Abstract
The global supply chain has been severely impacted with the outbreak of COVID-19. The continuous supply of essential products in the post-COVID-19 world is a truly effective and strategic contest. The security and useability of inventory management are a main burden for industries along with the pressure from the government to fulfil the targets of net-zero economy in an uncertain circumstance. One of the most potential keys to these issues is an accurate demand forecasting process by blockchain technology. This article addresses a basic outline for blockchain-based supply chain (SC) and reveals how blockchain technology (BCT) can aid policymakers to cut carbon footprint during and postpandemic time in a fuzzy environment. This study fuzzifies all the carbon factors as intuitionistic triangular fuzzy numbers and uses a signed distance method to defuzzify the model. We consider that the retailer can embrace BCT to enhance demand forecasting. The planned scenario is modeled as an optimization problem to maximize the profit with low carbon emissions and suggest a solution method to solve it. A numerical example is also given to validate the model. We compare the optimal decisions of the SC with and without BCT. We discover that the pandemic and BCT have considerable influences on the optimal results. The study also shows that practitioners should exercise caution when developing operational strategies for maximizing profit with the least amount of carbon emissions during and postpandemic time.
1. Introduction
The crisis COVID-19 had a demonstrable effect on global supply chains (GSCs) in addition to several other harmful effects on human health, ecology, and the economy. The most obvious sign of pervasive strains in GSC is the extension of the supplier’s delivery lead times across advanced economies from the end of 2020. The report stated according to Figure 1 that the supplier’s delivery lead times have lengthened massively in recent months and that the extension is turning out to be longer than it was with the first COVID-19 shock [1]. Figure 1 also shows that there is a substantial quantity of divergency between advanced and emerging economies. Then, another study explored the pre- and post-COVID-19 effects on the logistics markets as the COVID-19 turns further into the past [2]. The graphic picture of the analysis is pictured in Figure 2. Even as we move toward a postpandemic situation, we are still seeing exceptional challenges in various GSC sectors such as demand forecast and transportation. For example, the current conflicts among nations (Russia-Ukraine, Israel-Hamas, etc.) have been causing massive devastation and human gloom just as the postpandemic recovery was picking up speed. A major interruption to the GSC and an energy crisis have accompanied the conflicts. The same worries about consumers and global transport sources that we encountered during the pandemic are sparked by these disruptive acts. This paper contends that in emergency pandemic scenarios such as COVID-19 and conflicts, the transit cost of GSC needs to be correlated with the intensity of the pandemic.
A new study reveals the extent of multinational companies’ impact on climate change by estimating that a 1/5th of carbon emissions (CO2) originate from the GSC of these firms [3]. Many industrialized nations set goals to cut CO2 and achieve net-zero by 2050 at the COP26 summit in Glasgow. However, we fell short of the goals in 2022, and if the current emissions intensity trends continue, the level of CO2 will remain to rise and reach 62 GT by 2030. According to the World Emissions Clock [4], the estimate of CO2 is based on an econometric vector autoregressive model of five sectors (industry, transport, energy, buildings, and agriculture and forestry) and 24 subsectors, across 180 economies. It is shown in Figure 3 and indicates that previous patterns are being followed without any modifications to strategies or other aspects. It demonstrates that overall world CO2 is still increasing. The report also addressed the major sectors that make up the per capita CO2 of the selected countries, and it is visualized in Figure 4. In addition to operational and technological energy efficacy measures to reduce the CO2 intensity of all processes, a range of policies are required to support modal transitions to the least carbon-exhaustive SC policies to put GSC on the correct track for the net-zero scenario. Trade advisors feel that green technology might ease to lessen CO2 due to SC activities such as production, setup, and storage. Hence, all delegates (leaders of various countries) in the COP27 summit held in November 2022 agreed to invest in green technology to cut CO2 [5]. The most effective SC strategies for lowering CO2 through green technology investments have also been determined by multiple studies. Sarkis [6] asserted that the postpandemic economic recovery would not disregard established standards and unease for the ecology. This work connects the impacts of pandemics and efforts to reduce CO2 by green technology investment in GSC in the prepandemic situation.
One key issue with the works that have been published so far in the CO2 area is that they assume the amount of CO2 associated with various SC activities such as setup, production, storage, and transport are fixed known constants. In the real scenario, the amount of CO2 is uncertain due to factors such as the state of the CO2 filtration machine in the production firm, engine failure, quality of the raw material, nature of the fuel, or resources used. There may be fluctuations in the CO2 parameters within specific ranges [7]. Hence, in line with Ruidas et al. [7], this study fuzzifies all the carbon factors as intuitionistic triangular fuzzy numbers.
Among the different fiscal issues faced by enterprises in the postpandemic, tightening acceptable financing has become more vital to retain operations and ensure business stability. A vital source of funding for small- and medium-sized firms is trade credit [8]. It still serves as an outlet for suppliers to provide credit to buyers, permitting them to postpone paying for accepted items and services. Even with the significance of trade credit in emergency situations, no studies link the effects of pandemics and trade credit financing in the postpandemic economy.
