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Data-driven preventive maintenance for a heterogeneous machine portfolio |
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| Institution: | 1. Luxembourg Centre for Logistics and Supply Chain Management, University of Luxembourg, Luxembourg;2. Faculty of Economics and Business, KU Leuven, Belgium;3. Faculty of Economics and Business, University of Amsterdam, the Netherlands;4. Technology & Operations Management Area, Vlerick Business School, Belgium;5. VCCM, Flanders Make, Belgium |
| Abstract: | We describe a data-driven approach to optimize periodic maintenance policies for a heterogeneous portfolio with different machine profiles. When insufficient data are available per profile to assess failure intensities and costs accurately, we pool the data of all machine profiles and evaluate the effect of (observable) machine characteristics by calibrating appropriate statistical models. This reduces maintenance costs compared to a stratified approach that splits the data into subsets per profile and a uniform approach that treats all profiles the same. |
| Keywords: | Preventive maintenance Data pooling Proportional hazards Small data |
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