Narrow Results

This study presents a comprehensive analysis of how the financial and technological progress of major companies affects the innovation activities of smaller businesses both upstream and downstream. The study discovered that core enterprise financial and technology development greatly improved the innovative capacity of upstream, downstream and small firms. The development of advanced financial technology in core enterprises empowers small- and medium-sized enterprises (SMEs) through a variety of beneficial effects. These effects include expanding financing options via supply chain credit, optimizing resource allocation through information sharing, promoting cooperation incentives through innovation, and effectively managing risks. These efforts collectively contribute to enhancing the innovation capacity of downstream SMEs in supply chains.

In the field of supply chain management, efficient vehicle scheduling is essential to reduce operational costs. With the dynamic changes in the market environment and the diversification of customer needs, the traditional static vehicle scheduling method has been difficult to adapt to the complex realistic scene. Therefore, this paper proposes an optimal vehicle scheduling model based on Markov Decision Process (MDP) to address these challenges. This study conducted an in-depth analysis of vehicle scheduling in the supply chain, clarified the core elements and constraints of the problem, and then constructed the stochastic transfer probability of vehicle scheduling in the supply chain based on the MDP theory, taking into account a variety of influencing factors, such as vehicle number, transportation time, transportation cost and customer demand. The corresponding return function is set up to reflect the influence of these factors on the scheduling effect. The MDP model is solved by a dynamic programming algorithm, and the optimal vehicle scheduling strategy is obtained. The iterative update method is adopted in the study, and the strategy is constantly adjusted according to the current state and environmental information to achieve the long-term optimization goal. In addition, The experimental results show that the MDP-based optimal vehicle scheduling model can significantly reduce transportation cost, shorten transportation time and improve customer satisfaction. Compared with the traditional static scheduling method, this model has higher flexibility and adaptability, and can better cope with the change of market environment and the uncertainty of customer demand.

Prior studies suggest that strategic alignment in the supply chain (i.e., supply chain partners share similar strategic objectives) helps build trust between the partners, reduce transaction costs, and alleviate coordination problems. We posit that a closer strategic alignment helps strengthen supply chain relationships by making them more durable. We use regulation-mandated major customer disclosures to construct a dataset consisting of supplier-customer dyads to examine this proposition. After controlling for the characteristics of suppliers and customers, we find that greater strategic alignment between suppliers and their customers extends the duration of supply chain relationships. Additionally, we hypothesize that the advantages of strategic alignment should be greater for suppliers in more competitive product markets since customers face lower switching costs when their suppliers are based in industries with more competition. We find support for this hypothesis as well.

The rapid development of chemical enterprises has not only improved the material foundation of society, but also brought serious environmental and pollution problems. In order to provide decision-making basis for basic chemical enterprises to achieve green Supply Chain Management (SCM) and green innovation, the study proposes a hypothesis and conceptual model on the impact of green SCM on enterprise performance under the influence of green innovation. Measurement scales for each dimension are designed and a questionnaire survey is conducted. The reliability and validity of the sample data are examined in turn, the consistency of each dimension is determined through correlation analysis. Finally, a regression model is used to examine the correlation of the three variables. The results indicated that the cumulative total deviation variance explained by the dimensions of green supply chain innovation, business performance and green innovation in basic chemical enterprises were 80.101%, 77.425% and 74.526%, respectively. Green procurement had a significant negative impact on the performance of basic chemical enterprises. Green recycling and internal environmental management had a negative influence on enterprise performance. The research results contribute to promoting the sustainable development of chemical enterprises, providing new perspectives and methods for the practice of knowledge management, and offering guidance for chemical enterprises on how to better manage and apply knowledge, which is beneficial for the research and practice of knowledge management.

A three-layer supply chain model with a single supplier, manufacturer, and retailer is presented in this paper. This proposed model is analyzed under green investment along with trade credit and cap-trade policy. It is considered that the manufacturer’s production process is imperfect. Few inferior quality products are reworked, and the remaining defective items are discarded. The manufacturer makes investments in green technology to reduce carbon emissions. The product’s demand is influenced by selling price, advertisement, and green-technology level. The total average profit of the supply chain is constructed under three different scenarios of trade credit provided by the manufacturer to the retailer. The primary goal of this research is to obtain the optimal selling price, production rate, and green-technology level to maximize the system’s total average profit by using genetic algorithm. A numerical example is shown to demonstrate the model, and sensitivity analysis is carried out to analyze the impact of numerous input parameters. Managerial insights and conclusions are provided to make this model helpful for manufacturers.

