Inventory forecasting for spare parts orders

When a leading logistics company approached us, they were faced with a special challenge: the precise Spare parts order prediction in the near future to ensure optimal control of their processes. The core problem was not just the mere prediction, but the exact Quantification of the order quantity for each individual storage area.

Why was this so crucial? Accurate inventory forecasting enables optimised inventory management and thus has significant business benefits, especially in terms of resource planning and capital management.

Based on our expertise in data analysis and AI, we developed a targeted solution approach. The first step was to Identification of influencing variablesthat would help predict order volumes. This analysis was based on data from seven diversified sources that provided us with deep insights into ordering behaviour. To increase the accuracy and relevance of our predictions, we developed and trained a specialised machine learning forecasting model that could predict order quantities by warehouse area on a daily basis.

To ensure the continuous performance of our forecasts, we implemented metrics for ongoing evaluation. In addition, we established a pipeline for the weekly Automated creation of the inventory forecastembedded in a robust cloud-based architecture. Thanks to the integration of a CI/CD pipeline, updates and improvements could be seamlessly transferred into productive operation. An additional highlight of our solution was the integration of self-service capabilities that allowed users to create and customise individual forecasts via input parameters.

Our work enabled the logistics company to benefit from an end-to-end automated data pipeline implemented in a forward-looking cloud infrastructure. This meant a Seamless integration of data preparation, model training and forecast generation. The weekly forecasts provided by our system were directly integrated into the planning and control of operational supply chain management, resulting in a significant increase in efficiency in their business processes.

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