The intricacy of post-COVID-19 planning is much more multilayered and difficult than it was during the epidemic as time goes on. This is especially true for GSC members who have to predict customer expectations in the face of shifting markets and demand. There has never been more uncertainty about what to plan for and what data to use to achieve it. To manage the complexity and dangers in today’s SCs, technology is essential. SC operations have been optimized by data analytics. An emerging trend known as Industry 4.0 is bringing industries into the next generation [9]. Among the cutting-edge technologies of Industry 4.0 is blockchain [10]. Blockchain technology (BCT) has a remarkable ability to handle large volumes of formless data in real time. It offers a single source of truth for supply and demand data, which cuts errors, divergences, and disputes. That is, consumer demand forecasting based on BCT would expedite the process of making accurate decisions in the SC. Apart from the demand forecast, Suta and Tóth [11] stated that BCT can contribute to the efforts to cut CO2 by steadily tracing CO2 beliefs and aiding apparent and effective CO2 swap. The blockchain-based inventory model has been shown to be one of the most critical aspects in cutting CO2 in the SC.
We consider a GSC involving a supplier and a retailer under carbon taxation policy and multiperiod trade credit financing to control the shortage in an imperfect manufacturing system. The retailer remanufactures faulty items using his repair shop. The subsequent elements influence the operational decisions and coordination among the members of the SC: the pandemic effect, uncertain amount of carbon footprints, green technology investment, and application of blockchain technology. We examine these effects in this work by looking at the retailer’s selling price, cycle time, amount of green investment and fraction of the cycle time with positive inventory-level decisions, and the demand forecasting problem of such a blockchain-based postpandemic GSC. We perform the analysis in three dimensions. First, we design the model by considering the practical influence of the COVID-19 pandemic on the transport sector in a crisp sense. Second, we adapt the model into a fuzzy sense. Third, we differentiate the analysis by taking blockchain technology into account. No study has looked at all three dimensions simultaneously to the best of our knowledge. We aimed to respond to the following three research questions in this paper:(i)How should green technology investment be implemented to reduce CO2 with the carbon taxation policy when CO2 factors are uncertain?(ii)How to handle and reuse the faulty items received from a foreign supplier to make efficient use of natural resources in emergency pandemic situations such as COVID-19 and conflicts?(iii)How BCT changes decision-making in GSC?(iv)What are the innovative solutions by applying BCT to deal with the logistics and customer demand worries during the pandemic and in comparable circumstances that arise in the future but are unpredictable?
To answer the above questions, we first investigate the optimization model for the integrated GSC with transport cost as proportional to COVID-19 intensity under the carbon taxation policy and trade credit finance, without considering fuzziness and blockchain. Then, we conduct a similar analysis by fuzzifying all the carbon factors as intuitionistic triangular fuzzy numbers into the model. We then examine both cases with and without BCT use. Last, we make a comparison of the optimal selling price, cycle time, fraction of the cycle time with a positive inventory level, green investment, and total profit of the GSC with and without applying BCT.
The remnant of the article is shaped as follows: In Section 2, we frame our study by spotting the research gap and reviewing the relevant literature. In Section 3, we introduce the notations and assumptions of the study. Section 4 constructs the model with the real effect of the pandemic on the transport sector and defuzzify the model by a signed distance method. In Section 5, we incorporate the BCT into the model and derive the main results of the study. We conduct numerical analysis to generate sensible insights from the analytical derivatives in Section 6. At the end, we draw the conclusion of the work and offer potential directions for future studies.
2. Literature Review
Our research is focused on four streams of the literature: (i) inventory management for imperfect production process in a supply chain, (ii) supply chain model with pandemic effects and trade credit finance, (iii) sustainable supply chain with CO2 regulation and green investment, and (iv) application of BCT in the supply chain system.
2.1. Inventory Management for the Imperfect Production Process in a Supply Chain
Supply chain (SC) is the backbone of every business operation. It is seemingly more complicated and global, making it tough to keep track of inventory management. The ability to govern imperfect production processes is one of the aspects that affect SC’s success, along with effective inventory management. Many production firms exhibit imperfection in their production processes due to numerous factors such as breakdowns in machinery, erratic power outages, worker and operator skill deficits, the quantity and quality of supplied raw materials, unexpected episodes in competitive markets, natural disasters, and pandemics [12]. The manufacturer would typically have a few options for handling this issue, such as rejecting and scrapping the faulty items, selling the salvaged items as new, or reworking the faulty items. In this case, the rework processes typically reduce the waste of the production company. Rework is raising areas of interest for scholars related to inventory management nowadays. In this context, a few researchers (see [12–20]) developed inventory models that considered imperfect production processes and rework policies. For instance, Al-Salamah [13] developed a production inventory model for a flawed production process with synchronous and asynchronous flexible rework rates. Lin et al. [14] designed inventory models for flawed items with an equal-sized supply policy under the hypothesis that the producer oversees production, inspection, and reworking processes. Jauhari and Wangsa [15] developed a closed-loop SC with green technology investment to cut CO2 and take-back investment to raise the number of returned items amassed from the market by the manufacturer. Ali et al. [16] studied the optimal policy of a production firm based on production control, the greenness of the products, and the rework of faulty goods.
In this context, international trade has become extremely crucial for modern business. These days, a buyer buys goods from a global supplier, and these items could not be flawless due to some common causes such as cargo fork lifting, deficient binding, and faulty palletization. Now, it is not feasible to send these faulty items back to the supplier as they are located a thousand miles away. In most cases, the buyer sends these minor faulty items to a local repair store for rework and returns them. In this regard, unlike earlier studies, Dey et al. [17] derived a GSC where buyers use their repair shops to remanufacture faulty items under different CO2 reduction strategies and multiperiod trade credit financing. Similarly, Harun [18] proposed alternate shipment policies compared for the GSC model with a stochastic exchange rate, CO2, and flawed items. In this context, Ahmed et al. [20] explored the synergic effect of reworking for faulty items with the combination of multiperiod delay in payment and partial backordering in GSCs.
