Spare Parts Demand Forecasting in Maintenance, Repair & Overhaul

authored by: Torben Lucht, Volodymyr Alieksieiev, Tim Kämpfer, Peter Nyhuis
Abstract: Despite a high degree of uncertainty about the scope of future orders and the corresponding capacity and material demands, Maintenance, Repair & Overhaul (MRO) service providers face high expectations regarding due date reliability by their customers. To meet these requirements while at the same time keeping delivery times short, the availability of the required spare parts or pool parts is an essential success factor. As these cannot be kept in stock in large quantities due to their high monetary value, reliable spare parts demand forecasts are of vital importance for the profitability of MRO service providers. As a result of a high degree of information uncertainty and the mostly lumpy demand patterns, conventional time-based and statistical methods do not show sufficient forecasting quality for application in the MRO industry. Data-based approaches incorporating machine learning methods offer promising capabilities to achieve improved predictive accuracy but still need to be adequately linked to production planning and control to realize their full potential. This paper first analyses potential approaches to spare parts demand forecasting in the MRO industry, focusing on forecast accuracy and potential for integration into material and production planning. Based on this, a classification of demand forecasting approaches is presented and an approach for order-based material demand forecasting with two-step feature selection is proposed. Finally, the presented approach is applied on a real dataset provided by an MRO service provider.
Organisation(s): Institute of Production Systems and Logistics
Type: Conference contribution
Pages: 525-534
No. of pages: 10
Publication date: 2022
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Industrial and Manufacturing Engineering, Mechanical Engineering, Management of Technology and Innovation, Strategy and Management
Sustainable Development Goals: SDG 9 - Industry, Innovation, and Infrastructure
Electronic version(s): https://doi.org/10.15488/12179 (Access: Open)