Integrated location and two-echelon inventory network design under uncertainty

In this paper, we propose a two-stage stochastic model to address the design of an integrated location and two-echelon inventory network under uncertainty. The central issue in this problem is to design and operate an effective and efficient multi-echelon supply chain distribution network and to minimize the expected system-wide cost of warehouse location, the allocation of warehouses to retailers, transportation, and two-echelon inventory over an infinite planning horizon. We structure this problem as a two-stage nonlinear discrete optimization problem. The first stage decides the warehouses to open and the second decides the warehouse-retailer assignments and two-echelon inventory replenishment strategies. Our modeling strategy incorporates various probable scenarios in the integrated multi-echelon supply chain distribution network design to identify solutions that minimize the first stage costs plus the expected second stage costs. The two-echelon inventory cost considerations result in a nonlinear objective which we linearize with an exponential number of variables. We solve the problem using column generation. Our computational study indicates that our approach can solve practical problems of moderate-size with up to twenty warehouse candidate locations, eighty retailers, and ten scenarios efficiently.     

Coordinating a decentralized supply chain with customer returns and price-dependent stochastic demand using a buyback policy

We investigate a decentralized supply chain that consists of a manufacturer and a retailer where the retailer simultaneously determines the retail price and order quantity while experiencing customer returns and price dependent stochastic demand. We propose an agreement between the manufacturer and the retailer that includes two buyback prices, one for unsold inventory and a second for customer returns, and show that this type of easy-to-implement agreement can achieve perfect supply chain coordination and be a win-win for both manufacturer and retailer when a complementary profit-sharing agreement is included.     

Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains

Operational forecasting in supply chain management supports a variety of short-term planning decisions, such as production scheduling and inventory management. In this respect, improving short-term forecast accuracy is a way to build a more agile supply chain for manufacturing companies. Demand forecasting often relies on well-established univariate forecasting methods to extrapolate historical demand. Collaboration across the supply chain, including information sharing, is suggested in the literature to improve upon the forecast accuracy of such traditional methods. In this paper, we review empirical studies considering the use of downstream information in demand forecasting and investigate different modeling approaches and forecasting methods to incorporate such data. Where empirical findings on information sharing mainly focus on point-of-sale data in two-level supply chains, this research empirically investigates the added value of using sell-through data originating from intermediaries, next to historical demand figures, in a multi-echelon supply chain. In a case study concerning a US drug manufacturer, we evaluate different methods to incorporate this data and consider both time series methods and machine learning techniques to produce multi-step ahead weekly forecasts. The results show that the manufacturer can effectively improve its short-term forecast accuracy by integrating sell-through data into the forecasting process and provide useful insights as to the different modeling approaches used. The conclusion holds for all forecast horizons considered, though it is most pronounced for one-step ahead forecasts. Therefore, our research provides a clear incentive for manufacturers to assess the forecast accuracy that can be achieved by using sell-through data.     

Supply chain coordination with controllable lead time and asymmetric information

This paper considers coordinated decisions in a decentralized supply chain consisting of a vendor and a buyer with controllable lead time. We analyze two supply chain inventory models. In the first model we assume the vendor has complete information about the buyer’s cost structure. By taking both the vendor and the buyer’s individual rationalities into consideration, a side payment coordination mechanism is designed to realize supply chain Pareto dominance. In the second model we consider a setting where the buyer possesses private cost information. We design the coordination mechanism by using principal-agent model to induce the buyer to report his true cost structure. The solution procedures are also developed to get the optimal solutions of these two models. The results of numerical examples show that shortening lead time to certain extent can reduce inventory cost and the coordination mechanisms designed for both symmetric and asymmetric information situations are effective.     

On the effect of centralisation on expected profits in a multi-location Newsboy problem

This paper is a multi-location, single-period, single-product inventory problem with an opportunity for centralisation. The decentralised model in which a separate inventory is kept at every location is compared to the centralised system in which the demands are satisfied from one central warehouse. The two systems are then compared when excess demands are penalised and lost, and in the more general case where a portion or all of the excess demand at a location may be reallocated among other locations with the remaining inventory. The revenue, salvage, cost, and penalty functions at each location are assumed to be identical. Expected profits are evaluated and conditions under which the centralised model outperforms the decentralised one are derived.     

