Assembly Quality Methodolgy: An Application of the Defect Prediction to Audio Equipment Assembled at Various Manufacturing Sites

Shibata, Hirokazu; Cheldelin, Brent; Ishii, Kosuke

doi:10.1115/detc2001/dfm-21194

Cited by 6 publications

(5 citation statements)

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“…According to Swain and Guttmann [18], human errors can be defined as "any member of a set of human actions that exceed some limit of acceptability, i.e., an out-of-tolerance action, where the limits of tolerable performance are defined by the system." It has been acknowledged that assembly errors are one of the main causes of defects in manufacturing systems [14,[19][20][21][22]. For example, in research conducted at Fuji Xerox China, it was found that 20% of the total defects in copier assembly were caused by operator errors [19].…”

Section: Literature Reviewmentioning

confidence: 99%

“…For example, in research conducted at Fuji Xerox China, it was found that 20% of the total defects in copier assembly were caused by operator errors [19]. Similarly, data from a production facility of audio equipment in Japan showed that defects in manual assembly processes represented 17% of total defects [20]. In the case of car manufacturing, in a study conducted at a Volvo plant in Sweden, researchers analyzed the quality outcomes of a production volume of 47061 cars and found several errors in the assembly process, such as incorrectly installed parts, incorrect torque applied to components, missing bolts and clips, scratches and dirt on components, and insufficiently tightened cables or pipes [14].…”

Section: Literature Reviewmentioning

confidence: 99%

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Effect of Changes in the Sequence of Assembly Operations on Error Rates: A Case Study From the Car Manufacturing Industry

Real,

Torres

2024

IEEE Access

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Human operator-induced assembly errors affect the quality of car manufacturing. Understanding the factors influencing assembly errors is critical for quality improvement. The sequence of assembly operations, a factor markedly affecting cognitive load, remains largely understudied. We aimed to assess the effect of changing the sequence of assembly operations on error rates through four field experiments conducted in a car manufacturing plant. The parts (and errors) under study were child lock labels (missing labels), sunroofs (missing bolts), windshield wiper arms (loose bolts), and armrests (wrong selection). Parts were chosen based on data from quality records, and they represent different scenarios regarding the sequencing of assembly operations. Minitab was used to conduct the statistical test for two proportions at a significance level of 0.05. The experiments ran for a period varying from 9 to 25 weeks (22292 to 138456 cars). All four experiments exhibited significant differences in the proportion of errors. MODAPTS cycle-time calculations revealed no negative effect of assembly sequence variations on productivity. The study findings show that changing the assembly operation sequence can reduce the error rates, possibly due to the intermediary effect in reducing the operator's cognitive load. Overall, realizing quality improvement requires optimizing the assembly operation sequence in terms of time and productivity while considering its possible impact on error rates.INDEX TERMS Assembly systems, human factors, cognitive load, quality control, automobile manufacture.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Effect of Changes in the Sequence of Assembly Operations on Error Rates: A Case Study From the Car Manufacturing Industry

Real,

Torres

2024

IEEE Access

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“…With this consideration in mind, Hinckley (1993) found that defect per unit (DPU) was positively correlated with total assembly time and negatively correlated with number of assembly operations. Moreover, Shibata et al (2001) developed a quantitative predictor that worked not only on the whole product but also on its modular units. In this method, the newly proposed floating threshold assembly time improved the correlation between complexity factor and DPU.…”

Section: Previous Workmentioning

confidence: 99%

Measuring the assembly quality from the operator mistake view: a case study

Liu²,

Lai

2009

Assembly Automation

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PurposeThe purpose of this paper is to focus on the assembly quality of copier products, specifically, concentrating on the prediction of the operator‐induced assembly defect.Design/methodology/approachBased on the Shibata model, the design‐based assembly complexity is redesigned. And the Sony Standard Time is replaced by the Fuji Xerox Standard Time in the calculation of the process‐based assembly complexity. Furthermore, different correlation functions are attempted and comparatively studied in the regression analysis. Thereby, a new defect rate prediction model is proposed and validated with three copier assembly cases.FindingsThe new proposed model is much more accurate and stable in the human‐induced assembly defect prediction in copier production.Practical implicationsThe proposed model can be used to ensure the assembly quality by removing potential defects at the structure and process design stages. Meanwhile, with this model, the interactions between the engineers and designers can be more effective.Originality/valueThis paper presents a novel assembly defect rate prediction model for copier assembly quality management.

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“…The process-based complexity factor is derived from a time standard defined for a set of assembly tasks. Initially proposed by [Hinckley, 1998] and utilized by [Shibata et al, 2001], [Shibata, 2002], [Shibata et al, 2003a,b] and [Su et al, 2009] the process complexity factor (Cf P ) shows satisfactory correlation with defect rates and is defined as:…”

Section: Process-based Complexity Factormentioning

confidence: 99%

“…Research by [Shibata et al, 2001[Shibata et al, , 2003a, [Hinckley, 1998] and recently [Su et al, 2009] report a strong co-relation between the complexity factors (product and process) and Defects per Unit (DPU). These case studies focus on data of processes that had already reached a steady state.…”

Section: Steady-state Defects Per Unitmentioning

confidence: 99%

Learning and continuous process improvement during new product development

Χατζημιχάλη¹

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Consumer attitudes and fashion trends during the last decades increased the need for varied product designs, imposing severe constraints in manufacturing and especially in traditionally mass production processes. Additionally, the workforce is shifting increasingly towards more cognitive and problem-solving tasks that are di#cult to automate. In this context, learning faster than competition is one of the most critical and sustainable advantages that rms can develop. The purpose of this study is to research the implications of the learning curve theory in todays' manufacturing systems. In this research, I attempt to pose some unstructured research questions and explore rsthand uncharted research territories. Based on an empirical study in ceramic tile manufacturing, I critically examine the learning curve theory in its potential to address current issues in an actual production environment. The challenge was to discover new potentials of the learning curve and highlight the details of the learning process in micro scale to reveal phenomena usually neglected in the literature. Quantitative insights from this study have led to a better understanding of the role of learning in modern production systems. In essence, this research reveals a missing link between the role of speci c products and process improvement theories. As a result of this study, an integrative model of learning is proposed that takes under consideration both system dynamics and product idiosyncrasies

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Assembly Quality Methodolgy: An Application of the Defect Prediction to Audio Equipment Assembled at Various Manufacturing Sites

Cited by 6 publications

References 0 publications

Effect of Changes in the Sequence of Assembly Operations on Error Rates: A Case Study From the Car Manufacturing Industry

Effect of Changes in the Sequence of Assembly Operations on Error Rates: A Case Study From the Car Manufacturing Industry

Measuring the assembly quality from the operator mistake view: a case study

Learning and continuous process improvement during new product development

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