2.2. Supply Chain Model with Pandemic Effects and Trade Credit Finance
The COVID-19 pandemic constitutes a novel mega disruption with deadly ramifications that has significantly impacted GSC and intensified financial stresses in a variety of unified industries. Amid the numerous fiscal obstacles confronted by firms during the pandemic, acquiring enough financing has grown gradually crucial for preserving operations and ensuring SC continuity. Trade credit is an essential financing source. It is still used by suppliers to offer buyers an extension of credit, enabling them to postpone paying for collected products and services. Despite the significance of trade credit in emergency pandemic situations, no studies have considered the effects of the pandemic and trade credit financing simultaneously in the SC model. Conversely, several studies have considered the effects of the pandemic and trade credit financing independently ([21–27] for trade credit and [28–34] for pandemic effects). Specifically, Tiwari et al. [21] analyzed a green SC model for flawed products with a trade credit policy. They reduced the SC cost and made a contrast in various trade credit states. Mondal et al. [22] derived a model for decaying items and conferred the impact of trade credit on the total cost. Padiyar et al. [23] studied various circumstances under alternate trade credit schemes for a SC of a nondecaying item. Mittal and Sarkar [24] formed a GSC model where the retailer offers a certain credit period to the client to pay in full.
In the pandemic context, Mashud et al. [28] investigated optimum inventory decisions for SC with considerations of product deterioration, time-dependent holding costs, price-dependent demands, and CO2 under COVID-19 pandemic effects. They introduced a realistic transport cost that hinges on the rate of the COVID-19 outbreak. Priyamvada and Kumar [29] designed a dynamic optimal framework that allows businesses to allocate funds to marketing initiatives while upholding a sufficient standard of customer care during COVID-19. Valizadeh et al. [30] posed a solution for the vaccine SC to tackle the obstacles in the public immunization program considering the government and the system concern during COVID-19. Mashud et al. [31] studied a hybrid payment SC model for post-COVID-19 recovery taking price-sensitive customer demand, discounts, inflation, and conservation technology investment for noninstantaneously decaying items into account. Alkahtani et al. [32] derived a nonlinear SC t model with a controllable production rate in order to offer the trade company an economic advantage in terms of the optimum total cost of production and to address various scenarios such as COVID-19. Das et al. [33] proposed a recovery model employing two warehouse perishable inventory directives to assess the effects of SC disruptions during the COVID-19 lockdown. In contrast to others, Xie and Tian [34] studied the influence of the COVID-19 on the corporate trade credit policy.
2.3. Sustainable Supply Chain with CO2 Regulation and Green Investment
Organizations are under growing pressure to take the environmental effects of their SC operations into account since SC generates around 60% of all CO2 globally. Companies are therefore searching for methods to lower their carbon footprint to meet legal obligations and establish a reputation for corporate social responsibility. Increasing ecological alertness has provoked scholars in the field of inventory management to develop sustainable SC models that adapt for CO2. The effect of CO2 on inventory decisions has been greatly observed and linked to numerous key issues, including CO2 legislation ([7, 35–40]), green investment ([41–50]), and other SC activities. Lu et al. [36] took into consideration a GSC that encompasses material supply, producer production and delivery, and retailer ordering and sales to provide a cross-border production-inventory model for decaying goods under various CO2 policy mixtures. Asadkhani et al. [37] formed a SC model with extraction and package stock policies and executed various CO2 policies to control CO2. Jauhari et al. [38] used variable production rates and paid a hybrid production scheme to cut CO2 and SC costs. The effect of carbon tax legislation on CO2 and collaborative savings in a multiechelon SC was studied by Halat et al. [39]. They revealed that the planned rule might cut coalition expenses and lower rates of CO2. Ahmad et al. [40] recently developed a green inventory model with conservation technology for decaying goods in a CO2 tax regulation.
The goal of green technology is to protect the atmosphere, remedy ecological harm that has earlier been done, cut greenhouse gas CO2, and conserve the natural resources. Green technology has also become a burgeoning industry that has attracted enormous amounts of investment capital. There are several methods to invest in green technology, including putting up solar panels and wind turbines and utilizing electric cars and biomass energy. The advantages of investing in green technology include operational efficiency, various cost savings, favorable ratings in the ecosystem, social and corporate governance, and other various government incentives [17]. Huang et al. [41] examined the possibility of investing in green technology to find an integrated policy with consideration of ecological and economic merits under various CO2 laws. Jauhari [43] invented a mathematical model for a manufacturer-multiretailer system to optimize the inventory level and investments under a carbon tax policy. His study centered on how CO2 and energy consumption affected SC activities. Ruidas et al. [44] explored the effects of a concurrent investment in greening invention and CO2 decline technologies in a green production model where the various inventory cost and CO2 factors are considered as interval valued. Priyan [45] addressed an effect of green investment to reduce CO2 in a flawed system. Marchi and Zanoni [46] extended Huang et al.’s [41] model, which explored the effects of carbon rules and green technologies on the joined inventory system of a two-echelon SC with consideration of CO2 during the processes of production, transport, and storage. Jauhari et al. [47] proposed pricing and green inventory decisions for a SC with green investment and carbon tax regulations under hybrid production and stochastic demand. Alsaedi et al. [50] studied the effects of learning and CO2 on a green SC model for faulty items in fuzzy environments with shortage.
