Abstract

In the present era, many Petrochemical Industries (PIs) are driven energetically due to IIoT (Industrial Internet of Things) Communication Networks/Architectural Layers (CNs/ALs), abbreviated as PI4.0-CNs/ALs. PI4.0 fruitfully participated to achieve the Circular Economy (CE) by speeding the reutilization, recovery, and recycling of scrap materials by minimizing cost, unproductive operations, energy consumption, emission of flue gases, etc. Recently, it has been ascertained that the identification and measurement of Research Trends (RTs) of CNs-ALs help the PI4.0 to build the future CE. In addressing the said research challenge, the objective of this research dossier is turned towards inculcating into future PI4.0 researchers the RTs of CNs/ALs of PI4.0, so that the researches can be organized over the very weak and moderately performing CNs-ALs to hike the future CE. To materialize the RTs of PI4-CNs/ALs, the authors conducted the Systematic Literature Survey (SLS) focusing on PI4.0-CNs/ALs, i.e., Internet of Things (IoTs), Cyber Physical System (CPS), Virtual Reality (VR), Integration (I), Data Optimization (DO), Enterprise Resource Planning (ERP), Plant Control (PC), Data and Analytics (DA), Network (N), and Information and Data Management (IDM). The authors searched three hundred two research documents, wherein two hundred seventy-five research manuscripts qualified as RQ2. Next, the authors collected the DOIs/URLs corresponding to each CN-AL and explored the Sum of Digit Scoring System (SDSS) to summarize the DOIs/URLs of PI4.0-CNs/ALs. The RTs of DO have been determined as excellent and stronger over 2007-2017 than residue CNs/ALs. Eventually, the authors advised scholars to focus on the research areas of very weak and moderately weak performing CNs/ALs in order to attain future CE.

1. Introduction

In the last decade, the demand from customers for customized products with the lowest prices thrust Production Systems (PSs) into more digitalized capabilities with an effort of minimizing the wastefulness of industries. This approach is called the Circular Economy (CE). The growth of the population also propelled industrialists to establish, expand, and integrate their business empires by incorporating different varieties of customized products with the least waste. This is another way to describe CE. Today’s emergent demands of domestic oils and gas resources are stimulating the research communities towards devising appropriate means to monitor and evaluate the physical performance for saving energy, processes which are respected as CE as per [1, 2]. Firms adapt a technical and institutional framework for enrolling, recycling, and disposal monitoring systems to ensure utilization of waste oils and hazardous wastes with a huge public interest in developing CE [3, 4]. The employment of appropriate technology can radically reduce ecological pollution and wastes by improving connectivity, integration, and automation towards oil mill processors and allied systems for attaining CE [5, 6]. Today, it is necessary to react towards technological advancements and deployments for coping with the demand for liquid fuel, the shortage of crude oil, monitoring of liquid fuel routes, and the assessment of their life cycle’s estimation for addressing CE as per [7, 8]. Industries ought to focus on stimulating the biomass means, technical routing, byproduct synergy, carbon capture features, system automation, scientific implications, feedback mechanisms, connecting nodes, etc. in industries for attaining global CE as per [911]. Additionally, the diverse varieties of emissions of offensive gases from production should be managed and monitored by developing national strategies, control systems, technology, etc., as they can cause irreparable loss, wastes, and commensurate costs to the society and to industrial plants [12, 13]. Accordingly, technological proximity and means need to evolve and be incorporated into a firm’s system by modeling and integrating to build CE as per [14, 15]. An elevated level of connectivity, integration, and monitoring is evidently needed among business networks and is required for coping with the global CE, competition, and management of production costs as per [16, 17]. High-rank digitalization mechanisms, connecting technologies, and automation are needed at every corner of the organizational structure for attaining CE as per [1820].

