In today's volatile and complex international organization conditions, B2B manufacturing firms face increasing challenges in managing supply chains that are efficient, responsive and aligned with dynamic customer demands. The rapid changes in the market due to trade wars, pandemics and political instability add more problems to the customer-centric supply chain. This paper examines the function of big data analytics powered by artificial intelligence as a vital facilitator of supply chain agility and customer relationship efficacy in Chinese B2B manufacturing firms. Using the Dynamic Capabilities View, the study presents and experimentally evaluates a model that investigates the impact of big data analytics augmented to artificial intelligence on supply chain agility, both directly and indirectly, through supply chain alignment and Adaptability, while also accounting for market turbulence as a moderating factor between agility and customer outcomes. Data were collected from 434 B2B Chinese manufacturing firms involved in export and import activities through structured questionnaires. Expending Partial Least Squares Structural Equation Modelling, the conclusions confirm that big data analytics and artificial intelligence significantly enhance supply chain agility, supply chain adaptability and supply chain alignment, which in turn positively impact customer relationship performance. The moderating validity of market turbulence signifies that the impact of supply chain agility on customer relationships is particularly significant in conditions of high market volatility. The findings also highlight how critical big data analytics and artificial intelligence has become an essential dynamic capability that converts data into valuable actionable insights and as such, can enable firms align and redesign their supply chain business to achieve higher levels of customer performance. This research also helps digital supply chain literature by connecting advances in technology with the changes in the supply chain and being customer-focused, offering useful information for both theory and practical application in difficult industrial circumstances.

Industry 4.0, characterized by automation and digitalization, has become a key driver of transformation in China’s industrial sector. This study investigates the impact of $\mathbf{I 4. 0}$ technologies and Circular Economy practices in improving firm performance through supply chain management and market relations. The study used cross-sectional data collected from 411 firms with the help of a structured questionnaire, and analysis was done using Smart PLS-4 software. The findings reveal that adopting Industry 4.0 significantly enhances supply chain visibility, resilience, and operational efficiency, while circular economy practices contribute to sustainability and profitability by optimizing resource usage. The empirical results further confirm that Industry 4.0 positively implements circular economy practices and improves supply chain capabilities. Meanwhile, circular economy practices strongly correlate with operational and economic performance. Although supply chain capability improves operational performance, its relationship with market relations is weaker. Findings from this study offer crucial insights for firms aiming to achieve sustainable growth by adopting Industry 4.0 technologies to support sustainable growth.

The paper considers an online scheduling problem with the effects of both learning and deterioration to minimize the total completion time. More specifically, we assume that the actual processing length of job J(j) is p(jr) = p(j)r(b) thorn at, where p(j) is the initial processing time of J(j), t is the starting time of J(j), r is the seating arrangement position of J(j), b is the learning factor and a is the deterioration factor, respectively. For this problem, we show that the performance ratio of any deterministic online algorithm is not <2 and provide a best possible online algorithm DSBPT with a competitive ratio of 2. Furthermore, we also present a concise computational simulation study to verify the effectiveness and efficiency of the proposed algorithm DSBPT, as well as the management implications provided for decisionmakers to production optimization.

Blockchain technology is applied for product traceability in e-commerce supply chains. In this context, the paper investigates the channel problem and decision timing problem considering product traceability. Using game-theoretic framework, the manufacturer encroachment strategies, order quantities and profits under different channel structure, and traceability level are characterized. Research shows that selling traceability products can expand potential market demand and increase the sales volume of products and the profits of manufacturers and retailers. Under encroachment, members with information advantage can maximize their own profits. Additionally, manufacturers can regain the high ground by adjusting traceability levels to influence wholesale prices.

In this paper, an innovative fuzzy group decision-making model is designed for assessing regional transportation sustainability, focusing on the correlation between various attributes of the evaluation system. The focus of this model is the partitioned Maclaurin symmetric mean operator because of its better applicability when considering attribute correlation and attribute grouping. The modified spherical fuzzy partitioned Maclaurin symmetric mean operator is proposed, which has superior application scope. Its weighted form and special cases are discussed. Then, the extended statistical variance method and the evidence-based Bayes approximation method are used to obtain weight vectors of attributes and experts. In addition, a fuzzy assessment model of sustainable transportation is developed. Finally, a numerical example of regional transportation sustainability assessment and a comparison with previous studies are presented to illustrate the feasibility and universality of this method.

