PROMOT: Forecasting models for efficient resource planning that consider people, organisation and technology

Geopolitical changes, climatic influences, seasonal fluctuations - these are all factors that make market-oriented sales planning increasingly demanding and complex - also for Wöhler Technik. As part of the Arbeitswelt.Plus competence centre, the company is now working with the Institute Industrial IT (inIT) to use AI processes to optimise the forecast accuracy of sales planning in order to be prepared for market developments and optimise resource planning.

Wöhler Technik, a family-owned company, develops, manufactures and sells measuring, inspection and cleaning technology for heating, ventilation and building applications worldwide. Especially when it comes to sales forecasting, market and demand trends can lead to inventory problems that cause significant costs in the form of overstocks and understocks. Machine learning (ML) techniques offer great potential for improving forecasting accuracy.

AI methods support manual sales forecasting

AI methods are used to assist sales planning staff in forecasting future product demand. Historical data is used to identify patterns, trends and seasonal variations, as well as economic indicators, customer activity and industry trends. In addition, the ML process incorporates the experiential knowledge of employees. To ensure acceptance of the AI solution, employees are closely involved in the development of the AI.

Increased flexibility and customer understanding

By using AI techniques, the company can predict future demand trends more accurately and easily. By analysing customer data, the company can also better understand customer behaviour and make targeted adjustments to meet individual needs. This enables Wöhler Technik to react more flexibly to market developments and strengthen its competitiveness in the long term. Optimised sales planning also reduces inventory problems and the associated costs.