Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study
Source abstract
Abstract
Data availability is changing the way companies make decisions at various levels (e.g., strategical and operational). Researchers and practitioners are exploring how product–service system (PSS) providers can benefit from data availability and usage, especially when it comes to making decisions related to service delivery. One of the services that are expected to benefit most from data availability is maintenance. Through the analysis of the asset health status, service providers can make informed and timely decisions to prevent failures. Despite this, the offering of data-based maintenance service is not trivial, and requires providers to structure themselves to collect, analyze and use historical and real-time data properly (e.g., introducing suitable information flows, methods and competencies). The paper aims to investigate how a manufacturing company can re-engineer its maintenance service delivery process in a data-driven fashion. Thus, the paper presents a case study where, based on the Dual-perspective, Data-based, Decision-making process for Maintenance service delivery (D3M), an Italian manufacturing company reengineered its maintenance service delivery process in a data-driven fashion. The case study highlights the benefits and barriers coming with this transformation and aims at helping manufacturing companies in understanding how to address it.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Transformation in Industry · Service and Product Innovation · Quality and Supply Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Management, Information and Production Engineering, University of Bergamo, Viale Marconi, 5, 24044 Dalmine, BG, Italy
References (52)
Show source-provided references
1. Product–Service Systems Engineering: State of the art and research challenges10.1016/j.compind.2012.02.006 ↗
2. Literature review of Industry 4.0 and related technologies10.1007/s10845-018-1433-8 ↗
3. New Paradigm of Data-Driven Smart Customisation through Digital Twin10.1016/j.jmsy.2020.07.023 ↗
4. Big Data Analytics: Applications, Prospects and Challenges10.1007/978-3-319-67925-9_1 ↗
5. Zeiler, P., and Bertsche, B. (2014). Simulation-Based Risk Management of Product-Service Systems. Proceedings of the Annual Reliability and Maintainability Symposium, Colorado Springs, CO, USA, 27–30 January 2014, Institute of Electrical and Electronics Engineers Inc.10.1109/RAMS.2014.6798516 ↗
6. Aspects of Risk Management Implementation for Industry 4.010.1016/j.promfg.2017.07.248 ↗
7. Hybrid simulation modelling as a supporting tool for sustainable product service systems: A critical analysis10.1080/00207543.2017.1330569 ↗
8. Service-oriented collaboration framework based on cloud platform and critical factors identification10.1016/j.jmsy.2021.09.007 ↗
9. Digitalization for sustainable maintenance services: A systematic literature review10.1016/j.digbus.2021.100011 ↗
10. Data-based decision-making in maintenance service delivery: The D3M framework10.1108/JMTM-08-2020-0301 ↗
11. Design a personalised product service system utilising a multi-agent system10.1016/j.aei.2020.101036 ↗
12. Overcoming the Service Paradox in Manufacturing Companies10.1016/j.emj.2004.12.006 ↗
13. Suh, E.S. (2019). Product Service System Availability Improvement through Field Repair Kit Optimization: A Case Study. Appl. Sci., 9.10.3390/app9204272 ↗
14. Interactions between Service and Product Lifecycle Management10.1016/j.procir.2015.02.018 ↗
15. Midlife upgrade of capital equipment: A servitization-enabled, value-adding alternative to traditional equipment replacement strategies10.1016/j.cirpj.2019.09.001 ↗
16. The role of digital technologies for the service transformation of industrial companies10.1080/00207543.2017.1324224 ↗
17. Research on product-service systems: Topic landscape and future trends10.1108/JMTM-04-2020-0164 ↗
18. Challenges Building a Data Value Chain to Enable Data-Driven Decisions: A Predictive Maintenance Case in 5G-Enabled Manufacturing10.1016/j.promfg.2018.10.064 ↗
19. A service-oriented dynamic multi-level maintenance grouping strategy based on prediction information of multi-component systems10.1016/j.jmsy.2019.09.005 ↗
20. A systematic literature review of machine learning methods applied to predictive maintenance10.1016/j.cie.2019.106024 ↗
21. Pollak, A., Temich, S., Ptasiński, W., Kucharczyk, J., and Gąsiorek, D. (2021). Prediction of Belt Drive Faults in Case of Predictive Maintenance in Industry 4.0 Platform. Appl. Sci., 11.10.3390/app112110307 ↗
22. Operation-dependent maintenance scheduling in flexible manufacturing systems10.1016/j.cirpj.2012.09.005 ↗
23. Hajej, Z., Nyoungue, A.C., Abubakar, A.S., and Ali, K.M. (2021). An Integrated Model of Production, Maintenance, and Quality Control with Statistical Process Control Chart of a Supply Chain. Appl. Sci., 11.10.3390/app11094192 ↗
24. Circular production and maintenance of automotive parts: An Internet of Things (IoT) data framework and practice review10.1016/j.compind.2021.103593 ↗
25. Maintenance Analytics–The New Know in Maintenance10.1016/j.ifacol.2016.11.037 ↗
26. Jwo, J.-S., Lin, C.-S., Lee, C.-H., Zhang, L., and Huang, S.-M. (2021). Intelligent System for Railway Wheelset Press-Fit Inspection Using Deep Learning. Appl. Sci., 11.10.3390/app11178243 ↗
27. Smart Maintenance Decision Support Systems (SMDSS) based on corporate big data analytics10.1016/j.eswa.2017.08.025 ↗
28. Importance of Maintenance Data Quality in Extended Warranty Simulation
29. Ajaegbu, A., Uren, V., and Schroeder, A. (2020, January 29–30). A Capability-Based Framework of Utilising Data-in-Use to Create Business Value in Integrated Solutions, Advanced Service, or PSS. Proceedings of the EurOMA Conference 2020, Warwick, UK.
