Seizing opportunity: Advancing the science and practice of opportunistic maintenance in manufacturing

Abstract

Opportunistic Maintenance (OM) remains underutilized in manufacturing despite being introduced over half a century ago. To break new ground, this article seeks to provide concept clarity and demonstrate the potential of artificial intelligence techniques for OM in a manufacturing context. Through an analysis of the existing OM literature, we provide clarity in the OM theory and distinguish two separate views of OM that we dub the ‘component view’ and the ‘flow view’. We then integrate the two views into a new and unified conceptual definition of OM followed by expanding the OM concept by embedding four distinct time constructs: frequency, duration, sequence, and timing. To pave the way for novel OM tools, we demonstrate a real-world application of data-driven prediction of maintenance opportunity windows in an automotive manufacturing line using a long short-term memory algorithm. Evaluated against a naïve benchmark, our model showed quantitatively superior predictive performance on precision, recall, and F1 score. Our theoretical and practical implications relate to increasing the coherence in OM scholarship, making OM research easily understandable by working professionals, and creating new directions for OM tools capable of learning, adapting, and responding to changing production dynamics. We thereby offer a unified foundation for creating impactful OM theory and tools, aiming to inspire maintenance scholars to pursue the OM topic in their own research to deepen the understanding of OM and fully unlock its productivity potential in manufacturing.