Vinod Arekar is a service supply chain expert at PTC and as a Fellow he leads strategic initiatives overseeing several accounts to ensure success. Vinod’s strategic thinking and experience have helped bring the concept and application of Service Simulation to PTC. This and other innovations have helped propel Servigistics to be recognized as the industry leading service parts optimization solution. Vinod is a popular presenter at the annual LiveWorx event from sharing exciting success stories together with the clients with which he collaborates.
Manufacturers and OEMs of everything from aircraft engines to printers know that a business can earn much more from servicing equipment over its lifetime than from the original sale.
Optimizing and managing inventory of service parts, then, is critical to winning in the aftermarket. Inefficient service parts planning can lead to high inventory investment, low customer service levels, and high excess and obsolescence costs. To realize the full potential of multi-echelon inventory optimization, the problem needs to be modeled with correct business objectives and adapted to specific needs. Here are some things to keep in mind:
Optimizing location part mix
- What combined group or mix of parts should be stocked to provide an overall service target?
- Each part is not stocked and should not be stocked to the same target.
- It's too expensive to stock each part to the target service level.
- Can your system account for variability of demand for parts?
- You need a methodology to set different service levels for different parts.
Consider the interaction of echelons
- How does the stocking plan of a parent location affect the stocking plan of a child location?
- Lead time to a child location is reduced if its parent location has a high stocking level with correct business objectives and adapted to the specific needs. How?
- Can your system account for impact of a parent location’s service level?
- What is each location's variable lead time?
- Does your system allow for rich modeling of real-life targets, like SLAs and PBLs?
Part and location tradeoffs
In the process of choosing which parts to stock and how much, an inventory optimization algorithm considers trade-offs across several parts and across multiple echelons. If nuts, bolts and an engine are considered together in a tradeoff, the system would view the engine as an expensive proposition, and would likely recommend low or no stock level for it. In most cases, such a solution would be unacceptable to the business. A business-savvy approach is to model multiple part groups or segments, each within a band of part costs, so the tradeoff can be among parts of comparable cost.
To make inventory optimization work for the business, it is important to group parts into segments that are aligned with the business. As parts within a segment compete with one another for stocking, the segmentation scheme needs to group parts that can be acceptably traded off, and measured together, a sophisticated function that exceeds the capacity of ERPs and spreadsheets.
For example, if a business has two product lines, X-Ray and MRI equipment, it may not be acceptable to combine parts of the two product lines into one segment – the X-Ray business needs to meet its target independent of the MRI business. Depending on the industry, the segmentation scheme could be based on many different factors such as part type, sourcing, criticality, budgeting etc., but the central theme is that parts within a segment have common planning goals and can compete with one another.
Realizing the Potential
As the service matrix shifts from a transactional business of break-fix to a usability matrix of uptime and asset availability, the interdependence of parts and locations in global manufacturing operations becomes critical. Manufacturers need an advanced ability to understand how to stock service parts against component and asset, as well as a theater view of how components work together, which will depend on myriad factors, including location.
Capital equipment relies on having the right part at the right time, in the right place, which requires a holistic view at the entire service network and its levels of hierarchy. Added to this, manufacturers must also account for intermittent demand on different locations, and the dependencies between locations, to make accurate and profitable stocking and business decisions.
How do industry leaders manage their global service operations with an eye to multi-echelon optimization? Read the Qantas Airlines case study here, or download our infographic below to learn how to assess your organization's spare parts inventory management capabilities:
