Modeling and Analysis of Operator Effects on Process Quality and Throughput in Mixed Model Assembly Systems

Abstract

With the increase of market fiuctiiation, 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.

Lean Manual Assembly 4.0: A Systematic Review

“…Analyzing the human operator characteristics and the process complexity can be used to maintain the process KPIs [202], to predict operator overload [203], or to assess human-originated quality problems [204]. Operator walking distances are a key input for kitting vs. line stocking decisions [205], and JIT kitting can be optimized by incorporating hybrid HRC systems [206].…”

Linking Emergence to the Complex Product System

“…Considering that abnormal state (breakdown and awaiting resources) may occur in actual process, Zhang [18] evaluated the probabilities corresponding to the entropy based on practice. Abad et al [19] focused on the linkage of input/output relationship and assessed a divergence between what is demanded and what is produced by the quality rate of product. Modrak and Soltysova [20] stressed on the layout complexity and took into account the probability of parts being processed on individual machine according to scheduling order.…”

Surrogate Model-Based Control Considering Uncertainties for Composite Fuselage Assembly

“…It is difficult to obtain an accurate estimation of those parameters, especially for large composite parts with complex structures. Other than the SoV type of models [3][4][5][6]9,10], other types of models have been used to improve the quality control of assembly process, such as robust pattern-matching technique for variation source identification [11], adaptive product, process and tooling design strategy for optimal dimensional quality [12], modeling of operator effects on process quality [13], and variation analysis using component geometric covariance [14]. However, these models are mainly focused on variation analysis and modeling instead of shape control; thus they cannot be used directly in the composite fuselage shape control problem.…”