Sales forecasting can be enhanced through the use of machine learning algorithms that analyze various elements such as Seasonal Sales, Recurring Sales, Missed Sales, Related Sales, Marketing Promotions, Trend Impact, Abnormal Demand, and No Demand Cleanup. This process involves the automatic ranking of products at each outlet based on criteria like turnover, margin, and service level, facilitating a more strategic approach to inventory management. Additionally, it calculates the optimal safety stock levels for each product at each outlet to ensure sufficient supplies while minimizing excess. By determining the ideal reserve levels, resources can be utilized more effectively, leading to cost savings and improved efficiency. The system also assesses replenishment needs for all products across outlets and transit warehouses, generating comprehensive reports on product availability and liquidity. Important events trigger email notifications, ensuring relevant stakeholders stay informed. Furthermore, the system provides actionable recommendations for fulfilling calculated needs throughout the supply chain, while accounting for constraints such as priority, financial limits, service levels, and delivery methods. Automatically, it creates Supply and Sales Plans alongside Replenishment and Transfer Orders, streamlining operations and enhancing overall supply chain performance. Ultimately, this approach not only boosts operational efficiency but also improves decision-making processes across the board.
