Skip to main content
Article thumbnail
Location of Repository

End-of-Life Inventory Decisions for Consumer Electronics Service Parts

By M. Pourakbar, J.B.G. Frenk and R. Dekker


We consider a consumer electronics (CE) manufacturer’s problem of controlling the inventoryof spare parts in the final phase of the service life cycle. The final phase starts when thepart production is terminated and continues until the last service contract or warranty periodexpires. Placing final orders for service parts is considered to be a popular tactic to satisfy demandduring this period and to mitigate the effect of part obsolescence at the end of the servicelife cycle. To satisfy demand for service in the final phase, previous research focuses on repairingdefective products by replacing the defective parts with properly functioning spare ones.However, for consumer electronic products there is a remarkable price erosion while repaircosts may stay steady over time. As a consequence, this introduces the idea that there mightbe a point in time at which the unit price of the product is lower than repair associated costs.Therefore, it would be more cost effective to adopt an alternative policy to meet demands forservice such as offering customers a replacement of the defective product with a new one orgiving a discount on the next generation of the product. This paper examines the cost trade-offsof implementing alternative policies for the repair policy and develops an exact formulation forthe expected total cost function. Based on this developed cost function we propose policies tosimultaneously find the optimal final order quantity and the time to switch from the repair toan alternative replacement policy. Numerical analysis of a real world case study sheds lightover the effectiveness and advantage of these policies in terms of cost reduction and also yieldsinsights into the quantitative importance of the various cost parameters.consumer electronics;end-of-life inventory control;service parts

OAI identifier:

Suggested articles


  1. (1972). and each sample path of the increasing process t ! N¤1(t) is of bounded variation we obtain by Theorem 15a of Widder
  2. (1970). Applied Probability Models with Optimization Applications, Holden-Day,
  3. (2000). Electronic part life cycle concepts and obsolescence forecasting,
  4. (1999). End-of-Life Service,
  5. (1998). End-of-life service: A case study,
  6. (1971). Forecasting and Scheduling for Past-Model Replacement Parts,
  7. (2008). Forecasting service parts demand for a discontinued product,
  8. (2003). Good buy? Delaying end-of-life purchases,
  9. (2000). How to set the holding cost rates in average cost inventory models with reverse logistics?.
  10. (1975). Introduction to Stochastic Processes,
  11. (2002). Inventory control of service parts in the final phase,
  12. (2006). Lot Sizing for Optimal Collection and Useof Remanufacturable Returns over a Finite Life-Cycle,
  13. (1981). Reduction of All-time requirements for Spare Parts,
  14. (1978). Since the random variable ¿ ^ n is a Ft-stopping time we obtain by Lemma 2 applied to the non-stationary Poisson arrival process of non-repairable parts with mean value function ¤1 = (1¡q)¤ and the optional sampling theorem (Lipster and Shiryayev
  15. (1978). Statistics of random processes.
  16. (1992). Stochastic Integration and Differential Equations,
  17. (1980). The All-Time Requirement of Spare Parts for Service After SalesTheoretical Analysis and Practical Results,
  18. (2008). The Final Order Problem for Repairable Spare Parts under Condemnation.
  19. (1998). The Final Order Problem,
  20. (1946). The Laplace Transform,

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.