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  • 1.
    Alsyouf, Imad
    et al.
    Industrial Engineering and Engineering Management, University of Sharjah, Sharjah, United Arab Emirates.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Al-Ash, Lubna
    Industrial Engineering and Engineering Management, University of Sharjah, Sharjah, United Arab Emirates.
    Al-Hammadi, Muna
    Industrial Engineering and Engineering Management, University of Sharjah, Sharjah, United Arab Emirates.
    Improving baggage flow in the baggage handling system at a UAE-based airline using lean Six Sigma tools2019In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 30, no 3, p. 432-452Article in journal (Refereed)
    Abstract [en]

    This paper presents a real successful implementation of lean six sigma methodology to continuously improve the baggage flow in a baggage handling system (BHS), by identifying the causes of mishandled baggage, and deriving solutions to enhance BHS performance. The results show that the main critical problems were low system reliability and the high number of bags passing through manual-encoding-stations. This research illustrates how to avoid baggage congestion and provides applicable and cost-effective solutions. The success of this project made the organisation aware of the opportunities that the application of lean Six Sigma methodology created in the aviation and airport sector.

  • 2.
    Bergquist, Bjarne
    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.
    Nordenvaad, Magnus Lundberg
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A Bayesian analysis of unreplicated two-level factorials using effects sparsity, hierarchy, and heredity2011In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 23, no 2, p. 152-166Article in journal (Refereed)
    Abstract [en]

    This article proposes a Bayesian procedure to calculate posterior probabilities of active effects for unreplicated two-level factorials. The results from a literature survey are used to specify individual prior probabilities for the activity of effects and the posterior probabilities are then calculated in a three-step procedure where the principles of effects sparsity, hierarchy, and heredity are successively considered. We illustrate our approach by reanalyzing experiments found in the literature.

  • 3.
    Bisgaard, Søren
    et al.
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst.
    Kulahci, Murat
    Department of Industrial Engineering, Arizona State University, Tempe.
    Checking process stability with the variogram2005In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 17, no 2, p. 323-327Article in journal (Refereed)
    Abstract [en]

    Modern quality control methods are increasingly being used to monitor complex industrial processes. A key requirement for such methods is the derivation of long records. Once such records are obtained, the variogram becomes a simple and useful exploratory tool that can be used by quality professionals to investigate whether a process is stationary or not.

  • 4.
    Bisgaard, Søren
    et al.
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst, Institute for Technology Management, University of St. Gallen.
    Kulahci, Murat
    Institute for Technology Management, University of St. Gallen.
    Finding assignable causes2000In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 12, no 4, p. 633-640Article in journal (Refereed)
  • 5.
    Bisgaard, Søren
    et al.
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst, University of Amsterdam.
    Kulahci, Murat
    University of Wisconsin-Madison.
    Improving and controlling business processes2001In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 14, no 2, p. 341-344Article in journal (Refereed)
  • 6.
    Bisgaard, Søren
    et al.
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst.
    Kulahci, Murat
    Department of Industrial Engineering, Arizona State University, Tempe.
    Quality quandaries: Beware of autocorrelation in regression2007In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 19, no 2, p. 143-148Article in journal (Refereed)
    Abstract [en]

    In the quality engineering context, the problem of trying to assess whether there exists a relationship between several inputs and an output of a process is often encountered. The main reason for spurious relationships between time series is that two unrelated time series that are internally autocorrelated sometimes by chance can produce very large cross correlations. Perhaps the safest approach to assessing the relationship between the input and the output of a process when the data is autocorrelated is to use prewhitening

  • 7.
    Bisgaard, Søren
    et al.
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst.
    Kulahci, Murat
    Department of Informatics and Mathematical Modelling, Technical University of Denmark.
    Quality quandaries: Box-cox transformations and time series modeling - Part I2008In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 20, no 3, p. 376-388Article in journal (Refereed)
    Abstract [en]

    A demonstration in determining a Box-Cox transformation in the context of seasonal time series modeling has been provided. The first step is postulating a general class of statistical models and transformations, identifying a transformation and a model to be tentatively entertained, estimating parameters in tentatively entertained and fitted model, then checking the transformation and so on. Starting this iterative process a number of graphical methods are typically applied. Graphical determination of appropriate transformation may include log transformation and use of range-mean chart. Proceeding this step is identification of an appropriate ARIMA time series model. The Box-Cox transformation family of transformations is continuous in λ and contains the log transformation as a special case. It does have repeated model fittings but can be done relatively quick with standard time series software.

