Search results

With the rapid development of e-commerce, multi-sourcing with supply contracts and spot buying has become more and more popular in reality. The main purpose of the paper is to explore a loss-averse buyer's optimal procurement policy in a multi-sourcing under e-commerce surroundings.

The study introduces the loss aversion utility function to characterize the loss aversion effect and derives a loss-averse buyer's optimal procurement policy in a multi-sourcing with a wholesale price contract and spot market.

A loss-averse buyer could order no items in a wholesale price contract and only needs to replenish commodities from spot market under certain conditions. In addition, the study shows that spot capacity has important influences on a loss-averse buyer's optimal ordering decision in the wholesale price contract.

This is the first paper to study the loss aversion effect on a buyer's procurement decision in a multi-sourcing. The results present important managerial insights for a loss-averse buyer to devise optimal ordering policies in a multi-sourcing under e-commerce surroundings.

The purpose of this paper is to investigate the major factors influencing retailer’s optimal ordering strategy in a supply chain consisting of one supplier and one retailer, where the retailer is newsvendor-like and capital-constrained, and further explore the issue of supply chain coordination.

Based on bi-objective programming which is modeled under the mean-variance framework, the retailer’s optimal ordering strategy is derived. Furthermore, through comparative analysis between decentralized system and centralized system along with a numerical simulation, this study examines the theoretical conclusions about supply chain coordination.

This study shows that a poor retailer with a high Expected Terminal Wealth Target Threshold (ETWTT) would ignore bankruptcy risk and order more, whereas a rich retailer is relatively conservative. It also reveals that in some cases, the optimal order quantity and performance of decentralized system could be both improved. However, the centralized system can always get more profit than the decentralized one.

This study uses a bankruptcy threshold to describe retailer’s bankruptcy risk, and considers retailer’s wealth status to formulate the model as an innovative bi-objective programming. The type of retailer as rich or poor in terms of his wealth status and asset structure is distinguished. Moreover, the impacts of retailer’s type and ETWTT on ordering strategy are examined.

The purpose of this paper is to discuss the role of reference effect on newsvendor’s decision behavior in a market with strategic customers and work out the newsvendor’s optimal pricing policy and ordering quantity.

This study utilizes the prospect theory and strategic customer framework to analyze the decision-making behavior on the newsvendor’s optimal pricing policy and ordering quantity. The paper further presents an extension of newsvendor model and provides the model’s properties. The paper finally analyzes the results with various parameters on the model and reports on the insights generated by the model.

The paper indicates that the ordering quantity is not altered with the changing proportion of strategic customers and myopic customers, but the ordering quantity and the pricing strategy are influenced in terms of newsvendor’s reference effect, loss aversion, product cost, and salvage price.

The research findings have important implications for decision makers. Previous researches have studied the incomplete rationality newsvendor’s decision-making behavior mainly by analyzing the vendor’s risk preferences or loss aversion, but the effect of reference point also plays an important role in analyzing the decision-maker’s behavior. The paper provides the optimal pricing policy and ordering quantity with the reference effect considering the strategic customers behavior. This model is also a valid complementarity to behavioral operations management research area.

The paper examines the role of reference effect in newsvendor problem with the strategic customers and analyzes the impact of parameters such as loss aversion on the newsvendor’s decision behavior.

The purpose of this paper is to investigate the impact of backroom storage in supply chain replenishment decision parameters: the order quantity based on the well-established economic order quantity (EOQ) model.

The authors develop an EOQ-type model to investigate the operational cost impact of the order quantity with backroom storage. Because of the discrete and discontinuous nature of the problem, a modification of an existing algorithm is applied to obtain an optimal order quantity. Numerical experiments derived from a leading retailer in Thailand are used to study the cost impact of the backroom.

The paper shows that the backroom storage will significantly affect the decision regarding the order quantity. If its effect is ignored, the cost increase can be as high as 30 per cent. The costs and operations of additional shelf-refill trips from the backroom must be carefully analyzed and included in the decisions of replenishment operations.

