Value of supplier's capacity information in a two-echelon supply chain

In traditional supply chain models it is generally assumed that full information is available to all parties involved. Although this seems reasonable, there are cases where chain members are independent agents and possess different levels of information. In this study, we analyze a two-echelon, single supplier-multiple retailers supply chain in a single-period setting where the capacity of the supplier is limited. Embedding the lack of information about the capacity of the supplier in the model, we aim to analyze the reaction of the retailers, compare it with the full-information case, and assess the value of information and the effects of information asymmetry using game theoretic analysis. In our numerical studies, we conclude that the value of information is highly dependent on the capacity conditions and estimates of the retailers, and having information is not necessarily beneficial to the retailers

Value of supplier's capacity information in a two-echelon supply chain

Cataloged from PDF version of article.In traditional supply chain models it is generally assumed that full information is available to all parties involved. Although this seems reasonable, there are cases where chain members are independent agents and possess different levels of information. In this study, we analyze a two-echelon, single supplier-multiple retailers supply chain in a single-period setting where the capacity of the supplier is limited. Embedding the lack of information about the capacity of the supplier in the model, we aim to analyze the reaction of the retailers, compare it with the full-information case, and assess the value of information and the effects of information asymmetry using game theoretic analysis. In our numerical studies, we conclude that the value of information is highly dependent on the capacity conditions and estimates of the retailers, and having information is not necessarily beneficial to the retailers

Analysis of decentralized production-inventory system

"November 22, 1999."Includes bibliographical references (p. 30-32).René Caldentey, Lawrence M. Wein

Modeling inventory and responsiveness costs in a supply chain

Evaluation of supply chain performance is often complicated by the various interrelationships that exist within the network of suppliers. Currently many supply chain metrics cannot be analytically determined. Instead, metrics are derived from monitoring historical data, which is commonly referred to as Supply Chain Analytics. With these analytics it is possible to answer questions such as: What is the inventory cost distribution across the chain? What is the actual inventory turnover ratio? What is the cost of demand changes to individual suppliers? However, this approach requires a significant amount of historical data which must be continuously extracted from the associated Enterprise Resources Planning (ERP) system. In this dissertation models are developed for evaluating two Supply Chain metrics, as an alternative to the use of Supply Chain Analytics. First, inventory costs are estimated by supplier in a deterministic (Q , R, δ )2 supply chain. In this arrangement each part has two sequential reorder (R) inventory locations: (i) on the output side of the seller and (ii) on the input side of the buyer. In most cases the inventory policies are not synchronized and as a result the inventory behavior is not easily characterized and tends to exhibit long cycles. This is primarily due to the difference in production rates ( δ), production batch sizes, and the selection of supply order quantities (Q) for logistics convenience. The (Q , R, δ )2 model that is developed is an extension of the joint economic lot size (JELS) model first proposed by Banerjee (1986). JELS is derived as a compromise between the seller\u27s and the buyer\u27s economic lot sizes and therefore attempts to synchronize the supply policy. The (Q , R, δ )2 model is an approximation since it approximates the average inventory behavior across a range of supply cycles. Several supply relationships are considered by capturing the inventory behavior for each supplier in that relationship. For several case studies the joint inventory cost for a supply pair tends to be a stepped convex function. Second, a measure is derived for responsiveness of a supply chain as a function of the expected annual cost of making inventory and production capacity adjustments to account for a series of significant demand change events. Modern supply chains are expected to use changes in production capacity (as opposed to inventory) to react to significant demand changes. Significant demand changes are defined as shifts in market conditions that cannot be buffered by finished product inventory alone and require adjustments in the supply policy. These changes could involve a ± 25% change in the uniform demand level. The research question is what these costs are and how they are being shared within the network of suppliers. The developed measure is applicable in a multi-product supply chain and considers both demand correlations and resource commonality. Finally, the behavior of the two developed metrics is studied as a function of key supply chain parameters (e.g., reorder levels, batch sizes, and demand rate changes). A deterministic simulation model and program was developed for this purpose

Order Allocation Model Considering Transportation Alternatives and Lateral Transhipment

Intense competition among companies encourages them to provide the best quality of products in competitive price. It is important for company to manage supply chain properly in order to achieve that. Selecting the best reliable supplier is the key to reduce purchasing cost, increase customer satisfaction and improve the competitive ability. In this study, we develop an order allocation model in multi echelon environment which includes supplier, manufacturer, and retailer. We consider transportation alternatives for the shipment from supplier to manufacturer and also the shipment from manufacturer to retailer. This model allows lateral transshipment between retailers.  A Mixed Integer Linear Programming (MILP) is used to model the system. Sensitivity analysis is conducted at the end of the research. The result shows that the retailer demand, lead time, material variable price are sensitive to the objective function while the transportation costs from supplier to manufacturer, from manufacturer to retailers, and between retailers are not sensitive to the objective function. Retailer demand parameter is also sensitive to all decision variables. The transportation cost from supplier to manufacturer, material prices, and lead time are sensitive to the order allocation from manufacturer to supplier, while transportation cost from manufacturer to retailers and transportation cost between retailers are sensitive to the allocation of product sent from the manufacturer to retailers and the allocation of product sent between retailers