Approximate analysis and optimization of batch ordering policies in capacitated supply chains

Devising manufacturing/distribution strategies for supply chains and determining their parameter values have been challenging problems. Linking production management to stock keeping processes improves the planning of the supply chain activities, including material management, culminating in improved customer service levels. In this study, we investigate a multi-echelon supply chain consisting of a supplier, a plant, a distribution center and a retailer. Material flow between stages is driven by reorder point/order quantity inventory control policies. We develop a model to analyze supply chain behavior using some key performance metrics such as the time averages of inventory and backorder levels, as well as customer service levels at each echelon. The model is validated against simulation, yielding good agreement of robust performance metrics. The metrics are then used within an optimization framework to design the supply chain so as to minimize expected total system costs. The outcome of the optimization framework specifies how to move inventory throughout the supply chain and how to set inventory control parameters, i.e., reorder levels and replenishment batch sizes.     

缺货情形下轴辐式库存的纵横向协作调拨

电子商务发展要求缩短供需端距离,便于提高供应链柔性运作水平,以更好的响应市场需求,这使得仓配环节的纵横向库存协作调拨面临严峻挑战。为此,本文针对零售行业的轴辐式二级库存系统,考虑可用库存及调拨能力限制,以缺货情形下的额外履约成本及销量损失最小化为优化目标,构建纵向及横向综合库存调拨模型,优化每日库存调拨方案。在此基础上,利用改进的三层编码形式的遗传算法对模型进行求解,并通过算例验证了模型及算法的有效性。     

Inventory,channel coordination and bargaining in a manufacturer-retailer system

Substantial research literature has been developed over the years on the subject of inventory. The more recent literature has examined the fundamental relationships between inventory control and price theory. A significant portion of this literature assumes the ultimate consumer demand as a constant and characterizes the relationship between a manufacturer and a retailer as a leader-follower problem. A primary assumption in these studies is that the manufacturer, as the leader, exerts almost complete control over the behavior of the retailer. However, in practice, the retailer does exert some control over the manufacturer. This paper develops a framework that integrates inventory control with constant demand and the economic relationship between consumer demand and retail price. Within this framework, the impact of order quantity, wholesale price and retail price on the behavior of both the manufacturer and the retailer is investigated. Furthermore, this paper explores the issues and conclusions that results from coordinating the relationship between the manufacturer and the retailer. Our analyses demonstrate that channel coordination can be achieved by utilizing well-known bargaining models. A numerical example is provided to illustrate our theoretical findings.     

Optimal inventory management for a retail chain with diverse store demands

Item demands at individual retail stores in a chain often differ significantly, due to local economic conditions, cultural and demographic differences and variations in store format. Accounting for these variations appropriately in inventory management can significantly improve retailers’ profits. For example, it is shown that having greater differences across the mean store demands leads to a higher expected profit, for a given inventory and total mean demand. If more than one inventory shipment per season is possible, the analysis becomes dynamic by including updated demand forecasts for each store and re-optimizing store inventory policies in midseason. In this paper, we formulate a dynamic stochastic optimization model that determines the total order size and the optimal inventory allocation across nonidentical stores in each period. A generalized Bayesian inference model is used for demands that are partially correlated across the stores and time periods. We also derive a normal approximation for the excess inventory from the previous period, which allows the dynamic programming formulation to be easily solved. We analyze the tradeoffs between obtaining information and profitability, e.g., stocking more stores in period 1 provides more demand information for period 2, but does not necessarily lead to higher total profit. Numerical analyses compare the expected profits of alternative supply chain strategies, as well as the sensitivity to different distributions of demand across the stores. This leads to novel strategic insights that arise from adopting inventory policies that vary by store type.     

