The ISO 14064-1:2018, Greenhouse Gas Inventories for Organizations, provides a framework for quantifying and reporting greenhouse gas (GHG) emissions and removals. The standard emphasizes accuracy, consistency, and reproducibility in its “Quantification Approach” section, stating:

• “The organization shall select and use quantification methodologies that minimize uncertainty and yield accurate, consistent, and reproducible results.”
• “GHG emissions or removals can be obtained through measurement or modeling.”
• “A model is a simplification of physical processes with assumptions and limitations, converting source or sink data into emissions or removals.”

These requirements highlight the critical role of digital twins in supply chain modeling. Digital twins help organizations meet these guidelines by offering:

1. Minimization of uncertainty: A simulation model is tailored to the company’s specific characteristics, refined over time to represent actual and expected performance reliably. This process ensures a high degree of accuracy while balancing data availability, modeling effort, and result precision.
2. Consistency in results: Supply chain simulations validate traditional metrics (e.g., costs, service levels, and productivity) against real-world observations. Fine-tuning ensures models align operationally and environmentally, supporting consistent outcomes.
3. Reproducibility over time: Once validated, models can be reused periodically (e.g., monthly or quarterly) to monitor performance and adjust strategies based on evolving company needs.

Additionally, simulation models allow:

• Dynamic scenario analysis: Testing interventions for emissions reductions under varying operational conditions.
• Granular insights: Identifying inefficiencies across the supply chain, such as energy usage, transportation modes, or resource allocation, to improve overall performance.