It is ascertained that the industry 4.0 revolution captured entire industries and markets around the world. Industry 4.0 is described in terms of automations, information exchangers, cloud storages and computing, cyber tech and physical systems, robotic self-navigation phenomena, big data analysis, and IoTs to add values in flexible and efficient PSs. Technological advancement, innovation, evolution, etc., are required for effective interconnection and communication among different manufacturing systems, which are demanding the critical exploration of industry 4.0 as per [21, 22]. The reactions of digital CNs/ALs are propelling the significant advancements in industry 4.0 structures and are assisting industry 4.0 for driving PSs with flexibility with least waste output. Industry 4.0 introduces electronic data feedback and an integrated digital medium system across the functional units of industry. Industry 4.0 stimulates the computerization, monitoring, automation, and setting up of wide CNs for predicting and self-diagnosing deployed manufacturing devices. Ennis et al. [23] reveals that the collaboration and integration among organizations are possible today due to industry 4.0. Oztemel and Gursev [24] stated that industry 4.0 can make PSs more capable for performing activities and achieving CE. Yuan et al. [25] suggested that industry 4.0’s practices are the best for smart manufacturing and advised that industry 4.0 is a reliable and scalable platform (digitally operating hardware and software) to update the level of instrument technology for effective utilization of resources. Trotta and Garengo [26] and Maresova et al. [27] found that industry 4.0 is a complex and disruptive network, which helps the stakeholders of private and state companies to attain more revenue and develop the global CE.

It has been empirically investigated that Petrochemical Industries (PIs) have been counted as one of the significant sectors that fulfill the daily needs of society’s people. Presently, PIs are leading and immensely expanding their business globally by producing chemical compounds and petroleum-based solvents such as paints, coatings, detergents, and adhesives. Nowadays, most of the PIs are emergently focusing on manufacturing beverage products, i.e., oil and gas refinery, vegetable and palm oil, petroleum oil, ethylene refinery, and ink and paint production for attaining a strong CE. [2830]. A few PIs turn out ethane, ethylene, methane, and petrol to compensate for the daily needs of peoples. Today, PIs are engrossed into the industry 4.0 revolution or digitalization, which is called PI4.0. The majority of PI4.0 becomes sustainable and more lenient to flexible, agile, and lean manufacturing processes on account of digital transformation for attaining a strong CE. PI4.0 overcomes the prohibitive issues related to production and monitoring of PSs with great digital network and capabilities, which help industries meet the challenges of market demands.

Investigations have shown that the Communication Networks/Architectural Layers (CNs/ALs) with PI4.0 (which is abbreviated as PI4.0-CNs/ALs), such as IoT, CPS, VR, I, and DO, build the PSs of PIs more lenient to CE and the market’s demands. Therefore, the continuous digital advancement into CNs/ALs enabled PI4.0 to meet the future market’s demands. However, there is a need to map the RTs against CNs/ALs of PI4.0 to identify the areas where CNs/ALs are still poor, weak, and not at all improved. This rationale diverts the attentions of the authors to quantify the RTs of PI4.0-CNs/ALs. Gölzer et al. [31] argue that RT materialization is needed for a complete and detailed description of data processing requirements for all industry 4.0-based companies, to fully understand implications on IT-infrastructure and IT-solution-components. Van Tienen et al. [32] found that the digital advancement solution against CNs/ALs is a simplistic approach to improve industry 4.0 technologies and entrepreneurships and attain a high degree of CE. Groger [33] suggested that RT directions are useful for the successful implementation of industry 4.0, i.e., interdisciplinary research, specification of modular and reusable analytical services, appropriate tools, and organizational models and frameworks.

2. Literature Survey

The CNs/ALs augmented the convenience, handiness, and productivity across PIs due to intelligent interpretation of procured information and consequently aided the autonomous process [34, 35]. The apparent requirement for advanced digital technologies belonging to Internet of Things, cloud computing, information exchangers, etc., is embodying a novel paradigm and influencing countless aspects of the routine line of private and business users [36, 37]. Theoretical supports and academic researches provided the comprehensive pitch to be explored by smart industry 4.0 [3840]. An assessment model was built in [41], and it was used to measure and gauge the industry 4.0 technologies by considering three dimensions such as factory of the future, people and culture, and strategy. The eight chief attributes, i.e., cloud computing, manufacturing execution system, e-value chains, additive manufacturing, IoTs and cyber physical systems, big data, and sensors and autonomous robots are suggested as “Factory of the Future” and implemented as the significant dimensions of industry 4.0. Accordingly, enablers of industry 4.0, interconnected nodes and medium, research questions, technical frameworks, distribution patterns, computation mechanisms etc., are respected as effectual patterns of industry 4.0 evolution [4244].

