Predictive maintenance (PdM) is one of the core benefits of Industry 4.0, but on the shop floor, and in discussions involving management, we need to be able to quantify the impact of PdM.

Because IoT technology is constantly advancing – with better sensors, more data and improved analytics – there isn’t really a golden formula for calculating the return on investment (ROI) of PdM that will suit every type of use case. This is also because PdM differs greatly from one manufacturing scenario to another, being affected by equipment type, product sector, and overall facility conditions.

That being said, it’s important for manufacturing operations to construct ROI calculations for their specific predictive maintenance implementation in order to have benchmarks in place for business justification, analysis of actual results, and further improvement.

 

How Predictive Maintenance Cuts Costs

To quantify the ROI of PdM for your operation, it helps to first identify the main areas where PdM cuts costs.

  1. Reduced lost production time. PdM allows for preempted downtime that is typically much shorter than responsive repairs, and can be scheduled for times that are convenient and less costly. Unexpected failures become less common reducing overall production lost due to malfunction.
  2. Reduced maintenance costs. Instead of routine maintenance which in many cases is completely unnecessary, repairs are done when needed. Having a defined task, technicians perform in a more engaged manner instead of going through the motions and checking items off a list.
  3. Reduced labor costs. Since technicians are called upon for specific and focused tasks only, labor costs are reduced.
  4. Reduced equipment costs. Only the problematic parts are dealt with, saving money on unnecessary replacements and wear-and-tear of adjacent components caused by repairs.
  5. Reduced secondary damage. PdM identifies problems early on, before they escalate and cause more extensive damage to equipment. Quantifying savings made by reducing secondary damage is difficult, but a widely-accepted estimate is that repairs made to failed equipment cost up to 10 times more than repairs made before failure.
  6. Reduced inventory expenses. Instead of having to keep parts and materials in storage as backup, with PdM, orders can be made only for what’s necessary, significantly cutting down on inventory.
  7. Longer lasting machinery. Since disassembly is carried out less frequently, equipment lasts longer.
  8. Reduced risk-based costs. Fewer unplanned repairs reduce safety risks and the chance of damage being done to other parts or equipment.

 

Factors to Consider when Quantifying Predictive Maintenance

Here’s an example of how you can visualize your pre and post-PdM repair costs in a fairly simple manner by comparing main parameters:

The chart above compares repair costs for a single malfunction. Since predictive maintenance caught the malfunction early on, the damage was controlled, preventing Part B and Part C from also needing to be replaced. No production time was lost since the repair was scheduled during planned downtime, and although third party assistance was required, it was cheaper than calling for a rush job had the repair been responsive.

 

Unique Benefits for Manufacturers

Each manufacturer is likely to reap unique benefits from predictive maintenance and IoT. Beyond the measurable factors such as production time or labor costs, predictive maintenance can lead to better quality products, improved safety conditions, and more satisfied customers and employees.

Factors such as cuts in inventory expenses and extended equipment life are subjective to manufacturer so it’s important to quantify PdM value by taking into account the specifics of your enterprise.

 

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