  • 8.
    Bisgaard, Søren
    et al.
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst.
    Kulahci, Murat
    Department of Informatics and Mathematical Modelling, Technical University of Denmark.
    Quality quandaries: Box-cox transformations and time series modeling - Part II2008In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 20, no 4, p. 516-523Article in journal (Refereed)
    Abstract [en]

    The sales data from the engineering firm called Company X has a type of problem called "Big Q" that involve issues about time series analysis and the use of data transformations. According to Chatfield and Prothero (CP), the forecasts produced using a seasonal ARIMA model fitted to the log of the sales data produced unrealistic forecasts. The application of Box-Cox transformations to Company X's sales data provided a "useful" transformation of these data. The CP tried to find "useful" models that characterize the dynamics in the particular data appropriately, and thus produced sensible forecasts. The forecasting model proposed by CP and the alternative model proposed by Box and Jenkins were analyzed. As a result, both types of models provided to be quite good reasonable forecasts.

  • 9.
    Bisgaard, Søren
    et al.
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst.
    Kulahci, Murat
    Department of Informatics and Mathematical Modelling, Technical University of Denmark.
    Quality quandaries: Forecasting with seasonal time series models2008In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 20, no 2, p. 250-260Article in journal (Refereed)
    Abstract [en]

    Forecasting is increasingly a part of the standard list among quality engineers specifically the domain of Six Sigma and quality engineering that deals with operational problems in manufacturing and service organizations. One of the most versatile approaches is the so-called Box-Jenkins approach using regular and seasonal integrated autoregressive moving average. The international airline data has been used with a seasonal autoregressive integrated moving average time series model to demonstrate how seasonal ARIMA models can be used to model cyclic data and how the model can be used for short term forecasting.

  • 10.
    Bisgaard, Søren
    et al.
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst, University of Amsterdam.
    Kulahci, Murat
    University of Wisconsin-Madison.
    Quality quandaries: Improving and controlling business processes2001In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 14, no 2, p. 341-Article in journal (Refereed)
  • 11.
    Bisgaard, Søren
    et al.
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst.
    Kulahci, Murat
    Department of Industrial Engineering, Arizona State University, Tempe.
    Quality quandaries: Interpretation of time series models2005In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 17, no 4, p. 653-658Article in journal (Refereed)
  • 12.
    Bisgaard, Søren
    et al.
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst.
    Kulahci, Murat
    Department of Industrial Engineering, Arizona State University, Tempe.
    Quality quandaries: Practical time series modeling2007In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 19, no 3, p. 253-262Article in journal (Refereed)
    Abstract [en]

    Time series analysis is important in modern quality monitoring and control. The analysis has no precise methods and no single true, final answer. There are three general classes for stationary time series models: autoregressive (AR), moving average (MA) or the autoregressive moving average model. If in case a data is nonstationary, differencing before using ARMA model to fit to the data is necessary. The formulations for AR(p), MA(q) and the ARMA(p,q) has zero intercept which is attained by subtracting the average from the stationary data before modeling the process. If it is applied in a nonstationary data, there is a need to differenced either once or twice, adding a nonzero intercept term to the model. This implies that there is an underlying deterministic first or second order polynomial trend in the data. In reality, the type of model and the order necessary to adequately model a given process is not known. Hence, there is a need to determine the model that best fit the data based on looking at the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Since the time series modeling requires judgment and experience, an literative model is suggested. Once the model is fitted, diagnostic checks are conducted using the ACF and PACF. Series C consisting of 226 observations of the temperature of a chemical pilot plant has been used as an example.

  • 13.
    Bisgaard, Søren
    et al.
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst.
    Kulahci, Murat
    Industrial Engineering, Arizona State University, Tempe.
    Quality Quandaries: Practical Time Series Modeling II2007In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 19, no 4, p. 393-400Article in journal (Refereed)
  • 14.
    Bisgaard, Søren
    et al.
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst.
    Kulahci, Murat
    Department of Industrial Engineering, Arizona State University, Tempe.
    Quality quandaries: Process regime changes2007In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 19, no 1, p. 83-87Article in journal (Refereed)
    Abstract [en]

    Gaining understanding of process behavior and exploring the relationship between process variables are important prerequisites for quality improvement. In any diagnosis of a process, the quality engineer needs to try to understand and interpret relationships between inputs and outputs as well as between intermediate variables. Regime changes occassionally occur in the process engineering context. The tell tale sign of a regime change is most easily seen in scatter plots. Geographical analysis is proven to be useful in the early diagnostic phase of analyzing processes suspected ot having undergone regime changes.

