Abstract
Existing methods for evaluating manufacturing process chain complexity consider the number of machines, state of machines, number of parts, operation time, and processing sequence of parts. However, such evaluation methods ignore human factors. To consider human factors, human cognitive decision‐making process factors are considered in the complexity evaluation of production processes. Accordingly, a new objective evaluation method of the human factor complexity is proposed. In the proposed method, sewing operations are taken as an example, and the human factor complexity is classified into perceived and cognitive complexity. Information entropy is used to measure cognitive complexity according to the type and quantity of sewing workers’ cognitive activities. The results show that various methods have significant differences in the evaluation of the complexity level of the production process chain. Specifically, the calculation results of the proposed evaluation method are much greater than those of other methods. This indicates that human cognitive and perceived complexities account for a large proportion. Therefore, human factor complexity cannot be omitted.
Highlights
The applications of manufacturing system complexity are an active research area
Based on Method 4, we evaluate the manufacturing process chain complexity of garment production considering human factors
Evaluation Results of Method 4. e sewing process is similar to the assembly process, and the cut pieces can be regarded as individual parts
Summary
The applications of manufacturing system complexity are an active research area. Complexity negatively impacts the attributes of manufacturing systems, such as productivity [4], profit [5], and quality [6]. E research on the complexity of manufacturing systems mainly focuses on processing and assembly. Static complexity is structural complexity, which is related to the structure and configuration of manufacturing systems. It includes various elements such as people, machines, cache, logistics, and the relationship among them. Frizelle and Woodcock [2] were the first to use information entropy to evaluate manufacturing system complexity. Deshmukh [8] used information entropy to evaluate structural complexity and provided the basis for static and dynamic complexity evaluation. Modrak and Zuzana [9]
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