Performance Indicators and Digital Value Stream Mapping Implementation: An Industry 4.0 Application in SME
Bibliographic record
Abstract
Manufacturing companies aspiring to adopt digitization in their production are increasingly required to make the best selection that would align with their sustainability goals. This research tackles a case study in a small and medium enterprise (SME) with the aim to assess the adoption of digital technology by mapping the relation between selected key performance indicators and the digitization technologies. The effects of the digitization technology regarding information flow in the value streams were studied while generating the digital value stream map. The study has mapped the effects of Industry 4.0 tools on selected KPIs to identify the most suited I4.0 tools for the SME considered. Accordingly, the projected production lead time in this SME has dropped from 654 hours (current state) to 290.16 hours (future state). The leanness score (another metric considered) has improved from 0.41 to 0.57. This research work can be helpful to decision makers who are planning to implement digitization into their production processes and hence proceed towards reaping the benefits of Industry 4.0.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".