2.4. Application of BCT in the Supply Chain System
The intricacy of post-COVID planning is considerably more multifaceted and difficult than it was previously. This is particularly true for SC managers who are responsible for forecasting customer expectations in the face of shifting markets and demand. The inability of SC managers to make projections solely based on historical data presents one of the biggest obstacles to planning after two years of upheaval. They cannot disregard the preceding two years either. The pandemic has drastically altered consumer behavior, but no one anticipated how reopening would affect these habits. It has never been more difficult to plan and identify the data you need to do so. Keeping this in mind, scholars have studied SC inventory models with different demand nature (see [51–53]). For example, Jauhari [51] addressed a three-stage inventory production system with equal-sized deliveries. He assumed that the demand on the buyer’s side is stochastic. Then, Ruidas et al. [52] formed a production inventory model with stock- and selling price-dependent demands and variable production rates in an interval environment. Priyamvada and Kumar [29] developed an efficient optimal model for retailers with selling price and efficiency of the investment-dependent demand. Ruidas et al. [53] suggested pricing tactics in an interval-valued production inventory model for high-tech items under demand disruption and price revision.
Around the world, the epidemic upended people’s lives, and livelihoods, requiring customers and businesses to adopt new digital habits. Rehabbed homemakers gave up long-standing shopping habits, sending e-commerce into overdrive, and condensing ten years of digital adoption into a few short days. Businesses swiftly changed course and innovated to find new methods to engage with potential customers in response to the digital-first economy. The development of a digital SC imposes the approval of a few technologies, such as artificial intelligence (AI), blockchain technology (BCT), cloud computing, data science, 5G, IoT, and digital twins. Blockchain technology is one of the key technologies, and it plays an important role in initiating GSC strategies. It is remarkably adept at processing massive amounts of unstructured data in real time. Organizations miss out on valuable insights that can enhance their SC resilience and efficacy when they ignore such data. BCT helps firms to generate more dependable and trustworthy plans and forecasts by allowing them to regularly verify the legitimacy, originality, and integrity of data [54]. Implementing blockchain in SC is graphically shown in Figure 5. Most of the available studies on BCT adoption are carried out using case, simulation, or empirical studies, for example, benefits and challenges of introducing BCT (Azzi et al., [55]), classification of BCT applications (Helo and Hao [56]), and simulation of BCT (Longo et al. [54]).
There are some works analytically investigating SC optimization considering BCT ([57–63]). Wei et al. [57] presented some analytical results on production and order dynamics in the context of a discrete time vendor-managed inventory (VMI) SC. Manupati et al. [58] proposed the use of BCT to address various production allocation issues in a multiechelon SC under the carbon taxation policy. Zhu et al. [59] initially attempted to study the impacts of BCT on market share, and they focused on whether the powerful brand owner in a dual-channel SC should adopt BCT. Li [60] presented a conceptual outline for blockchain-based SCs and illustrated how BCT may facilitate the realization of key innovations, including contract automation, a shared platform, end-to-end information exchange and traceability, real-time updates, and quick and efficient asset transfers. Xu et al. [61] derived integration of a platform-based SC in the marketplace or reselling mode considering cross-channel effects and BCT. They considered that the manufacturer can adopt BCT to improve demand forecasting. Modares et al. [62] derived a new VMI model by applying BCT and considering ecological issues. They looked at BCT for SC management by taking into consideration the most important BCT execution criteria for retailer selection and optimization. According to Allenbrand [63], a consortium BCT with smart contracts integrated might create an incentive structure that would make forecast-based coordination attractive. Table 1 presents research gaps in relation to the body of literature.
2.5. Research Gaps
(i)The enormous effects of green technology investment to reduce CO2 on the functionality of GSC systems have been emphasized by research on inventory management with the carbon taxation policy. A significant gap in the literature is that it treats the quantity of emitted CO2 throughout SC activities as a fixed constant (Dey et al. [17]). However, in the real scenario, these quantities may not be fixed due to various issues. There may be fluctuations in the parameters within specific ranges (Ruidas et al. [7]). To fill this main research gap, this study fuzzifies all the carbon factors as intuitionistic triangular fuzzy numbers in the GSC.(ii)The regular GSC activities have been severely affected by the COVID-19 pandemic due to sudden and unplanned lockdowns. Even if the postpandemic era is rapidly approaching, we are still seeing the effects of the pandemic in the transport and financial sectors due to geopolitical issues such as the current conflicts (Russia-Ukraine and Israel-Hamas) and rising tensions among nations. These rambunctious acts raise the same issues that we encountered during the epidemic about customers, global transportation, and financial sources. Decision-makers should reconfigure their SC in both sectors to ensure business continuity in the post-COVID-19 era. Despite the importance of trade credit financing in emergency pandemic situations, no studies have considered the effects of the pandemic and trade credit financing simultaneously in the GSC model. This study fills this gap.(iii)Firms are having extraordinary difficulties predicting demand quickly based on changing client requirements and preferences as COVID-19 becomes the new normal. Introducing a smart SC is an essential for firms enabling their effectiveness in the market. Demand forecasting based on BCT will speed up the SC’s decision-making process. This research uses demand forecasting based on BCT to increase GSC efficiency.2.6. Contributions of This Study
The given model contributes to the existing literature by considering more realistic issues inspired by the GSC disruptions caused by the COVID-19 pandemic in a postpandemic world such as pandemic effects, trade credit financing, blockchain-based demand forecasting, green technology investment, flawed production and recovery processes, and transport disruption. The methodology that is being given has the potential to be utilized in relation to the COVID-19 pandemic, future conflicts, and other unforeseen events. The following are the principal contributions:(i)The connection between pandemic effects and the demand forecasting process has been studied since the pandemic has significantly changed the consumption pattern and accelerated the shift toward more health and ecologically conscious and online purchases.(ii)A convincing pandemic function is connected to the system’s CO2 to provide a clear image of the pandemic’s effects on the transportation industry. One aspect of the pandemic function is the interaction between immune-compromised and diseased populations.(iii)A GSC is formed under waste generation in terms of imperfect production and trade credit finance, where waste was generated during international trade and reworking is performed in the local repair shop of the retailer to avoid CO2 due to transport activities.(iv)An ecological fuzzy model based on blockchain technology is designed to control CO2 via green technology investments in emergency pandemic situations.
