Selection of the Most Suitable Statistical Process Control Approach for Short Production Runs: A Decision-Model

Abstract

Nowadays, customers are increasingly claiming not only for better quality products at the lowest possible cost, but also demanding customized solutions to satisfy their specific, sometimes unique, needs and wants. Due to this, manufacturing companies are seeking to adopt higher agile production models, such as mass customization strategies. In the quality control field, statistical process control (SPC) methods have been widely used to monitor process performance and detect abnormal situations in its behavior; however, traditional SPC approaches are usually not appropriate for small lot or batch sizes, for the start-up of a process, and for situations where a high variety of mixed products exist. Such situations are within the scope of the so called short production runs. Several SPC schemes have been proposed for short-run environments; all of them have their own advantages, shortcomings, and more suitable for certain production scenarios. This paper provides an up-to-date literature review on the topic, identifies classes of SPC short-run methods, and presents a decision-model that guides production managers in the choice of the most appropriate SPC short-run approach. The model was validated in a textile production company, and is being incorporated into a software package.