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Case Study

Demand Forecasting

To increase the accuracy of our client's demand planning, we developed a demand forecasting engine that combines a wide variety of machine learning and deep learning algorithms to predict the demand for over 20,000 products within the next 24 months.

  • Industry Automotive
  • Topic Forecasting
  • Tools Python, Tensorflow, Docker, Azure
  • Duration 1.5 years

Challenge

Demand planning is a central component of a smooth supply chain for the entire product range. In order to avoid inventory surpluses or supply bottlenecks, demand must ideally be precisely planned months or even years ahead of the delivery date. Our customer was faced with the challenge of manually planning several thousand products every month. This ties up a lot of resources, with the sheer volume making it difficult to take important external influencing factors into account. An automated forecasting engine was to remedy this situation, as historical patterns and external influencing factors can be used to accurately plan ahead demand on a monthly basis.

Approach

To support strategic demand planning, we have developed a forecasting engine that uses various machine learning and deep learning algorithms to project demand up to 24 months into the future. The engine automates data preparation, the selection of external influencing factors, model estimation, and model selection and combination. The solution combines different modeling approaches so that seasonality, trends and external influencing factors can be optimally estimated. In order to plan the complete product range, the engine was deployed in the cloud and forecasts are automatically transferred to target systems.

Results

Based on the engine’s forecasts, the accuracy of demand predictions across the entire product range was increased by around 10 percent. In addition, the patterns of the forecasts are convincing, as they take seasonality, trends and external influencing factors into account and extrapolate into the future. The engine’s predictions were tested in other markets during the course of the project, where they were able to achieve improvements in accuracy of around 10 percent, even without being adapted to the specific market. The predictions can now be used to validate and use forecasts in strategic planning to significantly reduce monthly planning time.

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