As the basic unit of urban governance, communities play an important role in the prevention and control of epidemics. Appropriate methods for assessing the status of community-based epidemic prevention and control will not only help to gain a comprehensive understanding of community preparedness, but also help the government to develop appropriate epidemic prevention strategies. A three-phase multi-attribute group decision extension VIKOR method based on spherical fuzzy normalized projection is proposed for the dilemma of community-based epidemic prevention and control assessment method selection. Meanwhile, a knowledge-based spherical fuzzy entropy measure is proposed to determine the expert weights, and a nonlinear programming model based on the extended spherical fuzzy normalized projection TOPSIS method is given to obtain the attribute weights. In addition, an example of community-based epidemic prevention and control is given to demonstrate the feasibility of the proposed approach. Subsequently, compared with the traditional methods, it is found that there are differences in the ranking of alternatives obtained by the method in this paper and those obtained by other methods, but the optimal solution obtained is the same. Dynamic analysis by adjusting relevant parameters and expert evaluation information reveals that changes in parameters and deviations in individual expert information have little effect on the final results, which further illustrates the stability of the proposed method.

PurposeThe promotion of new energy vehicles (EVs) is an effective way to achieve low carbon emission reduction. This paper aims to investigate the optimal pricing of automotive supply chain members in the context of dual policy implementation while considering consumers' low-carbon preferences.Design/methodology/approachThis article takes manufacturers, retailers and consumers in a main three-level supply chain as the research object. Stackelberg game theory is used as the theoretical guidance. A game model in which the manufacturer is the leader and the retailer is the follower is established. The author also considered the impact of carbon tax policies, subsidy policies and consumer preferences on the results. Furthermore, the author investigates the optimal decision-making problem under the profit maximization model.FindingsThrough model solving, it is found that the pricing of EVs is positively correlated with the unit price of carbon and the amount of subsidies. The following conclusions can be obtained by numerical analysis of each parameter. Changes in carbon prices have a greater impact on conventional gasoline vehicles. Based on the numerical analysis of parameter β, it is also found that when the government subsidizes consumers, supply chain members will increase their prices to obtain partial subsidies. Compared with retailers, low-carbon preferences have a greater impact on manufacturers.Research limitations/implicationsThe new energy automobile industry involves many policies, including tax cuts, tax exemptions and subsidies. The policy environment faced by the members of a supply chain is complex and diverse. Therefore, the analysis in this article is based only on partial policies.Originality/valueThe authors innovatively combine the three factors of subsidy policy, carbon tax policy and consumer low-carbon preference, with research on the pricing of EVs. The influence of policy factors and consumer preferences on the pricing of EVs is studied.

The transportation systems are facing major challenges due to changes social environment caused by the COVID-19 pandemic. How to construct a suitable evaluation criterion system and suitable assessment method to evaluate the status of the urban transportation resilience has become a predicament nowadays. Firstly, the criteria for evaluating the current state of transportation resilience involve many aspects. New features of transportation resilience under epidemic normalization are exposed, and previous summaries focusing on resilience characteristics under natural disasters can hardly reflect the current state of urban transportation resilience comprehensively. Based on this, this paper attempts to incorporate the new criteria (Dynamicity, Synergy, Policy) into the evaluation system. Secondly, the assessment of urban transportation resilience involves numerous indicators, which make it difficult to obtain quantitative figures for the criteria. With this background, a comprehensive multi-criteria assessment model based on q-rung orthopair 2-tuple linguistic sets is constructed to evaluate the status of transportation infrastructure from perspective on the COVID-19. Then, an example of urban transportation resilience is given to demonstrate the feasibility of the proposed approach. Subsequently, sensitivity analysis about parameters and global robust sensitivity analysis are conducted, and comparative analysis of existing method is given. The results reveal that the proposed method is sensitive to global criteria weights, so it is suggested that more attention should be paid to the rationality of the weight of criteria to avoid the influence on the results when solving MCDM problems. Finally, the policy implications regarding transport infrastructure resilience and appropriate model development are given.

Against the background of continuously rising energy carbon emissions, accelerated energy transformation in developed countries, and increased international attention to energy security, there is still a large amount of energy consumption in the manufacturing industry. Promoting the diffusion of green supply-chain management is becoming a powerful tool to support energy transformation and energy conservation and emission reduction in the manufacturing industry. Based on this, we first conducted a scientific metrological analysis of 4960 articles in relevant fields in the Web of Science database, presenting the research status of green supply-chain management diffusion in the context of energy transformation. Second, we identified factors that affect the implementation of green supply-chain management, and analyzed the diffusion path of green supply-chain management among enterprises. Finally, based on the energy situation, enterprise operation, and implementation of environmental protection laws and regulations in Shaanxi Province, China, we determined the current situation, obstacles, and development direction of green supply-chain management diffusion of enterprises in the context of energy transformation. The research found that: at this stage, there are still some deficiencies in the research on the mechanism of green supply-chain management in the internal communication of enterprises; in the future, the diffusion of green supply-chain management can be further developed around social performance and energy transformation technology; and we can help energy transformation by strengthening policy guidance and assisting enterprise reform.

