Engineering Implications of Semi-Repetitive Processes (4-part Series on “Wiley StatsRef: Statistics Reference Online”; Now Published)

I am pleased to share that my new 4-part series on semi-repetitive processes is now published (February 18, 2026).

Below, please find abstracts and links for all four parts.

Part 1: Engineering Implications of Semi-Repetitive Processes

Abstract: Process predictability may be impaired in two ways — by lack of process information and by lack of process repetitiveness (process is partially repetitive (semi-repetitive) or not repetitive at all). In this four-part series, we address statistical engineering implications of the latter, namely, how lack of complete repetitiveness affects engineering and managerial decisions, required in the analysis and design of semi-repetitive processes and in their management. In this first part, we deliver an overview of the other three parts of the series, addressing statistical engineering questions and problems this series is intended to respond to, and the adaptations needed (relative to repetitive or non-repetitive processes). In particular, we address the dual-component variation of semi-repetitive processes (second part), measuring process repetitiveness (third part) and assessing reliability of process-time predictions, as we move from repetitive to semi-repetitive to non-repetitive processes (fourth part).

Part 2: The Dual-Component Variation of Semi-Repetitive Processes

Abstarct: This is the second of a four-part series on engineering implications of semi-repetitive (SR) processes. In this part, we briefly summarize the “Random Identity Paradigm”, and in compliance with this paradigm make a distinction between two sources of variation affecting SR processes, identity/work-content instability and error. This dual-component variation affects appreciably distributions associated with SR processes. We formulate requirements for models of the dual-component variation and review examples of published models that fulfill these requirements. Adding a new requirement relating to error variation, a new model is partially developed that fulfills this requirement. The link between process repetitiveness and process predictability is addressed as preparation for the third part of this series.

Part 3: Measuring Repetitiveness of Semi-Repetitive Processes

Abstract: This is the third of a four-part series on engineering implications of semi-repetitive processes. In the fourth part, we address how process degree of repetitiveness affects its predictability. Here we explore measuring of process repetitiveness. A measure of the latter had been published, denoted Process Repetitiveness Measure (PRM). It is based on the standardized departure of the mode from the mean and is expressed in terms of the first four moments of the process distribution. Any measure that can be shown to be linearly related to PRM may obviously also serve to measure process repetitiveness. In this article, we explore two additional measures – a probability measure and one based on the coefficient of variation (CV). We show that CV is qualified for this role, having the added benefit of sparing the need to estimate third and fourth moments (known for their large standard errors). CV is appreciated both theoretically, by examining a small sample of arbitrarily selected statistical distributions, and empirically, using a database of surgery durations.

Part 4: Reliability of Process-Time Prediction for Semi-Repetitive Processes

Abstract: This is the fourth of a four-part series on “Engineering Implications of Semi-repetitive Processes”. In Part 3, we have examined and compared several candidate measures to evaluate process repetitiveness, the basis for evaluating predictability of a semi-repetitive process. In particular, we have evaluated the coefficient of variation (CV) and found it to be statistically linearly related to process repetitiveness measure (PRM), which measures process repetitiveness based on the standardized distance of the mode from the mean. In this entry, we employ CV to address how process degree of repetitiveness affects its predictability. More specifically, we formulate for semi-repetitive processes a statistical criterion by which to determine when process-time predictions cease to be acceptable due to insufficient process repetitiveness.