2.7. Motivation and Objective
The COVID-19 pandemic has thrust many deficiencies in SC networks into the spotlight, particularly around visibility and integrity of customer demand and carbon footprint data. Many SC leaders have realized that their organizations are not prepared to handle major disruptions to their networks in the postpandemic world. However, this realization has begun to find the digitization of SCs by adopting emerging technologies such as blockchain that will allow firms to adapt to argue in real time. Blockchain technology plays a vital part in the digital transformation of SCs rising in a postpandemic world. The simple fact is BCT can effectively manage workflow across the entire SC and, through its strewn ledger approach, guarantee that data are accurate, transparent, and immutable critical capabilities in SC redesign. The focal essence of the study is to address a basic outline for blockchain-based GSC and reveal how BCT can help policymakers to cut CO2 under uncertain postpandemic situations.
2.8. Research Methodology
Figure 6 provides a graphical illustration of the suggested methodology.
3. Notations and Assumptions
The following appropriate notations and assumptions are taken into consideration to formulate the mathematical model for the proposed realistic problem.
3.1. Notations
Parameters are as follows: is the delay in first period payout (time unit) is the screening cost ($/unit) is the buyer’s ordering cost ($/order) is the cost for the refund of faulty items ($/unit) is the cost for loss of goodwill ($/unit) is the proportion of delivered faulty items (%) is the holding cost for perfect items ($/unit per time unit) is the screening rate (unit) is the proportion of faulty items (%) is the emission due to the setup of the remanufacturing process (kg/setup) is the emission due to the repairing activities (kg/unit) is the emission due to the transportation actions (kg/unit) is the emission from holding inventories (kg/unit) is the markup fraction by a local store (%) is the reworking rate (unit/time time) is the setup cost of the rework shop ($/setup) is the cost of materials and labour to repair an item ($/unit) is the cost of holding items at a local store ($/unit per time unit) is the aggregate transport time of faulty items (time unit) is the fixed transport cost ($/trip) is the cost of shipping the faulty items ($/unit) is the carbon tax for unit carbon emissions ($/unit) is the fraction of backordered demand (%) is the cost of lost sales ($/unit per unit time) is the backordering cost ($/unit per time unit) is the size of the potential market is the price sensitivity of sales price ($) is the markup margin (%) is the order quantity, dependent variable is the fuzzy index
Decision variables are as follows: is the sales price is the green investment amount is the fraction of the cycle time with a positive inventory level (%) is the cycle time (year)
3.2. Assumptions
(a)A single type of item is sent to the retailer by a global supplier, and each lot contains some faulty items. It is more expensive to send these faulty items to the supplier than to have them repaired in a local shop.(b)The retailer gets an extra income from interest when the payment time () is longer than the cycle time (). On the other hand, if is the accepted time and the cycle length K is lower, the buyer gets fewer interest but pays extra in opportunity costs (Dey et al. [17]).(c)The inventory level remains positive for the proportion of the cycle period. If η is the fraction of faulty items, then K has two portions: and . Here, and must lie within a [0, 1] interval (Dey et al. [17]).(d)The vaccination has a 100% success rate. The initial vaccinated people must be less than the total people, i.e., . The infection rate lies in [0, 1], and the vaccination rate is positive, i.e., . No one will be infected if there are no infected people (Mashud et al. [28]).4. Model Formulation
A buyer will constantly try to sell the most consumer required items to their retail shop. The shop tried to pay as little as possible for the product the customer desired. Intercontinental trade is now extremely important for modern business in this setting. The things that a retailer now purchases from a global source might not be flawless due to freight fork lifting, inadequate packaging, false palletization, etc. Keeping this in mind, we investigate a global blockchain-based SC where retailers use their repair facilities to remanufacture defective goods while also considering the transport cost as a function of COVID-19 intensity. A trade credit is offered by the supplier to the retailer to make the payment in the emergency pandemic situation. We believe the retailer can embrace blockchain technology to enhance demand forecasting since lack of visibility into consumer behavior has made demand and supply unpredictable caused by the COVID-19 pandemic. We also assume that the uncertain amount of CO2 is emitted due to various factors (Ruidas et al. [7]) during SC activities, and the system follows a carbon taxation policy to reduce CO2 by investing in green technology. The considered scenario is modeled as an optimization problem to maximize the profit function while reducing CO2 and suggest a solution method to solve it. In this context, we have developed two fuzzy mathematical models with and without applying blockchain technology.
