When stopping production, the manufacturer has to decide on the lot size in the final production run to cover spare part demand during the end-of-life phase. This decision can be supported by forecasting how much demand is expected in the future. Forecasts can be obtained from the installed base of the product, that is, the number of products still in use. This type of information is relatively easily available in case of B2B maintenance contracts, but it is more complicated in B2C spare parts supply management. Consumer decisions on whether or not to repair a malfunctioning product depend on the specific product and spare part. Further, consumers may differ in their decisions, for example, for products with fast innovations and changing social trends. Consumer behavior can be accounted for by using appropriate types of installed base, for example, lifetime installed base for essential spare parts of expensive products with long lifecycle, and warranty installed base for products with short lifecycle. This paper proposes a set of installed base concepts with associated simple empirical forecasting methodologies that can be applied in practice for B2C spare parts supply management during the end-of-life phase of consumer products. The methodology is illustrated by case studies for eighteen spare parts of six products from a consumer electronics company. The research hypotheses on which installed base type performs best under which conditions are supported in the majority of cases, and forecasts obtained from installed base are substantially better than simple black box forecasts. Incorporating past sales via installed base therefore supports final production decisions to cover future consumer demand for spare parts.

Additional Metadata
Keywords Consumer goods, Decision support, End-of-life service, Installed base forecast, Spare parts
Persistent URL dx.doi.org/10.1016/j.cie.2016.11.014, hdl.handle.net/1765/94814
Journal Computers and Industrial Engineering
Citation
Kim, T.Y, Dekker, R, & Heij, C. (2017). Spare part demand forecasting for consumer goods using installed base information. Computers and Industrial Engineering, 103, 201–215. doi:10.1016/j.cie.2016.11.014