A new approach for evaluating experienced assembly complexity based on Multi Expert-Multi Criteria Decision Making method Article Swipe
Elisa Verna
,
Gianfranco Genta
,
Maurizio Galetto
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s00163-023-00409-3
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s00163-023-00409-3
In manufacturing, complexity is considered a key aspect that should be managed from the early phases of product and system design to improve performance, including productivity, efficiency, quality, and costs. The identification of suitable methods to assess complexity has always been of interest to researchers and practitioners. As complexity is affected by several aspects of different nature, it can be assessed from objective or subjective viewpoints or a combination of both. To assess experienced complexity, the analysis relies on the subjective evaluations given by practitioners, usually expressed on nominal or ordinal scales. However, methods found in the literature often violate the properties of the scales, potentially leading to bias in the results. This paper proposes a methodology based on the analysis of categorical data using the multi expert-multi criteria decision making method. A number of criteria are adopted to assess assembly complexity and, from subjective evaluations of operators, product assembly complexity is assessed at an individual level and then, aggregating results, at a global level. A comparison between experienced complexity and an objective assessment of complexity is also performed, highlighting similarities and differences. The assessment of experienced complexity is much more straightforward and less demanding than objective assessments. However, this study showed that it is preferable to use objective assessments for highly complex products as individuals do not discriminate between different complexity levels. An experimental campaign is conducted regarding a manual assembly of ball-and-stick products to show the applicability of the methodology and discuss the results.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s00163-023-00409-3
- https://link.springer.com/content/pdf/10.1007/s00163-023-00409-3.pdf
- OA Status
- hybrid
- Cited By
- 11
- References
- 82
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4320040334
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4320040334Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s00163-023-00409-3Digital Object Identifier
- Title
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A new approach for evaluating experienced assembly complexity based on Multi Expert-Multi Criteria Decision Making methodWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-02-11Full publication date if available
- Authors
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Elisa Verna, Gianfranco Genta, Maurizio GalettoList of authors in order
- Landing page
-
https://doi.org/10.1007/s00163-023-00409-3Publisher landing page
- PDF URL
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https://link.springer.com/content/pdf/10.1007/s00163-023-00409-3.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s00163-023-00409-3.pdfDirect OA link when available
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Viewpoints, Computer science, Categorical variable, Pairwise comparison, Quality (philosophy), Complexity management, Management science, Risk analysis (engineering), Machine learning, Artificial intelligence, Data mining, Engineering, Marketing, Philosophy, Visual arts, Epistemology, Medicine, Business, ArtTop concepts (fields/topics) attached by OpenAlex
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11Total citation count in OpenAlex
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2025: 1, 2024: 4, 2023: 6Per-year citation counts (last 5 years)
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82Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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