Data quality assessment of maintenance reporting procedures

Abstract

Today's largest and fastest growing companies' assets are no longer physical, but rather digital (software, algorithms. . . ). This is all the more true in the manufacturing, and particularly in the maintenance sector where quality of enterprise maintenance services are closely linked to the quality of maintenance data reporting procedures. If quality of the reported data is too low, it can results in wrong decision-making and loss of money. Furthermore, various maintenance experts are involved and directly concerned about the quality of enterprises' daily maintenance data reporting (e.g., maintenance planners, plant managers. . . ), each one having specific needs and responsibilities. To address this Multi-Criteria Decision Making (MCDM) problem, and since data quality is hardly considered in existing expert maintenance systems, this paper develops a Maintenance Reporting Quality Assessment (MRQA) dashboard that enables any company stakeholder to easily -and in real-time -assess/rank company branch offices in terms of maintenance reporting quality. From a theoretical standpoint, AHP is used to integrate various data quality dimensions as well as expert preferences. A use case describes how the proposed MRQA dashboard is being used by a Finnish multinational equipment manufacturer to assess and enhance reporting practices in a specific or a group of branch offices.

Computerised Software Systems in Replacement Maintenance Information Management: A South African Case Study

“…The work is very interesting, particularly for its explanation of how CMMS can be applied to total productive maintenance (TPM), with over 60 manufacturing companies being considered. There are other notable and excellent presentations on CMMS use in manufacturing and allied company maintenance activities in the literature [2], [27]- [29]. Apart from chronicling the efforts made in applying CMMS to maintenance planning, the geographical spread of those publications and examples provide handy material for researchers who are interested in the proper information management of maintenance systems.…”

Analysis of manual data collection in maintenance context

“…Data quality dimensions, such as accuracy, accessibility, relevance, completeness and timeliness, depend on the task the data is used for and their relative importance may change as work requirements change (Strong et al, 1997). In maintenance data context, Aljumaili et al (2018) used a set of 12 attributes (Completeness, Metadata constraints, Accuracy, Source reputation, Relevancy, Amount of data, Usability, Conciseness, Availability, Navigation, Security, Up to date) whereas Madhikermi et al (2016) identified the three most important attributes: believability, completeness and timeliness. The latter set is also used in this thesis.…”

“…However, there remains data that is difficult and expensive, if not impossible to acquire automatically. Manually collected data is required to supplement the vast amounts of automatically produced data in service provision and data-centric decision making (Madhikermi et al, 2016). Examples of such data are operational and cost related data from service operations, such as hours spent on maintenance tasks and spare parts used (Unsworth et al, 2011) or the reasons for an equipment not running (waiting for service, waiting for spare parts, or redundancy; Publication II).…”

Service-Oriented Computing for intelligent train maintenance

“…Madhikermi and his colleagues stress that the quality of enterprise maintenance services are closely linked to the quality of maintenance data reporting procedures. They propose the development of a maintenance reporting quality assessment (MRQA) dashboard enabling any company stakeholder assess/rank company subsidiaries in terms of maintenance reporting quality (Madhikermi et al 2016). Roy and his colleagues present advanced computing and visualisation technologies (like 3D scan and computer tomography data analysis systems) that will improve efficiency of the maintenance and reduce through-life cost of the product.…”