Battery distribution and market analysis
CLS has about 33 battery depots throughout the Netherlands where they supply battery’s to external parties like ANWB and to customers. They currently have to automated system which can predict when and how much to restock these battery depots. A team member of CLS (Ruud) is responsible for making these predictions purely based on experience. This however starts to be come more difficult now as CLS is distributing new types of battery’s which don’t have a track record from the past.
I was tasked with developing a “load balancing algorithm” which could predict and forecast how many battery’s would be needed at each battery depot for every 5 hours of the day (as CLS restocks 3 times a day every 5 hours). They currently had no way to know how many battery’s where present at each depot, as the distributers only record this once a day which meant I had to figure out how CLS could record this data in real-time. After some research I made a report specifying how CLS could make it easier for their employees working at these depots to record and publish changes (whenever they take a battery or return one) using a mobile application.
After this change in their process pipeline has been made, I could start analyzing the newly obtained data along side scraping external internet sources to make a predictions how many battery’s at what locations they would sell at specific moment in time. I had experiments with different time series and regression models but ended up using SARIMAX to predict total sales per region, city and province.
Based on these predictions Ruud could make better judgements what areas to restock and what the most optimal route would be to do this (based on distance an petrol used).