ML-Based Sales Forecasting for an Agriculture Giant

80-90% Sales Forecasting Accuracy for a Leading Agro Producer

Leading Agro-holding







About the Customer:

Our client is one of the largest agricultural holdings in Ukraine and one of the leading poultry producers in Europe. The company owns top food brands with a focus on food quality and advanced manufacturing technologies.

Executive Summary

Goals: Our client aimed to improve production planning and stock management based on the accurate prediction of sales volumes for each customer.

Solution: Infopulse data engineers developed a Machine Learning-based sales forecasting solution with an average accuracy of 80-90% for weekly and monthly forecasts.

Benefits: Optimizes production planning, improves warehouse stocks, space management, and logistics, and significantly accelerates time for sales forecasts from days to a few minutes.

Services delivered: Innovation Services, Intelligent Business, Intelligent Automation, Smart Insights, AI/ML services, Predictive Analytics, BI & Data Analytics, and Advanced Analytics.


Business Challenge

Our client, a large agriculture company, successfully collaborates with Infopulse on a number of projects within their digital transformation strategy. The solutions developed together with Infopulse help the company automate and streamline a variety of business processes.                 

Taking into account the extensive experience of Infopulse in developing prediction models for sales, our expertise in business intelligence, and a portfolio of solutions designed specifically for the agriculture industry, the client selected our team for their next project – a Machine Learning-based sales forecasting solution that would help our client to:    

  • Predict the sales volumes for the company customers with the maximum possible accuracy
  • Improve the logistics operating quality
  • Optimize the production planning by regions
  • Improve the stock management

One of our client's key goals was to ensure the maximum possible accuracy of sales forecasting, which would be critical for effective planning and business processes optimization.

Solution & Business Value

Infopulse data engineers developed a sales forecasting solution based on the Prophet model for weekly predictions and the XGBoost for monthly predictions.

The ML-based sales forecasting models implemented by Infopulse introduced a multitude of benefits for our client: 

  • Better sales planning: high sales forecast accuracy helps avoid over- or underproduction and minimizes stockout risks.
  • Streamlining production and sales processes: accurate sales predictions help optimize the required production volumes and simplify logistics.
  • Optimized warehouse stocks and space: minimized remainders, waste, and storage.
  • Improving the level of service (if not out-of-stock): ensuring the delivery of required product volumes to customers on time.
  • More efficient use of capital and higher profits: as the production is fully coordinated with accurate sales predictions, the resource waste is minimized.
  • Faster forecasts: compared with manual sales forecasting, our solution reduced the time for obtaining a sales forecast from a few days to a few minutes.
  • Better accuracy of predictions due to automated data processing, choosing a more precise model. The prediction accuracy also improves with each new dataset processed by our models.

Technical Details

Infopulse data scientists began the development of the sales forecasting solution with an in-depth analysis of the data set received from the client. We selected a standard but effective technology approach, using Python and Jupyter Notebook for prediction model development. To save development time and re-ensure the results, we also used Azure ML to train our models with AutoML.

Upon analyzing the obtained results, the team selected the best-performing models, built the analytical reports, and presented results using Microsoft Power BI:

  • Based on the model training results, Infopulse data scientists chose the Prophet model for weekly forecasting. 4 of 28 predicted cases showed up to a 90% accuracy, while the remaining 24 cases had 80-89% accuracy.
  • As for the monthly forecasting, the best model is XGBoost, producing up to 90% accuracy for 19 of 28 predicted cases, while 4 cases had 80-89% accuracy, and the remaining 5 had an accuracy of less than 80%.

The quality of weekly and monthly sales predictions


Thus, the Infopulse team developed ML-based sales forecasting models that provide the following:



The project took three months to implement. Together with the client, we decided to move in short sprints to ensure the transparency of our progress. We also wanted to provide our client with the ability to evaluate the results on the go and make decisions regarding further development and improvements.


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