Applying AcciMap and STAMP to the analysis of human error in complex manual assembly
Bibliographic record
Abstract
Abstract In sectors such as aerospace manufacturing, human errors in the assembly of complex products can negatively impact quality, productivity, and safety. Until now, the analysis of assembly errors has focused more on the immediate human‐system interface and less on broader organizational factors. This article presents a case study‐based analysis of assembly errors in the aeronautical industry using the systemic methods AcciMap and Systems‐Theoretic Accident Model and Processes (STAMP). We seek to provide the company with elements to build a quality improvement strategy that considers human factors and ergonomics from a systemic perspective. The data and information necessary to conduct the analysis came from a project carried out at an aerospace manufacturing facility over a period of 12 months. The team had direct and recurrent access to primary data sources and communication with various stakeholders. A total of 31 influencing factors were identified with AcciMap at different levels within the manufacturing system. STAMP made it possible to model the sociotechnical control structure of the assembly process and identify several control flaws leading to hazards. The analysis shows that systemic methods require a high level of understanding of the manufacturing system and access to relatively high amounts of data and information. Therefore, direct contact with the field and stakeholders is crucial. Training quality specialists on systemic methods could support its use and help to close the gap between theory and practice. Globally, the field of quality in manufacturing could benefit from using systemic methods when deemed necessary.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".