Data-driven machine criticality assessment – maintenance decision support for increased productivity

Data-driven decision support for maintenance management is necessary for modern digitalized production systems. The data-driven approach enables analyzing the dynamic production system in realtime. Common problems within maintenance management are that maintenance decisions are experience-driven, narrow-focussed and static. Specifically, machine criticality assessment is a tool that is used in manufacturing companies to plan and prioritize maintenance activities. The maintenance problems are well exemplified by this tool in industrial practice. The tool is not trustworthy, seldom updated and focuses on individual machines. Therefore, this paper aims at the development and validation of a framework for a data-driven machine criticality assessment tool. The tool supports prioritization and planning of maintenance decisions with a clear goal of increasing productivity. Four empirical cases were studied by employing a multiple case study methodology. The framework provides guidelines for maintenance decision-making by combining the Manufacturing Execution System (MES) and Computerized Maintenance Management System (CMMS) data with a systems perspective. The results show that by employing data-driven decision support within the maintenance organization, it can truly enable modern digitalized production systems to achieve higher levels of productivity

[1]  Minoru Tanaka,et al.  A practical bottleneck detection method , 2001, Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304).

[2]  Christopher A. Voss,et al.  Case research in operations management , 2002 .

[3]  Sigrid Wenzel,et al.  A new procedure model for verification and validation in production and logistics simulation , 2008, 2008 Winter Simulation Conference.

[4]  Johan Stahre,et al.  Smart Maintenance: an empirically grounded conceptualization , 2020, International Journal of Production Economics.

[5]  Rajkumar Roy,et al.  Continuous maintenance and the future – Foundations and technological challenges , 2016 .

[6]  Õrjan Ljungberg,et al.  Measurement of overall equipment effectiveness as a basis for TPM activities , 1998 .

[7]  Jun Ni,et al.  Decision support systems for effective maintenance operations , 2012 .

[8]  Felix T.S. Chan,et al.  A joint production scheduling approach considering multiple resources and preventive maintenance tasks , 2013 .

[9]  Semyon M. Meerkov,et al.  Bottlenecks in Markovian production lines: a systems approach , 1998, IEEE Trans. Robotics Autom..

[10]  Joachim Metternich,et al.  Development of bottleneck detection methods allowing for an effective fault repair prioritization in machining lines of the automobile industry , 2016, Prod. Eng..

[11]  Peter Kipruto Chemweno,et al.  Integrated maintenance policies for performance improvement of a multi-unit repairable, one product manufacturing system , 2020 .

[12]  Maheshwaran Gopalakrishnan,et al.  Identification of maintenance improvement potential using OEE assessment , 2017 .

[13]  David P. Christy,et al.  Incorporating maintenance activities into production planning; integration at the master schedule versus material requirements level , 1996 .

[14]  Carlo Batini,et al.  Methodologies for data quality assessment and improvement , 2009, CSUR.

[15]  Adolfo Crespo Márquez,et al.  The Maintenance Management Framework , 2009 .

[16]  Gunnar Bolmsjö,et al.  Improved efficiency with production disturbance reduction in manufacturing systems based on discrete‐event simulation , 2004 .

[17]  Soundar R. T. Kumara,et al.  Cyber-physical systems in manufacturing , 2016 .

[18]  P. O'Donovan,et al.  Big data in manufacturing: a systematic mapping study , 2015, Journal of Big Data.

[19]  T. R. Moss,et al.  Criticality analysis revisited , 1999 .

[20]  Jun Ni,et al.  Prediction of Passive Maintenance Opportunity Windows on Bottleneck Machines in Complex Manufacturing Systems , 2015 .

[21]  Barbara B. Flynn,et al.  Empirical research methods in operations management , 1990 .

[22]  Jun Ni,et al.  Short-term decision support system for maintenance task prioritization , 2009 .

[23]  Dorota Stadnicka,et al.  Development of an empirical formula for machine classification: Prioritization of maintenance tasks , 2014 .

[24]  H. Yamashina,et al.  Manufacturing cost deployment , 2002 .

[25]  Liliane Pintelon,et al.  A dynamic predictive maintenance policy for complex multi-component systems , 2013, Reliab. Eng. Syst. Saf..

[26]  Arne Ingemansson,et al.  On Reduction of Production Disturbances in Manufacturing Systems Based on Discrete-Event Simulation , 2004 .

[27]  Seungchul Lee,et al.  Hidden maintenance opportunities in discrete and complex production lines , 2013, Expert Syst. Appl..

[28]  S. Jack Hu,et al.  Allocation of maintenance resources in mixed model assembly systems , 2013 .

[29]  J.B. Bowles,et al.  Using fuzzy logic for system criticality analysis , 1994, Proceedings of Annual Reliability and Maintainability Symposium (RAMS).

[30]  Antti Salonen,et al.  Machine criticality assessment for productivity improvement , 2019, International Journal of Productivity and Performance Management.

[31]  A. Skoogh,et al.  Machine criticality based maintenance prioritization: Identifying productivity improvement potential , 2018 .

[32]  L. Pintelon,et al.  Maintenance: An Evolutionary Perspective , 2008 .

[33]  Anders Skoogh,et al.  Data-driven algorithm for throughput bottleneck analysis of production systems , 2018 .

[34]  Uday Kumar,et al.  Maintenance Analytics – The New Know in Maintenance , 2016 .

[35]  Stephan Biller,et al.  The Costs of Downtime Incidents in Serial Multistage Manufacturing Systems , 2012 .

[36]  Adolfo Crespo Marquez,et al.  Criticality Analysis for Maintenance Purposes: A Study for Complex In‐service Engineering Assets , 2016, Qual. Reliab. Eng. Int..

[37]  W. J. Moore,et al.  An intelligent maintenance system for continuous cost-based prioritisation of maintenance activities , 2006, Comput. Ind..

[38]  Stephan Biller,et al.  Simulation study of a bottleneck-based dispatching policy for a maintenance workforce , 2010 .

[39]  A. Skoogh,et al.  Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030 , 2017 .

[40]  Anders Skoogh,et al.  An algorithm for data-driven shifting bottleneck detection , 2016 .