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This paper aims to substantiate the need for additional research into a more holistic and multidisciplinary approach to managing the supporting supply chains that may also capture contextual information, also pointing out emerging avenues for further scholarly contributions.

The supply chain is viewed from a spare part consumer as well as from a supplier perspective. Key to the discussion is an accurate description of the maintenance demand pattern (MDP) known at the consumer's side as a valuable information source for the entire supply chain.

Solving the spare parts supply chain puzzle exceeds the realms of a single scientific discipline and involves hard and soft sciences. Besides, extending on the quantitative modelling aspects of MDPs, soft modelling and analysis is needed to define cooperative settings in which the supply chain parties can operate effectively.

In this paper, the authors argue for the sharing of the appropriately balanced combination of quantitative and qualitative information that is currently hidden, or exists in isolation, within supply chains. Debatably, such information sharing may potentially generate substantial benefits for all “players” within a given supply chain.

This contribution is unique in the sense that it provides a most accurate characterization of MDPs based on the proven maintenance concept design theory. In addition, the supply chain problem is analysed in a realistic context, with an open and broad mindset rather than approaching this issue from a single hard science perspective.

Ensuring high on-shelf availability at low inventory costs remains an important challenge in retailing. Inaccurate inventory records, i.e. discrepancies between the stock records displayed in the inventory system and the stock quantity actually found in the retail store, have been identified as one of the most important drivers of retail stockouts in the past. The purpose of this work is to investigate the causes of positive inventory discrepancies in retailing, i.e. where there is more inventory on-hand than identified by the inventory system.

Based on input from retailers, the authors develop a simulation model of a retail store that considers various error-prone processes and study in a full factorial test design how the different operational errors may drive inventory discrepancies, paying special attention to the sources of positive inventory record inaccuracies.

This makes it possible to gain insights into the process parameters retailers need to adjust to avoid inventory records becoming inaccurate. In addition, the authors analyze how positive inventory discrepancies relate to stockouts to further our understanding of the role so-called phantom products may play in a retailing context.

While negative inventory discrepancies (where the stock that is available in the store is less than what the system displays) and their sources (theft, shrinkage, etc.) have been discussed quite frequently in the literature, the causes of positive inventory discrepancies (where the available inventory exceeds the system inventory) have received much less attention.

Spare parts have become ubiquitous in modern societies and managing their requirements is an important and challenging task with tremendous cost implications for the organisations that are holding relevant inventories. An important operational issue involved in the management of spare parts is that of categorising the relevant stock keeping units (SKUs) in order to facilitate decision‐making with respect to forecasting and stock control and to enable managers to focus their attention on the most “important” SKUs. This issue has been overlooked in the academic literature although it constitutes a significant opportunity for increasing spare parts availability and/or reducing inventory costs. Moreover, and despite the huge literature developed since the 1970s on issues related to stock control for spare parts, very few studies actually consider empirical solution implementation and with few exceptions, case studies are lacking. Such a case study is described in this paper, the purpose of which is to offer insight into relevant business practices.

The issue of demand categorisation (including forecasting and stock control) for spare parts management is addressed and details reported of a project undertaken by an international business machine manufacturer for the purpose of improving its European spare parts logistics operations. The paper describes the actual intervention within the organisation in question, as well as the empirical benefits and the lessons learned from such a project.

This paper demonstrates the considerable scope that exists for improving relevant real word practices. It shows that simple well‐informed solutions result in substantial organisational savings.

This paper provides insight into the empirical utilisation of demand categorisation theory for forecasting and stock control and provides some very much needed empirical evidence on pertinent issues. In that respect, it should be of interest to both academics and practitioners.