Strategic safety stock placement in supply chain design with due-date based demand

We present a strategic safety stock placement model in supply chain design for assembly-type product with due-date based demand, where demand data are based on dates when company has to ship to customers rather than order receiving dates. We formulate multi-echelon stock placement by guaranteed-service model with demand propagation equations through backward explosion, where demand can be either stationary or nonstationary. The stock placement model is incorporated into network design problem and its optimization procedure is provided. We show effectiveness of the optimization procedure and other significant features of the model through numerical examples of a machinery product supply chain.     

A simple and accurate approximation for the order fill rates in lost-sales Assemble-to-Order systems

In this paper we consider an Assemble-to-Order system with multiple end-products. Demands for an end-product follow a Poisson process and each end-product requires a fixed set of components. We are interested in the order fill rates, i.e., the percentage of demands for which all requested components are available from stock. Requested components that are not in stock are supplied via an emergency shipment and the demand for these components is lost for the stockpoint under consideration. The component lead times are deterministic and may differ per component. The inventory of each component is controlled via a base stock policy. We show that the system decomposes into subsystems which can be analyzed independently. Each subsystem can be approximated by a subsystem with exponentially distributed lead times, for which an exact evaluation exists. For big subsystems, however, this method requires considerable computational effort. Therefore, we formulate a simple and accurate approximation for the order fill rates. Our approximation uses two estimates of which one generally gives an underestimation of the order fill rate and the other one an overestimation. A weighing factor is used to combine these two estimates into an approximate value. The approximation is shown to be accurate and requires little computational effort.     

基于收益共享的供应链中断协调研究

本文假定市场需求不确定和供给存在中断危机，同时考虑订货过剩损失和缺货成本，研究了由零售商、主供应商和备份供应商组成的供应链网络，构建了收益共享合同协调模型，通过相关参数的设定达到供应链协调。供应链协调时的供货量及整体利润均大于分散模型；备份供应商的供货量及利润随着中断危机的增大而增加，零售商利润也随之增加。     

Centralized and decentralized control polices for a two-stage stochastic supply chain with subcontracting

An analysis of control policies for a two-stage supply chain with subcontractors at each stage is presented when decisions at each stage concerning safety stocks, backorders, and subcontracting are made jointly or in a decentralized manner. The inventory/admission control policies considered are base stock, echelon base stock and partial backordering, and the objective is to maximize the mean profit rate of the system. The optimal control parameters are found by exhaustive search using Markov chains. From numerical examples it appears that the policies which manage jointly sales and production levels in each stage provide much higher overall (system) profits than decentralized policies, although the latter are individually more profitable for the second stage. In addition, partial backordering provides the system with an extra profit above those that result from the lost sales (no backordering) and complete backordering policies. Finally, a number of numerical results show the impact of variations in certain system parameters on the optimal control parameters and the corresponding profit.     

VMCI 下基于批发价决策权转移的供应链效率改进研究

本文在VMCI 模式下,考虑销售努力水平对市场需求的影响,研究批发价决策权由供应商转移至零售商的两级供应链效率改进问题。假定零售商决策批发价和销售努力水平,供应商决策寄售量,建立了零售商主导的Stackelberg 博弈模型,证明了集中决策下的最优解和分散决策下的均衡解均存在且唯一,集中决策下的寄售因子大于分散决策下的对应值。随后,引入收入共享与销售努力成本共担契约,论证了系统中寄售因子保持不变、批发价降低,且当契约参数满足一定条件时,可实现帕累托改进,实现供应链的效率改进。最后,分析了契约参数对于系统决策变量及利润的影响。     

Impact of RFID information-sharing strategies on a decentralized supply chain with reverse logistics operations

The integration of environmental practices in a supply chain has been study for the past few decades. However, most of the work relies on centralized decisions made by one player. Few papers address the complex dynamics of environmental decentralized supply chains and how these dynamics can affect environmental and economic outcomes. To study this problem, we consider a supply chain with a manufacturer and two different suppliers: a recycled-material and a raw-material supplier. The players make individual inventory decisions to satisfy demand and reduce cost. Further, this supply chain encompasses stochastic elements such as in demands, returns, and collection leadtimes. These decentralized decisions and random factors can cause underperforming results; therefore, new inventory models and technologies are needed to help companies increase coordination within these systems. We model the implementation of Radio Frequency Identification (RFID) in the supply chain to determine if real-time inventory monitoring and information sharing can help the system attain higher environmental benefits (more returns) and higher economic benefits (less cost). We study two scenarios through a simulation-based analysis: No RFID and RFID. Numerical studies show that environmental benefits are significantly increased with the attainment of more returns. However, although economic benefits are realized, they are less significant than the environmental benefits. Further regression and sensitivity analyses on the cost performance measures reveal that economic benefits depend on several drivers inside the system. We present managerial insights that illustrate what configurations within this complex system can lead to the achieving of environmental as well as economic benefits.     

