Strategic information sharing in a supply chain

We consider a two-member supply chain that manufactures and sells newsboy-type products and comprises a downstream retailer and an upstream vendor. In this supply chain, the vendor is responsible for making stock-level decisions and holding the inventory, and the retailer is better informed about market demand. In each period, the retailer receives a signal about market demand before the actual demand is realized, and must decide whether to reveal the information to the vendor, at a cost, before the vendor starts production. We assume that any information that the retailer reveals is truthful. We model the situation as a Bayesian game, and find that, in equilibrium, whether the retailer reveals or withholds the information depends on two things—the cost of revealing the information and the nature of market demand signal that the retailer receives. If the cost of sharing the information is sufficiently large, then the retailer will withhold the information from the vendor regardless of the type of signal that is received. If the cost of sharing the information is small, then the retailer will reveal the information to the vendor if a high demand is signaled, but will withhold it from the vendor if a low demand is signaled. In general, reducing the cost of sharing information and increasing the profit margin of either the retailer or the vendor (or reducing the cost of the vendor or retailer) will facilitate information sharing.     

Pricing policies for complementary products in a dual-channel supply chain

This paper investigates the pricing problem of complementary products in a supply chain with two manufacturers and one retailer, one of the two manufacturers uses dual channels including an online channel and a traditional retail channel to sell its product. We formulate four pricing game models through considering different market power structures of channel members, and derive the corresponding optimal pricing strategies. Based on the theoretical and numerical analysis, we study the effects of consumer channel loyalty, the level of complementarity, and the market power structures on the pricing strategies and the maximal profits of two complementary products. Some interesting and valuable managerial insights are obtained.     

Inventory competition in a dual-channel supply chain with delivery lead time consideration

Aimed at the inventory competition of perishable products in a dual-channel supply chain with consideration of the delivery lead time in the online direct channel, we extend the Newsvendor model considering stock-out-based consumer switching behavior to include the delivery lead time. We examine the retailer's optimal order quantity decision in the retail channel and the manufacturer's optimal inventory level decision in the online direct channel, explore the manufacturer's optimal delivery lead time decision in the online direct channel, discuss the impact of the product price and consumer switching behavior on the optimal decisions of supply chain members, and compare the optimal decisions between decentralized and centralized scenarios. The results show that, compared with the centralized scenario, at least one of the supply chain members will overstock in the decentralized scenario and that consumers in the online direct channel enjoy a shorter delivery lead time and hence better service in the decentralized scenario. Finally, we present numerical examples to analyze the impact of relevant parameters on the supply chain members’ profits and the supply chain efficiency.     

Managing a dual-channel supply chain under price and delivery-time dependent stochastic demand

Several leading manufacturers recently combined the traditional retail channel with a direct online channel to reach a wider range of customers. We examine such a dual-channel supply chain under price and delivery-time dependent stochastic customer demand. We consider five decision variables, the price and order quantity for both the retail and the online channels and the delivery time for the online channel. Uncertainty frequently arises in both retail and online channels and so additional inventory management is required to control shortage or overstock and that has an effect on the optimal order quantity, price, and lead time. We developed mathematical models with the profit maximization motive. We analyze both centralized and decentralized systems for unknown distribution function of the random variables through a distribution-free approach and also for known distribution function. We examine the effect of delivery lead time and customers’ channel preference on the optimal operation. For supply chain coordination a hybrid all-unit quantity discount along a franchise fee contract is used. Moreover, we use the generalized asymmetric Nash bargaining for surplus profit distribution. A numerical example illustrates the findings of the model and the managerial insights are summarized for centralized, decentralized, and coordinated scenarios.     

The impact of partial information sharing in a two-echelon supply chain

We consider a simple two-echelon supply chain composed of a manufacturer and a retailer in which the demand process of the retailer is an AR(1) where the random component is a function of both sides’ information. We focus on partial information sharing under which each side informs the other of an interval in which the exact value of its own component of demand lies. These various levels of information sharing can reduce the supply chain costs.     

Pricing and horizontal information sharing in a supply chain with capacity constraint

This paper aims to explore manufacturers horizontal information sharing strategy under competition. The model framework is based on a two-echelon supply chain composed of one upstream supplier and two downstream manufacturers with asymmetric capacity constraint. Analysis of the model establishes manufacturers’ information sharing strategies under different conditions and shows how supplier’s pricing decision can shape manufacturers’ information sharing incentives.     

