'My logisticians are a humorless lot. They know if my campaign fails, they are the first ones I will slay.'
- Alexander the Great
MOD-METRIC Multi-Echelon Optimization
In 1973 Jack Muckstadt, then a Captain in the US Air Force Material Command, developed a mathematical model, MOD-METRIC, for the control of a multi-item, multi-echelon, multi-indenture inventory-system for recoverable items, that is, items subject to repair when they fail.
A derivative of MOD-METRIC was implemented by the Air Force to compute recoverable spare stock levels for the F-15 weapon system, enabling the practice of “Readiness-Based Sparing” (RBS) for setting spares levels and locations to optimize combat readiness across the system. RBS has since been adopted by other branches of the US military, and those of our friends and allies. And of course, the same concepts are applicable to complex, commercial service parts supply chains.
Today PTC Servigistics incorporates MOD-METRIC concepts and extensions in its commercial Multi-Echelon Optimization offering. By determining the lowest-cost solution to achieve desired service levels across a multi-echelon service network, it calculates optimal target stocking levels required to meet customer-facing demand at specified fill-rate targets. The software also calculates expected back-order delay, effective lead-time and network-wide customer-facing fill-rates, and uses marginal analysis to find the optimal stocking strategy.
Service Parts Network Design Tool
The Service Parts Supply Chain Network Design Tool is a framework for exploring the tradeoffs of customer service objectives, inventory costs, safety stock levels, backorders, transit times and shipping costs among alternative supply chain configurations, and for evaluating the impact of different policy and investment decisions on fill rates. Optimal configurations and Target Stock Levels are dependent upon cost structures.
The tool was initially developed for service parts supply chains supporting Field Service Engineers (FSE’s), but the framework is generic and suitable for use across a wide range of supply chain types and configurations. As Peter Jackson says, “Parts is parts”.
Use cases include:
- Centralized vs. Decentralized (regional) parts stocking and resupply
- The impact of pooling
- Shipping vs. safety stock costs
- “Normal” direct vs. emergency and “flow through” shipments
- Stocking policies for expensive, high demand vs. low demand rate parts
The Service Parts Network Design tool allows for sensitivity analysis to evaluate and manage risk considering demand and supply uncertainty.
Field Service Workforce Planning Tool
Combines optimization and simulation models to determine optimal field workforce composition over time, considering customer service level expectations, skills requirements, labor costs, the use of full-, part- and contract workers and logistics constraints. Users can estimate future workforce composition under a variety of scenarios including, but not limited to:
- Changes in product mix, reliability, lifecycle and shifting demand
- Changes in labor mix, availability and cost structures
- Hiring and training requirements, costs and lead times
Uncertainty in demand and supply is explicitly represented in the models and considered in the decision-making process.