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Recovering 28% Inventory Carrying Cost Through MRP Intelligence at a Process Manufacturer
A Global Company Scaling Complexity in Process Manufacturing
A leading process manufacturer specializing in high-volume, multi-phased production operating across diverse product lines with variable demand patterns. The organization depended heavily on Material Requirements Planning (MRP) systems to manage the raw materials, intermediates, and finished goods across plants and distribution centers. With fluctuating demand, long lead times, and complex bill-of-materials (BOM) structures, maintaining optimal inventory levels was critical, yet increasingly difficult.
Inventory Management Turning into a Liability
Despite having MRP systems in place, the organization struggled with rising inventory carrying costs and inefficiencies in planning. Over-ordering caused by static planning parameters and demand uncertainty caused the build-up of excess inventory.
Poor demand variability and seasonality handling reduced forecast accuracy and tied up working capital in excess stock
Limited visibility into slow-moving, obsolete, and redundant inventory created MRP blind spots
Misaligned procurement and production schedules led to inefficient replenishment cycles
Increased carrying costs, constrained cash flow, and widespread operational inefficiencies
Next-Gen MRP Intelligence Layer to Rectify the Challenges
To address these inefficiencies, ThoughtMinds implemented an automated MRP intelligence layer on top of the existing planning systems, enabling a more adaptive and data-driven planning approach. The solution combined machine learning–driven demand forecasting with advanced inventory optimization to improve accuracy and recommend optimal stock levels based on variability and service targets.
Real-time anomaly detection identified excess, slow-moving, and obsolete inventory, while continuous policy optimization refined reorder points, safety stock, and lot sizes. Prescriptive insights further enabled actions like stock rebalancing, liquidation, and procurement adjustments, transforming MRP into a continuously learning, decision-intelligence system.
The Process of Embedding Intelligence into Planning
The transformation followed a phased and controlled rollout
Inventory, demand, procurement, and production data were unified across systems to establish a single source of data
Existing MRP parameters, inventory policies, and stock profiles were analyzed to identify inefficiencies and cost drivers
AI models were implemented to forecast demand, optimize inventory levels, and detect anomalies across SKUs and locations
Insights were embedded directly into planner workflows, enabling real-time recommendations within existing MRP systems
Models were refined using feedback from planners and actual outcomes, improving accuracy and adaptability over time
Value Unlocked
Reduction in Excess Inventory
Aligned Demand and Production
Enhanced Planning Accuracy
Better Stock Utilization
Improved Efficiency Through Intelligent Planning
The company experienced improved efficiency with the capital allotment and liquidity
The agility in responding to the fluctuations in demand was increased, while the waste and excess of stockpiling were reduced significantly
For the employees, the new system was able to reduce the manual effort in inventory analysis and planning
The company’s customers were able to see better availability of the products across service levels, where the lead times were reduced drastically
Improved Efficiency Through Intelligent Planning
28%
Reduction in inventory carrying costs
20%
Improvement in forecast accuracy
15%
Increase in inventory turnover ratio