Transferring and sharing exchange-rate risk in a risk-averse supply chain of a multinational firm

This paper analyzes risk management contracts used to handle currency risk in a decentralized supply chain that consists of risk-averse divisions in a multinational firm. Particular contracts of interest involve transferring risk to a third party by using risk-transfer contracts such as currency options and re-arranging risk between supply chain members using risk-sharing contracts. Due to decentralization, operational and risk management decisions are made locally; however, a headquarter who is interested in total supply chain profit has some controllability over those activities. We question if each kind of risk management contract can improve the utility of all supply chain members compared to the utility without any of those, and how the conditions to achieve such improvements are different. Further structural differences are investigated via sensitivity analysis with respect to the transfer price, the variability of exchange rates, and the location of the headquarter. We also find that using the two kinds of contracts jointly does not necessarily result in better outcomes.     

The impact of information sharing in a two-level supply chain with multiple retailers

Information sharing is an important component of cooperation in supply chain management. This paper presents a study to evaluate the impact of information sharing on inventory and expected cost in a two-level supply chain with multiple retailers. Three levels of information sharing are given and the optimal inventory policy under each level is derived. We show that both the inventory level and expected cost of the manufacturer decrease with an increase in the level of information sharing.     

BP神经网络预测在流程企业供应链中的应用研究

本针对流程制造企业的管理模式和相应的供应链特点，采用多智能体技术建立基于预测的流程供应链模型。在比较预测方法的基础上，采用BP神经网络技术建立客户分销的预测支持系统，并采用某造纸流程企业的周销量件数进行了实例预测仿真。     

Incorporating demand uncertainty and forecast error in supply chain planning models

This paper develops a framework for examining the effect of demand uncertainty and forecast error on unit costs and customer service levels in the supply chain, including Material Requirements Planning (MRP) type manufacturing systems. The aim is to overcome the methodological limitations and confusion that has arisen in much earlier research. To illustrate the issues, the problem of estimating the value of improving forecasting accuracy for a manufacturer was simulated. The topic is of practical importance because manufacturers spend large sums of money in purchasing and staffing forecasting support systems to achieve more accurate forecasts. In order to estimate the value a two-level MRP system with lot sizing where the product is manufactured for stock was simulated. Final product demand was generated by two commonly occurring stochastic processes and with different variances. Different levels of forecasting error were then introduced to arrive at corresponding values for improving forecasting accuracy. The quantitative estimates of improved accuracy were found to depend on both the demand generating process and the forecasting method. Within this more complete framework, the substantive results confirm earlier research that the best lot sizing rules for the deterministic situation are the worst whenever there is uncertainty in demand. However, size matters, both in the demand uncertainty and forecasting errors. The quantitative differences depend on service level and also the form of demand uncertainty. Unit costs for a given service level increase exponentially as the uncertainty in the demand data increases. The paper also estimates the effects of mis-specification of different sizes of forecast error in addition to demand uncertainty. In those manufacturing problems with high demand uncertainty and high forecast error, improved forecast accuracy should lead to substantial percentage improvements in unit costs. Methodologically, the results demonstrate the need to simulate demand uncertainty and the forecasting process separately.     

Price fluctuations,information sharing,and supply chain performance

We consider a supply chain consisting of one supplier with finite production capacity and a retailer facing independent and identically distributed demands from end-customers. Existing research advocates that, in a decentralized setting, the retailer and the supplier using stationary order up to policies is efficient. We show that in the presence of information sharing, the supply chain performance can be improved by the supplier offering fluctuating prices. We study two specific settings: (1) the supplier only knows the parameters of the retailer’s inventory policy; and (2) the supplier knows the day-to-day inventory levels at the retailer as well. After establishing structure of optimal policies and developing efficient solution procedures, we perform an extensive computational study to determine the extent of the improvements realizable in the supply chain. We observed that for setting 1, an improvement was realized only when the end-customer demands were highly variable. Even then, the improvement in supply chain performance was less than 1%. Whereas, for setting 2, the improvement in supply chain performance averaged around 5.0% with a maximum of 16.3%.     

The risk and information sharing of application services supply chain

We study an application services supply chain consisting of one application service provider (ASP) and one application infrastructure provider (AIP). The AIP supplies the computer capacity to the ASP that in turn sells the value-added application services to the market. The market is characterized by a price-sensitive random demand. The ASP’s objective is to determine the optimal price of its service to the market and the optimal capacity to purchase from the AIP. The AIP’s goal on the other hand is to maximize its profit from selling the capacity to the ASP.     