4.1. Model without Applying Blockchain
In the proposed GSC system, the retailer orders products from a global supplier and sells them through the online channel to consumers at the retail price . Then, we consider the online demand to be where is the retailer’s power to grow the size of the prospective retail market and is a normal random variable with mean 0 and variance , which can be viewed as the demand noise. Here, we use to denote the private market signal that results from channel and user interaction, such as past sales data. Following Yu et al. [64], we state , where is the instability of demand noise from distorted data and is a normal random variable with mean 0 and variance , which can be viewed as the signal noise. The retailer detects the demand signal and shares it with the supplier. The detected signal for the demand noise follows the Bayesian rule, which is an unbiased estimator of Then, we similarly have and , where . This approach to the information structure is widely used in earlier research, and Vives [65] provides a detailed proof. We can rewrite as , where follows a normal random distribution with mean 0 and variance .
Now, the demand function can be reexpressed as .
CO2 originates from the warehousing operation (which stores both high-quality items), repairs, remanufacturing setup, and defective item shipping. Below is the formulation of the corresponding CO2 process.
After screening, the portion of items is found faulty as Thus, CO2 associated with the repairs is given as . Shipment of faulty items emits CO2, which is calculated by , where is the amount of CO2 generated per unit item.
The warehouse, where both perfect and imperfect items are kept, releases certain emissions. Consequently, the amount of CO2 released during storage is given as .
Hence, total CO2 emitted during SC activities is
The SC members intend to invest in green technologies to transition to a more environmentally friendly industrial system. Green chemistry (biodegradable polymers and green chemical processes), renewable energy (solar, wind, and hydro), and technology that makes recycling easier are a few examples of the various types of green technology (100% recycled tin, precious metals). A well-known example of green technology is the solar cell. We consider the amount of carbon reduction investment function (Dey et al. [28]), where m implies the efficacy of the technology used for reduction of CO2. when and as The graphical visualization of the green investment function with respect to the efficiency of the technology is shown in Figure 7.
Now, as pointed out by Dey et al. [17], the total profit function of the entire GSC with the carbon taxation policy and trade credit finance in a crisp nature without pandemic effects is expressed aswhere
However, many areas are still partially or entirely locked down because of COVID-19 spreading across the world. The scenario increases the cost of transportation. This study considers the scenario where the outcome is dependent on the pandemic’s spread rate. There are totally people in a town. These people are exposed to COVID-19 at an infection rate of . Nevertheless, the inoculation process is still ongoing; the acceleration rate is . Then, like Mashud et al. [28], the derived COVID function with the initial infected people and the initial vaccinated people is stated as
The standardized COVID-infected function is expressed as
Taking the standardized COVID indicator function as an essential measure in mind, according to Mashud et al. [28], the transport cost rises times of the initial transport cost, i.e., . At the apex of the COVID indicator function, this transport cost rises. Consequently, the transport cost can be remodified as
It is observed from Figure 8 that in the real scenario, the amount of CO2 is uncertain due to the state of the carbon-filtering equipment employed by the firm, general production technology, the caliber of the raw materials utilized, machine failure, the kind of fuels or energy used, etc. As a result, we consider that the factors (CO2 emitted for setup of the repair store), (CO2 emitted for setup repair of one unit), (CO2 emitted for transport of defective items), and (CO2 emitted for holding) are all fuzzy, and we denote them here by the intuitionistic triangular fuzzy number as given below.
Intuitionistic fuzzy CO2 emitted for setup of the repair shop () is where .
Intuitionistic fuzzy CO2 emitted for setup repair of one unit () is where .
Intuitionistic fuzzy CO2 emitted for transport of the defective item () is where .
Intuitionistic fuzzy CO2 emitted for holding items () is where .
The CO2 factors , in equation (1) are intuitionistic fuzzified as derived above; we can obtain the total profit function in the intuitionistic fuzzy sense as
When the CO2 factors , , and are described with the intuitionistic triangular fuzzy number, by using the signed distance method, the defuzzified values of , , and are given by
Similarly, based on the above-defuzzified values, the defuzzified total profit function of the model (equation (6)) is obtained as
4.2. Model with Applying Blockchain
This subsection looks at how the retailer might use BCT to enhance demand forecasts. If a retailer decides to use blockchain, there will be fixed costs associated with putting it up as well as variable costs associated with each product when the system uploads its data to the blockchain. The reason for this situation is that implementing BCT entails a fixed cost where the retailer must purchase the mandatory equipment to afford digital authorization (Niu et al. [67]). In addition, implementing BCT entails a unit operation cost where the retailer must pay staff salaries for the BCT operations, a fee for the BCT operations, and other expenses (Wang et al. [68]).
