• Forecasting spare part demand with installed base information

      Van der Auweraer, Sarah; Boute, Robert; Syntetos, Aris (2017)
      Service maintenance is commonly used to extend the lifetime of capital assets, such as manufacturing equipment or heavy infrastructure. When a service part, necessary to perform the maintenance action, is required but not immediately available, the incurred shortage costs may be substantial. For this reason, companies keep large stock buffers to deal with uncertain demand of these spare parts. Specialized service parts models should therefore focus on improving the availability of parts whilst limiting the investment in inventories. An important characteristic of most service parts is their intermittent demand pattern, for which specific forecasting techniques have been developed (see e.g., the review of Boylan and Syntetos, 2010). Many of these methods, however, rely on the time series of the historical demand and do not take into account the factors that generate the spare part demand: the failure behaviour of the components, the maintenance policy, etc. We refer to these factors as the Installed Base information. In our work we provide an overview of the papers which use installed base information for forecasting future service parts demand and we develop a new model which incorporates this information. Dekker et al. (2013) define installed base information as the information on the set of systems or products for which a company provides after sales services. It can include the number of installed and serviced machines (i.e. the size of the installed base), its evolution over time, the failure behaviour of the parts, part age information, and the part replacement probability. In addition to that, it is also possible to include information on the sudden and scheduled service needs of the products. Because the maintenance policy in use has an impact on the demand of spare parts, taking this information into account will improve the predictability of service parts demand. We aim to provide a new model which uses installed base information to predict future demand. This model combines information on the maintenance policy, the size of the installed base and its evolution over time, the part failure behaviour, and the replacement probability, in order to capture the full picture of the demand generating process.
    • Forecasting spare part demand with installed base information: A review

      Van der Auweraer, Sarah; Boute, Robert; Syntetos, Aris (2017)
      Service maintenance is commonly used to extend the lifetime of capital assets, such as manufacturing equipment or heavy infrastructure. When a service part, necessary to perform the maintenance action, is required but not immediately available, the incurred shortage costs may be substantial. For this reason, companies keep large stock buffers to deal with uncertain demand of these spare parts. Specialized service parts models should therefore focus on improving the availability of parts whilst limiting the investment in inventories. An important characteristic of most service parts is their intermittent demand pattern, for which specific forecasting techniques have been developed (see e.g., the review of Boylan and Syntetos, 2010). Many of these methods, however, rely on the time series of the historical demand and do not take into account the factors that generate the spare part demand: the failure behaviour of the components, the maintenance policy, etc. We refer to these factors as the Installed Base information. In our work we provide an overview of the papers which use installed base information for forecasting future service parts demand and we develop a new model which incorporates this information. Dekker et al. (2013) define installed base information as the information on the set of systems or products for which a company provides after sales services. It can include the number of installed and serviced machines (i.e. the size of the installed base), its evolution over time, the failure behaviour of the parts, part age information, and the part replacement probability. In addition to that, it is also possible to include information on the sudden and scheduled service needs of the products. Because the maintenance policy in use has an impact on the demand of spare parts, taking this information into account will improve the predictability of service parts demand. We aim to provide a new model which uses installed base information to predict future demand. This model combines information on the maintenance policy, the size of the installed base and its evolution over time, the part failure behaviour, and the replacement probability, in order to capture the full picture of the demand generating process.
    • Forecasting spare part demand with installed base information: a Review

      Van der Auweraer, Sarah; Boute, Robert; Syntetos, Aris (International Journal of Forecasting, 2019)
      The classical spare part demand forecasting literature studies methods to forecast intermittent demand. The majority of these methods do not consider the underlying demand generating factors. Demand for spare parts originates from the part replacements of the installed base of machines, which are either done preventively or upon breakdown of the part. This information from service operations, which we refer to as installed base information, can be used to forecast future spare part demand. In this paper we review the literature on the use of such installed base information for spare part demand forecasting to asses (1) what type of installed base information can be useful; (2) how this information can be used to derive forecasts; (3) what is the value of using installed base information to improve forecasting; and (4) what are the limits of the currently existing methods. The latter serve as motivation for future research.