Our client is a company which produces biotech-based medicines for 70 million patients. They have been very attentive to innovations in the supply chain.
Recently, the board decided to target net 0 CO2 emissions by 2040.
Their preliminary analysis has shown that emissions in inbound logistics and distribution make up a big part of total emissions. More on the close relation between supply planning and emissions can be read here.
Our client already plans part of their portfolio through GenLots. So asked us how to reduce CO2 emissions in supply planning.
Results
GenLots uses machine learning to radically reduce total cost of ownership in supply planning. Luckily in supply chain, CO2 emissions and costs are very closely correlated.
The result of our investigation has been astonishing. Using GenLots resulted in 46% less inbound batches on the years 2021 – 2022.
On top of the direct financial impact through lower logistics costs, this contributes to the sustainability objectives by:
- reducing transports by a lot
- reducing quality controls
In addition, we could advise on how to:
- use external information sources to calculate CO2 emissions automatically. Our client applied those tools immediately and calculate emissions in their transportations.
- include a 100€/CO2 ton equivalent pricing into our intelligent algorithm to account for CO2 into our total cost of ownership optimization
What comes next
We established a clear roadmap together. We develop a reporting interface to present to users the CO2 emissions of each material and supply plan. Our simulator can help to assess the expected emissions of different supply planning parameters. In a further step, we can define automated CO2 reduction as an objective for our reinforcement learning algorithm.
