A data-driven approach to diagnosing throughput bottlenecks from a maintenance perspective

Abstract

Prioritising maintenance activities in throughput bottlenecks increases the throughput from the production system. To facilitate the planning and execution of maintenance activities, throughput bottlenecks in the production system must be identified and diagnosed. Various research efforts have developed data-driven approaches using real-time machine data to identify throughput bottlenecks in the system. However, these efforts have mainly focused on identifying bottlenecks and only offer limited maintenance-related diagnostics for them. Moreover, these research efforts have been proposed from an academic perspective using rigorous scientific methods. A number of challenges must be addressed, if existing data-driven approaches are to be adapted to real-world practice. These include identifying relevant data types, data pre-processing and data modelling. Such challenges can be better addressed by including maintenance-practitioner input when developing data-driven approaches. The aim of this paper is therefore to demonstrate a data-driven approach to diagnosing throughput bottlenecks, using the combined knowledge of the maintenance and data-science domains. Diagnostic insights into throughput bottlenecks are obtained using unsupervised machine-learning techniques. The demonstration uses real-world machine datasets extracted from the production line. The novelty of the research presented in this paper is that it shows how inputs from maintenance practitioners can be used to develop data-driven approaches for diagnosing throughput bottlenecks having more practical relevance. By gaining these diagnostic insights, maintenance practitioners can better understand shop-floor throughput bottleneck behaviours from a maintenance perspective and thus prioritise various maintenance actions.

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