Science and industry join forces to harvest business value
The University of Sunderland conducted an interdisciplinary research project to address the problems of data-based maintenance management in the European manufacturing sector, focusing on the aspects of business value management, reliability and maintenance engineering, and data sciences. The EU-funded Lead4Value initiative(opens in new window) aimed to function as a bridge between maintenance engineers and decision makers, providing managerial tools and methods for evaluating the value of data for specific organisations, aims and contexts. The project worked with a number of small- and medium-sized enterprises. The purpose of this collaboration was to collect and analyse manufacturing data used by senior managers when planning maintenance tasks. Subsequently, the next step was to determine the value and impact of data on maintenance task selection. The final deliverable was to provide the companies with a new system. This new system is tangible proof of how the business value of industrial maintenance can be maximised through adopting a new approach to data-based decision-making based upon lean principles of cost and resource efficiency.
Keeping up with the industry’s pace
“Fortunately, our problems were limited to access to companies during production changeover or shift changes involving key staff. Where possible, we planned 5 to 6 months in advance. Thankfully, we encountered very little delay due to these issues,” reports professor David Baglee, project coordinator and Professor in the Faculty of Technology at the University of Sunderland.
Developed by academia in partnership with industry to answer an industry challenge
The outcomes of this research were specially developed with industry support, with the partner organisations being a food manufacturing company and an automotive industry parts manufacturer. Specifically, the project developed a method to allow companies to analyse their existing and potential data exploitation paths, to determine data relevance and accuracy. In addition, analytical modelling as well as statistical analyses were used to construct maps and tools to support decision-making in future data-management. These tools revealed unnecessary maintenance tasks, unnecessary data collection tasks, missing data collection tasks, and the potential to increase the business value of maintenance.
Saving effort, costs and time
The actual results are found within the companies themselves, including a reduction in maintenance tasks, a reduction in overall maintenance expenses, a reduction in time to collect and analyse data and an identification of which data adds value to the overall decision-making process. The project produced a number of academic papers. The case study of the food manufacturing company, which focuses on corrective maintenance, is presented in a paper titled ‘An approach to identifying waste in data management processes’ by David Baglee and Salla Marttonen-Arola within the framework of the 14th International Conference on Data Science (ICDATA 2018), which took place in Las Vegas, Nevada, USA. The same authors presented their findings in a paper titled ‘Implementing a CMMS: an investment appraisal based on value of data’ at the 2019 World Congress on Resilience, Reliability and Asset Management in Singapore. The next step will be a follow-on project, which is currently being designed by the research team, aiming to expand this system to incorporate production, quality and other manufacturing systems rich in data.