30. A novel data-driven method for maintenance prioritization of circuit breakers based on the ranking SVM10.1016/j.ijepes.2021.106779 ↗
31. Antomarioni, S., Ciarapica, F.E., and Bevilacqua, M. (2022). Data-driven approach to predict the sequence of component failures: A framework and a case study on a process industry. Int. J. Qual. Reliab. Manag.10.1108/IJQRM-12-2020-0413 ↗
32. A decision-making model for knowledge collaboration and reuse through scientific workflow10.1016/j.aei.2021.101345 ↗
33. Planning of Maintenance Activities–A Current State Mapping in Industry10.1016/j.procir.2015.02.093 ↗
34. Designing and developing smart production planning and control systems in the industry 4.0 era: A methodology and case study10.1007/s10845-021-01808-w ↗
35. Agent-enabled service-oriented decision support systems10.1016/j.dss.2012.05.047 ↗
36. Specifying An Expanded Framework for Classifying and DescribingDecision Support Systems
37. Prediction maintenance integrated decision-making approach supported by digital twin-driven cooperative awareness and interconnection framework10.1016/j.jmsy.2020.08.001 ↗
38. Experienced knowledge for the description of maintenance packages10.1016/j.jmsy.2014.11.005 ↗
39. A knowledge-based collaborative platform for PSS design and production10.1016/j.cirpj.2018.08.004 ↗
40. A model-based Digital Twin to support responsive manufacturing systems10.1016/j.cirp.2021.04.043 ↗
41. A reliability diagnosis to support servitization decision-making process10.1108/JMTM-06-2015-0044 ↗
42. A fuzzy decision support system for managing maintenance activities of critical components in manufacturing systems10.1016/j.jmsy.2019.06.002 ↗
43. Data-driven decision-making for equipment maintenance10.1016/j.autcon.2020.103103 ↗
44. Data-driven model for maintenance decision support: A case study of railway signalling systems10.1177/0954409714533680 ↗
45. Data-driven machine criticality assessment—Maintenance decision support for increased productivity10.1080/09537287.2020.1817601 ↗
46. Yin, R.K. (2009). Case Study Research: Design and Methods; Applied Social Research Methods, SAGE Publications. [4th ed.].
47. A Dual Methodology for Case Studies: Synergistic Use of a Longitudinal Single Site with Replicated Multiple Sites10.1287/orsc.1.3.248 ↗
48. Ritchie, J., Lewis, J., Nicholls, C.M., and Ormston, R. (2013). Qualitative Research Practice: A Guide for Social Science Students and Researchers, SAGE Publications.
49. Kothari, C.R. (2004). Research Methodology: Methods and Techniques, New Age International. [2nd ed.].
50. Chen, Y., Ye, C., Liu, B., and Kang, R. (2012, January 23–25). Status of FMECA Research and Engineering Application. Proceedings of the IEEE 2012 Prognostics and System Health Management Conference, Beijing, China.10.1109/PHM.2012.6228914 ↗
51. Implementing a dynamic FMECA in the digital transformation era10.1016/j.ifacol.2019.11.206 ↗
52. Sala, R., Zambetti, M., Pirola, F., and Pinto, R. (2018, January 12–14). How to Select a Suitable Machine Learning Algorithm: A Feature-Based, Scope-Oriented Selection Framework. Proceedings of the Summer School Francesco Turco, Palermo, Italy.