In the current globalized business environment, multinational competition has become the norm for companies. This paper considers technology spillovers among manufacturers and develops a global supply chain network equilibrium model. Firstly, the optimal decision-making behaviors of manufacturers, retailers, and demand markets are characterized separately. Secondly, based on the variational inequality theory, the optimal decision-making behaviors of global supply chain members are transformed. Finally, the model is solved and analyzed using the Euler algorithm. The primary objective is to explore the impact of research and development (R&D) subsidies and intellectual property protection (IPP) strategies on manufacturers’ research and development technological levels. Furthermore, the study delves into their effects on the production and transactions of the global supply chain network and social welfare. The following conclusions are drawn: (1) Technology spillovers have a positive effect on the technological level achieved by manufacturers through research and development investment and social welfare. However, intense technological competition may harm manufacturers’ profits. (2) Under the symmetric subsidy policy, higher subsidies may lead to a decrease in social welfare. (3) Under symmetric intellectual property protection policies, increasing the intensity of intellectual property protection benefits manufacturers but is detrimental to retailers and social welfare. However, under an asymmetric intellectual property protection strategy, implementing high-intensity intellectual property protection by high-technology countries is advantageous for retailers and social welfare. This conclusion has contributed to the technical research and development and production operation decision making of global supply chain members, as well as government policy formulation, and has also provided a new perspective for theoretical research in the field of global supply networks.

The evaluation of manufacturing component suppliers is focused on economic indicators, with insufficient emphasis on green indicators and no consideration of the correlation between indicators. Firstly, indicators related to green production are incorporated into the supplier evaluation system. Then, for the problem that attributes in decision making can be divided into different categories and there are interrelationships between attributes of the same category, a multi-attribute decision-making (MADM) method based on the partitioned Maclaurin symmetric mean operator (PMSM) is proposed. Finally, the proposed MADM method was applied to the evaluation of component suppliers considering green production. Comparing popular decision methods with the newly proposed method for validation, it was demonstrated that the proposed multi-attribute decision method is highly flexible and versatile. Furthermore, the newly proposed aggregation operator can not only handle the correlation between multiple attributes, but also be converted to other general aggregation operators through parameter adjustment.

With improvements in consumers’ environmental awareness and the promulgation of environmental regulations, an increasing number of companies are beginning to pay attention to green product design, pricing, and purchasing strategies. However, due to demand fluctuations and cost changes brought about by green product design and manufacturing, understanding corporate behavior preferences and constructing non-single-period pricing and procurement strategies can profoundly affect long-term cooperation among green supply chain members. This paper constructs six scenarios in which decision-makers have altruistic preferences simultaneously or separately and whether the retailer adopts strategic inventory. In addition, the impact of altruistic preferences and strategic inventory on the decision-making and profits of the two-period supply chain for marginal cost-intensive green products (MIGPs) are analyzed. The results show that altruistic preferences and purchasing strategies do not affect MIGPs’ greening levels. Besides, the retailer’s strategic inventory is still an effective bargaining tool but is not necessarily beneficial to profits. Noteworthy, when deciders exhibit altruism simultaneously or alone, the effects on certain decisions and strategic inventory range are significantly different. Finally, the retailer’s altruistic preference may not affect the green supply chain’s profits, but the manufacturer’s altruism improves total profits.

This study examines the optimal pricing and production strategy of a closed-loop supply chain consisting of a manufacturer, a recycler, and consumers. Considering the cannibalization and promotion effects of remanufactured products on new and secondhand products, we constructed Stackelberg game models under different scenarios. We analyze the impact of the changes in the two effects on the optimal prices and production strategies of the manufacturer and recycler, as well as their countermeasures. We find that (i) how the cannibalization and promotional effects influence the manufacturer and the recycler's pricing and production strategies differ under different scenarios; (ii) when the two effects exceed a threshold, the manufacturer abandons new or remanufactured products, and the recycler prefers to stop production on its new products or continue to remanufacture products; and (iii) the two effects always reduce the profits of the manufacturer and increase the profits of the recycler.

With increased environmental protection awareness, sustainability has been incorporated into supply chain management. Sustainable supplier selection and evaluation have become an acritical part of supply chain management. They can significantly improve the supply chain’s operational performance and enhance enterprises’ competitiveness. Based on trapezoidal interval type-2 fuzzy numbers (TIT2FNs) and cloud probability dominance relations (PDR), manufacturers can make more tangible and environmentally friendly decisions in the SSSE process. In this paper, a SSSE indicator system is first established using the necessary economic, environmental, and social factors. The importance of the indicators described in linguistic terms is transformed into TIT2FNs, and the weight of each indicator is calculated. In order to prevent candidate suppliers from promoting performance maliciously, different weights are given according to the impact of the enterprise’s historical performance on the present. Finally, the cloud PDR method is used to determine the optimal sustainable supplier. A case study and analysis are provided to show the feasibility and superiority of the proposed method.