We are aware that the key characteristic of BCT is that it is exceedingly difficult or expensive to change the data stored there. According to Babich and Hilary [69], blockchain can also deliver more precise data. It leads to data transparency on BCT and data reliability, which further improves forecast accuracy [68]. The blockchain’s signal regarding demand change is more dependable because the data are trustworthy and cannot be modified unilaterally. So, the demand noise of the private market signal is lessened with the aid of blockchain, showing a better value for . Please be aware that the demand without blockchain is . We now employ to reflect the rise in demand brought on by the adoption of BCT. It is obvious that . Please take note that this approach to capturing blockchain characteristics is realistic and that many experts in the field of operations management have used it to assess the effect of BCT in their research (Xu et al. [61] and Yu [64]). Based on actual practice, the approach used to develop our model not only directs actual adoption of blockchain but also enhances prior research on the use of blockchain in operation management. The research presented here yields some noteworthy new findings. Figure 9 depicts the proposed BCT model.
Now, the defuzzified total profit function of the model (stated in equation (8)) by applying BCT is obtained aswhere
4.3. Model with the Cross-Channel Effect
In this case, the retailer sells goods through its own offline store where the retail price is hardly ever altered. There is often a base demand for this channel. In doing so, we can exactly capture the cross-channel effect as follows: The sales price of the offline channel is , and the corresponding demand is where is the base demand and captures the effect of online sales on offline demand, i.e., the cross-channel effect. Then, we formulate the profit of the system as follows:
5. Analytical Analysis for Optimality
The abovementioned problems (equations (8)–(10)) look to be nonlinear programming (NLP). We use the traditional calculus optimization approach to solve the proposed problem as derived in equation (9), and similarly, we can solve equations (8) and (10) as well. That is, it is essential to verify the necessary and sufficient conditions to determine the optimality of , G, and :where and where
Furthermore, Theorem 1 demonstrates the existence of second-order sufficient conditions.
Theorem 1. The profit function is concave with respect to , G, and .
Proof. See Appendix.
By solving equations (10)–(13), the optimal values of , G, and can be computed. The SC’s total profit can be determined by inserting those optimum values in equation (9). Similarly, we shall prove the concavity of other two cases and similar to Theorem 1.
6. Numerical Analysis for Optimality
The proposed optimization model’s effectiveness is numerically evaluated, and the outcomes of the existing model are utilized to improve decision-making. A GRG solver aids in the solution of the optimization model. This solver solution ensures that the Karush–Kuhn–Tucker requirements for local optimality have been met mathematically.
To illustrate the efficiency of our model, we consider Dey et al. [17] data. In addition, for the COVID-19 case, , , , , 0, ; for the fuzzy case, , ; and for the BCT case,
If industry adopted the carbon taxation policy under the COVID-19 effect and fuzzy environment, then the decision-maker can follow the proposed optimal strategy which is given in Table 2, and concavity of the profit function is graphically shown in Figure 10. The effects of the COVID-19 epidemic rate and vaccination on the total profit function are graphically depicted in Figures 11 and 12, respectively.
6.1. Discussion of Findings
(i)This study compares the retailer’s profit with and without BCT in terms of retailer power and price sensitivity . The values of each curve are obtained by deducting the retailer’s profit without applying BCT from the one with applying BCT, i.e., changes in the retailer’s profit after applying BCT. Without applying BCT, we set . With applying BCT, we set respectively. Figure 13 reveals that if then when and when . If then when and when . If then when and when . In reality, a lot of retailers take steps to strengthen their commercial power. If the retailer uses BCT, this behavior will therefore be advantageous to him.(ii)Figure 14 shows that if then when and when . If then when and when . If then when and when .(iii)The table and graphs show the optimal strategies of GSC with and without blockchain technology when the system runs in an emergency pandemic situation such as COVID-19 and conflicts. The model by Dey et al. [17] derived similar GSC behaviors and proposed optimal strategies. It needs to be noted that Dey et al. [17] do not consider the effects of the pandemic, blockchain, and uncertainty. The current optimal strategies are more applicable than the Dey et al. [17] model since we considered all the realistic issues which are facing GSC currently.(iv)From Table 1, the system considering BCT gives more profit than without BCT. That means the manager gets more profit by digitalizing the GSC system.(v)The total profit drops off dramatically when the rate of the COVID-19 outbreak rises. Figure 12 illustrates how the SC’s overall profits are affected by the rate of change of vaccine acceleration. With a rising rate of vaccine acceleration, the overall profit rises.(vi)According to Figure 11, a rise in the COVID rate causes a noticeable increase in the effects of COVID-19 on the overall profit and cost. As can be observed from the chart, the higher rate of COVID has a substantial impact on transport costs, although other circumstances have allowed for a less sensitive change of the COVID effect. The effects of the CO2 cost are evident as well, and appropriate management of the CO2 cost results in more profit.(vii)Figure 15 reveals that CO2 is less when we apply the BCT in the GSC system. It is not surprising because practically we know that CO2 can automatically reduce when we use the online process instead of offline.(viii)Figures 16 and 17 demonstrate the graphical visualization of profit function with associated cost.(ix)In model 5, we consider that the retailer sells its products through its own offline store with the data . The optimal solution of the special case is and . The outcome shows that the retailer’s profit drops when it sells products through its offline channel. This result is relevant because storing physical items in the warehouse leads to substantial CO2 and cost.(x)Since every study has different findings, it is impossible to compare directly with earlier research. But if certain ideas are added or ignored, the current study converges with those studies. This analysis converges with Dey et al. [17], for instance, if the idea of the pandemic and uncertainty effects is disregarded, as well as the use of BCT.(xi)In model 5, according to Figures 13 and 14, we conclude that (a) if the cross-channel effect is low (high), using BCT could result in more (less) CO2. (b) Applying BCT increases profit for the GSC under high (low) platform power if the cross-channel effect is modest (high).6.2. Managerial Insights
