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Managerial implications for improvingcontinuous production processes
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.ORCID iD: 0000-0003-3911-8009
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Data analytics remains essential for process improvement and optimization. Statistical process control and design of experiments are among the most powerful process and product improvement methods available. However, continuous process environments challenge the application of these methods. In this article, we highlight SPC and DoE implementation challenges described in the literature for managers, researchers and practitioners interested in continuous production process improvement. The results may help managers support the implementation of these methods and make researchers and practitioners aware of methodological challenges in continuous process environments.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Productivity, Statistical tools, Continuous processes
National Category
Engineering and Technology Reliability and Maintenance
Research subject
Quality Technology and Management
Identifiers
URN: urn:nbn:se:ltu:diva-65568OAI: oai:DiVA.org:ltu-65568DiVA: diva2:1140120
Conference
24th EurOMA Conference, Edinburgh, July 1-5, 2017
Projects
Statistical Methods for Improving Continuous Production
Funder
Swedish Research Council, 4731241
Available from: 2017-09-11 Created: 2017-09-11 Last updated: 2017-11-24Bibliographically approved
In thesis
1. Contributions to the Use of Statistical Methods for Improving Continuous Production
Open this publication in new window or tab >>Contributions to the Use of Statistical Methods for Improving Continuous Production
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Complexity of production processes, high computing capabilities, and massive datasets characterize today’s manufacturing environments, such as those of continuous andbatch production industries. Continuous production has spread gradually acrossdifferent industries, covering a significant part of today’s production. Commonconsumer goods such as food, drugs, and cosmetics, and industrial goods such as iron,chemicals, oil, and ore come from continuous processes. To stay competitive intoday’s market requires constant process improvements in terms of both effectivenessand efficiency. Statistical process control (SPC) and design of experiments (DoE)techniques can play an important role in this improvement strategy. SPC attempts toreduce process variation by eliminating assignable causes, while DoE is used toimprove products and processes by systematic experimentation and analysis. However,special issues emerge when applying these methods in continuous process settings.Highly automated and computerized processes provide an exorbitant amount ofserially dependent and cross-correlated data, which may be difficult to analyzesimultaneously. Time series data, transition times, and closed-loop operation areexamples of additional challenges that the analyst faces.The overall objective of this thesis is to contribute to using of statisticalmethods, namely SPC and DoE methods, to improve continuous production.Specifically, this research serves two aims: [1] to explore, identify, and outlinepotential challenges when applying SPC and DoE in continuous processes, and [2] topropose simulation tools and new or adapted methods to overcome the identifiedchallenges.The results are summarized in three appended papers. Through a literaturereview, Paper A outlines SPC and DoE implementation challenges for managers,researchers, and practitioners. For example, problems due to process transitions, themultivariate nature of data, serial correlation, and the presence of engineering processcontrol (EPC) are discussed. Paper B further explores one of the DoE challengesidentified in Paper A. Specifically, Paper B describes issues and potential strategieswhen designing and analyzing experiments in processes operating under closed-loopcontrol. Two simulated examples in the Tennessee Eastman (TE) process simulatorshow the benefits of using DoE techniques to improve and optimize such industrialprocesses. Finally, Paper C provides guidelines, using flow charts, on how to use thecontinuous process simulator, “The revised TE process simulator,” run with adecentralized control strategy as a test bed for developing SPC and DoE methods incontinuous processes. Simulated SPC and DoE examples are also discussed.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2017. 109 p.
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Keyword
Process industry, Continuous process, Statistical process control, Design of experiments, Process improvements, Simulation tool, Engineering process control
National Category
Other Engineering and Technologies
Research subject
Quality Technology and Management
Identifiers
urn:nbn:se:ltu:diva-66256 (URN)978-91-7583-996-7 (ISBN)978-91-7583-997-4 (ISBN)
Presentation
2017-11-27, A109, Luleå University of Technology, Luleå, 13:00 (English)
Opponent
Supervisors
Funder
Swedish Research Council, 4731241
Available from: 2017-10-25 Created: 2017-10-25 Last updated: 2017-11-27Bibliographically approved

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