  • 15.
    Bisgaard, Søren
    et al.
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst.
    Kulahci, Murat
    Department of Industrial Engineering, Arizona State University.
    Quality quandaries: Studying input-output relationships, part I2006In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 18, no 2, p. 273-281Article in journal (Refereed)
  • 16.
    Bisgaard, Søren
    et al.
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst.
    Kulahci, Murat
    Department of Industrial Engineering, Arizona State University, Tempe.
    Quality quandaries: Studying input-output relationships, part II2006In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 18, no 3, p. 405-410Article in journal (Refereed)
    Abstract [en]

    When analyzing process data to see if there exists an input variable that can be used to control an output variable, one should be aware of the possibility of spurious relationships. One way to check for this possibility is to carefully analyze the residuals. If they show signs of autocorrelation, the apparent relationship may be spurious. An effective method for checking such relationship is that of William S. Gosset.

  • 17.
    Bisgaard, Søren
    et al.
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst.
    Kulahci, Murat
    Department of Industrial Engineering, Arizona State University, Tempe.
    Quality quandaries: The application of principal component analysis for process monitoring2006In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 18, no 1, p. 95-103Article in journal (Refereed)
    Abstract [en]

    An overview of graphical techniques that are useful when dealing with process monitoring is given. Focus is on contemporaneous correlation. Specifically, principal component analysis (PCA), a method akin to a Pareto analysis is demonstrated. The geometry of PCA to enhance intuition is described.

  • 18.
    Bisgaard, Søren
    et al.
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst.
    Kulahci, Murat
    Department of Industrial Engineering, Arizona State University, Tempe.
    Quality Quandaries: The Effect of Autocorrelation on Statistical Process Control Procedures2005In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 17, no 3, p. 481-489Article in journal (Refereed)
  • 19.
    Bisgaard, Søren
    et al.
    Isenberg School of Management, University of Massachusetts Amherst.
    Kulahci, Murat
    Department of Informatics and Mathematical Modelling, Technical University of Denmark.
    Quality quandaries: Time series model selection and parsimony2009In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 21, no 3, p. 341-353Article in journal (Refereed)
    Abstract [en]

    Choosing an adequate model for a certain set of data is considered to be one of the more difficult tasks in time series analysis as experienced analysts are also having a hard time selecting such appropriate model. Thus, one popular approach have been discussed with the use of certain numerical criteria which is believed to be a useful input for the decision making process. However, using this technique solely is also not advisable on choosing a model but the use of judgement and the use of information criteria are more preferred. Specifically, the use of parsimonious mixed autoregressive model (ARMA) is more favorable to be used as it considers the context of the model as well as illustrating what is trying to be modeled and what model is to be used.

  • 20.
    Bisgaard, Søren
    et al.
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst.
    Kulahci, Murat
    Department of Informatics and Mathematical Modelling, Technical University of Denmark.
    Quality quandaries: Using a time series model for process adjustment and control2008In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 20, no 1, p. 134-141Article in journal (Refereed)
    Abstract [en]

    The behavior of a chemical manufacturing process can be characterized with a time series model. A time series model can control and adjust the manufacturing process. Time series control of a process is about the prediction that the process will deviate excessively from the target in the next time period and make the predicted difference to make compensatory adjustment in the opposite direction. A detailed example on how a nonstationary time series model can be be used to develop two types of charts has been provided, one for periodic adjustments of the process counteracting the naturally occurring common cause variability and one more traditional control chart based on the residuals to look for special causes. The nonstationary time series model requires the acknowledgment that processes are inherently nonstationary and work from that more realistic assumption rather than the traditional Shewhart model of a fixed error distribution around a constant mean. The process, once too far off from a given target, will be adjusted by bringing its level back to target while the data coming from the process continues to conform to the assumed nonstationary time series model.

  • 21.
    Bisgaard, Søren
    et al.
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst, University of St.Gallen.
    Kulahci, Murat
    University of St.Gallen.
    Robust product design: Saving trials with split-plot confounding2001In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 13, no 3, p. 525-530Article in journal (Refereed)
    Abstract [en]

    Robust production experimentation is described as an important quality engineering activity. A cake mix experiment performed to illustrate split-plot confounding was used to eliminate the low resolution of standard inner and outer array designs. A necessary amount of information was furnished by the split plot design due to switching between fractions. The robustness of the product was improved by identifying the interaction between enviromental and design factors

