFarlane, J. Strachan, J. Um, and R. Srinivasan, “Augmented reality in warehouse operations: opportunities and barriers,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 12979–12984, 2017. View at: Publisher Site | Google Scholar
  618. D. Puljiz, G. Gorbachev, and B. Hein, “Implementation of augmented reality in autonomous warehouses: challenges and opportunities,” 2018. View at: Google Scholar
  619. W. Wang, F. Wang, W. Song, and S. Su, “Application of augmented reality (AR) technologies in inhouse logistics,” E3S Web of Conferences, vol. 145, 2020. View at: Google Scholar
  620. M. Merlino and I. Sproģe, “The augmented supply chain,” Procedia Engineering, vol. 178, pp. 308–318, 2017. View at: Publisher Site | Google Scholar
  621. S. Koul, “Augmented reality in supply chain management and logistics,” International Journal of Recent Scientific Research, vol. 10, no. 2, pp. 30732–30734, 2019. View at: Google Scholar
  622. M. K. Williams, Augmented Reality Supported Batch Picking System, University of Twente, Twente, France, 2019.
  623. K. Govindan, T. C. E. Cheng, N. Mishra, and N. Shukla, “Big data analytics and application for logistics and supply chain management,” Transportation Research Part E: Logistics and Transportation Review, vol. 114, pp. 343–349, 2018. View at: Publisher Site | Google Scholar
  624. S. F. Wamba, A. Gunasekaran, T. Papadopoulos, and E. Ngai, “Guest editorial: big data analytics in logistics and supply chain management,” International Journal of Logistics Management, vol. 29, no. 2, pp. 478–484, 2018. View at: Google Scholar
  625. S. S. Darvazeh, I. R. Vanani, and F. M. Musolu, “Big data analytics and its applications in supply chain management,” IntechOpen, vol. 29, pp. 1–26, 2020. View at: Google Scholar
  626. S. Rowe and M. Pournader, “Supply Chain Big Data Series Part 1: How Big Data Is Shaping the Supply Chains of Tomorrow,” KPMG Australia and Macquarie Graduate School of Management, vol. 29, 2017. View at: Google Scholar
  627. G. Wang, A. Gunasekaran, E. W. T. Ngai, and T. Papadopoulos, “Big data analytics in logistics and supply chain management: certain investigations for research and applications,” International Journal of Production Economics, vol. 176, pp. 98–110, 2016. View at: Publisher Site | Google Scholar
  628. M. Brinch, J. Stentoft, and J. K. Jensen, “Practitioners understanding of big data and its applications in supply chain management,” The International Journal of Logistics Management, vol. 29, no. 2, pp. 555–574, 2018. View at: Publisher Site | Google Scholar
  629. Z. Rajkumar, H. Ismail, L. Chen, X. Zhao, and L. Wang, “The application of big data analytics in optimizing logistics: a developmental perspective review,” Journal of Data, Information and Management, vol. 1, no. 1-2, pp. 33–43, 2019. View at: Publisher Site | Google Scholar
  630. Y. Issaoui, A. Khiat, A. Bahnasse, and H. Ouajji, “Smart logistics: study of the application of blockchain technology,” Procedia Computer Science, vol. 160, pp. 266–271, 2019. View at: Publisher Site | Google Scholar
  631. A. Goudz and V. Steiner, “An evaluation for the use of blockchain technology in logistics,” International Journal of Transportation Engineering and Technology, vol. 5, no. 1, pp. 11–17, 2019. View at: Publisher Site | Google Scholar
  632. Capgemini Research Institute, “Does blockchain hold the key to a new age of supply chain transparency and trust?” How Organizations Have Moved from Blockchain Hype to Reality, vol. 29, 2018. View at: Google Scholar
  633. N. Kshetri, “1 Blockchain's roles in meeting key supply chain management objectives,” International Journal of Information Management, vol. 39, pp. 80–89, 2018. View at: Publisher Site | Google Scholar
  634. DHL and Accenture, “Blockchain in logistics: Perspectives on the Upcoming Impact of Blockchain Technology and Use Cases for the Logistics Industry,” 2018. View at: Google Scholar
  635. J. Duan, C. Zhang, Y. Gong, S. Brown, and Z. Li, “A content-analysis based literature review in blockchain adoption within food supply chain,” International Journal of Environmental Research and Public Health, vol. 17, 2020. View at: Google Scholar
  636. M. Dobrovnik, D. M. Herold, E. Fürst, and S. Kummer, “Blockchain for and in logistics: what to adopt and where to start,” Logistics, vol. 2, no. 18, 2018. View at: Publisher Site | Google Scholar
  637. Deloitte, “Continuous interconnected supply chain: using blockchain & internet-of-things in supply chain traceability,” 2017. View at: Google Scholar
  638. F. Poszler, A. Ritter, and I. Welpe, “Blockchain startups in the logistics industry: the technology’s potential to disrupt business models and supply chains,” 2019. View at: Google Scholar
  639. E. Tijan, S. Aksentijevi, K. Ivanic, and M. Jardas, “Blockchain Technology Implementation in Logistics sustainability Blockchain Technology Implementation in Logistics,” Sustainability, vol. 11, p. 1185, 2019. View at: Google Scholar
