Multi‐item service constrained ( s , S ) policies for spare parts logistics systems

Multi‐item service constrained ( s , S ) policies for spare parts logistics systems Many organizations providing service support for products or families of products must allocate inventory investment among the parts (or, identically, items) that make up those products or families. The allocation decision is crucial in today's competitive environment in which rapid response and low levels of inventory are both required for providing competitive levels of customer service in marketing a firm's products. This is particularly important in high‐tech industries, such as computers, military equipment, and consumer appliances. Such rapid response typically implies regional and local distribution points for final products and for spare parts for repairs. In this article we fix attention on a given product or product family at a single location. This single‐location problem is the basic building block of multi‐echelon inventory systems based on level‐by‐level decomposition, and our modeling approach is developed with this application in mind. The product consists of field‐replaceable units (i.e., parts), which are to be stocked as spares for field service repair. We assume that each part will be stocked at each location according to an (s, S) stocking policy. Moreover, we distinguish two classes of demand at each location: customer (or emergency) demand and normal replenishment demand from lower levels in the multiechelon system. The basic problem of interest is to determine the appropriate policies (si Si) for each part i in the product under consideration. We formulate an approximate cost function and service level constraint, and we present a greedy heuristic algorithm for solving the resulting approximate constrained optimization problem. We present experimental results showing that the heuristics developed have good cost performance relative to optimal. We also discuss extensions to the multiproduct component commonality problem. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Naval Research Logistics: An International Journal Wiley

Multi‐item service constrained ( s , S ) policies for spare parts logistics systems

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Publisher
Wiley
Copyright
Copyright © 1992 Wiley Periodicals, Inc., A Wiley Company
ISSN
0894-069X
eISSN
1520-6750
DOI
10.1002/1520-6750(199206)39:4<561::AID-NAV3220390409>3.0.CO;2-5
Publisher site
See Article on Publisher Site

Abstract

Many organizations providing service support for products or families of products must allocate inventory investment among the parts (or, identically, items) that make up those products or families. The allocation decision is crucial in today's competitive environment in which rapid response and low levels of inventory are both required for providing competitive levels of customer service in marketing a firm's products. This is particularly important in high‐tech industries, such as computers, military equipment, and consumer appliances. Such rapid response typically implies regional and local distribution points for final products and for spare parts for repairs. In this article we fix attention on a given product or product family at a single location. This single‐location problem is the basic building block of multi‐echelon inventory systems based on level‐by‐level decomposition, and our modeling approach is developed with this application in mind. The product consists of field‐replaceable units (i.e., parts), which are to be stocked as spares for field service repair. We assume that each part will be stocked at each location according to an (s, S) stocking policy. Moreover, we distinguish two classes of demand at each location: customer (or emergency) demand and normal replenishment demand from lower levels in the multiechelon system. The basic problem of interest is to determine the appropriate policies (si Si) for each part i in the product under consideration. We formulate an approximate cost function and service level constraint, and we present a greedy heuristic algorithm for solving the resulting approximate constrained optimization problem. We present experimental results showing that the heuristics developed have good cost performance relative to optimal. We also discuss extensions to the multiproduct component commonality problem.

Journal

Naval Research Logistics: An International JournalWiley

Published: Jun 1, 1992

References

  • Component Commonality in Assemble‐to‐Order Systems: Models and Properties
    Gerchak, Gerchak; Henig, Henig

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