Overall equipment effectiveness (OEE) and process capability (PC) measures: A relationship analysis

Abstract

Purpose

Overall equipment effectiveness (OEE) and process capability (PC) are commonly used and well‐accepted measures of performance in industry. These measures, however, are traditionally applied separately and with different purposes. The purpose of this paper is to investigate the relationship between OEE and PC, how they interact and impact each other, and the possible effect that this relationship may have on decision making.

Design/methodology/approach

The paper reviews the OEE and PC background. Then, a discrete‐event simulation model of a bottling line is developed. Using the model, a set of experiments are run and the results interpreted using graphical trend and impact analyses.

Findings

The paper demonstrates the relationship between OEE and PC and suggests the existence of a “cut‐off point” beyond which improvements in PC have little impact on OEE.

Practical implications

PC uses the capability indices (CI) to help in determining the suitability of a process to meet the required quality standards. Although statistically a Cp/Cpk equal to 1.0 indicates a capable process, the generally accepted minimum value in manufacturing industry is 1.33. The results of this investigation challenge the traditional and prevailing knowledge of considering this value as the best PC target in terms of OEE.

Originality/value

This paper presents a study where the relationship between two highly used measures of manufacturing performance is established. This provides a useful perspective and guide to understand the interaction of different elements of performance and help managers to take better decisions about how to run and improve their processes more efficiently and effectively.

Citation

Arturo Garza‐Reyes, J., Eldridge, S., Barber, K.D. and Soriano‐Meier, H. (2010), "Overall equipment effectiveness (OEE) and process capability (PC) measures: A relationship analysis", International Journal of Quality & Reliability Management, Vol. 27 No. 1, pp. 48-62. https://doi.org/10.1108/02656711011009308