With the background of implementing carbon peaking and carbon neutralization, identifying methods to realize energy-saving and carbon reduction effectively has become an important issue in the intelligent energy-conservation manufacturing industry. During the process of achieving this goal, determining an optimal location for a low-carbon and intelligent manufacturing industrial park is a foremost decision-making problem for manufacturing corporations’ energy-efficient development. The article established a multi-criteria decision framework to assist manufacturing companies when selecting suitable industrial park sites. To begin with, an evaluation criteria framework is confirmed by literature search. Then, a fuzzy optimization model, which combines the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is presented, where fuzzy TOPSIS is used to determine the decision-maker criteria weights. Then, criteria weights are calculated by the optimization model with construction of a Lagrange function. Moreover, the fuzzy VIKOR method is applied to sort alternatives and choose the best alternative location. In addition, five alternative sites for a manufacturing company are evaluated and ranked according to the values of the ranking index as a numerical case to demonstrate the proposed framework’s application. Finally, a comprehensive analysis of diverse methods and sensitivity analyses for the volatility in criteria weights and decision-maker weights is illustrated to confirm that the framework is practicable for the problem of intelligent and sustainable manufacturing industrial park-site selection.

The decision-making and coordination of an e-commerce supply chain (ECSC) with consumer preferences are studied. In this paper, the ECSC, consisting of a single manufacturer and a single e-commerce platform, is considered, and the optimal decisions are made for two scenarios, namely, decentralized and centralized decision models. Then, a comparative analysis is performed for two models. The coordination mechanism of "revenue sharing" is proposed based on the decentralization, and finally, a numerical analysis is used to verify the conclusions. The theoretical results show that the e-commerce platform is less profitable than the manufacturer because of its economies of scale. Interestingly, the quality of e-commerce services under decentralized decision is instead the highest, and improving the service level of e-commerce does not always benefit e-commerce platforms. Meanwhile, a revenue sharing contract will reduce the price and profit gap between the manufacturer and the e-commerce platform.

This paper studies the multi-level supply chain network equilibrium optimization problem of multi-energy-efficiency products under different government subsidies and demand scales. In the equilibrium optimization problem, manufacturers determine the production volume of the energy-saving products; retailers decide the transaction volume with manufacturers, distribution volume for markets, and marketing efforts of energy-saving products; markets determine the transaction price. Firstly, the optimal decision-making behaviors of manufacturers, retailers, and markets are described. Simultaneously, the global optimization problem is transformed into a finite-dimensional variational inequality formulation. Then, the equilibrium conditions of the whole supply chain network are derived by the Euler method. Finally, a case study verifies the effectiveness of the proposed method. Interestingly, we found that energy-saving subsidies and demand scales were negatively correlated with the marketing efforts of the subsidized retailers for high energy-efficient products and positively correlated with the marketing efforts of non-subsidized retailers for high energy-efficient products in the same market; the development of retailers in the same market tended to be consistent, and the differentiation of the demand scale eliminated the retailers without a competitive advantage.

Due to the price elasticity of demand for secondhand commodities, it is difficult to establish a quantitative model for the auction. This paper proposes an agent-based multiattribute reverse auction model to support multicommodity combinatorial auction. First, this paper establishes an agent-based reverse auction model and introduces the framework, procedures, and protocols of the model in detail. Second, in light of the multicommodity environment, the targets, protocols, auction strategies, and approaches are identified. Finally, by using the proposed agent-based auction model, both buyers and sellers will reach simultaneous agreements on the details of the commodities to complete the auction.

Strategic inventories are considered a vital bargaining tool for retailers and an essential means to promote supply chain coordination and reduce double marginal benefits. Most previous studies of strategic inventories have been based on two underlying assumptions: the perfect substitution of goods across periods and information symmetry in supply chains. This study examines the far-reaching effects of commodity deterioration and information asymmetry on strategic inventories for a two-period supply chain. In comparing different contracts across various cases, optimal pricing and ordering decisions are determined, and changes in supply chain profits are compared and analyzed. The results show that in the face of deteriorating goods, the manufacturer tends toward a supply chain with asymmetric information. By contrast, the retailer chooses to disclose private information when the deterioration of goods is slow or the holding cost is low. Second, the changes in decision thresholds between the retailer and manufacturer are not synchronous as a result of information asymmetry. In addition, interestingly, both consumer surplus and social welfare tend to have higher product iterations, which is the opposite of the manufacturer's trend. Finally, in terms of supply chain profits, a dynamic contract is always better than a commitment contract when the retailer holds the deteriorating strategic inventory under information asymmetry. Sensitivity analysis is carried out, and relevant managerial insights are provided. (C) 2021 Elsevier Ltd. All rights reserved.