A data-driven approach to diagnosing throughput bottlenecks from a maintenance perspective. (December 2020)

• Record Type: Journal Article
• Authors: Subramaniyan, Mukund; Skoogh, Anders; Muhammad, Azam Sheikh; Bokrantz, Jon; Johansson, Björn; Roser, Christoph
• Is Part Of: Computers & industrial engineering. Volume 150(2020)
• Publication Date: 2020-12
• Subjects: Throughput bottlenecks -- Production system -- Manufacturing system -- Maintenance -- Machine learning -- Data science; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2020.106851
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Developed data-driven way of diagnosing unplanned stops in throughput bottlenecks. Identified relevant input by maintenance practitioners to develop data-driven approach. Diagnostic insights gained by applying unsupervised machine-learning techniques. Visual analytics of clusters aids maintenance decisions on throughput bottlenecks. Shows integration of maintenance-practitioner knowledge into data-driven approaches. 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.