Modeling of Manufacturing Complexity in Mixed-Model Assembly Lines

Abstract

Mixed-model assembly lines have been recognized as a major enabler to handle product variety. However, the assembly process becomes very complex when the number of product variants is high, which, in turn, may impact the system performance in terms of quality and productivity. This paper considers the variety induced manufacturing complexity in manual mixed-model assembly lines where operators have to make choices for various assembly activities. A complexity measure called “operator choice complexity” (OCC) is proposed to quantify human performance in making choices. The OCC takes an analytical form as an information-theoretic entropy measure of the average randomness in a choice process. Meanwhile, empirical evidences are provided to support the proposed complexity measure. Based on the OCC, models are developed to evaluate the complexity at each station and for the entire assembly line. Consequently, complexity can be minimized by making system design and operation decisions, such as error-proof strategies and assembly sequence planning.