using-predictive-analytics-to-optimize-asset-maintenance-in-the-utilities-industry-codex964

Using-predictive-analytics-to-optimize-asset-maintenance-in-the-utilities-industry-codex964

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Using Predictive Analytics to Optimize Asset Maintenance in the Utilities Industry By working proactively to collect and distill digital information, transmission and distribution utilities can enhance customer satisfaction, reduce total cost of ownership, optimize the field force and improve compliance. Executive Summary Aging assets, an aging workforce, the introduction of networked smart grids and a proliferation of intelligent devices on the power grid are challeng- ing utilities to find more effective and efficient ways to maintain and monitor their critical assets — and to do so with high availability and reliability. The ultimate objective of traditional or smart asset management is to help reduce/minimize/ optimize asset lifecycle costs across all phases, from asset investment planning, network design, procurement, installation and commissioning, operation and maintenance through decommis- sioning and disposal/replacement. Optimizing the costs associated with each of these lifecycle phases remains among the key objectives of an asset-intensive utility organi- zation. Sadly, preventive maintenance sched- ules prescribed by manufacturers haven’t really helped utilities to avoid asset failures. Many utilities have realized that avoiding unexpected outages, managing asset risks and maintaining assets before failure strikes are critical goals to improve customer satisfaction. A recent survey 1 across 200 global utilities suggests that in the area of power distribution, reducing outages and shortening restoration times are the most significant challenges. Approx- imately 58% of surveyed utilities said they need a mechanism for predicting equipment failure. These challenges have forced utilities to leverage analytics to extend the life of assets and bring more predictability to their performance and health, which ultimately helps them plan and pri- oritize maintenance activities. Predictive analytics is a process of using statisti- cal and data mining techniques to analyze historic and current data sets, create rules and predic- tive models and predict future events. This white paper examines how transmission and distribu- tion (T&D) utilities can effectively apply predictive analytics to smart asset management to realize asset lifecycle cost reduction and improve the accuracy of their decision-making. Three mean- ingful types of predictive analytics benefits have been identified: Technology: The amount of money saved on technology or technology costs avoided by introducing the analytic solution. Productivity: Efficiency savings due to the reduced amount of time and effort required for particular tasks. cognizant 20-20 insights | december 2014 Cognizant 20-20 Insights
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cognizant 20-20 insights 2 Business process enhancement: All identifi- able annual savings that were realized due to changes in business process supported by the analytic application.
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  • Winter '16
  • Dr. Mohammad Habibi
  • Data Mining, Predictive Analytics Asset Management Environment

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