A Multi-Stage Supply Chain Network Optimization Using Genetic Algorithms

In today's global business market place, individual firms no longer compete as independent entities with unique brand names but as integral part of supply chain links. Key to success of any business is satisfying customer's demands on time which may result in cost reductions and increase in service level. In supply chain networks decisions are made with uncertainty about product's demands, costs, prices, lead times, quality in a competitive and collaborative environment. If poor decisions are made, they may lead to excess inventories that are costly or to insufficient inventory that cannot meet customer's demands. In this work we developed a bi-objective model that minimizes system wide costs of the supply chain and delays on delivery of products to distribution centers for a three echelon supply chain. Picking a set of Pareto front for multi-objective optimization problems require robust and efficient methods that can search an entire space. We used evolutionary algorithms to find the set of Pareto fronts which have proved to be effective in finding the entire set of Pareto fronts.Comment: 12 pages, 4 figure

Development of an information fractal to optimise inventory in the supply network

The aim of this research paper is to develop a new conceptual framework for an information fractal to optimise inventory including safety stock, cycle stock and prevent stock out at lowest logistics cost and further enhance integration within the network. The proposed framework consists of two levels; top and bottom level fractals. Fractals in the bottom level analyse demand, optimise safety stock and then transmit output to the top level fractal. Fractals in the top level investigate different replenishment frequencies to determine the optimum cycle stock for each fractal in the bottom level. The proposed conceptual framework and a hypothetical supply network are implemented and validated using mathematical modelling and Supply Chain GURU Simulation Software; in order to optimise inventory in the supply network during the demand test period. Experimental factorial design and statistical techniques (MANOVA) are used to generate and analyse the results

Virtual transshipments and revenue-sharing contracts in supply chain management

This dissertation presents the use of virtual transshipments and revenue-sharing contracts for inventory control in a small scale supply chain. The main objective is to maximize the total profit in a centralized supply chain or maximize the supply chain\u27s profit while keeping the individual components\u27 incentives in a decentralized supply chain. First, a centralized supply chain with two capacitated manufacturing plants situated in two distinct geographical regions is considered. Normally, demand in each region is mostly satisfied by the local plant. However, if the local plant is understocked while the remote one is overstocked, some of the newly generated demand can be assigned to be served by the more remote plant. The sources of the above virtual lateral transshipments, unlike the ones involved in real lateral transshipments, do not need to have nonnegative inventory levels throughout the transshipment process. Besides the theoretical analysis for this centralized supply chain, a computational study is conducted in detail to illustrate the ability of virtual lateral transshipments to reduce the total cost. The impacts of the parameters (unit holding cost, production cost, goodwill cost, etc.) on the cost savings that can be achieved by using the transshipment option are also assessed. Then, a supply chain with one supplier and one retailer is considered where a revenue-sharing contract is adopted. In this revenue-sharing contract, the retailer may obtain the product from the supplier at a less-than-production-cost price, but in exchange, the retailer must share the revenue with the supplier at a pre-set revenuesharing rate. The objective is to maximize the overall supply chain\u27s total profit while upholding the individual components\u27 incentives. A two-stage Stackelberg game is used for the analysis. In this game, one player is the leader and the other one is the follower. The analysis reveals that the party who keeps more than half of the revenue should also be the leader of the Stackelberg game. Furthermore, the adoption of a revenue-sharing contract in a supply chain with two suppliers and one retailer under a limited amount of available funds is analyzed. Using the revenue-sharing contract, the retailer pays a transfer cost rate of the production cost per unit when he obtains the items from the suppliers, and shares the revenue with the suppliers at a pre-set revenue-sharing rate. The two suppliers have different transfer cost rates and revenue-sharing rates. The retailer will earn more profit per unit with a higher transfer cost rate. How the retailer orders items from the two suppliers to maximize his expected profit under limited available funds is analyzed next. Conditions are shown under which the optimal way the retailer orders items from the two suppliers exists

Inventory optimization in a retail multi-echelon environment

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2008.Includes bibliographical references (leaf 66).The objective of the study is to find an optimal inventory distribution in a retail three-echelon environment, consisting of a supplier, a DC, and stores. An inventory model is built by replicating the echelons' periodic, order-up-to-level policies with all echelons' transactions integrated. Network carrying cost is set as an objective function, while the store target service level and the store's minimum order-up-to-levels are set as constraints. A heuristic approach, that combines the optimization and simulation methods, is used to find the optimal inventory distribution. The results show that the optimal network carrying cost can be achieved by having low inventory and low service level at the DC. In addition, the impact of the echelons' deviations from the optimal policies as well as the impact of the upstream echelon's service disruptions on the other echelons confirms the interrelation between the echelons in the network. The analyses also illustrate that high target service level can be accomplished by keeping high inventory at the stores and the DC.by Rintiya Arkaresvimun.M.Eng.in Logistic