The model is a simplified version of the actual replenishment process. Validation from a real-world setting should be used to confirm the results. There are many additional opportunities to further integrate other issues in this problem such as shelf space decisions or joint order quantity between vendors and retailers.

The insights gained from the model will help managers, both retailers and vendors or manufacturers, make better decisions with regard to the order quantity policy in the supply chain.

Problems with backroom storage have been qualitatively described in the literature in the past decade. This paper is an early attempt to develop a quantitative model to analytically study the cost impact of backroom on order quantity decisions.

This study aims to examine the value of the call option contract in hedging the risks in the supply chain. The decentralized supply chain without call option contract is first studied as the criterion model for evaluations. This paper addresses several questions: What will be the optimal manufacturer’s production quantity, retailer’s ordering and pricing policies in the presence of random demand and random yield by applying the downconversion approach? How will the call option contract influence the optimal decisions for the members of the supply chain? Can the risk from randomness be divided among the members in the supply chain through the call option contract?

This paper considers a two-level decentralized supply chain under random yield and random demand in which the manufacturer takes advantage of the downconversion approach with two scenarios, with and without option contract. To the best of the authors’ knowledge, no article or study uses the downconversion approach in a supply chain regarding random yield and random demand. Furthermore, the paper considers pricing with option contract in the supply chain, which makes this article stands out significantly from other articles in the literature.

This study shows that the downconversion approach would reduce the risk caused by the random yield, which appears to be the appropriate method for the environmental goal of the supply chains. Moreover, adopting a call option contract can increase flexibility and mitigate risks, resulting in more expected members’ profits.

To simplify the model, the authors assume one manufacturer and one retailer, so extending the model to consider multiple retailers instead of one retailer and inventory sharing between them would be interesting. Considering the option and exercise prices as decision variables would be important future research topics. Put option and bidirectional option contracts could be investigated in the future. Another extension is modeling asymmetry of information in supply chain.

This paper provides managerial insights on dealing with both demand and yield risks in a manufacturer–retailer supply chain. The manufacturer has a random yield production and produces two types of vertical products: low-end and high-end. To reduce waste caused by the random yield, the manufacturer uses a downconversion approach in which low-end products are made by converting the defective high-end products. The manufacturer purchased a shortage of high-end products from the secondary market (i.e. emergency sourcing). High-end products are sold through the retailer, and low-end products are sold directly by the manufacturer. The customer demand for high-end products in the end market is random and depends on the selling price, and the customer demand for the low-end products in the secondary market is independent and random. The retailer contracts the manufacturer with the call option to obtain high-end products to meet a random demand; in fact, by using the call option contract, the authors try to balance the risks between two members. Two scenarios of with and without call option contract are proposed. After the high-end product demand is observed, the retailer would exercise the option order quantity in the call option contract scenario and then place an instant order with the manufacturer if necessary. In each scenario, the manufacturer and the retailer make their decisions simultaneously (static game) to determine the retailer’s optimal ordering and pricing policies and the optimal production quantity of the manufacturer (Nash equilibrium) by maximizing their expected profits. Finally, the impact of the model parameters on the supply chain is expressed through numerical examples. The numerical analysis shows that the call option contract provides greater profit than the wholesale price contract.

This study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.

This study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.

The study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

This study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

Optimal ordering decision for a retailer in a dual-sourcing procurement is an important research area. The main purpose of this paper is to explore a loss-averse retailer’s ordering decision in a dual-sourcing problem.

For a loss-averse retailer, the study obtains the optimal ordering decision to maximize expected utility. Based on sensitivity analysis, the properties of the optimal ordering decision are well discussed.

Under the optimal ordering quantity that maximizes expected loss aversion utility, the relevant expected profit of a retailer turns to be smaller under a bigger loss aversion coefficient. For this point, a retailer needs to balance between expected loss aversion utility maximization and expected profit maximization in deciding the optimal ordering policy in a dual-sourcing problem.

