That Pesky Math is Critical to Precision in Service Supply Chain Optimization

Written by: Ed Wodarski

Read Time: 3 minutes

In supply chain planning there is a constant struggle to increase the precision of the results we provide. In today’s experience economy, service customers have higher expectations than ever. Our goals and objectives are centered around delighting customers by meeting their expectations. It’s a balancing act to deliver maximum value to the customer while minimizing costs and the amount of inventory. The foundational process determines the optimized target stocking levels that meet a set of service goals, representing those customer expectations.

What that means for Servigistics is we must continue to research and develop the most advanced Multi-Echelon Optimization (MEO) models to ensure that our customers achieve their service goals with the absolute least amount of inventory. What a daunting task given the numerous unique nuances of an aftermarket supply chain, e.g., large item, and location populations, reverse logistics and repairable parts, complex supersession, network rebalancing, decades-long service lives, etc. Achieving optimal results has grown increasingly challenging as Service Level Agreements (SLA) and other outcome-based goals have begun to encroach on the dominance of simpler metrics like warehouse fill rates.

Complexity Delivers Precision

What this all means is that a service-centric MEO model is complicated. It is very complicated, and it’s not just the math that is complex. The technical architecture and processing logic must yield results for a network of hundreds of locations and hundreds of thousands of parts, modeled over a multi-year planning horizon within a practical window of time. Yes, this is very complicated. 

Fortunately, though, Servigistics insulates the inventory analyst or planner from having to be fluent in this high-order math.

Once the assumptions, service goals, and other tuning variables are established, optimization functions much like a black box. The planning team members determine the inputs, and planners leverage the optimized outputs. It’s complicated math presented through a straightforward process.

Servigistics is the only vendor to have a validated MEO optimization engine specifically designed for Service. It took us hundreds of person-years of research and development and many, many millions of dollars. We can now quantify superiority over any other level setting model on the market. The precision of our stocking levels is a direct consequence of the complexity of our optimization logic, and they represent the critical value fulcrum of our offering.

Some vendors choose not to invest the required time, workforce, resources, and funding to develop such a model. Instead, they choose to disparage our model by labeling it as too complex to be practically used. Ironically, they position their solutions as “almost as accurate,” but without all of the pesky math. Sorry, it doesn’t work that way! Planning systems exist to resolve uncertainty, and precision is critical to optimal results, not just a “nice to have” feature.

Celestial Navigation

To put precision into perspective, let’s consider an analogy to the evolution of navigational tools. Over millennia sailors have leveraged increasingly sophisticated and complicated technology to improve the precision of their navigation. Celestial observation and simple charts are a very basic approach, but the results can be off by miles. The compass and sextant refined directionality and positioning but still had large margins of error. The Astrolabe gave us latitude, and the ground-breaking Chronograph finally solved the longitude riddle, but the results would not be considered precise. To get there, we must jump ahead to the last century, where advanced science made quantum leaps in precision but was based upon staggeringly complicated technologies. Radar gave way to Loran, which bowed to Global Positioning System (GPS). The level of precision in positioning and navigation has gone from miles to microns. It is impossible to calculate the incremental value of that increased precision, but no one would want to settle for less than the best.


I cannot imagine a more complicated process than having a receiver listen for signals from satellites in space, have it registered by locating at least four of those satellites to determine my precise location and automate navigational steps. But is this amazingly complex technology hard to use? Hardly. Most children could open their parent’s smartphone and “navigate” Google maps.

While it didn’t take Millennia to evolve, a similar series of milestones in setting stocking levels, driven by increased precision, has been achieved. For decades planners were limited to statistical safety stock. This was understandable because it was easy to compute, required little data or computing power, but the results were comparable to celestial navigation. Refinements to include elements like ABC classification drove minor improvements. However, true change was evidenced only when the journey to deliver optimized results was undertaken.

As in navigation tools, there would be a number of breakthroughs, each more complex, but further increasing the precision of the results. Single item optimization (SIO) yielded better values for a service goal for a part at a location, but among other shortcomings ignores that optimization must focus on the mix of parts, not an individual one. Multi-item optimization allowed for an improved parts mix at a location but does not account for the ability to buffer demand and source supply from a vibrant network of locations. Single-echelon models accounted for the ability to rebalance stock across an echelon and leverage supply within that echelon to satisfy demand, but they ignored the hierarchical network of the multiple echelons of locations.

Multi-Echelon Optimization’s Massive Impact

Finally, we arrive at Multi-Echelon Optimization or MEO. Each of these improvements in math reduced the amount of inventory required to meet an array of service goals. Since MEO simultaneously models every part at every location in one run, it can guarantee the lowest inventory investment for the Enterprise. The story continues with Asset Availability Optimization which accounts for the additional layer of complexity optimizing for the availability of the aircraft, network, or weapons system, for example, instead of the constituent parts. This requires incorporating the bills of materials or product configurations into the model as well as how and where modules can be repaired. Only Servigistics provides the GPS equivalents of Asset Availability and Service-Centric Multi-Echelon Optimization. Fact.

Don’t Compromise

As you evaluate solutions for improving your service parts planning, make sure you verify the level of precision. With the precision of Servigistics’ sophisticated tools, you’ll meet your goal delighting customers, and better yet, do it at least cost. With technology innovation and digital transformation radically changing the business climate, you’ll need precision to thrive. We’ll handle the complexity.


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About the Author

Ed Wodarski

Ed Wodarski is a Service Parts Planning (SPM) expert for Servigistics with a special focus on the commercial aviation ecosystem. Ed has over 36 years of experience in SPM software design, deployment and sales support. Starting his career at Xerox in 1981 as a part of the design team for the first bespoke global parts planning system, Ed is widely acknowledged as an industry founder. He later then designed the first commercial offering for LPA/Xelus which has since been incorporated into the Servigistics platform. Ed has also been a Senior Executive at Accenture consulting globally on parts planning best practices. At PTC, Ed has worked closely with a number of leading aviation enterprises including Boeing, Aviall, JetBlue, and Southwest.