Efficient versus Responsive Supply Chain Choice: An Empirical Examination of Influential Factors

Contemporary strategies in operations management suggest that successful firms align supply chain assets with product demand characteristics in order to exploit the profit potential of product lines fully. However, observation suggests that supply chain assets often are longer lived than product line decisions. This suggests that alignment between supply chain assets and demand characteristics is most likely to occur at the time of initial market entry. This article examines the association between product demand characteristics and the initial investment in a supply chain at the time of market entry. We characterize supply chains as responsive or efficient. A responsive supply chain is distinguished by short production lead‐times, low set‐up costs, and small batch sizes that allow the responsive firm to adapt quickly to market demand, but often at a higher unit cost. An efficient supply chain is distinguished by longer production lead‐times, high set‐up costs, and larger batch sizes that allow the efficient firm to produce at a low unit cost, but often at the expense of market responsiveness. We hypothesize that a firm's choice of responsive supply chain will be associated with lower industry growth rates, higher contribution margins, higher product variety, and higher demand or technological uncertainty. We further hypothesize that interactions among these variables either can reinforce or can temper the main effects. We report that lower industry growth rates are associated with responsive market entry, but this effect is offset if growth occurs during periods of high variety and high demand uncertainty. We report that higher contribution margins are associated with responsive market entry and that this effect is more pronounced when occurring with periods of high variety. Finally, we report that responsive market entry also is correlated positively with higher technological demand uncertainty. These results are found using data from the North American mountain bike industry.     

Distribution contracts to support optimal inventory holdings under demand uncertainty

When suppliers produce products for which demand is uncertain, they face a problem of inducing downstream distributors to stock inventory levels that the suppliers prefer. This paper considers a wide array of alternative supply contracts, each of which consists of a mixture of constant per-unit wholesale prices, buy-back arrangements, and post sale payments contingent on sales made, such as revenue sharing or buybacks. We show that linear supply contracts specifying any combination of two of these three instruments can implement the vertical integrated outcome for a monopoly, thereby generating the supplier's preferred inventory configuration and price distribution. We extend our results to differentiated product oligopoly, demonstrating that each supplier obtains its preferred inventory configuration and price distribution, given the choices of its rival. Distributors choose optimal inventories from the suppliers' standpoint, even if suppliers do not know the distribution of demand uncertainty, and, given the perfect competition among distributors, all profits in the supply chain are captured by suppliers. Thus, suppliers are able to deal with demand uncertainty with remarkably little information about demand, and without the need to control dealer actions in detail. In particular, suppliers need not specify either dealer inventories or resale prices, but instead encourage distributors to order based on information in their possession and to set prices that generate desirable resale price dispersion.     

Coordination of a supply chain with one manufacturer and multiple competing retailers under simultaneous demand and cost disruptions

This paper develops a coordination mechanism for a supply chain consisting of one manufacturer and n Cournot competing retailers when the production cost and demands are simultaneously disrupted. This differs from traditional supply chain coordination models under a static case and the case with only demand or cost disruption. The coordination mechanism with revenue sharing is considered, and the effects of production cost and demand disruptions on revenue sharing contract are discussed to investigate the optimal strategies of players with disruptions. The penalty cost is introduced explicitly to obtain the production deviation cost caused by the disruptions. In this study, it is obtained that the coordination contract considering the production deviation cost differs from that without disruption. Besides that, the disruptions may affect the order quantities, wholesale prices as well as revenue sharing contract. Then, the optimal strategies for different disruption levels under the centralized decision-making mode are proposed. Concerning the decentralized mode, the improved revenue sharing contract can be used to coordinate the decentralized decision-making supply chain effectively. Finally, the theoretical results are illustrated by conducting some numerical examples.     

