The organization operates a diverse business model, supplying products across multiple channels. As a result, demand for individual SKUs can fluctuate significantly, making accurate forecasting and inventory management crucial for sustaining growth.

The existing tools lacked flexibility, making it difficult and time-consuming to identify and respond to external factors influencing demand fluctuations.

Following the project to restructure the data architecture and governance, the business was well-positioned to segment data and identify deviations and trends effectively.

Given the high cost of integrating business data with off-the-shelf planning tools, we opted to develop our own planning and forecasting solutions in-house.

We created statistical models using Python and developed visualizations with Power BI. This internal development allowed us to produce customized reports that addressed the specific needs of the business more effectively.

The complexity of the organization's channels and product range needed continuous refinement of forecasting and demand planning tools.

Following the development of a structured framework and statistical models, the business achieved a 50% reduction in out-of-stock rates and established a more stable purchasing process. These improvements enhanced inventory management and cash flow.

The tools now offer long-term visibility into potential stockouts and overstock situations, enabling proactive and strategic management of each product and category. This includes identifying demand peaks and troughs influenced by specific customer behaviors