In this paper, we consider a seller–buyer channel in which marketing expenditure is an endogenous decision for the buyer. We assume that both the unit marketing expenditure and the unit price charged by the buyer influence the end demand for the product. We model the seller–buyer relationship as a non-cooperative as well as a cooperative game. We investigate the non-cooperative game from two perspectives: the Seller–Stackelberg model and the Buyer–Stackelberg model. In the cooperative game, we provide a procedure for outlining Pareto efficient solutions. For each model, we present a numerical example as well as sensitivity analysis with respect to the two key parameters in the model.

The Internet is becoming increasingly important as a sales channel. Thus, most large retail firms have adopted a multi-channel strategy that includes both web-based channels and pre-existing offline channels. In this paper, we consider joint pricing and inventory/production decision problems for members in a monopoly two-stage dual-channel retailer supply chain. For a dual-channel retailer, pricing in one channel will affect the demand in the other channel. This subsequently affects the retailer's replenishment (ordering) decisions, which have an impact on the producer's inventory/production plans and wholesale price decisions. It is clear then that pricing decisions and inventory/production decisions are interacting in each member of the supply chain and among the members in the chain as well. In this paper, we analyze joint pricing and inventory/production problems under three scenarios by incorporating intra-product line price interaction in the EOQ model. We show that a unique equilibrium exists under certain realistic conditions. We also provide numerical results that offer insights for pricing strategies for the dual-channel retailer supply chain and for product design for different channels.

We model the impact of information visibility in a two-level supply chain consisting of independent retailers who share upstream supply. The manufacturer supplies similar products to the two retailers and each retailer serves its independent end market. Retailers face one period of demand and satisfy the demand by ordering in the first period or back-ordering some of the demand and satisfying it in the second period. The wholesale price in the second period is decreasing in the total order size across the two retailers in the first period. This decrease in wholesale price captures the market learning effect of aggregate orders that has been extensively documented in empirical literature. We use a game-theoretic framework to investigate the ex-ante incentives for retailers to share their private demand information. We show that: (1) retailers have no incentives to share information about their private values when equilibrium order quantities are interior, i.e., the order size is between zero and the demand; (2) partial information sharing may be the equilibrium strategy for retailers when equilibrium order quantities are binding on the demand. Finally, numerical examples are provided for illustration. This paper thus identifies conditions under which different levels of information sharing may be the equilibrium outcomes in a supply chain.

This study considers the supplier's warranty return and the buyer's rebate policy as business-return policy in the supplier–buyer supply chain. The policy as well as the displayed stock level influences the behavior of the buyer. Distinct from the traditional single-stage inventory model, we investigate a two-stage replenishment policy model for deteriorating item considering the effect of imperfect production process, product design, stock level and business-return. A mathematical model utilizing the time weighted inventory (TWI) approach and two-variables fixed point optimization technique is developed to derive the optimal inspection starting time, the number of deliveries, the optimal delivery-time interval and the optimal business-return period. A numerical example is presented to illustrate the model developed. The result shows that the fixed demand rate, the unit holding cost and the unit inspection cost are the critical factors affecting the scheduling and performance of the two-stage supply chain.

This paper investigates the impact of partial information sharing in a three-echelon supply chain. Partial information sharing means that information sharing occurs only between the distributor and the retailer, but not between the distributor and the manufacturer. This paper contributes to the literature by summarizing the circumstances in which information sharing between the retailer and the distributor benefits the manufacturer. In addition, our study points out that such information sharing does not always bring benefits to the manufacturer and that in some cases the information sharing may harm the manufacturer. We explain the reasons why this can happen and give managerial intuition for our results. Using numerical analysis, we illustrate the impact of partial information sharing on the agents in the supply chain with the change of the autoregressive coefficient in the demand process.

