Organizations need to respond to customer needs faster and more accurately in today’s economy. This can be challenging to accomplish in spare parts management because maintaining the right level of spare parts is tricky. The orders tend to be more sporadic in volume, and it’s harder to predict which components will be needed. As a solution, many companies are adopting alternative spare parts management models like predictive maintenance.
Author Muge Hizal Dogaroglu | Copperberg
Reading time: 2 minutes
One of the primary necessities to stay competitive in today’s economy is to be able to respond to customer needs faster and more accurately. This can be challenging to accomplish in spare parts management because maintaining the right level of spare parts is tricky; the orders tend to be more sporadic in volume, and it’s harder to predict which components will be needed. Moreover, having too little in the inventory is risky because it can result in delayed orders and hurt customer relationships. However, overstocking spare parts can drive up the costs both in terms of production
and using up the inventory space.
As a solution, many companies are adopting alternative spare parts management models that help them predict customer needs and become better at acting on them. One of these models is called predictive maintenance. And it enables companies to foresee spare part and maintenance needs before they happen. The idea behind it is to use cutting-edge technology like IoT, machine learning, and AI for simultaneously collecting and analyzing data on every product and service, the company sell so that they can predict and know — before the customer — which parts are likely to need repair or upgrade. Many companies are already moving away from the traditional break/fix model of maintenance to more proactive ones such as predictive maintenance. This trend was also apparent in the results of our Aftermarket 2019 Benchmark Survey.
We asked our survey takers, who are mainly from machinery/industrial equipment, automotive, marine and other manufacturing industries, to describe their service models now and the way it was two years ago. Their answers showed a significant shift towards proactive and predictive maintenance model from break/fix models with proactive maintenance rising from 13% to 31% and predictive maintenance going up from
5% to 9%. It should also be noted that these percentages are highly related to the availability and usage of technology.
As the technology becomes more available and catered towards spare parts management needs, predictive maintenance will likely be more commonly used.