This paper offers the following managerial strategies that may be used in the global SC during and postpandemic to adapt to interruptions in transportation networks:(i)In emergency pandemic situations, it is imperative to have knowledge of the transportation cost for each trip, as viewed from an industrial standpoint. With the use of this model, the store can quickly determine the cost of transportation and overall profit in any emergency.(ii)The importance of the delay period cannot be overstated in trade credit finance. Players stand to gain greater profit if the delay time is extended.(iii)The current economy places a great deal of importance on international trade, and as a result, SC management is critical to modern business. Due to a few factors, including shipping and repair or remanufacturing, the rate of CO2 during worldwide trade significantly grows. Green technology can be used to manage CO2 properly, and managers must know how much to invest in cutting CO2. Today, the international business enterprises, such as Intel, Google, Microsoft, and Apple, are currently adopting green technology. With the use of wind, solar, hydro, and biomass in the production of its goods, Intel is the industry leader in using renewable energy. Google is making changes to its processes to be more responsible and environmentally beneficial. It is still dedicated to achieving carbon neutrality and creating goods that support energy efficiency. Microsoft supports corporate ecoinitiatives using its own technologies. It also mentioned how its data centers would evolve, with a focus on research and development (R&D) for renewable energy and energy efficiency. Apple has committed to using entirely renewable energy to power its data centers, and it has encouraged the bulk of its suppliers to follow suit. The company has also set a target of producing no trash to save water and reduce CO2. These companies might adopt operational strategies for maximizing profit with the least amount of CO2 during and postpandemic time with the aid of the current study’s policy recommendations.(iv)Nowadays, the quantity of CO2 is not fixed due to several issues. Therefore, decision-makers require expert knowledge to make the optimal strategy when the SC operates with an uncertain amount of CO2. The policymakers of SC that operate with an uncertain amount of CO2 can discover the appropriate optimal strategy by implementing this model.(v)The study leads us to the conclusion that using BCT results in lower overall CO2 and more profits for businesses in sectors with a pandemic effect and an uncertain amount of CO2.(vi)The utilization of BCT can enhance the lucid display of the product transportation routes from global suppliers to domestic customers via digital transactions. This would enable a re-evaluation of the GSC network’s vulnerabilities and lower the risk of counterfeiting as well as the expense of correcting the harm.(vii)Product acquisition and transport via the GSC are subject to strict observation. It is also feasible to maintain tabs on the stakeholders participating in the SC. BCT might be able to verify which stakeholder the product went through most recently if an issue is encountered during supply.
7. Conclusion
We study a blockchain-based GSC by incorporating pandemic effects and trade credit financing in an uncertain carbon footprint environment. We consider that retailers use their repair facilities to remanufacture defective goods and the transport cost as a function of COVID-19 intensity. Also, the retailer sells its products through its own online channel. The demand effect is exacerbated when basic forecasting techniques are used since the variation of the prediction inaccuracy is predicted to be greater than optimal. We take into consideration a demand forecasting process by BCT to address this problem. That is, the retailer can apply BCT to detect more market signals. This study fuzzifies all the carbon factors as intuitionistic triangular fuzzy numbers and uses a signed distance method to defuzzify the model. We initially study the basic model of the proposed problem without taking blockchain and fuzziness into account. We then investigate the mathematical model and the solution approach for the given problem with and without applying BCT by introducing fuzziness. Finally, we compare the optimal strategies with and without applying BCT. We observe that the pandemic and BCT have considerable impacts on the optimal decisions. The study also shows that the policymakers should be cautious in accounting uncertainty in the input carbon factors to develop operational strategies for maximizing profit at the lowest CO2 during and postpandemic time.
Two major shortcomings of this study are that it does not account for selling price-dependent demand and no competition. This research can be expanded by considering selling price, customer service, and demand that is influenced by advertisements (Sarkar and Dey [70]). Another intriguing avenue for research is to include competition among multiple shops. Furthermore, as they are prevalent in practice, the inventory problem and product quality enhancement could be investigated. It is possible to examine the effects of further emerging technologies, such as artificial intelligence and big data, on supplier and retailer cooperation. This study can be expanded in the future by considering third-party logistics for the home delivery policy. A dynamic GSC system could be taken into consideration to take this research in a new direction.
Appendix
A. First-Ordered Derivatives (Necessary Conditions)
where
B. Sufficient Condition of Concavity
To prove the sufficient part for the classical optimization approach, it has to find the values of the principal minors of order 4 × 4; i.e., it has to calculate the values of the principal minors of the following Hessian matrix:
Now, it can be claimed that the profit function is concave, only when the values of the principal minors are alternate in sign.
Proof. For the optimal values of the decision variables, all principal minors of the Hessian matrices are alternate in sign. The detailed calculation along with second-ordered derivatives is provided as follows.
This section presents the Hessian computation for the optimality test and the derivative calculation:where .Hence, the profit function is concave with respect to , G, and .
Data Availability
The data used to support the findings of this study are available from the corresponding author upon request.
Conflicts of Interest
The authors declare that there are no conflicts of interest regarding the publication of this article.