Demand forecasting of spare parts through machine learning

In the past, a renowned distributor of construction equipment spare parts faced a significant business challenge: it sought to accurately forecast the quantities of demand for its products in the following months at different locations. The goal was to use this forecast to improve its Stocking the warehouse optimally and according to demand and thus achieve the highest possible efficiency.

Our experienced team of data analytics and AI specialists has taken on this challenge with a particular focus on demand forecasting. Based on a variety of in-house data, such as historical demand quantities, detailed product master data and master data on sales locations, we have developed an deep data analysis carried out.

We also integrated external data sources, such as relevant weather and economic data, to better understand the context and potential external drivers. By combining these extensive data sources relevant predictive indicators were identified. The application of a machine learning algorithm enabled us to forecast spare parts demand at all locations for the next 12 months with a precision that previously seemed unattainable.

Thanks to our solution, the retailer was able to realise considerable added business value. The increased predictive accuracy of our demand forecasting solution enabled the retailer to increase its Storage strategies more efficient to manage.

In concrete terms, the added value for the company was reflected in critical business indicators: Parts availability, also known as the service level, improved significantly, the Inventory turnover was optimised and lost sales from avoiding empty warehouses could be drastically reduced. This illustrates how our advanced data-driven solutions can help companies transform their business processes and gain a competitive advantage in the market.

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