Modeling impact of choice complexity on production rate in mixed-model assembly system

Abstract

The increasing variety of products complicates the mixed-model assembly process and affected the mixedmodel assembly system in terms of product quality and productivity. Choice complexity comes from the process of making choices for various assembly operations due to the product variety and impacts production rate which is the performance measure of the system. The choice complexity is measured with information entropy, and the relational expression between choice complexity and error rate is analyzed by means of those research finds on average reaction time and speed-accuracy trade-off. The main achievement of our study is establishing an artificial neural network meta-model for the impact of choice complexity on production rate. The meta-model performs better than a multiple linear regression meta-model in terms of experiment results and appears to be the optimal model of the impact of choice complexity on production rate in the mixed-model assembly system.

Managing Product Complexity and Variety for Operational Performance through an Integrated Green Supply Chain

“…The finding of the current study shows an alignment with the empirical conclusion of Fan, Cheng, Li, and Lee (2016) that product complexity is positively related with operational performance. Empirical observations on similar grounds cited by Wang, Rao, and Wang (2012) revealed a positive impact of product complexity and operational performance. According to Moseley (2017), although a significant impact exists between product complexity and operational performance, the direction (positive/negative) varies from industry to industry and firm's ability to cope up with the stress of variations at the base model level without compromising the operational performance.…”

Identifying Optimal Granularity Level of Modular Assembly Supply Chains Based on Complexity-Modularity Trade-Off

“…To formally define the complexity of modular ASCs, we adopt the following definition proposed by [32] as a base frame: "Complexity of a network-based system is a function of i) the complexities of individual components, ii) the complexities of pair-wise interactions, and iii) the effects of the system's architectural pattern, which makes the management of the system mentally difficult and error-prone". Following the definition given above, the complexity of an ASC (C) is defined as a combination of the inherent complexity of system entities [17] 2002 Vachon and Klassen [13] 2002 Blecker et al [24] 2005 Hoole [25] 2005 Sivadasan et al [26] 2006 Hu et al [2] 2008 Bozarth et al [10] 2009 Sivadasan et al [18] 2006 Isik [27] 2010 Wang and Cheng [28] 2010 Isik [6] 2011 Wang et al [29] 2012 Cheng et al [30] 2014 Bode and Wagner [9] 2015 Modrak and Soltysova [22] 2017 Hamta et al [31] 2018…”

“…In this research, the OCC model proposed by [36] is adopted to assess the inherent complexity of assembly systems. OCC is a widely used information-theoretic complexity quantification model for mixed-model manual assembly systems [2], [19], [20], [28], [29], [31], [36]- [42] relating complexity to the varietyinduced operational uncertainty associated with assembly choices that are made by assembly operators. In such a sense, OCC is only able to measure variety-induced complexity in manual assembly systems.…”