  640. T. Leonard, Blockchain for Transportation: Where the Future Starts, Cleveland/Dallas/Nashville, Switzerland, 2017.
  641. World Economic Forum, Inclusive Deployment of Blockchain for Supply Chains: Part 1-Introduction, Cologny, Geneva, Switzerland, 2019.
  642. A. Sivula, A. Shamsuzzoha, and P. Helo, “Blockchain in logistics: mapping the opportunities in con- struction industry,” Proceedings of the International Conference on Industrial Engineering and Operations Management September, vol. 27–29, pp. 1954–1960, 2018. View at: Google Scholar
  643. L. Koh, A. Dolgui, and J. Sarkis, “Blockchain in transport and logistics-paradigms and transitions,” International Journal of Production Research, vol. 58, no. 7, pp. 2054–2062, 2020. View at: Publisher Site | Google Scholar
  644. G. Blossey, J. Eisenhardt, and G. J. Hahn, “Blockchain technology in supply chain management: an application perspective,” 2019. View at: Google Scholar
  645. S. Agarwal, Blockchain Technology in Supply Chain and Logistics, Massachusetts Institute of Technology, Massachusetts, MA, USA, 2018.
  646. A. Schmahl, Resolving the Blockchain Paradox in Transportation and Logistics, Boston Consulting Group (BCG), Boston, MA, USA, 2019.
  647. M. Pournader, Y. Shi, S. Seuring, and S. C. L. Koh, “Blockchain applications in supply chains, transport and logistics: a systematic review of the literature,” International Journal of Production Research, vol. 7543, no. 58, p. 7, 2020. View at: Google Scholar
  648. D. Dujak and D. Sajter, “Blockchain applications in supply,” Springer International Publishing AG, vol. 75, pp. 21–46, 2019. View at: Google Scholar
  649. A. Jabbari and P. Kaminsky, “Blockchain and supply chain management,” 2018. View at: Google Scholar
  650. DAC, “Blockchain in Transport, Shipping and Logistics,” 2019. View at: Google Scholar
  651. E. Petersson and K. Baur, Impacts of Blockchain Technology on Supply Chain Collaboration: A Study on the Use of Blockchain Technology in Supply Chains and How it Influences Supply Chain Collaboration, Jonkoping University, Boston, MA, USA, 2018.
  652. C. F. Durach, T. Blesik, M. Von During, and M. Bick, “Blockchain Applications in Supply Chain Transactions,” Journal of Business Logistics, vol. 16, 2020. View at: Google Scholar
  653. G. Perboli, S. Musso, and M. Rosano, “Blockchain in logistics and supply chain: a lean approach for designing real-world use cases,” IEEE Access, vol. 6, pp. 62018–62028, 2018. View at: Publisher Site | Google Scholar
  654. G. Niharika and V. Ritu, “Cloud Architecture for the Logistics Business,” Procedia-Procedia Computer Science, vol. 50, pp. 414–420, 2015. View at: Google Scholar
  655. C. G. Kochan, The Impact of Cloud Based Supply Chain Management on Supply Chain Resilience, University of North Texas, Boston, MA, USA, 2015.
