Businesses, today, struggle with ensuring forecast accuracy and visibility in calculating market demand and retailing estimates when trying to achieve their supply chain goals. Often, stakeholder buy-in for forecast modification is not available. Calculating market demand in real-time and basing forecast on a single error metric is another challenge. The inability to select dynamic models or handle correlation information for new stock keeping units (SKUs) has also become a major hindrance in demand forecasting in supply chain.
TCS has developed an integrated distributor management and collaboration platform for businesses to improve their demand forecasting capacity, reduce manual efforts, and enhance collaboration with channel partners. The platform calculates market demand based on weekly market trends. It uses descriptive analytics to generate monthly forecasts at SKU level and enables workflow approval and rule-based forecast modification.
By combining machine learning and time-series models, the platform generates a forecast based on the best fit error matrix, historical data, and correlation information of all SKUs to accurately predict demand.
The modular design of this enterprise supply chain data platform helps in future extensibility as well as integration with multiple ERP and DMS systems. With ready-to-deploy components, this cloud-based Platform-as-a-Service is accessible on any device, anywhere.
Improved forecast accuracy by up to 10%
Reduced manual efforts through automation
Improved collaboration with channel partners
Better adaptability to changing business scenarios