Assessment of manual assembly complexity: a theoretical and empirical comparison of two methods

Falck, Ann-Christine; Tarrar, Malin; Mattsson, Sandra; Andersson, Lars Gustaf; Rosenqvist, Mikael; Söderberg, Rikard

doi:10.1080/00207543.2017.1330571

Cited by 17 publications

(14 citation statements)

References 30 publications

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“…Another possibility would be the creation of a method, guide or checklist tool from this more holistic perspective. Many different methods for mapping cognitive and/or mental workload already exist (e.g., [ 13 , 19 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ]), but the vast majority of them avoid combining multiple demand/resource factors with a multi-level (micro to macro) responsibility perspective. It would in that case be crucial to define which roles in a manufacturing organization are to be involved in the use of the tool, as the broad scope of what would be considered a cognitive demand and/or resource could befall very different areas of responsibility.…”

Section: Discussionmentioning

confidence: 99%

“…With regard to cognitive and/or mental loading in manufacturing contexts, several previous works bring up a variety facets to consider: how assemblers handle disruptions [ 6 , 7 , 8 ], overall process flow [ 9 ], effects of assembler age [ 10 , 11 , 12 ], interactions between physical and mental loading [ 8 ], assembler experience [ 13 ], boredom [ 14 ], efficacy and appropriateness of tools, materials and instructions [ 15 , 16 , 17 ], time pressure [ 18 ] and the effects of task complexity [ 1 , 13 , 19 , 20 ], just to name a few. This variety in the literature suggests that the scope of what constitutes mental or cognitive loading for assembly workers emerges from a systemic combination of any of these factors.…”

Section: Introductionmentioning

confidence: 99%

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Cognitive Ergonomics of Assembly Work from a Job Demands–Resources Perspective: Three Qualitative Case Studies

Bergman

Berlin

Chafi

et al. 2021

IJERPH

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In manufacturing companies, cognitive processing is required from assembly workers to perform correct and timely assembly of complex products, often with varied specifications and high quality demands. This paper explores assembly operators’ perceptions of cognitive/mental workload to provide a holistic understanding of the work conditions that affect cognitive demands and performance. While the physical loading aspects of assembly work are well known, most empirical literature dealing with cognitive/mental loading in manufacturing tends to examine a few particular aspects, rather than address the issue with a holistic system view. This semi-structured interview study, involving 50 industrial assembly operators from three Swedish companies, explores how assemblers perceive that their cognitive performance and well-being is influenced by a wide variety of factors within the context of mechanical product assembly. The interview transcripts were analysed using a priori coding, followed by bottom-up Thematic Analysis. The results indicate that a variety of systemic effects on assemblers’ cognitive performance can be classified as job demands or resources. Quite often, the absence of a resource mirrors a related demand, and “good assembly conditions”, as described by the interviewees, often re-frame demands as desirable challenges that foster motivation and positive feelings towards the work. The identified demands and resources stem from task design, timing, physical loading, intrinsic and extrinsic motivators, social teamwork and the product’s “interface” design. Despite organisational differences and conditions between the three companies that took part in the study, the results are largely consistent.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cognitive Ergonomics of Assembly Work from a Job Demands–Resources Perspective: Three Qualitative Case Studies

Bergman

Berlin

Chafi

et al. 2021

IJERPH

Self Cite

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show abstract

“…Samy and ElMaraghy (2012) utilized regression analysis to reveal the effect of part complexity on assembly equipment complexity, and they found a positive relation as expected. Falck et al (2017) compared the two methods, i.e. basic assembly complexity and complexity index, to obtain feedback and learn from operators' experience to develop better predictive assessment criteria.…”

Section: Manufacturing Complexitymentioning

confidence: 99%

A decision support system for proactive failure prevention: a case in a leading automotive company