  656. O. Akinrolabu, S. New, and A. Martin, “Cyber supply chain risks in cloud computing-bridging the risk assessment gap,” Open Journal of Cloud Computing, vol. 5, no. 1, pp. 1–19, 2018. View at: Google Scholar
  657. E. Aghamohammadzadeh, M. Malek, and O. F. Valilai, “A novel model for optimisation of logistics and manufacturing operation service composition in Cloud manufacturing system focusing on cloud-entropy,” International Journal of Production Research ISSN, vol. 58, no. 7, p. 1987, 2020. View at: Google Scholar
  658. Z. Benotmane, G. Belalem, and A. Neki, “A cloud computing model for optimization of transport logistics process,” Transport and Telecommunication Journal, vol. 18, no. 3, pp. 194–206, 2017. View at: Publisher Site | Google Scholar
  659. J. Yang, “Construction and optimization of port logistics service supply chain based on cloud computing,” Journal of Coastal Research, vol. S1, no. 98, pp. 83–86, 2019. View at: Google Scholar
  660. B. E. Al-jawazneh, “The prospects of cloud computing in supply chain management (A theoretical perspective),” Journal of Management Research, vol. 8, no. 4, pp. 145–158, 2016. View at: Publisher Site | Google Scholar
  661. S. K. Singh, P. S. Srinivasan, and D. Kaur, “SOA cloud computing: modernized the supply chain management applications,” IOSR Journal of Engineering, vol. 09, no. 3, pp. 67–74, 2019. View at: Google Scholar
  662. B. Nicoletti, “Cloud computing and procurement,” 2016. View at: Google Scholar
  663. FlytBase, “2020 Guide: Inventory Counts Using Drones,” 2020. View at: Google Scholar
  664. E. Companik, M. J. Gravier, and M. T. Farris II., “Feasibility of warehouse drone adoption and implementation,” Journal of Transportation Management, vol. 28, no. 2, pp. 33–50, 2018. View at: Publisher Site | Google Scholar
  665. E. J. U. Hernández, J. A. S. Martínez, and J. A. M. Saucedo, “Optimization of the distribution network using an emerging technology,” Applied Sciences, vol. 10, no. 857, 2020. View at: Google Scholar
  666. V. Olivares, F. Cordova, J. M. Sepúlveda, and I. Derpich, “Modeling internal logistics by using drones on the stage of assembly of products,” Procedia Computer Science, vol. 55, pp. 1240–1249, 2015. View at: Publisher Site | Google Scholar
  667. UNICEF, “Innovation Case Study: Drones for Delivering Results for Children,” 2019. View at: Google Scholar
  668. K. Kuru, D. Ansell, W. Khan, and H. Yetgin, “Analysis and optimization of unmanned aerial vehicle swarms in logistics: an intelligent delivery platform,” IEEE Access, vol. 7, pp. 15804–15831, 2019. View at: Publisher Site | Google Scholar
  669. B. A. Mccunney and K. P. Van Cauwenberghe, “Simulation Test Bed for Drone-Supported Logistics Systems,” Massachusetts Institute of Technology, vol. 7, 2019. View at: Google Scholar
  670. A. J. Lohn, What’s the Buzz? The City-Scale Impacts of Drone Delivery, RAND Corporation: Santa Monica, Califonia, 2017.
  671. M. Roca-Riu and M. Menendez, “Logistic deliveries with Drones. State of the art of practice and research,” 2019. View at: Google Scholar
  672. J. Aurambout, K. Gkoumas, and B. Ciuffo, “Last mile delivery by drones: an estimation of viable market potential and access to citizens across European cities,” European Transport Research Review, vol. 11, no. 30, pp. 1–21, 2019. View at: Publisher Site | Google Scholar
  673. I. Zubin, “introduction of drones in the last-mile logistic process of medical product delivery: a feasibility assessment applied to the case study of BENU’t slag,” Delft University of Technology, vol. 11, 2019. View at: Google Scholar
  674. I. Marsh, “Drones-a View into the Future for the Logistics Sector,” 2015. View at: Google Scholar
  675. A. Kujawski, J. Lemke, and T. Dudek, “Concept of using unmanned aerial vehicle (UAV) in the analysis of traffic parameters on Oder Waterway,” Transportation Research Procedia, vol. 39, pp. 231–241, 2019. View at: Publisher Site | Google Scholar
  676. L. Juntao and M. Yinbo, “Research on internet of things technology application status in the warehouse operation,” International Journal of Science, Technology and Society, vol. 4, no. 4, pp. 63–66, 2016. View at: Publisher Site | Google Scholar
  677. S. Trab, E. Bajic, A. Zouinkhi et al., “A communicating object's approach for smart logistics and safety issues in warehouses,” Concurrent Engineering, vol. 25, no. 1, pp. 53–67, 2017. View at: Publisher Site | Google Scholar
  678. Zebra Technologies, How the Internet of Things Is Improving Transportation and Logistics, ZIH Corporation, California, 2015.
