Inventory Control Models with Motivational Policies


Book Description

This book examines the different motivational policies used for inventory management. In many competitive markets, sellers use motivational policies to encourage the customers to buy more and these kinds of strategies are used as competitive tools. This book brings together all the motivational policies for lot sizing decisions and offers a useful guide for inventory control. Each chapter applies deterministic inventory models such as economic order quantity (EOQ) and economic production quantity (EPQ), but also stochastic models for the motivational policy covered. The book begins exploring quantity discounts such as all-unit and incremental discounts. It then looks at delayed payment or trade credit policies that are applied by many suppliers and/or wholesalers to increase their sales. The motivational policies covered in the following chapters are dedicated to advance payment/prepayment schemes and also special sales offered by retailers to increase sales levels or decrease the inventory level. Finally the book concludes with a review of announced price increases, which persuades customers to buy a product at the current price, rather than paying more for it in the future. Inventory Control Models with Motivational Policies should be useful for professionals working on supply chains, but also researchers in operations research and inventory management.




A Note on the Applicability of Federgruen and Zheng S Algorithm for Computing Optimal (R,Q) Inventory Policies


Book Description

Care must be taken in extending the formulation and solution methodology of Federgruen and Zheng (1992) to include a stockout cost of a different dimensionality: $/unit, rather than $/unit/year. Federgruen and Zheng formulate this extended model for Poisson demand. We modify their formulation to allow for a much broader class of demand processes, and show via counter examples that the elegant solution methodology developed in Federgruen and Zheng cannot be brought to bear upon the more general problems.




An Empirical Comparison of Two Approximately Optimal (s, S) Inventory Policies


Book Description

In this paper, we present an empirical comparison of two approximately optimal rules for computing (s, S) policies for single items under periodic review with a setup cost, linear holding and storage costs, fixed replenishment lead time, and backlogging of unfilled demand. The Naddor Approximation, originally designed for holding and shortage costs based on period-average inventory levels, is transformed for use in a system where these costs are based on period-end inventory. It is compared empirically with the Power Approximation and with exactly optimal policies in a system of independent inventory items having 576 distinct parameter settings. The Power Approximation yields lower expected total costs than the Naddor Approximation in 456 of the 576 cases. The cost differences tend to be rather small, however. When total costs are aggregated over the entire system, the Power Approximation is 1.65% above optimal, as compared with 2.34% for the Naddor Approximation. Significant differences appear only when components of total cost are examined. The robustness of the policies is examined by analyzing their performance when statistical estimates are used in place of the actual mean and variance of demand. We also discuss the sensitivity to parameter settings of the performance of the two rules. (Author).







Optimal Inventory Policies when the Demand Distribution is not Known


Book Description

This paper analyzes the stochastic inventory control problem when the demand distribution is not known. In contrast to previous Bayesian inventory models, this paper adopts a non-parametric Bayesian approach in which the firm’s prior information is characterized by a Dirichlet process prior. This provides considerable freedom in the specification of prior information about demand and it permits the accommodation of fixed order costs. As information on the demand distribution accumulates, optimal history-dependent (s,S) rules are shown to converge to an (s,S) rule that is optimal when the underlying demand distribution is known.