Leveraging digital twins for enhanced sustainable warehouse management
Bibliographic record
Abstract
Warehouses are increasingly under pressure to reduce their carbon footprint. Yet, traditional carbon accounting approaches remain ill-suited to support real-time or operational decision-making dedicated to lower their environmental impacts. These methods typically rely on aggregated, static data and offer vague emission estimates. To address this issue, this paper introduces a bottom-up carbon accounting framework embedded within a warehouse Digital Twin (DT), enabling real-time, resource-level emissions tracking and scenario analysis. The framework builds upon the Toyota Business Practices (TBP) method to analyze the results of traditional carbon accounting by integrating data streams from Warehouse Management Systems (WMS) and sensor inputs into DT simulation modules to allocate emissions at the level of equipment and processes. A case study conducted in a 3PL warehouse in France demonstrates the model’s ability to match aggregate estimates from conventional carbon accounting (CCA) tools, while delivering substantially higher resolution. Notably, the DT identified overlooked emission hotspots, including employee commuting and the use of packaging materials made from wood and plastic, to support operational “what-if” analysis and evaluate the carbon and cost trade-offs of alternative scenarios. These findings highlight the potential of Warehouse DTs to shift carbon accounting from a static reporting function to an actionable sustainability management tool.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".