This paper reveals the influence of loss aversion on a retailer’s ordering decision in a dual-sourcing problem. Managerial insights are suggested to devise the optimal ordering policy for retailers in practice.

The purpose of this paper is to study integrated inventory system and pricing and ordering strategy for vendor‐buyer supply chain system. Here, the vendor offers a trade credit to the buyer when the buyer's order quantity exceeds a given pre‐specified quantity. Therefore, to incorporate the concept of vendor‐buyer integration and trade credit linked, the authors analyze the model to determine the optimal strategy for an integrated vendor‐buyer inventory system under the condition of credit linked to the order quantity when demand is quadratic.

A mathematical model for integrated inventory system is developed when demand rate is increasing function of the time and decreasing function of the retail price. By analyzing the total channel profit function, the authors developed some useful results to characterize the optimal solution and provide an iterative algorithm to find the retail price, buyer's order quantity and the number of shipments per production run from the vendor to the buyer.

By developing a solution algorithm, the optimal retail price, order quantity and number of shipments from the vendor to the buyer are provided. Numerical examples and sensitivity analyses are presented to validate the proposed model. Through extensive numerical analyses, it is observed that a longer credit term increases profits of the player for the entire supply chain. The vendor should establish the threshold for allowing trade credit comprehensively to ensure the greatest benefit for both players.

Most of the research articles available in the literature considered the constant demand or linearly changing demand. In this paper, a mathematical model is developed considering time dependent quadratic demand. Very few researchers have investigated joint optimal policy in vendor‐buyer supply chain system, considering trade credit is linked to order quantity, and still there are not many findings on the benefit of integrated policy and trade credit.

The purpose of this paper is to examine how customers’ self-control affects their purchase decisions and to discuss the pricing decisions of the retailer under different forms of contract.

The authors use the literature on hyperbolic discounting to model customers’ self-control problems. In this framework, the authors examine how the customers’ self-control affects the optimal pricing decision and the selection of the optimal contract form when there is a supplier and a retailer in the supply chain.

The study’s results show that when wholesale price contract is compared with buyback contract, buyback contract is better when customers’ self-control is weak; when quantity-discount contract is compared with wholesale price contract and buyback contract, although quantity discount can encourage customers to purchase more units of products, but both wholesale price contract and buyback contract can be better than quantity-discount contract in some cases. Additionally, the authors demonstrate that revenue sharing contract can increase the supply chain’s profit. The authors also find that sometimes customers’ preplan will lead to the result that the supplier produces more unhealthy products.

To the best of the authors’ knowledge, this is the first study to analyze the decision-making of the retailer by developing an analytical framework combining customer’s self-control and supply chain contract. These results have important implications for the supplier and the retailer that sell vice goods.

Purpose: The purpose of this paper is to describe a decision support tool based on various types of contracts in a two‐level supply chain. A supply chain (SC) consists of disparate but interdependent members, dependent on each other to manage various resources (inventory, money and information). The conflicting objectives between these members may cause uncertainties in supply and demand, which can be managed by adopting coordination with the help of contracts (such as buyback, revenue sharing and quantity flexibility). Design/methodology/approach: A decision support tool for SC coordination using contracts (DSTSCCC) has been developed to explore the applicability of contracts and to compare different types of contracts in various situations. The DSTSCC is comprised of an analytical module, which is an extension of the classical newsboy problem and a simulation module. Findings: DSTSCCC helps in determining decision variables for different scenarios of contracts in the best interest of all SC members as well as whole SC. Practical implications: DSTSCCC is a simple‐to‐use and easy‐to‐implement decision making tool which helps in taking decisions prior to the actual start of SC activities. The prior decisions may help to handle future exceptions. SC members may jointly select the most profitable contract to share risks and rewards. Originality/value: DSTSCCC comprised of analytical module and simulation module presents an Integrative framework which cannot be dealt in isolation. The output of analytical module becomes input for simulation to quantify performance measures. The improvement in performance measures after satisfying the objectives of all SC members helps in realizing coordination in SC.