Abstract. Systems thinking is commonly accepted as the backbone of a successful systems engineering approach. As such, the Body of Knowledge and Curriculum to Advance Systems Engineering (BKCASE) team chose to leverage a systems thinking based tool, called Systemitool, to describe our project to the vast audience that would potentially become involved directly or indirectly in the success of the project. This paper describes the process and steps used by the authors and the BKCASE team to develop the project's systemic diagram, or Systemigram TM , and the story behind the project, the products, and the vision of the BKCASE project. The goal of the paper is to provide guidance so that readers can leverage the lessons learned from this effort to successfully develop their own project definitions and stories.
Cumulative sum (CUSUM) control charts are very effective at detecting persisting special causes. The most common CUSUM chart assumes that the process measurement being monitored follows the normal distribution. Many industrial problems yield measures with skewed, positive distributionsÐexamples are component reliabilities, times to completion of tasks and insurance claims. Non-normal measures such as these should not be monitored using procedures based on the normal distribution. The inverse Gaussian distribution provides a¯exible distribution that can be used to model positive skew quantities, and therefore provides an effective framework for statistical process control on processes producing such measures. This paper de®nes the optimal CUSUM control chart schemes for location and shape of the inverse Gaussian distribution and evaluates its performance in detecting step changes in each of these parameters. The inverse Gaussian distribution has been shown to be a good ®t to a long record of task completion times on a General Motors assembly line. We extend this application, showing how our CUSUMS may be used to detect changes in the distribution of these task completion times.
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