To read more about this sustainable supply chain template with geospatial calculations in python, see the supporting article on Towards Data Science for more background information. https://medium.com/towards-data-science/a-sustainable-supply-chain-template-with-geospatial-calculations-in-python-f99a20a43df4
The python notebook sustainable_supply_chain_template.ipynb can be run in a jupyter notebook.
Data is located in the data folder.
This notebook serves as a template with sample data, to demonstrate that both transportation and facility greenhouse gas emissions should be included in a supply chain carbon evaluation.
For example, here are the results of four scenarios and the accompanying maps
| Scenario | Transportation Emissions | Transportation and DC Emissions Total |
|---|---|---|
| 3 DCs | 11 kg CO2e | 779 kg CO2e |
| Seattle DC | 51 kg CO2e | 494 kg CO2e |
| Memphis DC | 23 kg CO2e | 466 kg CO2e |
| St. Louis DC | 24 kg CO2e | 467 kg CO2e |
Three Distribution Centers (DCs) versus one DC reduced the transportation emissions by more than 50% compared. But the total emissions increased by 40% due to the additional DCs.
Under certain conditions, more DCs and shorter shipping distances can lead to less greenhouse gas emissions. But this limited example demonstrates that shorter final-shipping destinations may not always be indicative of a supply chain network with a lower carbon footprint.
In order to design a sustainable supply chain, the considerations of both transportation and facility impacts should be considered, along with other factors.
3 DCs
Seattle DC
Memphis DC
St. Louis DC