  679. N. Mostafa, W. Hamdy, and H. Alawady, “Impacts of internet of things on supply chains: a framework for warehousing,” Social Sciences, vol. 8, no. 84, pp. 1–10, 2019. View at: Publisher Site | Google Scholar
  680. AT&T, IoT: A Strategic Approach to Logistics: A Focus on Innovation to Meet the Mission, Gallows Road, Vienna, 2016.
  681. T. Khare and R. K. Dubey, Leveraging IoT for Logistics, NEC Corporation, Vienna, 2017.
  682. P. Tadejko, “Application of internet of things in logistics-current challenges,” Economics and Management, vol. 7, no. 4, pp. 54–64, 2015. View at: Google Scholar
  683. AT&T and Eft, “The Internet of Things (IoT) in Supply Chain and Logistics,” 2016. View at: Google Scholar
  684. H. S. Birkel and E. Hartmann, “Internet of Things-the future of managing supply chain risks,” Supply Chain Management: An International Journal, vol. 7, 2020. View at: Google Scholar
  685. M. Tu, “An exploratory study of Internet of Things (IoT) adoption intention in logistics and supply chain management,” The International Journal of Logistics Management, vol. 29, no. 1, pp. 131–151, 2018. View at: Publisher Site | Google Scholar
  686. C. Liu, Y. Feng, D. Lin, L. Wu, and M. Guo, “Iot based laundry services: an application of big data analytics, intelligent logistics management, and machine learning techniques,” International Journal of Production Research, vol. 7, pp. 1–20, 2020. View at: Google Scholar
  687. G. Xie, “Smart logistics management of hazardous chemicals based on internet of things,” Chemical Engineering Transactions, vol. 67, pp. 85–90, 2018. View at: Google Scholar
  688. M. Ben-daya, E. Hassini, and Z. Bahroun, “Internet of things and supply chain management: a literature review,” International Journal of Production Research, vol. 57, no. 15-16, pp. 4719–4742, 2019. View at: Publisher Site | Google Scholar
  689. Y. Cui, Supply Chain Innovation with IoT, IntechOpen, London, UK, 2018.
  690. Z. Zhao, M. Zhang, G. Xu, D. Zhang, and G. Q. Huang, “Logistics sustainability practices: an IoT-enabled smart indoor parking system for industrial hazardous chemical vehicles,” International Journal of Production Research, vol. 7, pp. 1–17, 2020. View at: Publisher Site | Google Scholar
  691. K. Buntak, M. Kovačić, and M. Mutavdžija, “Internet of things and smart warehouses as the future of logistics,” Tehnički Glasnik, vol. 13, no. 3, pp. 248–253, 2019. View at: Publisher Site | Google Scholar
  692. M. Tu, M. Lim, and M.-F. Yang, “Internet of Things-Based Production Logistics and Supply Chain System-Part 2: IoT-Based Cyber-Physical System: A Framework and Evaluation,” Industrial Management & Data Systems, vol. 7, pp. 1–30, 2016. View at: Google Scholar
  693. S. Zaib and J. Iqbal, Nanotechnology: Applications, Techniques, Approaches, & the Advancement in Toxicology and Environmental Impact of Engineered Nanomaterials, MedDocs Publishers LLC, London, UK, 2019.
  694. J. E. M. Allan, Technology Transfer in Nanotechnology, Publication Office of the European Union, Luxembourg, 2019.
  695. J. T. Li and H. Liu, “Design optimization of amazon robotics,” Automation, Control and Intelligent Systems, vol. 4, no. 2, pp. 48–52, 2016. View at: Publisher Site | Google Scholar
  696. M. Johnson, “Quiet Logistics’ Next Step into Robotics: Modern System Report,” 2016. View at: Google Scholar
  697. T. Wozniakowski, K. Zmarzlowski, and M. Nowakowska, “Automation and innovations in logistic processes of electronic commerce,” Information Systems in Management, vol. 7, no. 1, pp. 72–82, 2018. View at: Google Scholar
  698. A. Dekhne, G. Hastings, J. Murnane, and F. Neuhaus, “Automation in Logistics: Big Opportunity, Bigger Uncertainty,” 2019. View at: Google Scholar
