Modelling, monitoring and evaluation to support automatic engineering process management

Lei Shi, Linda Newnes, Stephen Culley, Bruce Allen

Research output: Contribution to journalArticle

1 Citation (Scopus)
73 Downloads (Pure)

Abstract

Process management is considered to be an essential approach to improve the performance of an enterprise. The process of an engineering project is considered to be a formalised workflow accompanied by a set of decisions. With decisions being made by taking account of information from various sources, the operation and management of modern engineering projects has to deal with increasing amounts of dynamic and changing project information. Understanding and interpreting this information for use in process management can generate challenges in practice. This might be caused by constraints of time and resource, the distributed structure of the information and a lack of modelled domain knowledge. To address these challenges, the research described in this paper focuses on techniques that support automation of the process management of engineering projects, from a data-driven perspective. The research includes elements of process modelling, monitoring and evaluation of such projects, through a proposed automatic process analysis system. The proposed system works with live and historical data. Within this paper, the design and implementation of the system is described. The use of techniques such as autonomic computing, data mining and KM technologies are shown, and the system functionality is demonstrated through the use of a dataset from an aerospace organisation.
Original languageEnglish
Pages (from-to)17-31
Number of pages14
JournalProceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
Volume232
Issue number1
Early online date12 Aug 2016
DOIs
Publication statusPublished - 1 Jan 2018

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Monitoring
Data mining
Automation
Industry

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