What is the difference between the Flowlity approach and the DDMRP methodology?

Flowlity and DDMRP (Demand Driven MRP) share a common goal: to better position buffer stocks to absorb uncertainties and avoid the bullwhip effect in the supply chain. However, their methodological approaches differ significantly. DDMRP is a deterministic method that defines stock buffers at fixed decoupling points and adjusts these buffers mainly according to predefined rules (green-yellow-red colors based on consumption, for example). This works well for products with relatively stable demand, but can show its limitations on products with high volume volatility. Flowlity, on the other hand, adopts a dynamic and probabilistic approach: the solution continuously calculates optimized safety stocks based on updated consumption forecasts and uncertainty assessment via AI. In practice, Flowlity will dynamically adjust your buffer stocks based on detected risks (sudden increase in demand, supplier delays) rather than sticking to a fixed buffer size until the next review. This is a “flow-driven” approach where buffers are recalculated frequently thanks to forecasts and early detection of variations, whereas classic DDMRP often provides for a more spaced periodic review. Note that Flowlity also identifies critical decoupling points in the chain (as recommended by DDMRP) in order to decouple demand and supply in the right places, but the difference is that these points are managed in a more intelligent and adaptable way thanks to machine learning. In short, Flowlity takes the spirit of demand-driven while adding the power of AI to improve responsiveness. Companies that find DDMRP too rigid or manual will appreciate Flowlity's ability to automate the recalculation of parameters (buffers, replenishments) on a continuous basis. (Moreover, according to Flowlity, pure DDMRP "finds its limits" on highly volatile products - this is precisely where Flowlity's AI approach makes the difference by better absorbing uncertainty.)