Mixed model manufacturing systems are increasingly used to meet global competition by providing a broad variety of products to customers. The increase of product variety adds more complexity to production processes, thus, leading to a negative effect on the performance of production processes. Therefore, it is of great interest to effectively measure such complexity and to quantify its effect on manufacturing system performance. In this paper, a set of complexity metrics are proposed for measuring the complexity of different elements in a manufacturing system. These metrics were defined by constructing a linkage with the communication system's framework. Different from those existing complexity measures defined in the literature, this paper considers production quality into the measure of the process capability on handling the complexity induced by the input demand variety. Examples are given in the paper to discuss different properties of the defined metrics and their potential applications.

With the increase of market fluctuation, assembly systems moved from a mass production scheme to a mass customization scheme. Mixed model assembly systems (MMASs) have been recognized as enablers of mass customization manufacturing. However, effective implementation of MMASs requires, among other things, a highly proactive and knowledgeable workforce. Hence, modeling the performance of human operators is critically important for effectively operating these manufacturing systems. But, certain cognitive factors have seldom been considered when it comes to modeling process quality of MMASs. Thus, the objective of this paper is to introduce an integrated modeling framework by considering the factors—both intrinsic (such as work experience, mental deliberation time, etc.) and extrinsic (such as task complexity)—that affect the operator’s performance. The proposed model is justified based on the findings presented in the psychological literature. The effect of these factors on process operation performance is also investigated; these performance measures include process quality, throughput, and process capability in regard to handling complexity induced by product variety in MMASs. Two examples are used to demonstrate potential applications of the proposed model.

Mass customization appeared in the 1990’s as a necessary production paradigm to satisfy an increase in the market demand’s variability. Mixed model assembly systems were introduced to adopt mass customization schemes. One of the key requirements of a correct implementation of mixed model assembly systems is a high performance workforce [1], with special emphasis on assembly systems operator’s performance.

Modeling of human operators’ performance in an assembly environment is a particularly difficult task, mainly because the variables involved come from very different sources. Furthermore, a model that successfully characterizes operator’s performance must include variables that are intrinsic and extrinsic to the operator.

In this work we will consider two intrinsic variables: the experience and time used to think before performing a task; and one extrinsic variable: the demand uncertainty.

Increasing global competition demands that the manufacturing industry move from mass production into mass customization production in order to provide more varieties of products and thus satisfy customer demands. It has been shown that the increase of product variety has a negative impact on manufacturing system performance. Therefore, it is essential to understand how product variety complicates an assembly system, affecting its operation performance. Such knowledge, once validated, can be further used to improve manufacturing system design and operation.

The objective of this dissertation is to develop an enhanced general methodology for modeling and analyzing process complexity for mixed model assembly systems. The following fundamental research has been conducted:

(*) A set of complexity metrics are proposed for measuring the complexity of various elements in a manufacturing system. These metrics are proposed by constructing a linkage with the communication system framework. Unlike the existing complexity measures defined in the literature, this research is the first effort to include production quality into the measurement of how well a manufacturing system can handle the process complexity induced by the input demand variety.

(*) A systematic method is developed for efficiently and explicitly representing complex hybrid assembly system configurations by the use of algebraic expressions, which can overcome drawbacks of two widely used representation methods: block diagrams and adjacency matrices. By further extending the algebraic configuration operators, the algebraic performance operators are defined for the first time for the systematic evaluation of system performance metrics; these metrics include quality conforming rates for individual product types at each station, process capability for handling complexity, and production cycle time for various product types. Therefore, when compared to other methods, the proposed algebraic expression modeling method also has a unique merit in providing computational capability for automatically evaluating various system performance metrics.

(*) An integrated model is introduced for the first time to describe the effect of operator‟s factors on the process operation performance. The model includes intrinsic factors such as the operators‟ thinking time and experience; and extrinsic factors such as the choice task complexity induced by the product variety in mixed model assembly systems.