  • 22.
    Box, George E.P
    et al.
    Center for Quality and Productivity, University of Wisconsin-Madison.
    Bisgaard, Søren
    Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst.
    Graves, Spencer B.
    PDF Solutions, Inc., San Jose, CA.
    Kulahci, Murat
    Department of Industrial Engineering, Arizona State University, Tempe.
    Marko, Kenneth A.
    ETAS Group, Ann Arbor, MI , Ford Scientific Research, Dearborn, MI.
    James, John V.
    Ford Research Labs., Dearborn, MI.
    Gilder, John F. van
    General Motors Proving Ground, Milford, MI.
    Ting, Tom
    General Motors Research, Development and Planning, Warren, MI.
    Zatorski, Hal
    DaimlerChrysler, Auburn Hills, MI.
    Wu, Cuiping
    DaimlerChrysler Proving Grounds, Chelsea, MI.
    Performance Evaluation of Dynamic Monitoring Systems: The Waterfall Chart2003In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 16, no 2, p. 183-191Article in journal (Refereed)
    Abstract [en]

    Computers are increasingly employed to monitor the performance of complex systems. An important issue is how to evaluate the performance of such monitors. In this article we introduce a three-dimensional representation that we call a "waterfall chart" of the probability of an alarm as a function of time and the condition of the system. It combines and shows the conceptual relationship between the cumulative distribution function of the run length and the power function. The value of this tool is illustrated with an application to Page's one-sided Cusum algorithm. However, it can be applied in general for any monitoring system.

  • 23.
    Capaci, Francesca
    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.
    Kulahci, Murat
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering. Technical university of Denmark .
    Bergquist, Bjarne
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    The Revised Tennessee Eastman Process Simulator as Testbed for SPC and DoE Methods2019In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 31, no 2, p. 212-229Article in journal (Refereed)
    Abstract [en]

    Engineering process control and high-dimensional, time-dependent data present great methodological challenges when applying statistical process control (SPC) and design of experiments (DoE) in continuous industrial processes. Process simulators with an ability to mimic these challenges are instrumental in research and education. This article focuses on the revised Tennessee Eastman process simulator providing guidelines for its use as a testbed for SPC and DoE methods. We provide flowcharts that can support new users to get started in the Simulink/Matlab framework, and illustrate how to run stochastic simulations for SPC and DoE applications using the Tennessee Eastman process.

  • 24.
    Klefsjö, Bengt
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Westberg, Ulf
    Luleå tekniska universitet.
    TTT plotting and maintenance policies1996In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 9, no 2, p. 229-235Article in journal (Refereed)
    Abstract [en]

    One well-known maintenance policy, useful for nonrepayable parts primarily, is the age replacement policy. In that policy, the part is replaced (or repaired to an as-good-as-new state) at failure or when the part has reached the age T, whichever occurs first. The aim is to find the optimal replacement age T which minimizes the long-run maintenance cost per unit time. This maintenance policy was discussed in some recent papers by Brick et al. (1989) and Dodson (1994), and different solutions to obtain estimates of the optimal replacement age were suggested. However, there is another useful technique to handle this problem, namely to use TTT plotting. By using TTT plotting, it is easy to get a nonparametric estimate of the optimal replacement age under the age replacement policy when you have failure data or test data. The plotting technique had also been generalized recently to include censored data.

  • 25.
    Kulahci, Murat
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Discussion of “The Statistical Evaluation of Categorical Measurements: Simple Scales, but Treacherous Complexity Underneath’”2014In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 26, no 1, p. 40-43Article in journal (Refereed)
  • 26.
    Kulahci, Murat
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering. Technical University of Denmark, Lyngby, Denmark .
    Discussion on "Søren Bisgaard's contributions to Quality Engineering: Design of experiments"2019In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 31, no 1, p. 149-153Article in journal (Refereed)
  • 27.
    Kulahci, Murat
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Guest editorial2015In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 27, no 1, p. 1-Article in journal (Other academic)
  • 28.
    Kulahci, Murat
    Department of Informatics and Mathematical Modelling, Technical University of Denmark.
    Split-Plot Experiments with Unusual Numbers of Subplot Runs2007In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 19, no 4, p. 363-371Article in journal (Refereed)
    Abstract [en]

    In many experimental situations, it may not be feasible or even possible to run experiments in a completely randomized fashion as usually recommended. Under these circumstances, split-plot experiments in which certain factors are changed less frequently than the others are often used. Most of the literature on split-plot designs is based on 2-level factorials. For those designs, the number of subplots is a power of 2. There may however be some situations where for cost purposes or physical constraints, we may need to have unusual number of subplots such as 3, 5, 6, etc. In this article, we explore this issue and provide some examples based on the Plackett and Burman designs. Also algorithmically constructed D-optimal split-plot designs are compared to those based on Plackett and Burman designs.