Unver

Kabak

Topçu

et al. 2020

JEIM

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PurposeIn the automotive industry, the high process complexity becomes an important issue because of the increased number of product and process variants demanded by the customers. To avoid quality defects in assembly and losses in such a complex manufacturing environment, new predictive support systems are required. This study aims to develop a multiple attribute decision support system (DSS) for the prediction and quantification of the risk of failures on the workstations of a leading Turkish automotive manufacturing company.Design/methodology/approachInitially, the factors affecting the failures in workstations and the attributes to evaluate the factors are identified. Subsequently, the relations among the attributes are specified and priorities of them are calculated. Finally, the risk of failures is calculated and tested in a pilot study and validated with real production data.FindingsTo the best of authors’ knowledge, this is a unique study that computes the risk scores on the workstations via DSS. The DSS has various advantages for improvements of the manufacturing quality: the risk of failures can be detectable and comparable, the effect of changes in the design of new workstations can be observed. Stations that have medium or high complexity scores demonstrated strong correlation with failure rates. A sensitivity analysis is conducted to predict the effect of improvement actions on the riskiness of the workstations.Originality/valueHigh level of production complexity becomes a crucial issue for companies that use various production processes. Considering this fact, it is a requirement for companies to observe and monitor the risk factors, especially in the assembly lines to be able to eliminate failures derived from complexity. Accordingly, to measure risk scores of the workstations in the assembly lines, a decision support for companies aids executives to manage the complexity level in a reliable and effective way. In this study, the authors develop such a DSS for TOFAS, a leading Turkish automotive company. The proposed DSS is verified and applied through a pilot study on a specific basic production unit. A sensitivity analysis is also conducted to see the effects of potential improvements on the risk scores. Additionally, the trend of risk scores for the stations can also give valuable information for tracing the changes in the time horizon. The proposed DSS also enables an opportunity for the executives in their decision of design processes of new production lines by allocating limited resources in an appropriate way based on the risk scores of possible workstations. The proposed DSS is the first and unique proactive failure prevention model developed in a Fiat Chrysler Automobiles (FCA) plant across the world. TOFAS executives also plan to introduce and enlarge the usage of the model to other FCA plants. It may also be possible to apply the model to other assembly lines in any sector. Another plan of the executives of TOFAS is developing a software, which manages each parameter, to constitute data to the DSS to run this system more instantly and effectively. Moreover, they can take integration actions of the software with world-class manufacturing problem management system that is currently in use in TOFAS.

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“…Si se pretende reducir los errores, la reducción de la complejidad puede representar una alternativa eficaz. Por esta razón el estudio de la complejidad en el ensamblaje manual es un tema que ha ganado importancia en los últimos tiempos [13,[28][29][30] y el cual se discute con más detalles en los acápites subsiguientes.…”

Section: Factores Que Influyen En El Error Humanounclassified

“…El creciente interés por el tema de la complejidad en el ensamblaje manual ha favorecido el desarrollo de varios métodos de evaluación de la complejidad adaptados al contexto de la manufactura industrial. Falck et al [30] presenta dos de estos métodos. El primero, el Índice de complejidad o CompleXity Index (CXI) en inglés, evalúa la percepción del operador de la complejidad del ensamblaje durante el proceso de producción.…”

Section: Evaluación De La Complejidadunclassified

El análisis del error humano en la manufactura: un elemento clave para mejorar la calidad de la producción

Torres-Medina¹

2020

revuin

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A pesar del creciente nivel de automatización industrial, el ensamblaje manual continúa desempeñando un rol fundamental en diversos sectores de la manufactura. Sin embargo, las operaciones de tipo manual son susceptibles de errores humanos que ocasionan problemas de calidad y pérdidas económicas. El presente artículo se propone mostrar algunos métodos que permiten identificar diferentes tipos de errores y evaluar la influencia de factores que afectan el desempeño del trabajador. Se muestran, en particular, los métodos SHERPA y HEART. Igualmente se discute sobre la importancia de considerar la complejidad del ensamblaje por su negativo impacto en la carga cognitiva del trabajador lo que puede aumentar la probabilidad de error. En el artículo se emplean conceptos provenientes de la literatura especializada y se realiza una articulación de varias ramas del conocimiento tales como la ergonomía, la ingeniería industrial y la fiabilidad de sistema

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Assessment of manual assembly complexity: a theoretical and empirical comparison of two methods

Cited by 17 publications

References 30 publications

Cognitive Ergonomics of Assembly Work from a Job Demands–Resources Perspective: Three Qualitative Case Studies

Cognitive Ergonomics of Assembly Work from a Job Demands–Resources Perspective: Three Qualitative Case Studies

A decision support system for proactive failure prevention: a case in a leading automotive company

El análisis del error humano en la manufactura: un elemento clave para mejorar la calidad de la producción

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