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The Applicability of Statistical Process Control to Systems Involving People Processes and Business Rhythms

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  • Jason Dever
  • Thomas A. Mazzuchi
  • Shahram Sarkani
  • Matthew J. Mihalcin
  • Alex Loewenthal

Abstract

The operation and maintenance (O&M) activities of systems can account for 75% of total lifecycle cost. To effectively manage cost, optimize system “on” time, and mitigate defects/failures during the O&M phase of a system's lifecycle, the application of systems monitoring and control is encouraged. Statistical process control (SPC) in general, the control chart specifically, is the most common monitoring approach. The control chart provides alerts with respect to the behavior of systems and processes, as well as changes in process variability. Data applied to control charts is assumed to adhere to a normal distribution, a constraint often satisfied in manufacturing and similar industries where the natural variation in the process or system follows the Gaussian distribution. Systems involving people processes and business rhythms can compromise the normality assumption, reducing the reliability of SPC. Through the application of SPC, this paper proposes a novel approach to monitoring operational systems in the systems engineering O&M phase for the express purpose of reducing high costs by mitigating system discrepancies and uncovering inefficiencies. This paper focuses on processes that require 100% system data sampling due to the operational nature of the system.

Suggested Citation

  • Jason Dever & Thomas A. Mazzuchi & Shahram Sarkani & Matthew J. Mihalcin & Alex Loewenthal, 2014. "The Applicability of Statistical Process Control to Systems Involving People Processes and Business Rhythms," Systems Engineering, John Wiley & Sons, vol. 17(2), pages 193-203, June.
    • Handle: RePEc:wly:syseng:v:17:y:2014:i:2:p:193-203
    DOI: 10.1002/sys.21262
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    References listed on IDEAS

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    1. B L MacCarthy & T Wasusri, 2001. "Statistical process control for monitoring scheduling performance—addressing the problem of correlated data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(7), pages 810-820, July.
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