Demand Driven Material Requirements Planning: An Inventory Optimization Model

When dealing with stochastic multi-state production and distribution systems, determining the appropriate quantity of safety stocks on hand may be tricky. We often limit existing solutions for maximizing safety stock levels while keeping cost, objectives and service level constraints in mind to a subset of multi-stage inventory challenges, such as strictly serial architectures or specialist two-stage production/distribution systems. This may make achieving ideal stock levels while meeting cost objectives and sustaining service levels problematic. This is due to the underlying assumption in these systems that cost and service level are directly proportional to one another. Controlling unpredictability and satisfying customers are inventory management?s most important tasks. DDMRP (Demand-Driven Material Requirements Planning) dynamically adjusts inventories to handle unpredictability and improve customer service. This is done to improve crisis management. This achieves ?demand-driven material requirements planning.? This study presents a DDMRP-specific safety stock calculation. The inclusion of this formula aids in the preservation of mathematical consistency. When dealing with a model based on normal probability distributions, the major focus is discovering and applying the parts necessary for safety stock computation. One of the most well-known fast-moving consumer goods (FMCG) firms in South East Asia is the major focus of a case study to validate the proposed strategy. To build a scientific model, the study adopts a probabilistic strategy. The primary goal of this model is to aid in the process of identifying appropriate levels of safety stock and inventory at the distributor level, which will ultimately allow for a customer fill rate of 99%. A distribution network will be employed to achieve this purpose. The model provides a complete framework for estimating optimum safety stock levels and reorder points while accounting for the stochastic nature of demand and including critical elements. This enables more precise forecasting of future demand. This contributes to better inventory management practices and promotes customer satisfaction.. KEYWORDS :DDRMP, Demand level, Distribution network, Safety Stock Model.