We investigate a two-stage serial supply chain with stationary stochastic demand and fixed transportation times. Inventory holding costs are charged at each stage, and each stage may incur a consumer backorder penalty cost, e.g. the upper stage (the supplier) may dislike backorders at the lower stage (the retailer). We consider two games. In both, the stages independently choose base stock policies to minimize their costs. The games differ in how the firms track their inventory levels (in one, the firms are committed to tracking echelon inventory; in the other they track local inventory). We compare the policies chosen under this competitive regime to those selected to minimize total supply chain costs, i.e., the optimal solution. We show that the games (nearly always) have a unique Nash equilibrium, and it differs from the optimal solution. Hence, competition reduces efficiency. Furthermore, the two games ’ equilibria are different, so the tracking method influences strategic behavior. We show that the system optimal solution can be achieved as a Nash equilibrium using simple linear transfer payments. The value of cooperation is context specific: In some settings competition increases total cost by only a fraction of a percent, whereas in other settings the cost increase is enormous. We also discuss Stackelberg equilibria.

In this paper, we consider an adaptive base-stock policy for a single-item inventory system, where the demand process is non-stationary. In particular, the demand process is an integrated moving average process of order (0, 1, 1), for which an exponential-weighted moving average provides the optimal forecast. For the assumed control policy we characterize the inventory random variable and use this to find the safety stock requirements for the system. From this characterization, we see that the required inventory, both in absolute terms and as it depends on the replenishment lead-time, behaves much differently for this case of non-stationary demand compared with stationary demand. We then show how the single-item model extends to a multistage, or supply-chain context; in particular we see that the demand process for the upstream stage is not only non-stationary but also more variable than that for the downstream stage. We also show that for this model there is no value from letting the upstream stages see the exogenous demand. The paper concludes with some observations about the practical implications of this work.

this paper is to review the research about the latter.

Fund. Nelson Repenning graciously permitted us to administer the tasks in his introductory system dynamics class. We also thank Jim Doyle, Michael Radzicki, Terry Tivnan the referees for helpful comments. Christopher Hunter assisted with data entry.

In this study, we model an electronic supply chain that is managed by artificial agents. We investigate whether artificial agents do better than humans when playing the MIT Beer Game. Can the artificial agents mitigate the Bullwhip effect or discover good and effective business strategies? In particular, we study the following questions: Can agents learn reasonably good policies in the face of deterministic demand with fixed lead-time? Can agents cope reasonably well in the face of stochastic demand with stochastic lead-time? Can agents learn and adapt in various contexts to play the game? Can agents cooperate across the supply chain? 2 1.

This study explores the systematic variation in inventory record inaccuracy (IRI) observed both within and across stores. Traditional inventory models, with a few exceptions, do not account for the existence of IRI and those that do treat record inaccuracy as random. Examining nearly 370,000 inventory records from 37 stores of one retailer, we found 65 % to be inaccurate. That is, the recorded inventory quantity of an item fails to match the quantity found in the store. We identify factors associated with this inaccuracy that are stock keeping unit- (SKU) and store-specific. SKU-specific factors such as item cost, selling quantity, and method of distribution account for the observed variation in IRI within stores. Store-specific factors such as the density and variety of inventory observed at each store account for the variation in IRI across stores. 1

Abstract not found

In this paper, we focus on the supply chain as a multi-agent system and we propose a new coordination technique to reduce the fluctuations of orders placed by each company to its suppliers in such a supply chain. This problem of amplification of the demand variability is called the bullwhip effect. To reduce such a bullwhip effect, we propose a technique based on tokens to achieve a decentralized coordination. Precisely, classical orders manage the demand itself whereas tokens manage effects on company inventory due to variations of this demand. Finally, the proposed approach is validated by the Wood Supply Game, which is a supply chain model used to make players aware of the bullwhip effect. We experimentally verify that our coordination technique leads to less variable orders (i.e. the standard deviation of orders is reduced) while inventory levels are not excessively high but sufficient to avoid backorders.

Holland's Adaptation in Natural and Artificial Systems largely dealt with how systems, comprised of many self-interested entities, can and should adapt as a whole. This seminal book led to the last 25 years of work in genetic algorithms (GAs), and related forms of evolutionary computation (EC). In recent years, the expansions of the Internet, other telecommunications technologies, and other large scale networks, have led to a world where large numbers of semi-autonomous software entities (i.e., agents) will be interacting in an open, universal system. This development cast the importance of Holland's legacy in a new light. This paper argues that Holland's fundamental arguments, and the years of developments that have followed, have a direct impact on systems of general network agents, regardless of whether they explicitly exploit EC. However, it also argues that the techniques and theories of EC cannot be directly transferred to the world of general (rather than EC-specific) agents, without examination of e#ects that are embodied in general software agents. This paper introduces a framework for EC interchanges between general-purpose software agents. Preliminary results are shown that illustrate the EC e#ects of asynchronous actions of agents within this framework.

We describe the development and implementation of a Java-based, multi player, multi-group, distributed simulation and game. The Supply Chain game described here is based on the famous "Beer Distribution Game" (Sterman, 1989). Group, synchronous, distributed Java-based applications are both feasible and useful for gaming and management simulation processes in both learning and research capacities. Early results from actual running of the "Supply Chain game" are in line with data reported by Sterman (1989, 1992a,b). The Supply Chain game can be used as a teaching and research tool, generally in organizational processes and especially for e-commerce applications.