Hybrid Lean Decision-Making Framework Integrating Value Stream Mapping and Simulation: A Manufacturing Case Study
Bibliographic record
Abstract
Lean value stream mapping has been applied extensively in manufacturing settings to benefit the industrial sector by boosting productivity, improving product quality, and decreasing capital costs, in turn leading to customer satisfaction and manufacturer profitability.Notwithstanding the benefits, lean value stream mapping can be enhanced to dynamically reflect the statistical productivity and economic improvements to enhance the process efficiency of production lines.Findings reported in the literature points to the benefits of integrating simulation-based tools with traditional lean value stream mapping in a hybrid framework to validate the feasibility of a given improvement.The main criteria are to reduce lean waste, increase productivity, and dynamically optimize manufacturing trade-offs for push-pull and just-in-time production systems by enhancing the efficacy of lean value stream mapping using a simulation-based approach.In this context, the proposed framework leverages value stream mapping to visualize the production system's current state.It then integrates the discrete-event simulation model in order to assess the various lean improvement scenarios proposed that to transform the system to its future state.The framework is implemented in a window manufacturing production stream to test and validate its feasibility in a mass customization environment.The case study results demonstrate the value of the framework in assisting decisionsmakers to evaluate different scenarios and visualize their impact for better transformation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".