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Application of Statistical Methods: Challenges Related to Continuous Industrial Processes
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

For decades, many efficient statistical improvement methods have been available to improve the quality of processes and products. Statistical process control (SPC), process capability analysis (CA), and design of experiments (DoE) are among the most powerful process monitoring and problem-solving methods in the quality engineering toolbox. SPC and CA are methods that are more directed toward monitoring existing processes and assessing their capability related to customer requirements, while DoE typically is used to improve products and processes. It is increasingly difficult to understand and control industrial processes and products because of the increasing complexity of technical systems. Among the complications for statistical analysis of measurements in continuous industrial processes are the multitude of variables and the combination of high-frequency sampling of the measurement systems and process dynamics. Therefore, in industry today, process data are often multivariate as well as autocorrelated (i.e., dependent in time).The purpose of this research is to support the application of SPC, CA, and DoE. More specifically, the aims of this research are: [1] to analyze the use, and related barriers, of SPC, CA, and DoE in organizations; [2] to provide guidance in selection of appropriate decision methods for Cpk when data are autocorrelated; and [3] to adapt methods for analyzing designed experiments to manage dynamic process behavior and autocorrelation in continuous processes.The main contribution of this research is that it explicitly illustrates and describes special considerations and problems that can be encountered when planning, conducting, and analyzing real experiments in continuous industrial processes. Other contributions of this research are: the practical use and development of adapted analysis procedures for experiments in continuous processes; the presentation of comparative data that helps in the selection of decision methods for Cpk when data are autocorrelated; and the analysis of barriers that hinder the use of statistical methods in Swedish organizations.

Place, publisher, year, edition, pages
Luleå tekniska universitet, 2015.
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Research subject
Quality Technology and Management
URN: urn:nbn:se:ltu:diva-17048Local ID: 14ed19b3-12d2-4855-87ba-7104e5fde858ISBN: 978-91-7583-372-9ISBN: 978-91-7583-373-6 (PDF)OAI: diva2:990042
Godkänd; 2015; 20150420 (pedlun); Nedanstående person kommer att disputera för avläggande av teknologie doktorsexamen. Namn: Peder Lundkvist Ämne: Kvalitetsteknik/Quality Technology & Management Avhandling: Application of Statistical Methods Challenges Related to Continuous Industrial Processes Opponent: Dr Bart De Ketelaere, Research Manager, Division Mechatronics, Biostatistics and Sensors (MeBioS), Faculty of Bioscience Engineering, Katholieke, Universiteit Leuven, Leuven, Belgium Ordförande: Professor Bjarne Bergquist, Avd för industriell ekonomi, Institutionen för ekonomi, teknik och samhälle, Luleå tekniska universitet, Luleå Tid: Onsdag 16 september kl 13.00 Plats: A109, Luleå tekniska universitetAvailable from: 2016-09-29 Created: 2016-09-29Bibliographically approved

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