  699. D. Küpper, Advanced Robotics in the Factory of the Future, Boston Consulting Group (BCG), Boston, MA, USA, 2019.
  700. Institute for Supply Management, “New types of robots are providing opportunities for logistics, manufacturing and other industries, while enabling workers to do what they do best: interacting and performing more highly skilled jobs,” 2018. View at: Google Scholar
  701. G. Q. Huang, M. Z. Q. Chen, and J. Pan, “Robotics in ecommerce logistics,” HKIE Transactions ISSN:, vol. 22, no. 2, pp. 68–77, 2015. View at: Google Scholar
  702. T. Bonkenburg, “Robotics in Logistics: A DPDHL Perspective on Implications and Use Cases for the Logistics Industry,” 2016. View at: Google Scholar
  703. R. Strulak-Wójcikiewicz and J. Lemke, “Concept of a simulation model for assessing the sustainable development of urban transport,” Transportation Research Procedia, vol. 39, pp. 502–513, 2019. View at: Publisher Site | Google Scholar
  704. J. Maina and P. Mwangangi, “A critical review of simulation applications in supply chain management,” Journal of Logistics Management, vol. 9, no. 1, pp. 1–6, 2020. View at: Google Scholar
  705. N. Belyak, Simulation Methods for Transport Logistics, Lappeenranta University of Technology School, London, UK, 2017.
  706. C. Fu and Z. Shuai, “The simulation and optimization research on manufacturing enterprise’s supply chain process from the perspective of social network,” Journal of Industrial Engineering and Management, vol. 8, no. 3, pp. 963–980, 2015. View at: Publisher Site | Google Scholar
  707. M. Zhang and C. S. Liu, “Cost simulation and optimization of fresh cold chain logistics enterprises based on SD,” IOP Conference Series: Materials Science and Engineering, vol. 392, 2018. View at: Publisher Site | Google Scholar
  708. F. S. Yanikara and M. E. Kuhl, “A simulation framework for the comparison of rverse logistic network configurations,” 2015. View at: Google Scholar
  709. A. Ghadge, M. Er Kara, and H. Moradlou, “The impact of Industry 4.0 implementation on supply chains,” Journal of Manufacturing Technology Management, vol. 31, no. 4, pp. 669–686, 2020. View at: Publisher Site | Google Scholar
  710. M. F. Goswami, K. K. Castillo-villar, M. Aboytes-ojeda, and M. H. Giacomoni, “Simulation-optimization approach for the logistics network design of biomass Co-firing with coal at power plants,” Sustainability, vol. 10, 2018. View at: Publisher Site | Google Scholar
  711. J. González-reséndiz, K. C. Arredondo-soto, A. Realyvásquez-vargas, H. Híjar-rivera, and T. Carrillo-gutiérrez, “Integrating simulation-based optimization for lean logistics: a case study,” Applied Sciences, vol. 8, no. 2448, 2018. View at: Publisher Site | Google Scholar
  712. S. Mutke, C. Augenstein, M. Roth, A. Ludwig, and B. Franczyk, “Real-time information acquisition in a model-based integrated planning environment for logistics contracts,” Journal of Object Technology, vol. 14, no. 1, pp. 1–25, 2015. View at: Publisher Site | Google Scholar
  713. A. Muravjovs, Inventory Control System Analysis Using Different Simulation Modelling Paradigms, Transport and Telecommunication Institute, Berlin, Germany, 2015.
  714. Department of Defense for Research & Engineering, “Technical Assessment: Synthetic Biology,” 2015. View at: Google Scholar
  715. IICA, “Proceedings of the first seminar on synthetic biology for biotechnology-regulatory decision makers from the americas,” IICA, Berlin, Germany, 2017. View at: Google Scholar
  716. P. Sachsenmeier, “Industry 5.0-the relevance and implications of bionics and synthetic biology,” Engineering, vol. 2, no. 2, pp. 225–229, 2016. View at: Publisher Site | Google Scholar
  717. A. Tinafar, K. Jaenes, and K. Pardee, “Synthetic biology goes cell-free,” BMC Biology, vol. 17, no. 64, 2019. View at: Publisher Site | Google Scholar

Copyright © 2020 Ocident Bongomin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views3441
Downloads1440
Citations

Related articles

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.