Production system state analysis and improvement using VSM, simulation, and fuzzy logic
Bibliographic record
Abstract
Purpose The paper aims to provide a door-to-door holistic perspective on the state of a production system based on several metrics and their relationships, to utilize a combination of tools and techniques and to develop a unique model for production system assessment. The study addresses the limitations of narrow approaches by proposing a new metric as a single indicator of the production system’s overall state. Design/methodology/approach The research methodology adopted is design science research. It proposes a framework that combines value stream mapping, simulation and fuzzy logic to study the impact of different lean interventions on the overall state. An offsite construction facility was used as a case study. Data were gathered using methods such as ethnography, interviews, time and motion studies, shopfloor observations, video surveillance and database exploration. Findings The paper highlights how some interventions can have local improvements but lead to negative impacts on the overall system state. It emphasizes the importance of having a holistic approach to analyze and improve the true state of a production system. Research limitations/implications The study excludes impacts of the supply chain and assumes the system to be confined within the shopfloor. Researchers are encouraged to include those variables in future studies. Practical implications The study presents a practical framework and tool that can be tailored to any production system and be used to improve its performance. Originality/value This paper proposes a unique framework and a new metric for system state assessment and improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".