PREDICTIVE MODEL FOR SALES
We make sales forecasting model for distribution and auto spare part company.
The difference between classic ERP extrapolation prediction and machine learning forecast
- Classic ERP systems use for forecast extrapolation of past numbers
- This becomes an issue when some variables as weather is significantly changed
- Profio engine adds the variables in the forecasting
- This makes it more accurate (even by 40%)
How it works
- We run the data and variables through statistical modelling each day.
- We then choose the best model for each item.
- The system learns by itself from the past. It improves each day.
This has these implications for the company
- Increase of Service Level up to 99%
- Elimination of the stock-outs
- Inventory decrease by 25%
- They increase the sales by increasing the availability of goods on shelves for clients
- Big Data variables
- Neuro Networks
- Machine learning
- Statistical Modeling
Other possible clients
- The ideal client is a company with more than 700-1000 items in the warehouse.
- Spare part distribution
- Retail stores chains (food, drugstores, pharmacies)
- Pharmacy distribution
- Food production
- Generally distribution companies
McKinsey predicts machine learning will reduce supply chain forecasting errors by 50% and reduce lost sales by 65% with better product availability.
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