Currently, distributed supply chain production planning (SCPP) is a practical problem affecting global supply chain production. Distributed SCPP is a complex constraint satisfaction problem (CSP) comprising a series of constraints intra- and inter-production members on orders, capacity, and materials. Previous studies have proposed various advanced planning and scheduling (APS) algorithms for companies to solve internal CSPs related to production planning. Distributed SCPP problems additionally require a coordination approach to resolve conflicts among the production plans of individual production members. Several distributed CSP (DCSP) algorithms have been proposed to guide the coordination for various DCSP conditions and objectives. Based on these techniques, we propose a coordination approach for achieving consistency among individual production plans to develop an integral supply chain production plan. First, we employed an APS system to resolve the internal production planning constraints and to develop a feasible production plan for each production member. Next, we developed a hybrid DCSP coordination algorithm based on agent-ordering asynchronous weak-commitment search (AWS) algorithm and asynchronous incremental relaxation (IR) algorithm concepts to facilitate production planning coordination and resolve constraint conflicts among individual production plans. Finally, a thin-film transistor liquid crystal display (TFT-LCD) supply chain was used to illustrate and verify the proposed coordination approach.

In this paper, a revenue sharing contract is designed to coordinate a distribution channel where the demand of the product is ramp-type price and effort sensitive. It is shown that traditional revenue sharing contract does not coordinate the system. As an alternative, two new mechanisms are proposed (i) revenue sharing with coordinated effort of the retailer alone and (ii) both revenue and effort sharing contract. In addition, a crucial modification of revenue sharing fraction is also proposed. To enhance the applicability of revenue sharing contract, the contract parameters are determined by using bi-level multi-objective fuzzy goal programming technique where manufacturer sets the wholesale price greater than the marginal cost. Numerical examples are presented to illustrate all the models.

Product design decisions have significant impact on a firm's competitive edge. This paper investigates the condition under which component commonality is a profitable product design strategy for a firm by considering customer-choice behavior in the supply chain environment. The design configuration with commonality can lower manufacturing cost, but it also reduces product differentiation and revenue. While the customer-choice behavior with utility maximization will impact the retailer's product pricing, we analyze a stylized model of the manufacturer who designs a product family consisting of two products for two market segments with different valuations of quality. By explicitly considering the interrelationship between customer's utility and retail price, we find that commonality strategy is conditionally profitable. Theoretical research reveals that the optimal product design strategy depends on the marginal valuations of customer-choice behavior and the cost-coefficient of the common component determined by the supplier. An example is used to illustrate how some supply chain parameters impact the optimal product design strategy.

This paper develops the game models of two symmetric supply chains, each consisting of one manufacturer and one retailer, while both retailers compete in the market with a linear function. The disclosure mechanism is designed when the information of the disrupted demand is asymmetric between supply side and retail side. We first study the model with the full information as a benchmark to explore the effect of asymmetric information on the system. In the case, each manufacturer maximizes her profit while the downstream retailer only obtains the reservation profit. For the case of asymmetric information, each manufacturer can obtain the real information of the disrupted demand by using a menu of contract bundles. For each information structure, there are always robust regions for each manufacturer’s original trading quantity scheme. That is, when the disrupted amount of the demand is sufficiently small, the trading quantity will be unchanged. However, some special measures, e.g., the higher unit wholesale price, should be taken to prevent the retailer from deviating the trading quantity scheme. The high-disruption retailer gets the higher profit due to the information rent. Compared with a single supply chain, Cournot competition results in the less retail price and the lower performance for the whole system.

Sharing forecast information helps supply chain parties to better match demand and supply. The extant literature has shown that sharing forecast information improves supply chain performance. In the big data era, supply chain managers have the ability to deal with a massive amount of data by big data technologies and analytics. Big data technologies and analytics provide more accurate forecast information and give an opportunity to transform business models. In this paper, a comprehensive review on forecast information sharing for managing supply chain in the big data era is conducted. The value and obstacles of sharing forecast information are discussed. Given the sufficient data, the appropriate approaches of analyzing and sharing forecast information are highlighted. Insights on the current state of knowledge in each respective area are discussed and some associated pertinent challenges are explored. Inspired by various timely and important issues, future research directions are suggested.

In this paper, location–allocation problem of a three-stage supply chain network, including suppliers, plants, distribution centers (DCs) and customers is investigated. With respect to the total cost, the aim is determining opened plants and DCs and designing transportation trees between the facilities. Considering the capacity of suppliers, plants and DCs are limited and there is a limitation on the maximum number of opened plants and DCs, a mixed-integer linear programming (MILP) model of the problem is presented. Since multi-stage supply chain networks have been recognized as NP-hard problems, applying priority-based encoding and a four-step backward decoding procedure, a meta-heuristic algorithm, namely GAIWO, based on the best features of genetic algorithm (GA) and invasive weed optimization (IWO) is designed to solve the problem. In small size problems, the efficiency of the GAIWO is checked by solutions of GAMS software. For larger size problems, the performance of the proposed approach is compared with four evolutionary algorithms in both aspects of the structure of the GAIWO and the efficiency of the proposed encoding–decoding procedure. Besides usual evaluation criteria, Wilcoxon test and a chess rating system are used for evaluating and ranking the algorithms. The results show higher efficiency of the proposed approach.