A progressive elementary aspect can assist in visualizing, monitoring, and recovering the characteristics of a manufacturing system [45, 46]. The revolutionary concepts and creative tools are required to identify the significant enablers for future success of organizations [47, 48]. Multiple flow control mechanisms and flexibility in production, decision-making, and problem solving are needed for executing the production processes efficiently based on the operational models [49]; consequently, a novel operational model called the Internet Fulfillment Warehouses (IFWs) was devised in [50] for effectively optimizing the operational model of warehouses.

Internet is the key for technical innovation, and it possesses the capability to synchronize and optimize the static and dynamic constraints to enable the technical execution of industry 4.0 [51, 52]. Internet is presently serving as a novel operational configuration by linking smart apparatuses, machineries, and systems [53, 54]. Comprehensive integrated tools, models, strategies, policies, and techniques need to be integrated with industry 4.0 when tackling human resources [55, 56].

The authors adapted the internet-based research search engine [57] to accumulate databases related to research documents. The internet-based search engine has evidently been adapted in previous research works and has eliminated the drawback of the expert’s panel and furcating schemes. The authors further conducted a Systematic Literature Survey (SLS) to recognize the momentous CNs/ALs of PIs and to quantify the RTs of PI4.0-CNs/ALs. Matthew et al. [58] emphasized the failure detection analytics sensors to monitor the oil production failures in digitized oil fields. The research work suggested the use of thousands of sensors and gauges with equipment to map the physical and chemical characteristics of oil and gas extracted from underground reservoirs. John and David [59] proposed a simulation model to fruitfully analyze the dynamic magnitude of water and sand collected by extracting the oil from a pool of oil. An analysis was conducted to choose the optimum technology for coping with oil extraction challenges. Li et al. [60] proposed extensible X-3D software for building an interactive and dynamic virtual oil and gas pipeline system. The X-3D application was applied in designing a piping system to illustrate the virtual reality application in PI4.0. Meng et al. [61] stated that IoT is a significant indicator for taking care of information technology and is respected as a crucial AL for executing industrial operations effectively. The authors demonstrated an IoT AL reference model to investigate IoT growth. A case study of a PI4.0 is illustrated to reveal the research challenges and opportunities associated with IoT.

Hemant et al. [62] proposed an infeasibility driven evolutionary algorithm to resolve the efficiency evaluation problem of fifty-six oil pools under a single-level constraint variable. The authors have suggested to frame a multiobjective problem to define effective results and to eliminate the drawback of an earlier formulated objective. David [63] explained a key CNs/ALs of PI4.0 proposed by the American National Standards Institute, the American Petroleum Institute, and Standard 780 to build security risk assessment methods for systematically identifying suitable measures and eliminating future threats. Parolini et al. [64] proposed a new Cyber Physical Index (CPI) for measuring the effects of a combined distribution of a Cyber Physical System (CPS) in a given data. A case study is conducted by the authors to demonstrate how the CPI indicates the potential impact of CPS control strategies and cyber cum physical control as well. Gholian [65] developed a mathematical model for establishing the optimized operational sequences for industrial load control operation. Yatin and Clifford [66] proposed a game theory algorithm to allocate the cybersecurity controls in the oil pipelines. The proposed algorithm assisted the oil pipeline cyber physical system to allocate the cybersecurity control teams around high-risk regions. Ahmed and Kim [67] described the Named Data Networking (NDN) with its applications in smart home communications for critically evaluating and defining the aspects to address the future challenges of NDN.

Robin and Chunyan [68] utilized the ERP (Enterprise Resource Planning) system and suggested its implementation over Continuous Auditing (CA) of oil refinery processes. It has been concluded that ERP increases the efficiency, fraud risk reduction, knowledge application, and credibility of the auditing team. Jeon et al. [69] proposed a specific plan to effectively implement ERP for controlling the shop floor of PI4.0. In the proposed plan, an advanced MES is added for collecting, measuring, and analyzing shop floor controlling. Niggermann et al. [70] determined that data-driven approaches to analysis and diagnosis of CPSs are always inferior when compared with classical model-based approaches, constituted by experts. Trappey et al. [71] evaluated the critical international standards and intellectual property rights (associated with CPS patents) to benefit academic scholars and industry practitioners. Hassani et al. [72] focused on the evaluation of the significant innovations, technological drivers, and CNs/ALs of PI4.0s. The authors searched the quantifiable and nonquantifiable impacts of innovation, technological drivers, and CNs/ALs that benefit PI4.0s.