Optimizing service-level and relevant cost for a stochastic multi-item cyclic production system

A multi-item make-to-order production system in a stochastic environment is analyzed. Assuming a common cycle production approach, the impact of safety stock, cycle time, demand, processing time and setup time on service-level and total relevant cost (holding, setup and backorder cost) is determined. To illustrate this relationship a trajectory for the service-level with respect to the relevant cost (holding and setup) is presented. Furthermore algorithms to calculate the cycle time which leads to maximum service-level at constant safety stock and to calculate the pair cycle time and safety stock which minimize total relevant cost are introduced.     

Timing production in LP models in a rolling schedule

We consider an arbitrary supply chain structure under demand uncertainty. We look at a setting where the supply chain planning is executed periodically and where the demand of end items in subsequent periods is stochastic. In contrast to other linear programming-based approaches, we assume constant planned lead times. In this paper we discuss the timing of production during the planned lead times of items. If production starts immediately, the work-in-process inventory costs are higher, but producing later will result in higher safety stocks. We look at the influence of the demand variation, the planned lead time of the items, the utilization rate of resources, and the added value when merging items, on the inventory costs. The results indicate that, for higher utilization rates, producing early is more suitable.     

A stochastic inventory model of dual sourced supply chain with lead-time reduction

Recently, the amount of literature on analyzing the effects of investment strategies to control lead times has been increasing. Issues on investment to reduce lead times are important because variability in lead time between successive stages often has a great effect on the coordination of supply chain.This paper considers dual-sourcing models with stochastic lead times and constant unit demand in which lead times are reduced at a cost that can be viewed as an investment. In order to obtain an analytically tractable model, the distributions of lead times for two suppliers are assumed to be exponential. In our two-supplier model, we will concentrate on lead times as random variables, which are made endogenous in the stochastic lead-time model through “expediting factors”, the constants of proportionality between the expedited lead times and ordinary lead times, as was done by Bookbinder and Çakanyildirim (Eur. J. Oper. Res. 115 (1999) 300).Firstly, we determine the order quantity (Q), reorder point level (r), and order splitting proportion (k₁) in the case of no lead-time reduction. Using the (Q,r) found, we decide the expediting factors and new k₁ in the case of lead-time reduction. We compare the expected total cost per unit time for the two models and investigate savings. Additionally, sensitivity analyses are conducted with respect to the various cost parameter ranges, and remarks are made for further research.     

A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty

Manufacturers need to satisfy consumer demands in order to compete in the real world. This requires the efficient operation of a supply chain planning. In this research we consider a supply chain including multiple suppliers, multiple manufacturers and multiple customers, addressing a multi-site, multi-period, multi-product aggregate production planning (APP) problem under uncertainty. First a new robust multi-objective mixed integer nonlinear programming model is proposed to deal with APP considering two conflicting objectives simultaneously, as well as the uncertain nature of the supply chain. Cost parameters of the supply chain and demand fluctuations are subject to uncertainty. Then the problem transformed into a multi-objective linear one. The first objective function aims to minimize total losses of supply chain including production cost, hiring, firing and training cost, raw material and end product inventory holding cost, transportation and shortage cost. The second objective function considers customer satisfaction through minimizing sum of the maximum amount of shortages among the customers’ zones in all periods. Working levels, workers productivity, overtime, subcontracting, storage capacity and lead time are also considered. Finally, the proposed model is solved as a single-objective mixed integer programming model applying the LP-metrics method. The practicability of the proposed model is demonstrated through its application in solving an APP problem in an industrial case study. The results indicate that the proposed model can provide a promising approach to fulfill an efficient production planning in a supply chain.     

Structuring and contracting in competing supply chains

This paper considers two competing supply chains, each with multiple upstream suppliers producing complementary products and selling to a single buyer (e.g., assembler or retailer), who then sells the finished assembled product to a market that involves both demand uncertainty and competition. Our main research questions focus on what supply chain structure (integration vs. decentralization) and which contracting strategy a business should choose. We find that supply chains that decentralize perform better under strong market competition (i.e., high degree of product substitution between supply chains). However, when a large number of suppliers exist, supply chains that integrate perform better. When decentralized structures are used for both supply chains, a consignment with revenue sharing contract generally outperforms a wholesale price contract from the downstream retailer's point of view. Interestingly, for a supplier, a wholesale price contract, which pushes all demand risks to the downstream retailer, might not be preferred. For the entire supply chain, one contract strategy can outperform another depending on the degree of competition, the cost share of the buyer, and the number of suppliers.     