突发事件下不对称信息供应链协调机制研究

针对一个由供应商和一个零售商构成的供应链,在零售商成本为私有信息条件下,假设产品的市场需求为零售价格的指数函数,研究如何协调供应链应对突发事件。首先,给出了对称信息下供应链协调模型;然后,研究了不对称信息下集权式与分权式供应链的协调机制;再次,在突发事件引起零售商成本分布函数扰动情况下,通过引入供应商由于可能需要调整生产计划而产生的偏差成本,研究了供应链的最优应对策略。 研究表明,供应链的最优生产计划、最优批发价格和最优零售价格均具有一定的鲁棒性,当突发事件造成零售商期望成本在一定范围内发生扰动时,三者可以保持不变,当零售商期望成本扰动超过一定范围内时,则需要对之加以调整,才能有效应对突发事件。最后,通过数值仿真验证了相关结论。     

Scheduling coordination problems in supply chain planning

Scheduling algorithms and their role in supply chain planning are topics that have been discussed in scheduling literature for many years. Based on examples and experience with commercial supply chain planning software, this paper presents background information about production planning and scheduling functionality in commercial supply chain planning software and interesting scheduling coordination problems in supply chain planning for researchers. We first provide an overview of different planning activities in supply chain planning, while taking into consideration existing functionalities that are available in commercial supply chain planning software. As a second step, we show three scheduling coordination problems in supply chain planning, namely the integration of production planning and production scheduling, the integration of sales order confirmation and production scheduling and the integration of VMI planning and production scheduling. We conclude this paper with a detailed discussion of an implementation of a supply chain planning solution at the tissue producer SCA Hygiene in Sweden. This paper expresses the authors opinion and does not represent an official statement from SAP.     

Coordination strategies in a three-echelon reverse supply chain for economic and social benefit

In addition to seeking profit maximization, reverse supply chains should further seek to maximize social benefit. Consequently, this paper investigates the coordination strategies among different parties in a three-echelon reverse supply chain consisting of a single collector, a single remanufacturer, and two retailers with complete information sharing. Additionally, this paper discusses four coordination strategies and performs a detailed comparative analysis on the optimal decisions of different models. Three numerical analyses are performed to show how the potential market demand of remanufactured products and the utilization ratio of used products affect the optimal total channel profit, recycle quantity and retail prices among four models in a reverse supply chain. The results indicate that optimal total channel profit and recycle quantity in a reverse supply chain are maximized in a centralized model. With the same potential market demand of remanufactured products and utilization ratio of used products, a centralized model maximizes the economic and social benefit compared to the other three models.     

The coordinating contracts for a fuzzy supply chain with effort and price dependent demand

This paper investigates the coordination of a two-echelon supply chain with fuzzy demand that is dependent on both retail price and sales effort. In contrast with the centralized and decentralized decision models, two coordinating models based on symmetric information and asymmetric information about retailer’s scale parameter are developed by game theory, and the corresponding analytical solutions are obtained. Theoretical analysis and numerical examples yield the maximal supply chain profits in two coordination situations are equal to that in the centralized situation and greater than that in the decentralized situation. Furthermore, under asymmetric information contract, the maximal expected profit obtained by the low-scale-level retailer is higher than that under symmetric information contract.     

非对称信息和模糊需求下的闭环供应链协调决策研究

在模糊需求和非对称销售价格信息下,研究了由单一制造商和零售商组成的两级闭环供应链系统的协调问题。通过建立不确定环境下的集中决策和收益共享-费用共担契约决策模型,分别得到了不同模型下闭环供应链系统的最优策略。并进一步通过算例分析了契约下供应链各方收益随模糊需求和价格的变化情况,以及该契约对整个系统决策效率的影响。结果表明:收益共享-费用共担契约能有效实现模糊闭环供应链系统的协调,实现闭环供应链系统决策的最优化;其次,需求和零售价格的不确定性程度越高,对零售商的最优订购量、供应链成员及系统利润的影响也越大。     

Demand forecast sharing in a dual-channel supply chain

We assess the benefits of sharing demand forecast information in a manufacturer–retailer supply chain, consisting of a traditional retail channel and a direct channel. The demand is a linear function of price with a Gaussian primary demand (i.e., zero-price market potential). Both the manufacturer and the retailer set their price based on their forecast of the primary demand. In this setting, we investigate the value of sharing demand forecasts. We analyze the ‘make-to-order’ scenario, in which prices are set before and production takes place after the primary demand is known, and the ‘make-to-stock’ scenario, in which production takes place and prices are set before the primary demand is known. We also compare the supply chain performance with and without the direct channel under some assumptions (production cost is zero, and each demand function has the same slope of price). We find that the direct channel has a negative impact on the retailer’s performance, and, under some conditions, the manufacturer and the whole supply chain are better off. Our research extends and complements prior research that has investigated only the inventory and replenishment-related benefits of information sharing.     

