Case study card:

Demand forecasting for parcel delivery

Demand forecasting for parcel delivery

Vertical

Business Intelligence

Business Challenge
The main need is to create an algorithm that, based on historical data, will be able to forecast the daily sales volume for a particular customer (parcel lockers and couriers).

InPost was unable to compete with its competition because of problems with meeting the demand and appropriate pricing strategy for individual sellers. That caused delays in delivery and customer churn.


Supplier Solution
The team built and implemented a Machine Learning prediction model based on the historical data, macroeconomics factors, and other 3rd party data. Rare events such as Covid also was taken into account.
The model predicts demand and calculates the optimal price for private sellers.

The solution automatically forecasts monthly and weekly sales by individual products and companies. Based on the demand and supply analysis, the optimal price is chosen.

Benefits
The solution helps managers in better and more accurate planning, saves the time of manual work, eliminates constantly making predictions in excel, and considers Covid-19 and macroeconomics factors that have a huge impact on sales.

Industry

Supply chain management

Client

InPost

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