Due to double marginalization effect, the wholesale price contract has been proved that it cannot coordinate a decentralized supply chain (DSC) based on the framework of Stackelberg game, in which the upstream firm acts as a leader and the downstream firm acts as a follower. Nevertheless, it has shown that the partnership between the enterprises tends to be equality. Motivated by this factor, this paper studies the coordination of wholesale price contract under the perspective of equality between enterprises. First, an innovative wholesale price contract is constructed and to prove that the constructed contract can flexibly coordinate the DSC. Second, the adaptability of the constructed contract is analyzed and compared with the revenue sharing contract, which is designed under the framework of Stackelberg game. Third, numerical analysis is calculated to verify the effectiveness and operation of the model.

In a framework where both power structure and consumer heterogeneity (brand loyalty and different willingness-to-pay for perceived quality) count, we study six noncooperative games (two Stackerberg and one Nash games with and without a store brand) between a national-brand manufacturer and a store-brand retailer. Our results contribute to the theoretical literature on store brand in four aspects: (a) revealing that the mechanism whereby store brands alleviate double-marginalization problem varies with supply-chain power structures; (b) finding that it is the power manufacturer with a low-loyalty national brand that suffers most from the store brand entry; (c) finding that a category follower retailer is more inclined to introduce its store brand than a power one, and should optimally position its store brand close to the low-loyalty national brand supplied by a power manufacturer; (d) showing that the store brand may lead the manufacturer and the retailer to be trapped in the prisoners’ dilemma.

With the gradual improvement of living standards, people’s consumption levels and habits are changing. One notable fact is that the demand for fresh products is growing steadily. Accordingly, fresh-product preservation and logistics distribution also require higher standards. Based on the practice of fresh domestic transport and preservation, for which the producer and the distributor are responsible, this paper discusses their optimal decisions taking into account the freshness-keeping effort of the distributor. Our main contributions include the derivations of the optimal decisions of the order quantity and the freshness-keeping effort in both the pull and push models, which are common in practice but have not been studied in the literature. Our analytical models lead to the result that, all other settings being the same, the distributor always puts a greater effort into preserving the product quality in the pull model than in the push model. This phenomenon results in a greater distributor’s order quantity and producer’s shipping quantity in the pull model. We also conduct a comprehensive numerical comparison of the effects of different modulating factors, including the price and the proportion and variation of surviving quantity, in these two settings. We find that the profits of the participants and the supply chain are always larger in the pull model, which indicates that the pull model is a better choice for the supply chain.

Risks play an important role in supply chain vulnerability, performance maintenance, and competitive advantage. Therefore, appropriate risk assessment and ranking are among the top priorities of managers and experts in the supply chain. Because there are several quantitative and qualitative risks in the supply chain, multi-criteria decision-making (MCDM) and, in particular, multi attribute decision making (MADM) methods are used to rank them. Depending on the type of supply chain, the difference in criteria and information available, it is possible to use existing MCDM methods to rank supply chain risks or introduce new methods. In these methods, options are compared on the basis of different criteria according to mathematical methods. Each of these methods uses their own approaches and assumptions. Therefore, there will be different solutions to the ranking. In this case, one or more methods may be selected as the best method. But it cannot be sure that the superior method is chosen correctly. In this paper, decision making methods are compared with each other for an appropriate method to be chosen. Case examples from the related literature have been compared with the proposed method (three-dimensional integrated mean method). To evaluate the validity of the proposed method, the results were sent to experts in various industries along with the proposed method. For analysis the accuracy of experts’ opinions, One-Sample Kolmogorov-Smirnov, Frequencies and Explore tests have been used in SPSS software with 95% confidence level. The obtained results show high reliability of the proposed method for determining an appropriate decision-making method in supply chain risk assessment. However, the proposed method is used for ranking of supplier selection in this paper, but it can be used for many decision-making procedures.