多因素扰动下同价双渠道供应链协调应对突发事件

一条双渠道供应链存在一个传统零售商和一个拥有电子渠道的制造商,制造商采用了跟随零售商线上线下同价的定价策略。通过建立市场需求模型,求出供应链的最优价格、生产数量,利用博弈论和合同理论的原理设计收益共享契约。研究表明,当供应链处于稳定状态时,收益共享契约可以用来实现同价双渠道供应链的协调。当突发事件导致市场规模和制造成本发生扰动时,同价双渠道供应链存在鲁棒性,而收益共享契约也能够很好地协调同价机制下的双渠道分权供应链,最后给出一个算例验证了结论。     

A two-stage supply chain coordination mechanism considering price sensitive demand and quantity discounts

This paper explores the coordination between a supplier and a buyer within a decentralized supply chain, through the use of quantity discounts in a game theoretic model. Within this model, the players face inventory and pricing decisions. We propose both cooperative and non-cooperative approaches considering that the product traded experiences a price sensitive demand. In the first case, we study the dynamics of the game from the supplier's side as the leader in the negotiation obtaining a Stackelberg equilibrium, and then show how the payoff of this player could still improve from this point. In the second case, a cooperative model is formulated, where decisions are taken simultaneously, emulating a centralized firm, showing the benefits of the cooperation between the players. We further formulate a pricing game, where the buyer is allowed to set different prices to the final customer as a reaction to the supplier's discount decisions. For the latter we investigate the difference between feasibility of implementing a retail discount given a current coordination mechanism and without it. Finally the implications of transportation costs are analyzed in the quantity discount schedule. Our findings are illustrated with a numerical example showing the difference in the players’ payoff in each case and the optimal strategies, comparing in each case our results with existing work.     

Coordination mechanism for a deteriorating item in a two-level supply chain system

The subject of the coordination between the suppliers and the buyers in multi-echelon inventory-distribution systems has been studied by many researchers. This paper considers a supply chain including a manufacturer and several buyers and assumes that the inventory items deteriorate over time and its inventory level decreases. In order to determine the order policies, coordination over the supply chain is achieved by scheduling the buyers’ delivery days and their coordination with the manufacturer’s production cycle. A mathematical model is developed and analyzed. To test the efficiency of the proposed model, two other models with the supposition of lot-sizing policies with common order cycle and independent deciding are also developed. In comparison to the other two models, the numerical results show that the synchronizing model of production and delivery cycles works better and has less total cost. In addition, in order to encourage the buyers in cooperation, a model on profit sharing is proposed that equitably shares the total savings with all the parties.     

A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment

This paper models supply chain (SC) uncertainties by fuzzy sets and develops a fuzzy linear programming model for tactical supply chain planning in a multi-echelon, multi-product, multi-level, multi-period supply chain network. In this approach, the demand, process and supply uncertainties are jointly considered. The aim is to centralize multi-node decisions simultaneously to achieve the best use of the available resources along the time horizon so that customer demands are met at a minimum cost. This proposal is tested by using data from a real automobile SC. The fuzzy model provides the decision maker (DM) with alternative decision plans with different degrees of satisfaction.     

随机弹性需求下供应链销售价格与提前期联合决策及协调机制

研究了市场需求随机且对销售价格及提前期敏感的条件下,供应链如何制定最优的销售价格、提前期以及库存因子使得利润最大化。首先,分析了集中决策与分散决策供应链的最优决策,发现集中决策与分散决策模式下的提前期与库存因子是相同的,但分散决策下的销售价格更高、期望利润更低,且分散决策与集中决策供应链的利润之比随着需求对价格的敏感程度的增大而增大。然后建立了收入共享与成本分担的协调机制,并分析了其最优决策。研究发现,协调决策机制可以有效地压缩提前期,并能同时使供应链达到帕累托改进,但不能达到完美协调,而且还发现协调决策下制定的最优销售价格比分散决策要低。最后通过数值计算对三种供应链决策模式下的绩效进行了比较分析,结果表明供应链利润随着需求方差增大而减小,提前期压缩程度随着材料成本承担比例增大而增大。上述结论可以为企业制定销售价格与提前期决策以及企业间的协调提供有益的指导。