Develop a supplier collaboration model that rewards suppliers for reducing their Minimum Lead Time (Min LT) and Minimum Order Quantity (MOQ) by offering an extended fixed order horizon. This concept builds on the principles of Stable Planning to ensure a balanced, predictable supply chain while aligning supplier incentives with network efficiency.
Ensure that network capacity meets demand in an affordable way, using AI to forecast, model scenarios, and balance supply and demand. This system allows businesses to rapidly adjust plans without recalculation delays or the need for real-time data, while maintaining alignment with long-term strategic goals.
Address instability in DDMRP and MRP models by introducing stable demand signals to suppliers, specifically focusing on stabilizing the plan outside of lead time (LT). This reduces the bullwhip effect, improves supplier performance, and leads to more predictable supply chains. Current approaches do not attempt to stabilize the plan beyond LT, even though these numbers are critical for upstream suppliers and their own planning.
Enhance operational efficiency and business growth through agile execution and AI-driven optimization. The S&OE solution integrates advanced AI technologies across key operational areas, enabling predictive analytics, automated decision-making, and optimized workflows.
Provide planners with a speech-to-text-powered mobile app that allows them to quickly inquire about the planning status of supply chain elements. This AI-powered app offers contextually accurate responses, enabling planners to make informed decisions on the go.
Develop a robust mechanism for creating a supply chain plan that incorporates variability and addresses the challenges of simulating complex networks. The focus is on ensuring that planning accounts for the inherent volatility of supply chains through AI-driven insights, using a cycle of Plan → Test the Plan → Revise the Plan → Retest until a good plan is established. This approach helps determine whether a plan is effective by simulating the variability in supply chain nodes and continuously improving the plan.