After passing through the aforesaid literature survey, the authors built a research methodology and listed four Research Inquiries (RIs) to effectively grab the significance for commencing the research work. The four research inquiries and manuscript filtering/screening guidelines for quantifying the RTs against PI4.0-CNs/ALs are framed. The authors reported a research approach and structure for successfully materializing the RTs and suggesting the future research challenges of PI4.0-CNs/ALs to attain CE.

3. Research Methods and PI4.0-CN/AL Contribution towards Building the CE

CE is motivated to eliminate several industrial wastes, i.e., idle times, waste materials, and emission of carbon gases (emissions can be minimized by enhancing the reusing, repairing, refurbishing, remanufacturing, and recycling systems in production systems of companies). CE develops economic, natural, and social capital by addressing the three challenges of companies, i.e., concentrating on elimination of waste and pollution, recycling products and materials, and generating energy. In the present PI4.0, CNs/ALs contribute towards balancing the advance of manufacturing and the reutilization, recovery, remanufacturing, and recycling (reverse manufacturing) of scraps/parts aiming at forming the CE of industries. CE improvement across PI4.0 can be attained by enriching and advancing the CNs/ALs through identifying the RT levels. In the research forum presented in this study, the authors built and proposed a research method and four Research Inquiries (RIs) for quantifying the RTs of PI4.0-CNs/ALs. The research structure is illustrated in Figure 1. The four RIs are shown below: (i)Research Inquiry (RI1): How does research work address the most significant PI4.0-CNs/ALs?(ii)Research Inquiry (RQ2): What is the research policy to search the database of only scientific research-oriented documents against a framed PI4.0-CN/AL model?(iii)Research Inquiry (RQ3): How do we segregate the RTs corresponding to each PI4.0-CN/AL?(iv)Research Inquiry (RQ4): How do we recognize the very weak, moderate, and strong research areas of PI4.0-CNs/ALs?

4. Research Inquiries (RQs) and Responses

4.1. Research Inquiry (RQ1): How Does Research Work Address the Most Significant PI4.0-CNs/ALs?

From the Internet, the authors accessed Google, Yahoo, DuckDuckGo, and Ask.com, which are interdisciplinary scientific database search engines. The authors fruitfully explored the Google (open) internet-based research database search engine to conduct SLS in addressing PI4.0-CNs/ALs. The authors extracted and reviewed the research manuscripts. Eventually, the authors scrutinized the most significant PI4.0-CNs/ALs based on architectural celebrity, fame, population, and identification in most of the research manuscripts. Tables 1 and 2 illustrated the nomenclatures of PI4.0-CNs/ALs for addressing RQ1. Table 3 reflects the definitions of all scrutinized PI4.0-CNs/ALs. The research structure is illustrated in Tables 1, 2, and 3 and Figure 1.

4.2. Research Inquiry (RQ2): What Is the Research Policy to Search the Database of Only Scientific Research-Oriented Documents against a Framed PI4.0-CN/AL Model?

In order to extract the scientific research documents against each CN/AL, the authors adopted inclusion and exclusion parameters. Non-research-oriented scientific materials, i.e., magazines, posters, ppts, reports, and news reports are excluded. Only conference and journal manuscripts are respected under the inclusion parameters. The reason to pursue only research-oriented scientific materials is that most of the non-research-oriented scientific materials illustrate the newspaper or represent unreliable, unreadable works and non-value-added research activities. Table 4 illustrates the list of inclusion and exclusion parameters to justify RQ2.

4.3. Research Inquiry (RQ3): How Do We Segregate the RTs Corresponding to Each PI4.0-CN/AL?

Table 5 describes the specific research search engines [57], which are targeted in this research to store the relevant database and thus satisfying RQ2. The authors collected only DOIs/URLs against PI4.0-CNs/ALs over years 2007-2017. The authors explored primary, secondary, and tertiary protocols as discussed in Table 6 for searching research documents.

