Predictive Maintenance (PdM) solutions assist decision-makers by predicting equipment health and scheduling maintenance actions, but their implementation in industry remains problematic. Specifically, prior research repeatedly indicates that decision-makers often refuse to adopt the data-driven, system-generated advice in their working procedures. In this paper, we address these acceptance issues by studying how PdM implementation changes the nature of decision-makers’ work and how these changes affect their acceptance of PdM systems. We build on the human-centric Smith-Carayon Work System model to synthesise literature from research areas where system acceptance has been explored in more detail. Consequently, we expand the maintenance literature by investigating the human-, task-, and organisational characteristics of PdM implementation. Following the literature review, we distil ten propositions regarding decision-making behaviour in PdM settings. Next, we verify each proposition’s relevance through in-depth interviews with experts from both academia and industry. Based on the propositions and interviews, we identify four factors that facilitate PdM adoption: trust between decision-maker and model (maker), control in the decision-making process, availability of sufficient cognitive resources, and proper organisational allocation of decision-making. Our results contribute to a fundamental understanding of acceptance behaviour in a PdM context and provide recommendations to increase the effectiveness of PdM implementations.

We investigate the benefits of on‐site printing at remote geographic locations, where access to spare parts is intermittent and supplies are replenished at fixed intervals. Organizations typically have no solution to spare parts shortages other than expensive expediting orders, or waiting for a part to arrive with the next replenishment. We investigate whether on‐site three‐dimensional (3D) printing of spare parts can bring relief. Our work extends dual‐sourcing literature with fixed order cycles by considering two emergency supply options: expediting regular parts and 3D printing lower quality parts. We model the replenishment and emergency supply decisions as a Markov decision process and find that the optimal inventory control policy consists of two thresholds that control when to expedite, when to print and when to wait for regular parts via the next replenishment. We apply our model to a case study of the Royal Netherlands Army (RNLA) for her United Nations peacekeeping mission in Mali. Our results show that on‐site 3D printing, much more so than expediting, leads to large operational cost savings through on‐site inventory reductions and increased asset availability, thus increasing the ability of the RNLA to operate in remote locations. These results extend to many other organizations that operate in remote locations, for example, those in the mining and offshore industry.