Industry 4.0 - Asset management



When it comes to assets management, it is about changing the approach from reactive to proactive. Proactive assets management allows for:

  • Reduced failure-related repair or replacement costs
  • Reduced inspection and maintenance costs
  • Reduced asset lifetime cost
  • Increased asset reliability and availability

Today's technologies permit assets management to operate on condition based or predictive maintenance, thereby cutting costs associated to asset maintenance, while maximizing the productivity of the asset and reducing asset fail-rate.

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Asset Management Solutions

Condition Based Maintenance

Condition Based Maintenance

Condition Based Maintenance (CBM) is based on using real-time data to prioritize and optimize maintenance resources. Through condition monitoring, CBM allows asset managers to plan interventions based on the condition of the asset to prevent failures. 

With CBM, maintenance personnel will be able to minimize spare parts cost, system down time and overall costs associated to maintenance by doing the right thing, at the right time.

Predictive Maintenance

Predictive Maintenance

Predictive maintenance allows for intervention only when necessary. Thus, reducing the intervention times on the entire production chain and optimizing the costs related to the purchase of spare parts necessary in case of failure.

This has a direct benefit on the extension of the machinery life cycle, while indirectly there is a reduction in inventory management costs.

Condition Based Maintenance vs. Predictive Maintenance

Both condition based maintenance and predictive maintenance fall into the category of "preventative maintenance", that is: working to prevent failure of an asset before it occurs based on data regarding asset condition.

This is different from the category of "corrective maintenance" which relies on an asset to fail before maintenance is deployed.

What is the Main Difference?

In past, the terms have been used interchangeably - but today, real differences are emerging between condition based and predictive maintenance thanks to the rise in equipment sensor (IIoT) affordability and artificial intelligence techniques like machine learning. 

For both types of maintenance, intervention is predicted based on changes in the assets condition or performance. In a predictive maintenance scenario, the model is based on a smart system that continuously learns in order to provide increasingly more precise indications around maintenance interventions.


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Make Smart Manufacturing a Reality

Industry 4.0 is smart manufacturing for the future - and thanks to the features of SAP S/4 HANA, discrete manufacturers can begin making these scenarios a reality in their business.