Bang for the Buck a g f he c
How to radically improve the performance of your legacy
spares planning system without buying a new one.
By Robert Giacobbe, Managing Director for Accenture and the global lead for Accenture’s
Service Strategy and Operations (SSO) consulting practice
At one time or another, a maintenance organization has struggled with getting the best cost and service performance out of its spares inventory. Often, the
penalties for not getting spares “right” are significant. And with
the emergence of new service models such as “power by the
hour,” providers are signing contracts that carry a hefty penalty
for below-target performance.
But despite these factors, it’s extremely rare to find a maintenance organization demonstrating an advanced capability in spares
optimization. That is especially true when the typical maintenance
department is compared to their for-profit aftermarket brethren
who have been running large-scale parts businesses for decades.
There are several contributing factors. One is the very nature of
equipment maintenance. Randomized failures, sporadic demand,
and bad data are common, all of which complicate traditional plan-
ning techniques and places the emphasis on agility and execution.
Two, the criticality of needing high in-stock positions for critical
spares oftentimes reduces the concept of planning to a simplistic “just cover the worst-case scenario with lots of inventory”
approach. Three, some of the science of spares planning, such as
probabilistic modeling and advanced statistics, is too difficult to
sustain in many organizations. And last, with the potential cost of
deploying best-of-breed planning solutions now approaching several million dollars, many organizations choose to stick with their
legacy systems regardless of performance.
But there is a light is at the end of the tunnel: Specific analytic
techniques and new processes can be used to significantly improve
a legacy spares system’s performance. These changes can be made
with a low-to-modest investment that is a fraction of the complexity of a new system install. Planners can conduct these analytics