Measuring and Querying Process Performance in Supply Chains: An Approach for Mining Big-Data Cloud Storages

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Abstract

Survival in today's global environment means continuously improving processes, identifying and eliminating inefficiencies wherever they occur. With so many companies operating as part or all of complex distributed supply chain, gathering, collating and analyzing the necessary data to identify such improvement opportunities is extremely complex and costly. Although few solutions exist to correlate the data, it continues to be generated in vast quantities, rendering the use of highly scalable, cloud-based solutions for process analysis a necessity. In this paper we present an overview of an analytical framework for business activity monitoring and analysis, which has been realized using extremely scalable, cloud-based technologies. It provides a low-latency solution for entire supply chains or individual nodes in such chains to query process data stores in order to deliver business insight. A custom query language has been implemented which allows business analysts to design custom queries on processes and activities based on a standard set of process metrics. Ongoing developments are focused on testing and improving the scalability and latency of the system, as well as extending the query engine to increase its flexibility and performance.

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APA

Vera-Baquero, A., Colomo-Palacios, R., & Molloy, O. (2015). Measuring and Querying Process Performance in Supply Chains: An Approach for Mining Big-Data Cloud Storages. In Procedia Computer Science (Vol. 64, pp. 1026–1034). Elsevier B.V. https://doi.org/10.1016/j.procs.2015.08.623

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