Optimize Maintenance Costs through Predictive Maintenance

Predictive Maintenance:

Predictive maintenance, a strategy to service the equipment only when needed, reducing the unexpected outages.  These proactive analysis can help to increase the equipment life along with the reduction in the product delays with the reduction in the equipment changeovers/downtime.

Following are the few highlights on Predictive Maintenance

  • Enables the organization in monitoring assets remotely and that too in real time and also maintain a digital record of the transaction details.
  • Helps to monitors the asset’s location and its utilization in integration with IoT.
  • End-to-end visibility with real-time analytics enables improved productivity
  • Optimize logistics of the parts and ensure proper maintenance planning.

Significance and Management of Predictive Maintenance:

Predictive maintenance, a key component of Industry 4.0.  Improper maintenance management and strategies can impact the operational efficiency of the organization along with its profitability . i.e., The effective maintenance practices determine the ability to operate reliably and profitably. To be very competitive, companies need to minimize the plant/equipment unplanned downtime and inturn optimize maintenance costs. Implementing best maintenance practices, processes, and applications can yield good returns.

Predictive analytics is used to predict the assets failure and to generate actionable insights in real time. Different data sources can be used to get the raw data based on which the decision of whether to have maintenance operations needed i.e., data from different sources like IoT, M2M etc., is required to establish an effective predictive maintenance system. For example, maintenance mgmt., systems contain information on maintenance manual, parts of equipment, maintenance reports etc.

Advanced analytics capabilities (like Oracle Analytics Cloud) are very critical for maintenance optimization, i.e., for analytics and visualizations.  This helps the organization in predictive analytics besides descriptive analytics.  Machine learning and data science methods are used to build the predictive maintenance models.

Highlighting elements of the Predictive Maintenance system:

Asset Monitoring: Monitor Assets remotely in real time, collect the information from physical world into digital form and Optimize the Asset lifecycle by leveraging Artificial Intelligence

Data Analytics: Analyze asset data streams and analytics tools to deliver  visualizations of real-time data and failures prediction by advanced analytics and ML algorithms, before the failure happen and resulting in maintenance planning optimization.

Maintenance Optimization: Based on the AI, data insights and subsequent predictive actions, automation (like using sensor data) of the maintenance Work Order creation, technicians’ assignment and optimal maintenance schedule recommendations.

Integrate with IoT: Monitors the asset’s location, and its utilization in association with IoT. Real time data integration with the physical asset and the IoT application.

Optimized Operations:  Optimization of business process through real time data driven decision.  This also reduces the operations cost. Complete view of assets and equipment helps the organization to know the location of an asset and its lifecycle details.

Key Benefits:

Following are key benefits from Predictive Maintenance

  • Equipment Uptime increase
  • Reduction in breakdown
  •  Increase in Productivity
  • Reduction in Maintenance Costs

Poor maintenance strategies can affect the maintenance operations efficiencies and have impact on the organizational profits.  In today’s world, to be very competitive, the organizations in asset oriented industries, need have a proper maintenance strategy which results in reduction of unplanned downtime and optimization of the maintenance costs.  Hence have an effective Predictive Maintenance strategy in place which enable organizations in monitoring their assets in real time, assets integration and data collection from different sources, analyze the data and translate it into meaningful insights and finally convert those insights into very prescriptive actions in an automated manner, in optimizing maintenance activities and costs.






Author Details

Asif Basha Syed

Asif has 17+ years of experience in Oracle Practice with multiple end-to-end implementation, upgrade and support engagements across the Globe, besides 2 years of industry experience in Manufacturing domain. He is an Oracle Certified Specialist (EBS & Cloud) and has led solution design of several complex Oracle ERP implementations and designed multiple complex solutions spanning SCM/Manufacturing/Planning tracks.

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