Predictive maintenance (PdM) is a data-driven maintenance strategy that aims to avoid unplanned downtimes by predicting the remaining lifetime of maintenance objects. Thus, unnecessary replacements of spare parts and critical process disturbances due to breakdowns can be avoided. Despite the widely recognized advantages of this technology, the number of successful applications in practice is still very limited. Our study aims to address the theory-practice gap by conducting a comprehensive case study involving 15 expert interviews with industry professionals to uncover critical factors that hinder the successful implementation of PdM. Our findings shed light on the underlying reasons for a hesitant PdM implementation, including challenges related to digital readiness, data quality and accessibility, technological integration, and maintenance organization. By providing an in-depth analysis of these factors, our study offers valuable insights and guidelines to improve the implementation success rate of PdM in the industrial context. Based on the empirical findings, we present critical implementation factors and develop a framework with ten propositions that aim to dismantle barriers in the industrial application process of PdM and stimulate further research in academia.

The application of Artificial Intelligence (AI) approaches in industrial maintenance for fault detection and prediction has gained much attention from scholars and practitioners. This survey systematically assesses and classifies the state-of-the-art algorithms applied to data-driven maintenance in recent literature. The taxonomy provides a so far not existing overview and decision aid for research and practice regarding suitable AI approaches for each maintenance application. Moreover, we consider trends and further research demand in this area. Finally, a newly developed holistic maintenance framework contributes to a practice-oriented implementation of AI and considers crucial managerial aspects of an efficient maintenance system.

One prospect of additive manufacturing (AM) is location-independent and on-demand production from digital files. Implementing the technology influences production in the company but also simplifies and shortens the holistic supply chain. While these impacts have been increasingly studied, there is a lack of contributions that look at the impact of AM on internal logistical processes. For this reason, an empirical study was conducted, whereby semi-structured expert interviews were chosen as the survey instrument to extend the existing theory. As a result, procurement, warehousing, production, and distribution effects can be shown. The results also compare current potentials and barriers in practice with the literature. The contribution is rounded with practical implications and further research to advance the development in the AM field.

Predictive maintenance is considered an effective maintenance strategy to avoid unplanned downtimes in production processes. This strategy has proven promising results in forecasting machine failure to enable more efficient maintenance management, avoiding costly breakdowns. Furthermore, necessary maintenance measures can be planned, resources can be procured or scheduled on-demand, and fewer spare parts must be kept in stock. Best practice case studies for the successful implementation of smart maintenance management solutions are still rare in this research field. Therefore, this study aims to validate a framework by an industrial case study and give insights into the practical implementation process of predictive maintenance. A co-validation study of the framework based on empirical data of an automotive production process is proposed. The study is based on plant maintenance management data and the evaluation and adaption of the framework are grounded on maintenance expert consultation. The results show that the application of the proposed framework is feasible.

ZusammenfassungDer Anteil der Wertschöpfung an Produkten durch Lieferanten hat in den vergangenen Jahren stetig zugenommen. Dies bedingt eine hohe Komplexität von Lieferketten und stellt das strategische und operative Beschaffungswesen vor große Herausforderungen. Gleichzeitig steht heute eine Vielzahl an Technologien zur Verfügung, um diese Komplexität zu bewältigen, Informationsasymmetrien abzubauen und transparente, fehlersichere Prozesse zu ermöglichen.Während die Potenziale digitaler Lieferantennetzwerke weitgehend evident sind, sind digitale Technologien wie Internet of Things (IoT), Blockchain oder künstliche Intelligenz (KI) bisher kaum praktisch in Unternehmen implementiert.Mit Hilfe einer empirischen Fallstudie wurde untersucht, inwieweit die Digitalisierung als strategisches Unternehmensziel verfolgt wird und welche Hindernisse bei der Einführung digitaler Lieferantennetzwerke bestehen. Dazu wurden elf Experten aus der strategischen und operativen Beschaffung von acht Unternehmen des produzierenden Gewerbes in Form einer leitfadengestützten Interviewstudie befragt und deren Erfahrungen ausgewertet.Die Ergebnisse implizieren, dass in vielen Fällen grundlegende Voraussetzungen für eine erfolgreiche digitale Transformation fehlen und in vielen Unternehmen keine ausreichenden Ressourcen dafür zur Verfügung stehen. Weiterhin wurde festgestellt, dass der Nutzen digitaler Technologien in der Beschaffung häufig sehr einseitig ist und bisher nur selten Netzwerkvorteile genutzt werden. Um dieser zögerlichen Entwicklung entgegenzuwirken, werden Handlungsempfehlungen für eine erfolgreiche Implementierung und stärkere Kooperation in der Lieferkette aufgestellt und weiterer Forschungsbedarf identifiziert.

Machine failure can have significant impacts on increasingly global orientated supply chains in the producing industry. Predictive maintenance (PdM) is a powerful method to avoid economic damage that can occur as a consequence of critical system breakdowns. The latest research shows that only a minority of industrial companies use an approach of residual lifetime prognosis in maintenance. Especially smaller and mid-sized enterprises have a lack of resources and knowledge to focus on a PdM strategy. The purpose of this article is to provide a structured approach on how to implement a PdM strategy in industrial companies in order to reduce maintenance costs and resources. It contains practical orientated recommendations for analyzing, decision making, and implementation of a smart data-based maintenance strategy. Most of the relevant literature in this field focuses on operational decision making in maintenance and residual lifetime prognosis. This chapter provides a structured integrative managerial approach of PdM with a focus on the implementation process of this strategy in an industrial context.