On a method to improve your service BOMs within spare parts management

Joan Stip, Geert-Jan van Houtum (Corresponding author)

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Abstract

For advanced capital goods with high system availability requirements, it is common that all customers have service contracts with the Original Equipment Manufacturer (OEM). These service contracts include service level agreements on spare parts supply. The OEM operates a service network to support these logistic contracts. To determine spare parts stock levels the OEM needs to forecast spare parts demand. An important input for this forecast is the service Bill Of Material (BOM) per installed machine in the field, which specifies the applicable spare parts for a machine, and is usually derived from the machine configuration. Because of a growing installed base, increasing machine complexity, and an increasing number of machine variants, companies face a challenge in defining and maintaining machine configurations, which is why the service BOM is not always in line with the actual installed machine. An incorrect service BOM results in either a too low or a too high forecast for spare parts demand, and will result in under- or overstock.

In this paper we study the service BOMs at ASML, a large OEM in the semiconductor industry. We develop a method to generate alerts for possible errors. This method builds on multiple sources of machine information. Our method was tested in a pilot study, and found to be very effective. 95% of the generated alerts were correctly triggered and did result in actions that improved the service BOM. As a result, the method has been implemented by ASML. By this method, ASML reduced spare part non-availabilities by approximately 4-5 percent per year.
Original languageEnglish
Article number107466
Number of pages8
JournalInternational Journal of Production Economics
Volume221
Issue number1
Early online date19 Aug 2019
DOIs
Publication statusPublished - 5 Mar 2020

Keywords

  • Configuration management
  • Spare parts
  • inventory management
  • Forecasting
  • Data science
  • Inventory management

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