A novel approach to modeling steady-state process-time with smooth transition from repetitive to semi-repetitive to non-repetitive (memoryless) processes

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Defining properly the time distribution of a steady-state process is crucial to its management. A common practice is to assume that process-time is normally distributed for repetitive processes (process has constant work-content), and exponentially for non-repetitive processes (memoryless; no characteristic work-content). This dichotomous distinction ignores the majority of processes, residing in between the two extreme scenarios, the semi-repetitive ones. These processes own a characteristic duration time (as reflected in the mode), yet part of work-content (“process identity”) randomly varies between cycles. In this paper, we develop a unified platform to model process-time, comprising all three types of processes. The effects of work-content instability on shape characteristics of process-time distribution are studied, and process repetitiveness measure, possibly to be used to monitor work-content instability, is defined. The generalized gamma distribution is employed to approximate the (unknown) distribution of process-time sample mean, becoming exact for the two extreme scenarios (repetitive and non-repetitive processes).

Original languageEnglish
Pages (from-to)220-235
Number of pages16
JournalQuality and Reliability Engineering International
Volume40
Issue number1
DOIs
StatePublished - 1 Feb 2024

Keywords

  • generalized gamma distribution
  • generalized memoryless property
  • identity-full/less distributions
  • modeling process-time
  • process repetitiveness measure
  • process work-content instability

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research

Fingerprint

Dive into the research topics of 'A novel approach to modeling steady-state process-time with smooth transition from repetitive to semi-repetitive to non-repetitive (memoryless) processes'. Together they form a unique fingerprint.

Cite this