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Creation of sales/demand forecasting model  based on machine learning and big data. We have used all possible variables as weather, economic situation, demographics, promotions, competition, price sensitivity) to create a model that forecasts the demand for products  with much better results.

We make sales forecasting model for distribution and auto spare part companies.

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 the weather are 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 modeling 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 decreased by 25%
• They increase sales by increasing the availability of goods on shelves for clients

Technology

• 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.