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(Below is part two of our series on the economics of the Smart Factory.   For the part one of the series, please click on this link.)

The Complexity of ROI Calculations and why RAV should not be Ignored

When considering a new solution, it is important to evaluate all the cost factors.  In the case of asset maintenance, it is widely recognized that unscheduled machine downtime costs industrial plants billions of dollars a year in lost production.  As can be seen in the chart below, on average 17 days of production are lost in industrial plants.

Reducing average downtime is a major driver in adopting new Predictive Maintenance solutions. When calculating the financial implications of a new PdM system, the easiest way to achieve a positive ROI estimate is to focus on improvements to factory yields.   For instance, in one study it was estimated that 75% of breakdowns were eliminated by energy companies implementing Predictive Maintenance programs.

Applying these types of estimates, it may be tempting to get swept up in the hype of new PdM solutions for the Smart Factory.  However, there is another calculation to consider:  Total Maintenance cost as a Percentage of Replacement Asset Value (RAV).  This metric, developed by the Society for Maintenance and Reliability Professionals (SMRP), is used to determine the annual cost of maintenance relative to the cost of new machinery.   For instance, if it will cost $1 million to replace a factory asset and it costs the factory $50,000 to maintain the asset on an annual basis, then the Total Maintenance cost as a Percentage of Replacement Asset Value (RAV) is 5% ($50,000/$1,000,000).

At 5%, the lifetime of the asset is estimated to be 20 years.   If the maintenance cost is $10,000 a year, then the Total Maintenance cost as a Percentage of Replacement Asset Value (RAV) is 1% and the lifetime of the asset is estimated at 100 years.

It may be correct that Total Maintenance cost as a Percentage of Replacement Asset Value (RAV) varies by asset class and it is not a perfect metric.  However, it should also not be ignored.

Why?  Although CapEx and depreciation strategies are not the topics of this article, facility owners are well-aware that massive investments required to support some of the more ambitious Smart Factory solutions cannot be hidden by creative accounting practices.

TCO and the Digital Twin

From a technology perspective, robust solutions for the Smart Factory are gaining market attention.   According to Gartner, the Digital Twin is one of the top strategic technology initiatives in 2017.   The Digital Twin provides a factory owner with a virtual clone of the physical machine asset and can monitor the asset performance in real time.  For some, this could be considered the ultimate Predictive Maintenance tool because any abnormal machine behavior can be identified in real time and technicians can remediate before degradation turns to failure.

If our description of the Digital Twin is accurate, then what’s the catch?   Without disputing the value of virtual machine asset clone to the facility owner, it is important to consider the Total Cost of Ownership (TCO).

Putting aside the specific vendor solution, there are two major cost areas that need to be calculated:

  • Implementation. To deploy the Digital Twin, the actual architectural blueprints of the machine are required.   CAD technicians manually create 3D models using the factory blueprints.  Any discrepancy between the actual machine and the original blueprints will need to be accounted for. Creating a Digital Twin requires multiple external resources including big data engineers, data scientists, and other consultants.  Furthermore, the Machine Learning algorithm at the core of the Digital Twin requires significant input from internal maintenance staff to support the vendor in physical process modeling.

Digital Twin Process

  • Ongoing Costs. The massive investments by industry juggernauts such as GE and Siemens are designed to create a platform for the Smart Factory.    While addressing the need for more accurate and robust Predictive Maintenance, these vendors are creating an infrastructure to support a vendor ecosystem of solutions covering a range of technologies.  The licensing and infrastructure maintenance cost will need to be carefully evaluated as part of the TCO calculation.

Ultimately, the issue that factory owners will need to consider is the cost of a Digital Twin as a function of Total Maintenance cost as a Percentage of Replacement Asset Value (RAV).  No matter how compelling the technology, it becomes increasingly difficult for owners to justify the Digital Twin as it increases relative to the cost of RAV.

 The Issue of Scalability and the Impact on ROI      

The Digital Twin is a customized virtual clone based on the unique conditions of each factory asset.   Over time, we expect that certain factory assets be sold with corresponding Digital Twins.  However, if a factory procures a Digital Twin for existing assets, then the costly and labor-intensive deployment described above is required for each machine asset.   In the illustration below, multiple separate Digital Twins are needed, each operating independently.   Few, if any benefits can be gained from economies of scale, because of the high-level of customization required for each Digital Twin.

The alternative to the Digital Twin will be big data solutions that can serve an entire factory and are agnostic to sensor type or asset class.   With Presenso, all the existing factory sensor data is continuously streamed to the data is run through an ensemble of unique “Machine Learning” algorithms.  When anomalous sensor data is detected (or patterns of anomalous behavior), the system sends an alert to the facility in real time so that degradation can be remediated before breakdown occurs.

Do you wish to learn more about Machine Learning for Asset Maintenance? Click here to schedule a free demo.