The authors applied the Sum of Digit (SD) technique to quantify the research manuscripts, published over the years 2007-2017. Table 7 summarizes the results against the scientific research databases I and II with respect to the exclusion parameters, full text search, and primary search. Figure 2 illustrates the database using the Sankey flow diagram, which shows the total research documents and its scattering record. Figure 3 reveals the total database of PI4.0-CNs/ALs by bar chart. Figure 4 evaluates the research documents by PRISMA 2009 flow chart, which shows the research documents’ refinery process (inclusion and exclusion documents) and the research documents to be considered for studied, quantitative, and qualitative analysis.

4.4. Research Inquiry (RQ4): How Do We Recognize the Very Weak, Moderate, and Strong Research Areas of PI4.0-CNs/ALs?

The authors summarized the research documents with respect to each PI4.0-CN/AL as discussed in RQ3 to recognize the weakly, moderately, and strongly performing PI4.0-CNs/ALs. The RT data are depicted in Tables 810 which exhibit the DOIs/URLs of research documents, existing under the exclusion parameters. The results and discussions are carried out in Section 5. Suggestions to improve the very weak and moderately performing PI4.0-CNs/ALs are discussed in Section 5.1.

5. Result and Discussions

The line charts are presented in Figures 57 which illustrate the RTs. The results are briefly articulated as follows: (a)PI4.0-DO-CN/AL: the total number of RMs (Research Manuscripts) has been found (fifty-five) in the case of DO-CN/AL. The RTs have been found consistent and stable more than twice, i.e., 2007-2009 and 2013-2015, respectively. It has been proven that the RTs from 2013 to 2015 are stronger than those from 2007 to 2009. Next, the RTs have been found with strong acceleration over 2016-2017. As a result, the RTs of DO have been traced as more credible than the residue of all CNs/ALs(b)PI4.0-N-CN/AL: the total number of RMs has been found (fifty) in the case of N-CN/AL. The RTs have been found excellent in 2016. In addition, the RTs have also been observed stable over 2008, 2010, and 2017 with six to eight RMs, but the RT of 2016 could not be accessed. As a result, the RTs of N have been traced to be slightly less than those of DO-CNs/ALs(c)PI4.0-DA-CN/AL: this layer reserved forty-one RMs. The research has no sound from 2007 to 2008. Later, RTs have been found constant from 2009 to 2012 excluding 2011 which reflected only four RMs. RTs over 2013-2017 have been found stronger (wherein 2016 and 2017 seem to be the most significant years). As a result, the RTs of PC have been found lesser than those of N and DO(d)PI4.0-PC-CN/AL: this layer reserved thirty-three RMs. The RTs have been found constant over the years 2007-2014. RTs are slightly accelerated over 2015-2017. As a result, the RTs of PC have been found prosier than N, DO, and DA(e)PI4.0-IoT-CN/AL: this CN/AL stands with twenty-two RMs. The RTs illustrated upward and downward slopes throughout 2010-2015. However, RTs have been found strong for over two years (2016 and 2017). As a result, this layer RT has been found inferior to that of DO, N, DA, and PC(f)PI4.0-VR-CN/AL: the total number of RMs has been found (twenty-two). The RTs are continuously upward and downward over 2010-2017. The RTs have been found the same over the years 2007, 2010, 2013, and 2015. RTs resonate with a good sound for over two years (2012 and 2016). As a result, the RTs of DO, N, DA, PC, and IoT have been found superior to that of VR(g)PI4.0-I-CN/AL: this AL stands with only twenty RMs. The RTs have been observed with no sound/null or hardly with one RM over the years 2007-2014. The RTs highly accelerated over 2015-2017 in comparison with 2010-2014. As a result, the RTs of DO, N, DA, PC, IoT, and VR have been found superior to that of I(h)PI4.0-CPS-ERP-IDM-CNs/ALs: all said, ALs seemed to be without sense to the researchers. The RTs are the same in terms of RM publications. The researchers must focus on CPS-ERP-IDM-CNs/ALs to make the smart PI4.0 for the future. As a result, the RTs of CPS, ERP, IDM have been found at the level of the same RTs, but inferior to that of DO, N, DA, PC, IoT, VR, and I

