Richard Ehrhardt

s, S Policies for a Dynamic Inventory Model with Stochastic Lead Times

This study analyzes a stochastic lead time inventory model under the assumptions that a replenishment orders do not cross in time and b the lead time distribution for a given order is independent of the number and sizes of outstanding orders. The study extends the existing literature on the finite-horizon version of the model and yields an intuitively appealing dynamic program that is nearly identical to one that would apply in a transformed model with all lead times fixed at zero. Hence, many results that have been derived for fixed lead time models generalize easily. Conditions for the optimality of myopic base-stock policies, and for the optimality of s, S policies are established for both finite and infinite planning horizons. The infinite-horizon model analysis is extended by adapting the fixed lead time results for the efficient computation of optimal and approximately optimal s, S policies.

A dynamic inventory model with random replenishment quantities

A periodic-review, random-demand inventory model is analyzed under the assumption that replenishment quantities are random fractions of the amounts ordered. Results of a previous study of a single-period model are generalized to form an easily computed heuristic adaptation of the (s, S) policy for use in this environment. The heuristic is based on the simple practice of scaling down pipeline inventories to estimate the inventory position, and scaling up the order quantity in anticipation of an average replenishment yield. Simulation experiments are used to estimate the most cost-efficient (s, S) policies and to estimate the performance of heuristic policies in environments where replenishment randomness ranges from mild (0-20% defectives) to moderate (0-50% defectives). The heuristic is shown to perform quite well, with expected total costs typically within a few percent of the best (s, S) costs. The results tend to support common practice in industry which is similar to the approach studied here. Although the heuristic is naive in the sense that it ignores the degree of randomness in the replenishment quantity, the simulation results support the speculation that unless the target service level is extremely high, the replenishment process must be extremely random for its variability to be a significant explicit factor in the selection of a practical, cost-effective policy.

Finished goods management for JIT production: new models for analysis

A firm is considered that manages its internal manufacturing operations according to a just-in-time system, but maintains an inventory of finished goods as a buffer against random demands from external customers. The finished goods inventory may be analysed by the methods of classical inventory theory in order to characterize the trade-off between inventory costs and schedule stability. A model is formulated in which the supply of finished goods is replenished by a small fixed quantity each time period. The size of the replenishment quantity may be revised only at pre-specified intervals. The single-interval problem is analysed, the cost-minimizing value of the replenishment quantity for a given revision interval length is computed, and the optimal cost is characterized as a function of the revision interval length. The dynamic problem is shown to be convex, with relatively easily computed optima. Finally, alternative formulations of the problem are described and suggestions made for further research.

Discovery Process in a B2B eMarketplace: A Semantic Matchmaking Approach

In the context of a customer-oriented value chain, companies must effectively address customers changing information needs during the process of acquiring a product or service to remain competitive. The ultimate goal of semantic matchmaking is to identify the best resources (supply) that fully meet the requirements (demand); however, such a goal is very difficult to achieve due to information distributed over disparate systems. To alleviate this problem in the context of eMarketplaces, the authors suggest an agent-enabled infomediary-based eMarketplace that enables semantic matchmaking. They extend and apply the exact, partial, and potential match algorithms developed in Di Noia et al. (2004) to show how partial and potential matches can become full matches. Specifically, the authors show how multi-criteria decision making techniques can be utilized to rank matches. They describe mechanisms for knowledge representation and exchange to allow partner organizations to seamlessly share information and knowledge to facilitate the discovery process in an eMarketplace context.

Indifference Functions For Price-Adjusted Acceptance Sampling

Abstract We present an approach for deriving indifference functions for use in implementing price-adjusted acceptance sampling plans. The key element underlying the application of PASS (Price Adjusted Single Sampling) methodology is an economic model of the ordering process that specifies the costs and benefits as functions of the amount of acceptable material received. The basic idea in deriving an indifference function is to adjust the price of the lot to compensate for departures from the planned amount of acceptable material received. Two simple scenarios are analyzed to illustrate the application of trie approach.