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Predictive maintenance as an artificial intelligence service: a study of value creation

In: Handbook of Services and Artificial Intelligence

Author

Listed:
  • Kathrin Kirchner
  • Grzegorz Leszczyński
  • Marek Zieliński
  • Bartosz Jędrzejczak

Abstract

Companies using technical equipment must ensure ongoing maintenance and access to spare parts. Poorly maintained utilities are more likely to malfunction or break down, which can cause severe work stability and quality assurance problems. The development of modern technologies, such as the Internet of Things, sensors, and artificial intelligence (AI), led to a transition from corrective maintenance to predictive maintenance. The ability to predict when equipment breakdowns will occur and prevent them enables smooth operations for customers. This chapter presents the transition to AI-based maintenance services. Based on a case study of the Polish company Signal Group, which implemented predictive maintenance in a large convenience store chain, this chapter investigates the challenges of introducing and using AI-based predictive maintenance services. The most prevalent challenges are the availability and quality of data, cooperation between departments, and taking the customer’s perspective so that the AI-based service can create value.

Suggested Citation

  • Kathrin Kirchner & Grzegorz Leszczyński & Marek Zieliński & Bartosz Jędrzejczak, 2024. "Predictive maintenance as an artificial intelligence service: a study of value creation," Chapters, in: Ada Scupola & Jon Sundbo & Lars Fuglsang & Anders Henten (ed.), Handbook of Services and Artificial Intelligence, chapter 3, pages 31-52, Edward Elgar Publishing.
    • Handle: RePEc:elg:eechap:21983_3
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    File URL: https://www.elgaronline.com/doi/10.4337/9781035301973.00010
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