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  • 1.
    Lundkvist, Peder
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Application of Statistical Methods: Challenges Related to Continuous Industrial Processes2015Doctoral 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.

  • 2.
    Lundkvist, Peder
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Experiments and Capability Analysis in Process Industry2012Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The existence of variation has been a major problem in industry since the industrial revolution. Hence, many organizations try to find strategies to master and reduce the variation. Statistical analysis, such as process capability analysis and Design of Experiments (DoE), often plays an important role in such a strategy. Process capability analysis can determine how the process performs relative to its requirements or specifications, where an important part is the use of process capability indices. DoE includes powerful methods, such as factorial designs, which helps experimenters to maximize the information output from conducted experiments and minimize the experimental work required to reach statistically significant results.Continuous processes, frequently found in the process industry, highlight special issues that are typically not addressed in the DoE literature, for example, autocorrelation and dynamics. The overall purpose of this research is to contribute to an increased knowledge of analyzing DoE and capability in process industry, which is achieved through simulations and case studies of real industrial processes. This research focus on developing analysis procedures adapted for experiments and comparing decision methods for capability analysis in process industry.The results of this research are presented in three appended papers. Paper A shows how the use of a two-level factorial experiment can be used to identifying factors that affect the depth and variation of the oscillation mark that arises from the steel casting process. Four factors were studied; stroke length of the mold, oscillation frequency, motion pattern of the mold (sinus factor), and casting speed. The ANOVA analysis turned out to be problematic because of a non- orthogonal experimental design due to loss of experimental runs. Nevertheless, no earlier studies where found that shows how the sinus factor is changed in combination with the oscillation frequency so that the interaction effect could be studied. Paper B develops a method to analyze factorial experiments, affected by process interruptions and loss of experimental runs, by using time series analysis. Paper C compares four different methods for capability analysis, when data are autocorrelated, through simulations and case study of a real industrial process. In summary, it is hard to recommend one single method that works well in all situations. However, two methods appeared to be better than the others. Keywords: Process industry, Continuous processes, Autocorrelation, Design of Experiments, Process capability, Time series analysis.

  • 3.
    Lundkvist, Peder
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Forskningsrapport: Planering av brikettförsök vid masugn 32012Report (Other academic)
  • 4.
    Lundkvist, Peder
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Bergquist, Bjarne
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Experimental Study of Oscillation Mark Depth in Continuous Casting of Steel2014In: Ironmaking & steelmaking, ISSN 0301-9233, E-ISSN 1743-2812, Vol. 41, no 4, p. 304-309Article in journal (Refereed)
    Abstract [en]

    Mould oscillation is needed to reduce friction and thus prevent sticking and breakout of the liquid metal during casting. However, this oscillation is known to cause surface defects in the solidified steel slabs, so called oscillation marks. In this paper, the depth and the depth variation of these oscillation marks were studied using a two-level full factorial experiment (24) with four additional centre point runs. Four factors were studied: stroke length of the mould, oscillation frequency, motion pattern (strip factor) and casting speed. The stroke length affected the depth of the marks the most, where larger strokes created deeper marks. The interaction between the oscillation frequency and the strip factor of the mould also affected the oscillation mark depth. The oscillation mark depth variation was also increased by increased stroke lengths and at higher oscillation frequencies. The largest effect on the oscillation depth variation was found for the interaction between the stroke length and the oscillation frequency.

  • 5.
    Lundkvist, Peder
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Bergquist, Bjarne
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Statistical Methods - Still Ignored?: The Testimony of Swedish Alumni2020In: Total Quality Management and Business Excellence, ISSN 1478-3363, E-ISSN 1478-3371, Vol. 31, no 3-4, p. 245-262Article in journal (Refereed)
    Abstract [en]

    Researchers have promoted statistical improvement methods as essential for product and process improvement for decades. However, studies show that their use has been moderate at best. This study aims to assess the use of statistical process control (SPC), process capability analysis, and design of experiments (DoE) over time. The study also highlights important barriers for the wider use of these methods in Sweden as a follow-up study of a similar Swedish study performed in 2005 and of two Basque-based studies performed in 2009 and 2010. While the survey includes open-ended questions, the results are mainly descriptive and confirm results of previous studies. This study shows that the use of the methods has become more frequent compared to the 2005 study. Larger organisations (>250 employees) use the methods more frequently than smaller organisations, and the methods are more widely utilised in the industry than in the service sector. SPC is the most commonly used of the three methods while DoE is least used. Finally, the greatest barriers to increasing the use of statistical methods were: insufficient resources regarding time and money, low commitment of middle and senior managers, inadequate statistical knowledge, and lack of methods to guide the user through experimentations.

  • 6.
    Lundkvist, Peder
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vanhatalo, Erik
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Identifying Process Dynamics through a Two-Level Factorial Experiment2014In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 26, no 2, p. 154-167Article in journal (Refereed)
    Abstract [en]

    Industrial experiments are often subjected to critical disturbances and in a small design with few runs the loss of experimental runs may dramatically reduce analysis power. This article considers a common situation in process industry where the observed responses are represented by time series. A time series analysis approach to analyze two-level factorial designs affected by disturbances is developed and illustrated by analyzing a blast furnace experiment. In particular, a method based on transfer function-noise modeling is compared with a ‘traditional’ analysis using averages of the response in each run as the single response in an analysis of variance (ANOVA).

  • 7.
    Lundkvist, Peder
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vännman, Kerstin
    Kulahci, Murat
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    A Comparison of Decision Methods for Cpk When Data are Autocorrelated2012In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 24, no 4, p. 460-472Article in journal (Refereed)
    Abstract [en]

    In many industrial applications, autocorrelated data are becoming increasingly common due to, for example, on-line data collection systems with high-frequency sampling. Therefore the basic assumption of independent observations for process capability analysis is not valid. The purpose of this article is to compare decision methods using the process capability index Cpk, when data are autocorrelated. This is done through a case study followed by a simulation study. In the simulation study the actual significance level and power of the decision methods are investigated. The outcome of the article is that two methods appeared to be better than the others.

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