A data model for carbon footprint simulation in consumer goods supply chains

CO₂ efficiency is currently a popular topic in supply chain management. Most approaches are based on the Life Cycle Assessment (LCA) which usually exploits data from a static database. This approach is effective when estimating the carbon footprint of products or groups of products in general. Simulation has been a proper method for metering the effectiveness of logistics systems, and could thus be expected to also support the analysis of CO₂ efficiency in supply chains (SC) when combined with an LCA database. However, research shows that this combination does not deliver reliable results when the target of the study is improvement of the logistics in the SC. The paper demonstrates the shortcomings of the LCA-analogous approach and proposes a data model that enables discrete event simulation of SC logistics including its impact on the carbon footprint that is under development in the e-SAVE joint project funded by the European Commission.