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 industrial IoT technology is constantly advancing – with better, cheaper sensors, better-secured data communication, and improved analytics – there isn’t really a golden formula for calculating the return on investment (ROI) of PdM. 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 said, it’s important for manufacturing operations to construct ROI models for their specific predictive maintenance implementation. This will provide a backbone for business justification, analysis of actual results, and further improvement.

 

8 Key Benefits of Predictive Maintenance

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 planned 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, increasing overall equipment effectiveness and overall line efficiency.
  2. Reduced maintenance costs: Instead of routine maintenance, which in many cases is redundant, repairs are done when needed. Having a well-defined corrective action, technicians perform in a more engaged manner instead of simply going through the motions.
  3. Reduced labor costs: Since technicians are called upon for specific and focused tasks, 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 expensive parts and materials in inventory, with PdM orders can be made only for what’s needed, cutting inventory costs.
  7. Longer lasting machinery: Since disassembly is carried out less frequently, equipment lasts longer, increasing remaining useful life (RUL).
  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 Benefits

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

Comparison and benefits of predictive maintenance and responsive maintenance

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 repair was scheduled during planned downtime, and although third-party assistance was required, it was cheaper than calling for a rush job.

 

Unique Predictive Maintenance Benefits for Your Operations

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

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

 

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Why predictive maintenance is driving industry 4.0 - iot resource