After identifying as well as discussing the RTs of PI4.0-CNs/ALs, the authors focused on suggestions, provided by published articles in improving the very weak and moderately performing PI4.0-CNs/ALs, linked to CE. The authors present Section 5.1, which directs the scholars towards conducting research over very weak performing PI4.0-CNs/ALs such as CPS-ERP-IDM and subsequently focusing on the moderately weak performing PI4.0-CNs/ALs such as N, DA, PC, IoT, VR, and I.

5.1. Suggested Research Areas to Be Focused for Improving the Very Weak Performing PI4.0-CNs/ALs Such as CPS-ERP-IDM

Asongu and le Roux [73] and Miksa et al. [74] focused on the information and communication system and advised scholars to focus on information technology to enable effective communication systems in IT sectors. The presented research suggests that future scholars should focus on electronic devices, security risks, wireless monitoring control, knowledge and big data management, maintenance systems, and smart manufacturing architectures for improving the future performance of PI4.0-IDM-CN/AL and CE.

Robin and Chunyan [68] investigated the ERP system of oil and gas industries in Houston and analyzed the RTs and RTs of models, mathematical modeling, and applications of algorithmic techniques under I4 structures. The authors advised scholars to focus more towards path planning, machine learning process, and ERP software benefits for enhancing the future performance of PI4.0-ERP-CN/AL.

Mbohwa and Sahu [75] suggested that researchers should work on cyber physical security risk, cybersensor nodes, application of CPS principles, and polymorphic wireless receivers to improve the future performance of PI4.0-CPS-CN/AL and CE.

5.1.1. Suggested Research Areas to Be Focused for Improving the Moderately Weak Performing PI4.0-CNs/ALs Such as N, DA, PC, IoT, and VR, I

Lu [42] suggested that scholars should focus on such areas as development of models and data modeling, application of techniques/methods/algorithms, fuel market integration, integration of biofuel filtration, new technology, the best decision styles, and design integration and vertical integration in order to improve the future performance of PI4.0-I-CN/AL and CE.

Nazari et al. [76] advised researchers to follow up areas such as technological aspects, virtual reality architectures, analytical simulation or virtual testing of oil dynamic aspects and application of software model development, fault monitoring and diagnostics, Java-based toolkit, proportions, virtual line process monitoring, virtual reality-based education program, analytical simulation for parameter optimization, and data sharing to social network websites for escalating the future performance of PI4.0-VR-CN/AL and CE.

Meng et al. [61] and Celia and Cungang [77] proposed that scholars should focus on research areas such as IoT application to digital manufacturing, programming for production plans, IoT-based intelligent sensor systems, IoT architectures, IoT thinking and principles, supervisor control and data acquisition, operational analysis by IoT software, smart network applications and IoT simulators, and applications of programming in order to improve the future performance of PI4.0-IoT-CN/AL and CE.

Mraz et al. in 2017 advised scholars to work on the development of bench and site acceptance testing techniques, mathematical modeling, development of architectures for improving plant production and control, safety and controlling of operations in oil refineries, web servers and database information systems, internet technological design, monitoring technologies, energy controlling, multiagent systems, discharge and architecture loss control system, control systems for accidents and failures, scheduling programming, design and application of physical or soft controllers, improvement in industrial network, algorithm/programming configuration of plant system, modeling of hybrid internet and intranet, monitoring the gasification processes, plant control principles and advanced technology design, and application of physical controllers for improving the future performance of PI4.0-PC-CN/AL and CE.

Ahmed and Kim [67] advised scholars to concentrate on such techniques as smart network, application for monitoring processes, models as well as mathematical modeling, sensor network for monitoring, WSN applications, network privacy and security, network principles and intelligent agent network, sensor networks to the refinery, ZigBee technology problems, network architectures, mini adapter network, network topology technique, fault limiter network, object identification technique, mining network models and mathematical modeling, cosine similarity learning network, deployment of sensors and gauges on equipment, network privacy and security, multiagent and network nodes, neural network, Gaussian implication, mixture and Markov modeling, principles of networking, WSN nodes, technologies and network simulators, outlier design of WSN, ultrawide band WSN, technologies and network procedures, voltage networks, novel cascade neural network, multiagent model to network nodes, multiagent system network, weighted oil trade network, intelligent sensor network application, network for incident reduction, and fuzzy-technique-based network in order to enrich the future performance of PI4.0-N-CN/AL and CE.

