Micro-Motion Lean using MODAPTS: Enhancing Productivity in a Tier-1 Automotive Seatbelt Assembly Plant
Bibliographic record
Abstract
This paper presents a focused lean improvement case on a two-operator seatbelt sub-assembly cell at a North American Tier-1 automotive supplier, producing above its designed combined cycle time (actual ~32.2 sec vs. designed 30.3 sec).Hidden losses were identified in motion, layout, and sequencing rather than machine performance.A structured improvement approach combined Value Stream Mapping (VSM), Modular Arrangement of Predetermined Time Standards (MODAPTS) motion coding, Yamazumi load analysis, and low-cost physical rearrangement.Baseline MODAPTS breakdown revealed avoidable walking, redundant reaches, one-handed idle, and a micro-wait at a shared press between the two workstations representing on average 3-6 theoretical seconds of recoverable time per cycle.Targeted countermeasures such as point-of-use material repositioning, two-hand synchronous picking, workload balancing, press control relocation and standardized micro-sequence delivered a 13.2% cycle time reduction, lifted balance efficiency to 99.6%, and reduced lead time by over 21.9%, all without capital automation.A disciplined combination of micro-motion analysis and lean flow tools is shown to significantly enhance manual assembly performance.This case demonstrates that targeted method and layout refinements are sufficient to unlock latent capacity, enabling the system to exceed its initial design specifications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".