Installed Base Information Research Articles

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.

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 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.

Spare part demand forecasting for consumer goods using installed base information

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.

Understanding the Aspects of Collecting Installed Base Information in Manufacturing Context – Towards a Future Research Agenda

Previous studies in the manufacturing context have highlighted the important role of maintenance work, or field technicians, in manually collected installed base information (IBI). These studies have brought up various challenges related to accuracy and availability of this manually gathered IBI. However, the current research has not yet managed to identify underlying cultural and contextual aspects affecting this manual data collection work. Therefore this paper proposes a research agenda focusing on gaining deeper understanding of this data collection work by ethnographic research setting opening up new theoretical and practical insights to this part of modern maintenance work.

Human Role in Industrial Installed Base Information Gathering

Manual data gathering and data quality in industrial context have been, to some extent, discussed in the existing literature. The focus has been on companies’ own staff as data collectors. In the case of industrial companies, own staff might often be the only resource that can be harnessed to data gathering. However, this does not always hold true, and hence exploring also other alternatives is worthwhile. We have identified four data collector groups that can possibly be involved in the manual data gathering process, namely own staff, customers, equipment users and other parties. This paper explores the characteristics of the identified groups as potential data gatherers and provides our insights on how manual installed base information gathering utilizing multiple data collector groups could be approached.

Assessing the value of an installed base information management system: a simulation-based case study

this paper presents the results of a simulation study concerned with the assessment of the cost and the benefits associated with the implementation and operation of Installed Base Information Management systems. This study demonstrates that geo-referred discrete event simulation models can be successfully used to assess the value of these systems especially when the installed bases are big and geographically dispersed. This study is based on the real case of a product manufacturer providing field service to its installed base.

On the use of installed base information for spare parts logistics: A review of ideas and industry practice

Demand for spare parts is often difficult to forecast using historical data only. In this paper, we give an overview of installed based management and provide several ways in which installed base information can be used to support forecasting. We discuss cases where installed base information is used in forecasting at four companies and list the issues involved. Moreover, we review some models to illustrate the potential value of the installed base information and conclude that forecasts of spare parts demand and return can be made considerably more timely and accurate using installed base information.

Spare parts logistics and installed base information

Many of the challenges in spare parts logistics emerge due to the combination of large service networks, and sporadic/slow-moving demand. Customer heterogeneity and stringent service deadlines entail further challenges. Meanwhile, high revenue rates in service operations motivate companies to invest and optimize the service logistics function. An important aspect of the spare parts logistics function is its ability to support customer-specific requirements with respect to service deadlines. To support customer specific operations, many companies are actively maintaining and utilizing installed base data during forecasting, planning and execution stages. In this paper, we highlight the potential economic value of installed base data for spare parts logistics. We also discuss various data quality issues that are associated with the use of installed base data and show that planning performance depends on the quality dimensions.