Integration of financial statement analysis in the optimal design of supply chain networks under demand uncertainty

Models that aim to optimize the design of supply chain networks have become a mainstream in the supply chain literature. This paper aims to fill a gap in the literature by introducing a mathematical model that integrates financial considerations with supply chain design decisions under demand uncertainty. The proposed Mixed-Integer Linear Programming (MILP) problem enchases financial statement analysis through financial ratios and demand uncertainty through scenario analysis. The applicability of the model is illustrated by using a case study along with a sensitivity analysis on financial parameters expressing the business environment. The model could be used as an effective and convenient strategic decision tool by supply chain managers.     

Integrated inventory models with controllable lead time and backorder discount considerations

Many companies use time as a means of differentiating themselves in the marketplace. In many literatures, the controllable lead time is regarded as a decision variable and decomposed into several components, each having a crashing cost function for the respective reduced lead time. When an item is out of stock, the loyal, patient and captive customers will wait until the outstanding orders arrive and are served from them. To compensate for the inconvenience of backordering and to secure orders, the supplier may offer a price discount on the stockout item. In this paper, an integrated inventory system in which shortage is allowed and both lead time and backordering are negotiable is investigated. The lead time crashing cost is represented as a function of reduced lead time and the quantities in the orders. There are two inventory models proposed in the paper, one with normally distributed demand, and another with generally distributed demand.     

A discounted cash flow approach to the base stock inventory model

A widespread approach to inventory modelling is to associate costs with measures of system performance and determine the control policy which minimises the long run average cost per unit time. This type of approach ignores the impact of a control policy on the timing of the cash flows associated with payments to suppliers and revenue streams from customers. The approach in this paper is to concentrate on cash flows and determine the control policy which maximises the expected net present value of the cash flows associated with a demand, valued at the time when that demand occurs. There is a Poisson demand process, a fixed lead time, unsatisfied demand is backordered and the system is controlled using a base stock policy. A solution procedure is given and a comparison is made with an equivalent simple interest model and with the standard cost model with linear holding and shortage costs.     

A serial supply chain of newsvendor problem with safety stocks under complete and partial information sharing

We consider a supply chain where multiple members are serially connected. The decision is to determine the ordering quantity of a member to the next upstream member in the supply chain. The basic cost model is similar to the newsvendor problem with additional consideration to safety stock. This paper presents optimal approaches for coordination of the supply chain under both complete and partial information sharing in order to maximize the total expected benefit. For complete information sharing we develop an optimal coordination algorithm. For partial information sharing, we propose an optimal coordination algorithm based on the Alternating Direction Method and the Diagonal Quadratic Approximation Method. A numerical example is discussed to show the optimal convergence of ordering quantities and discuss the properties of the proposed algorithms.     

A hierarchical process industry production–distribution planning model

Global and multinational companies are subject to government regulations in addition to other international uncertainties due to operation in diverse geographic locations. Such government regulations often affect the cost of raw materials adversely which in turn creates adverse impact on product cost and forces the decision makers to re-evaluate current production–distribution plan. This paper presents an integrated supply chain model for simultaneous strategic and operational planning of a strategic business unit (SBU) in a global supply chain affected by government regulations. The model incorporates impact of changes in cost of inputs on expected product cost and solves for an optimal strategic and operational plan for the entire supply chain. In addition, the model includes exchange rates, border crossing costs and solves a multi-period model with due consideration of uncertainties in demand and transportation time.     

An integrated production–distribution model for the dynamic location and allocation problem with safety stock optimization

The design and management of a multi-stage production–distribution system is one of the most critical problems in logistics and in facility management. Companies need to be able to evaluate and design different configurations for their logistic networks as quickly as possible. This means coordinating the entire supply chain effectively in order to minimize costs and simultaneously optimize facilities location, the allocation of customer demand to production/distribution centers, the inbound and outbound transportation activities, the product flows between production and/or warehousing facilities, the reverse logistics activities, etc.Full optimization of supply chain is achieved by integrating strategic, tactical, and operational decision-making in terms of the design, management, and control of activities. The cost-based and mixed-integer programming model presented in this study has been developed to support management in making the following decisions: the number of facilities (e.g. warehousing systems, distribution centers), the choice of their locations and the assignment of customer demand to them, and also incorporate tactical decisions regarding inventory control, production rates, and service-level determination in a stochastic environment. This paper presents an original model for the dynamic location–allocation problem with control of customer service level and safety stock optimization. An experimental analysis identifies the most critical factors affecting the logistics cost, and to finish, an industrial application is illustrated demonstrating the effectiveness of the proposed optimization approach.