  • 29.
    Kulahci, Murat
    et al.
    Department of Industrial Engineering, Arizona State University, Tempe.
    Box, George E.P
    Center for Quality and Productivity, University of Wisconsin-Madison.
    Catalysis of discovery and development in engineering and industry2003In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 15, no 3, p. 513-517Article in journal (Refereed)
    Abstract [en]

    study was performed on the catalysis of discovery and development in engineering and industry. It was found that the standard ideas of statistical design and analysis had less success in engineering because the courses offered by statistics departments depended on the one-shot paradigm, which was inappropriate for most of engineering experimentation. Although industry has long recognized the need for engineers possessing appropriate statistical skills, engineering departments in universities had been slow to appreciate that

  • 30.
    Kulahci, Murat
    et al.
    Department of Industrial Engineering, Arizona State University, Tempe.
    Box, George E.P
    Center for Quality and Productivity, University of Wisconsin-Madison.
    Erratum: Catalysis of discovery and development in engineering and industry (Quality Quandaries) (Quality Engineering 15:3 (513-517))2003In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 16, no 1, p. 165-Article in journal (Refereed)
    Abstract [en]

    study was performed on the catalysis of discovery and development in engineering and industry. It was found that the standard ideas of statistical design and analysis had less success in engineering because the courses offered by statistics departments depended on the one-shot paradigm, which was inappropriate for most of engineering experimentation. Although industry has long recognized the need for engineers possessing appropriate statistical skills, engineering departments in universities had been slow to appreciate that

  • 31.
    Kulahci, Murat
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Menon, Anil
    Celgene Corporation, 556 Morris Ave, Summit, NJ.
    Trellis Plots as Visual Aids for Analyzing Split Plot Experiments2016In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 29, no 2, p. 211-225Article in journal (Refereed)
    Abstract [en]

    The analysis of split plot experiments can be challenging due to a complicated error structure resulting from restrictions on complete randomization. Similarly, standard visualization methods do not provide the insight practitioners desire to understand the data, think of explanations, generate hypotheses, build models, or decide on next steps. This article demonstrates the effective use of trellis plots in the preliminary data analysis for split plot experiments to address this problem. Trellis displays help to visualize multivariate data by allowing for conditioning in a general way. They can also be used after the statistical analysis for verification, clarification, and communication.

  • 32. Kvarnström, Björn
    et al.
    Bergquist, Bjarne
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Vännman, Kerstin
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    RFID to improve traceability in continuous granular flows: an experimental case study2011In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 23, no 4, p. 343-357Article in journal (Refereed)
    Abstract [en]

    Traceability is important for identifying the root-causes of production related quality problems. Traceability can often be reached by adding identification markers on products, but this is not a solution when the value of the individual product is much lower than the incurred cost of a marking system. This is the case for continuous production of granular media. The use of Radio Frequency Identification (RFID) technique to achieve traceability in continuous granular flows has been proposed in the literature. We study through experiments different methods to improve the performance of such an RFID system. For example, larger transponders and multiple readers are shown to improve the RFID system performance.

  • 33.
    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).

  • 34.
    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.

  • 35.
    Vanhatalo, Erik
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Bergquist, Bjarne
    Special considerations when planning experiments in a continuous process2007In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 19, no 3, p. 155-169Article in journal (Refereed)
    Abstract [en]

    Discontinuous processes dominate experimental applications in practice as well as in literature. Continuous processes constitute a significant part of goods production, and the need to gain knowledge using experiments are as relevant in such environments as in, for example, parts production. We argue that the characteristics of continuous processes affect the prerequisites for experimental efforts to such an extent that they need special attention. To describe considerations when planning experiments in a continuous process, experiments performed in a blast furnace process are studied. We propose a tentative list of special considerations, which are discussed and summarized in a thirteen-step check list.

  • 36.
    Vanhatalo, Erik
    et al.
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Kvarnström, Björn
    Bergquist, Bjarne
    Vännman, Kerstin
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    A method to determine transition time for experiments in dynamic processes2011In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 23, no 1, p. 30-45Article in journal (Refereed)
    Abstract [en]

    Process dynamics is an important consideration during the planning phase of designed experiments in dynamic processes. After changes of experimental factors, dynamic processes undergo a transition time before reaching a new steady state. To minimize experimental time and reduce costs and for experimental design and analysis, knowledge about this transition time is important. In this article, we propose a method to analyze process dynamics and estimate the transition time by combining principal component analysis and transfer function-noise modeling or intervention analysis. We illustrate the method by estimating transition times for a planned experiment in an experimental blast furnace.

1 - 36 of 36
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