A major objective of most manufacturing companies’ digital initiatives is increasing operating margins. Cost reduction through production process efficiency improvements, effective asset management, and sourcing optimization for direct and indirect items are some of the primary ways that organizations are successfully improving their profitability and competitiveness.
Unlike for direct items, demand and usage for indirect items (also referred to as Maintenance, Repair and Operations, or MRO) varies depending on equipment failures and unpredictable factors. Forecasting the equipment failure and the need for required spare items is difficult. Companies tend to overstock repair parts, but still face stockout situations. Overstocking increases expense budget, hurting the bottom-line results; running out of an MRO item can cause downtime.
Hamiltonian’s MRO Optimizer analyzes the Min/Max ordering and other planning rules, and then adjusts the necessary parameters to suggest the appropriate quantity for safety stock and replenishment. Finding the magic number for every part is the key to optimize MRO purchases. The safety stock levels and the quantities of purchase derived by replenishment methods are based on certain assumptions and individuals’ subjective decisions. The forecasts which drive the ordering rules originally start with assumptions for initial inventory levels. Subsequent ongoing replenishment should be based on:
Equipment Reliability and Usage
Preventive and Predictive Maintenance work orders
Material lead time
Future Cognitive Maintenance
MRO Optimizer has built-in, rule-based algorithms and machine learning to optimize the replenishment of every item, helping users maintain desired plant service levels while reducing stockouts. This business process automation improves maintenance efficiency and reduces the subjectivity of MRO inventory replenishment decisions.