PHARMACEUTICALS Supply Chain Model The company faced a situation where production and sales decisions were based on manual estimations, leading to significant deviations from actual demand and disruptions throughout the entire supply chain. Context Modern sales models in the pharmaceutical industry require full data integration to ensure accurate forecasts and enable proper resource allocation. The lack of an integrated and automated forecasting approach resulted in a high risk of errors, leading to both inventory surpluses and product shortages, which had a direct impact on the company’s operational capabilities. Operational Challenge The key challenge was to create a solution that would enable demand forecasting in a more structured, regular, and data-driven manner. The previous approach relied primarily on intuition and manual estimates, causing the company to react with delays to seasonality, promotional activities, and changing market conditions. Solution An analytical module was designed, based on econometric algorithms and ML, which predicts sales in subsequent periods based on sales results from recent months. The module relies on historical data and accounts for seasonal variables, demand changes, and external factors. 25% reduction in average forecast error compared to previous manual estimations, enabling the team to plan production and inventory more accurately Forecasting automation integration with operational processes enabled time savings in the planning team and improved operational process stability Inventory optimization reduction in the risk of out-of-stock situations A conversation is the first step to identifying the organization’s needs and assessing the project’s feasibility. Case study
Sales Forecasting
What have we achieved?
Contact