Triantafillou [78] emphasized to scholars the importance of focusing on areas such as technologies and strategies, resilience-based modeling, application of strategy performance measurement, programming, machine learning integration, standard principles and procedures and application of techniques/methods and fuzzy classification modeling, accident practice-based model development, application of evolutionary algorithms, analysis-based vibration monitoring, international electrotechnical commission protocols, benefits of logistic system for oil industry, literature survey report, general management, technical architectures, feasibility analysis of processes, antitheft system application, production management, stochastic frontier analysis, functional network intelligent clustering system, data envelope system modeling, human practice analysis, and applications of structural integrity analysis to augment the future performance of PI4.0-DA-CN/AL and CE.

Agrifoglio et al. [36] stimulated the scholars to focus on areas such as the effects of digital technologies on optimization of multiple operations, high-level architecture simulation, theoretical knowledge, operation management by hydrogen networks, illustration of industrial tools and techniques, infrared spectroscopy applications, illustration of cogeneration applications, regenerated spent catalyst scheme applications, and group decision-making algorithms to improve the future performance of PI4.0-IDM-CN/AL and CE.

6. Managerial Implication/Research Values for Scholars

The presented research work suggested the moderate and very weak performing research areas corresponding to PI4.0-CN/ALs. This work advises researchers to accept as a research gap the very weak performing research areas of PI4.0 and to focus on them to enhance the future performance of PI4.0-CNs/ALs, linked to CE. The research work also provides a new research methodology to PI4.0 researchers for materializing the future RTs of PI4.0 under multiple/different CNs/ALs. Researchers can avail the same methodology to address the future RTs and improve CE. The work has iconic value if it can be conducted without using any bibliographic software tools.

7. Conclusions

Investigation has shown that strong CNs/ALs lead a vital role in driving the processes of PIs at a faster and quicker rate. Machine learning, big data analysis, signal analysis of sensors, machine to machine virtual interaction, and mechanical automation thrust PI4.0 to turnout the standard/predicted outputs for attaining CE. Strong CNs/ALs aid PI4.0 in controlling the quality of refined beverage items, escalating green practices, and stimulating the overall sustainable traceability of PSs. In the presented research forum, the authors built a PI4.0-CN/AL model by gratifying RQ1 and archiving 302 research documents on conducting SLS over 2007-2017; however 275 were respected under the inclusion parameters to represent the RTs of the CN/AL model by satisfying RQ2. Later, the RTs of the presented CN/AL model materialized by addressing RQ3. It has been concluded that the RTs of a particular DO is dazzling among defined PI4.0-CNs/ALs. The DO-RT has been found with consistent acceleration and momentum. RTs of residue CNs/ALs are expressed in descending orders, i.e., N>D>A>PC>IoT/VR>I>CPS/ERP/IDM (discussed in Section 5). The authors also bifurcated RTs under two aspects, where N>D>A>PC>IoT/VR>I are introduced under moderately weak performing research areas/CNs/ALs, whereas CPS/ERP/IDM are introduced under very weak performing research areas/CNs/ALs.

In continuation of above, the authors suggested which areas the scholars should focus on to reform and amend the RT’s level of moderate and very weak performing CNs/ALs (discussed in Section 5.1) thus hiking and improving CE. The research work can aid future scholars with methodology to materialize the RTs of any interdisciplinary research area and topic focusing on CE. The presented CN/AL model also assists PI4.0 researchers and managers to explore the same model for investigating and mapping the performance of PI4.0 by using expert’s opinion/subjective data with focus on CE aspects.

Data Availability

Highlights: this work proposes a novel method to measure and identify the growths and trends of IIoT communication networks for petrochemical companies

Conflicts of Interest

The authors declare that they have no conflicts of interest.