Forecasting spare part demand with installed base information: A review

The classical spare part demand forecasting literature studies methods for forecasting intermittent demand. However, the majority of these methods do not consider the underlying demand-generating factors. The demand for spare parts originates from the replacement of parts in the installed base of machines, either 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 the future demand for spare parts. This paper reviews the literature on the use of such installed base information for spare part demand forecasting in order to asses (1) what type of installed base information can be useful; (2) how this information can be used to derive forecasts; (3) the value of using installed base information to improve forecasting; and (4) the limits of the existing methods. This serves as motivation for future research

Forecasting spare part demand using service maintenance information

We focus on the inventory management of critical spare parts that are used for service maintenance. These parts are commonly characterised by a large variety, an intermittent demand pattern and oftentimes a high shortage cost. Specialized service parts models focus on improving the availability of parts whilst limiting the investment in inventories. We develop a method to forecast the demand of these spare parts by linking it to the service maintenance policy. The demand of these parts originates from the maintenance activities that require their use, and is thus related to the number of machines in the field that make use of this part (known as the active installed base), in combination with the part's failure behaviour and the maintenance plan. We use this information to predict future demand. By tracking the active installed base and estimating the part failure behaviour, we provide a forecast of the distribution of the future spare parts demand during the upcoming lead time. This forecast is in turn used to manage inventories using a base-stock policy. Through a simulation experiment, we show that our method has the potential to improve the inventory-service trade-off, i.e., it can achieve a certain cycle service level with lower inventory levels compared to the traditional forecasting techniques for intermittent spare part demand. The magnitude of the improvement increases for spare parts that have a large installed base and for parts with longer replenishment lead times

Forecasting spare part demand with Installed Base information

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.status: publishe

Forecasting spare part demand with installed base information

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

The majority of the spare part literature studies intermittent demand forecasting. However, these methods typically do not consider the underlying demand generating factors. It seems reasonable to presume that the spare part demand can be explained by the service operations that require the use of these parts. This information can be helpful for forecasting purposes. In this article, we review the literature of such information from service operations, which we refer to as installed base information. The aim is twofold: First, by providing the academic community with a review on the literature concerning the use of installed base information (which is to the best of our knowledge non-existent), it can serve as a foundation for future academic research. Second, we offer practitioners insight on the potential of installed base information to improve their forecasts. This way we aim to stimulate appropriate data collection and implementation of installed base forecasting methods.nrpages: 35status: publishe

Forecasting spare part demand with installed base information: A review

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

The value of installed base information for spare part inventory control

This paper analyzes the value of different sources of installed base information for spare part demand forecasting and inventory control. The installed base is defined as the set of products (or machines) in use where the part is installed. Information on the number of products still in use, the age of the products, the age of their parts, as well as the part reliability may indicate when a part will fail and trigger a demand for a new spare part. The current literature is unclear which of this installed base information adds most value – and should thus be collected – for inventory control purposes. For this reason, we evaluate the inventory performance of eight methods that include different sets of installed base information in their demand forecasts. Using a comparative simulation study we identify that knowing the size of the active installed base is most valuable, especially when the installed base changes over time. We also find that when a failure-based prediction model is used, it is important to work with the part age itself, rather than the machine age. When one is not able to collect information on the part age, a logistic regression on the machine age might be a valuable alternative to a failure-based prediction model. Our findings may support the prioritization of data collection for spare part demand forecasting and inventory control