How to Maximize ROI on Preventive Maintenance Solutions?

Author: 
Larry Xu

To protect the investment on high dollar equipment and machinery asset, and most importantly, to ensure efficient and continuous operation of the businesses, most enterprises have established some sort of preventive maintenance (PM) programs in their organizations. Return on Investment (ROI) is a major KPI (Key Performance Indicator) to track the performance of the asset. However, the preventive maintenance solution itself needs a ROI to measure the effectiveness of the program.

Similar to the scheduled car engine oil change, the traditional enterprise level preventive maintenance programs are calendar or usage based. Although these programs play an important role in prolonging the usage life of the equipment and preventing unexpected breakdowns, they are only supported by past experience or a rule of thumb, without knowing the real “condition” of the protected equipment before scheduled service or replacement.

The emergence of new IT technology and hardware, together with the omnipotent Big Data analytics, has revolutionized the concept of preventive maintenance. More adequately, it has become a program of predictive maintenance (PdM).

Predictive maintenance techniques are designed to help determine the condition of in-service equipment in order to predict when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted by the conclusion of the powerful Big Data predictive analysis.

In order to maximize the ROI of a preventive maintenance project, a company can either reduce the initial amount of investment by limiting the scope of the project or leveraging the existing hardware infrastructure and systems, given the estimated future cash flow is constant; or it can increase the future cash flow by improving the operational efficiency in saving costs.

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