Maintenance Data Management: The Potential Effect of Blockchain Technology

Abstract

Product data management is the practice of assimilating, processing, protecting, and storing product data. One of the main concepts of Industry 4.0 is the application of data-driven policies to optimise industrial processes and monitor product life cycles. Consequently, a data management discipline within an organisation has become increasingly prioritised to address significant challenges such as data silos, security risks and general decision-making bottlenecks. The application of digital transformation technologies is needed to capture data from various industrial operations and product status in a smarter way. In addition, the data that is collected can be very useful in the maintenance phase of a product; in fact, it allows us to know the 'history' of the product under consideration, thus making risk factors understandable and giving us the possibility of defining intervention methods well in advance. The implementation of Blockchain (BC) technology in the storage of data that can be used for the maintenance of a product, can be seen as a solution to the problems behind the management of product data, in fact, it allows the acquisition, storage and processing of these in a secure, transparent, and decentralised environment. The present work in this regard studies the effects of BC on the performance of a product data management system in the maintenance process. The main critical issues in maintenance data management were identified and the potential of using blockchain technology was studied. A framework was developed to reproduce the operation of a BC for maintenance data management, and a set of key performance indicators (KPIs) were outlined to assess the effects of BC on the performance of a product data management system in the maintenance process. The results demonstrate that it is possible to improve a company's performance and make it more resilient through the collection of data within the BC, as it allows in-depth analysis during product maintenance planning and provides decision-makers with a single source of truth and insight to make complex decisions.