Improve material availability by implementing age based spare parts forecasting – ASML



Company Name / Department ASML
Contact Person

Roy van Hugten

Location Veldhoven, Netherlands
Study programme(s)

Supply Chain Management

Community ESCF
Start Date Around February 2020
Housing arranged by company No


 500 Eur per month

Company Description

ASML develops, produces, markets, sells, and services advanced semiconductor equipment systems consisting of lithography related systems for memory and logic chipmakers. It also offers metrology and inspection systems, including optical metrology solutions to measure the quality of patterns on the wafers; and e-beam solutions to locate and analyze individual chip defects. In addition, the company provides computational lithography and software solutions to create applications that enhance the setup of the lithography system; and mature products and services that refurbish used lithography equipment and offers associated services.


Project Description

The Service Inventory Management department at ASML is responsible for the planning of spare parts and service tools in order to meet service level agreements with ASML’s customers. The forecast of spare parts demand is an important driver for these planning activities. The current method used for forecasting does not take into account the age of the part or the machine.

ASML thinks that using this information can lead to a higher forecast accuracy and thereby improve material availability for our customers.


Goals of the Project

A method to improve forecast accuracy by using part/machine age data, that fits in the current framework for forecasting and planning, making use of the available data within ASML.



  • Literature study to determine to determine which methods can be used to include part/machine age in spare parts forecasting
  • Conduct interviews within ASML to determine which methods are already considered and which business data is available to support these methods
  • A proposal for a method
  • Model and analysis to evaluate the effectiveness of the proposed method(s)
  • Guidelines for implementation of the method


    Essential Student Knowledge

    Strong capital goods background with passion for data analytics and programming


     More information: