Developing analytics-driven maintenance data mining processes: a design science approach

Abstract

PurposeData mining has been well-applied by maintenance service providers in identifying data patterns and supporting decision-making. However, when applying data mining for analytics-driven maintenance, maintenance service providers often adopt data mining with unstructured “trial-and-error” approaches. In response, we have followed design science to develop a comprehensive approach to diagnosing the problems with the existing data mining processes model for analytics-driven maintenance service.Design/methodology/approachThis study conducted an in-depth case study with Siemens in the UK for data collection in order to apply a two-cycle build-and-evaluate design process. Based on the literature, the preliminary model is built. It is evaluated through the case company in the first cycle. In the second cycle, the model is refined based on the comments from the case company and then re-evaluated from both business management and information technology perspectives to ensure the applicability of the designed model in a real business environment.FindingsFirstly, this study identifies three main shortcomings in the existing data mining process models for analytics-driven maintenance. Secondly, this study develops the “Gear-Wheel Model”, with a customer-oriented cycle, a project planning cycle and a machine comprehension cycle, to overcome all these shortcomings simultaneously and provide improvement solutions. Thirdly, this study highlighted that the data mining processes for analytics-driven maintenance service need interactions from different functional departments and supports of successive data collection.Originality/valueThe study expands data mining analysis beyond a single business function to include interactions with other internal functions and external customers. It contributes to existing knowledge by focusing on the managerial aspects of data mining and integrating maintenance service providers with their business customers.