Demand disruption and coordination of the supply chain with a dominant retailer

This paper develops two coordination models of a supply chain consisting of one manufacturer, one dominant retailer and multiple fringe retailers to investigate how to coordinate the supply chain after demand disruption. We consider two coordination schedules, linear quantity discount schedule and Groves wholesale price schedule. We find that, under the linear quantity discount schedule, the manufacturer only needs to adjust the maximum variable wholesale price after demand disruption. For each case of the disrupted amount of demand, the higher the market share of the dominant retailer, the lower its average wholesale price and the subsidy will be under the linear quantity discount schedule, while the higher its fraction of the supply chain’s profit will be under Groves wholesale price schedule. When the increased amount of demand is very large and production cost is sufficiently low, linear quantity discount schedule is better for the manufacturer. However, when the production cost is sufficiently large, Groves wholesale price schedule is always better. We also find that the disrupted amount of demand largely affects the allocation of the supply chain’s profit.     

Entropy as an assessment tool of supply chain information sharing

As the order fulfillment process needs to be managed in a seamless way, the level of information sharing among the partners of the supply chain needs to be increased. An entropy-based formulation is proposed as the basis of a methodology for comparing different information sharing approaches in a supply chain environment. A step-by-step comparison of two different information sharing approaches is presented to exemplify the use of the methodology. A computer simulation is used to validate the usefulness of the proposed methodology.     

Understanding the value of upstream inventory information sharing in supply chain networks

Global competition has caused a paradigm shift in a firm’s outlook from a product-centric to a customer-centric view. With this shift, the availability of inventory at a store is a prime concern for firms in a supply chain network (SCN) as it affects customer goodwill and market share. Information sharing among the SCN partners is a key strategy to address this issue. In this study, we focus on the impact of sharing upstream inventory information in a SCN on its overall performance. A two-echelon SCN configuration with one retail store and two production facilities was used as an experimental test bed. To assess the marginal benefits of sharing additional information, three levels of information sharing were considered along with the base case of no information sharing. The information shared ranged from stock-out information at the lowest level to inventory and backorder levels at the highest level. Continuous Time Markov Chain models of the SCN were developed and analyzed to gain insights into the value of inventory information sharing. Numerical experiments were conducted to show that inventory information can be an effective substitute for physical inventory and to assess the impact of backorder limits on the SCN performance.     

The impact of sharing customer returns information in a supply chain with and without a buyback policy

In this paper, we examine a single period problem in a supply chain in which a Stackelberg manufacturer supplies a product to a retailer who faces customer returns and demand uncertainty. We show that the manufacturer incurs a significant profit loss with and without a buyback policy if it fails to account for customer returns in the wholesale price decision. Under the assumption that the retailer is better informed than the manufacturer on customer returns information, we show that without a buyback policy, the retailer prefers not to share if the manufacturer overestimates while it prefers to share customer returns information if the manufacturer underestimates this information. If the manufacturer offers a buyback policy, we have the opposite results. We also discuss incentives to share the customer returns information and some of the issues that are raised in sharing this information.     

The impact of forecasting model selection on the value of information sharing in a supply chain

This paper presents a study on the impact of forecasting model selection on the value of information sharing in a supply chain with one capacitated supplier and multiple retailers. Using a computer simulation model, this study examines demand forecasting and inventory replenishment decisions by the retailers, and production decisions by the supplier under different demand patterns and capacity tightness. Analyses of the simulation output indicate that the selection of the forecasting model significantly influences the performance of the supply chain and the value of information sharing. Furthermore, demand patterns faced by retailers and capacity tightness faced by the supplier also significantly influence the value of information sharing. The result also shows that substantial cost savings can be realized through information sharing and thus help to motivate trading partners to share information in the supply chain. The findings can also help supply chain managers select suitable forecasting models to improve supply chain performance.     

Demand forecasting and sharing strategies to reduce fluctuations and the bullwhip effect in supply chains

Supply chain inventories are prone to fluctuations and instability. Known as the bullwhip effect, small variations in the end item demand create oscillations that amplify throughout the chain. By using system dynamics simulation, we investigate some of the structural sources of the bullwhip effect, and explore the effectiveness of information sharing to eliminate the undesirable fluctuations. Extensive simulation analysis is carried out on parameters of some standard ordering policies, as well as external demand and lead-time parameters. Simulation results show that (i) a major structural cause of the bullwhip effect is isolated demand forecasting performed at each echelon of the supply chain, and (ii) demand and forecast sharing strategies can significantly reduce the bullwhip effect, even though they cannot completely eliminate it. We specifically show how each policy is improved by demand and forecast sharing. Future research involves more advanced ordering and forecasting methods, modelling of other well-known sources of bullwhip, and more complex supply network structures.