{"title":"Effects of product complexity on human learning in assembly and disassembly operations","authors":"E. Verna, G. Genta, M. Galetto","doi":"10.1108/jmtm-04-2023-0135","DOIUrl":null,"url":null,"abstract":"PurposeThe purpose of this paper is to investigate and quantify the impact of product complexity, including architectural complexity, on operator learning, productivity and quality performance in both assembly and disassembly operations. This topic has not been extensively investigated in previous research.Design/methodology/approachAn extensive experimental campaign involving 84 operators was conducted to repeatedly assemble and disassemble six different products of varying complexity to construct productivity and quality learning curves. Data from the experiment were analysed using statistical methods.FindingsThe human learning factor of productivity increases superlinearly with the increasing architectural complexity of products, i.e. from centralised to distributed architectures, both in assembly and disassembly, regardless of the level of overall product complexity. On the other hand, the human learning factor of quality performance decreases superlinearly as the architectural complexity of products increases. The intrinsic characteristics of product architecture are the reasons for this difference in learning factor.Practical implicationsThe results of the study suggest that considering product complexity, particularly architectural complexity, in the design and planning of manufacturing processes can optimise operator learning, productivity and quality performance, and inform decisions about improving manufacturing operations.Originality/valueWhile previous research has focussed on the effects of complexity on process time and defect generation, this study is amongst the first to investigate and quantify the effects of product complexity, including architectural complexity, on operator learning using an extensive experimental campaign.","PeriodicalId":16301,"journal":{"name":"Journal of Manufacturing Technology Management","volume":null,"pages":null},"PeriodicalIF":7.3000,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Manufacturing Technology Management","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1108/jmtm-04-2023-0135","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
PurposeThe purpose of this paper is to investigate and quantify the impact of product complexity, including architectural complexity, on operator learning, productivity and quality performance in both assembly and disassembly operations. This topic has not been extensively investigated in previous research.Design/methodology/approachAn extensive experimental campaign involving 84 operators was conducted to repeatedly assemble and disassemble six different products of varying complexity to construct productivity and quality learning curves. Data from the experiment were analysed using statistical methods.FindingsThe human learning factor of productivity increases superlinearly with the increasing architectural complexity of products, i.e. from centralised to distributed architectures, both in assembly and disassembly, regardless of the level of overall product complexity. On the other hand, the human learning factor of quality performance decreases superlinearly as the architectural complexity of products increases. The intrinsic characteristics of product architecture are the reasons for this difference in learning factor.Practical implicationsThe results of the study suggest that considering product complexity, particularly architectural complexity, in the design and planning of manufacturing processes can optimise operator learning, productivity and quality performance, and inform decisions about improving manufacturing operations.Originality/valueWhile previous research has focussed on the effects of complexity on process time and defect generation, this study is amongst the first to investigate and quantify the effects of product complexity, including architectural complexity, on operator learning using an extensive experimental campaign.
期刊介绍:
The Journal of Manufacturing Technology Management (JMTM) aspires to be the premier destination for impactful manufacturing-related research. JMTM provides comprehensive international coverage of topics pertaining to the management of manufacturing technology, focusing on bridging theoretical advancements with practical applications to enhance manufacturing practices.
JMTM seeks articles grounded in empirical evidence, such as surveys, case studies, and action research, to ensure relevance and applicability. All submissions should include a thorough literature review to contextualize the study within the field and clearly demonstrate how the research contributes significantly and originally by comparing and contrasting its findings with existing knowledge. Articles should directly address management of manufacturing technology and offer insights with broad applicability.