Abstract
Enterprise Resource Planning (ERP) systems offer firms a wealth of readily available transactional data. However, deriving insights from such data often demands the examination of multiple issues simultaneously. In this paper we use simple data mining to analyze ERP data from 27 service shops over a period of 35 months. The data has been used to provide valuable business performance insights to the service shop managers. Though the granular ERP data needed to be supplemented by further data in some instances, we found it has the potential to provide real insights into a firm's performance. Such simple data mining approaches can be standardized and automated across service centers for insights that can be used to drive continuous improvement activities within and across sites. We also suggest that this initial, exploratory study opens exciting avenues for further research into business analytics and, business intelligence pipelines.
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West, S., Powell, D., Fabian, I. (2021). Service Shop Performance Insights from ERP Data. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 631. Springer, Cham. https://doi.org/10.1007/978-3-030-85902-2_18
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