VR Games for Teaching Lean Manufacturing Tools: A Case Study of Stool Manufacturing
Bibliographic record
Abstract
This study investigates the efficacy of Virtual Reality (VR) in enhancing lean manufacturing training. By integrating VR with lean manufacturing principles, the aim is to compare performance and learning outcomes in traditional and lean scenarios. The research highlights the limitations of conventional training approaches in fully engaging learners and keeping pace with rapid technological advancements in manufacturing processes. Through the development of an interactive VR game focused on a stool manufacturing process, the study advances the use of PDCA framework to incorporate key lean manufacturing tools such as 5S Principles, Kanban, Poka-Yoke, and ergonomic improvements. The game development process is detailed, covering the preparation of 3D models, set up of virtual scenes, development of game function, design of user interface, and deployment. User testing reveals significant improvements in process efficiency and knowledge acquisition when employing lean-inspired scenarios within the VR environment. The study concludes with promising results, demonstrating the potential of VR in lean manufacturing training while also acknowledging the need for further research to validate these findings across a wider range of manufacturing processes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".