SOLUTIONS

Network Optimization

Enhances the efficiency of the supply chain by optimizing internal transfers and improving safety stock management. This process also aims to minimize out-of-stock situations and optimizes operational lead times, including truck routing and load management.

Safety Stock

Leverage AI-powered algorithms to maintain optimal safety stock levels, balancing inventory costs with service levels. These intelligent systems dynamically adjust safety stock to prevent both overstocking and stockouts, ensuring products are available when needed without tying up excess capital. The approach optimizes inventory management, leading to more efficient and responsive supply chain operations.

Master Planning

Focuses on effective item classification using deep clustering techniques and implements safety stock strategies such as EOQ-SS and cross-filling. This planning ensures optimal inventory levels and minimizes stockouts through methods like sample average approximation.

Capacity Planning

Employs advanced forecasting techniques, including machine learning and statistical models, to generate demand forecasts. These forecasts are enhanced with prediction intervals and standard deviation calculations, improving accuracy and reliability in supply chain decisions.

Aims to minimize out-of-stock situations and ensure smooth inventory flow through effective replenishment planning. It also involves scheduling and balancing between intra and inter-family products, ensuring the supply chain meets demand efficiently.

Demand Planning

Focuses on identifying and recommending top strategies to improve forecasting accuracy and manage lead-time variability. This planning ensures that processes are continuously refined to meet the dynamic needs of the supply chain.

Flow Planning – Process Improvement

Re-Planning

Involves revising and refining existing plans to improve safety stock levels and inventory management. The process includes detailed replenishment planning and scheduling, ensuring that the supply chain remains responsive to changing demands.

Integrates digital twin technology to create a virtual model of the network for continuous monitoring and risk assessment. This approach strengthens the supply chain's ability to withstand disruptions and enhances overall network resilience, applicable in both on-premise and cloud implementations.

Resilience Management