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Using Analytics to Drive Predictive Maintenance

February 27, 2014

Predictive Analytics, Predictive Analytics Demystified

Avoid Costly Downtime and Reduce Maintenance Costs

Given the current economic climate, there is continued pressure for companies to limit capital and operational costs. Some companies have focused on specific capabilities and technologies to predict equipment or asset failure so they can avoid costly downtime while reducing maintenance costs. Read this Analyst Insight by the Aberdeen Group based on responses from 140 executives to learn how they use data and analytics to manage the reliability and maintenance of their assets.

 

Business Context

Predictive Maintenance can help companies with assets or manufacturing operations reduce costs and maximize Return on Assets (RoA) for Best-in-Class performance.

Recommended Audience

Senior Executives; Finance, Operations and Supply Chain Managers; IT Managers; Business Intelligence Managers; Business Analysts

Outline

Forces Driving Predictive Maintenance

Reduced Operational budgets                                                40%

Need to Maximize Return on Assets (RoA)                            37%

Reduced Capital Budgets                                                       28%

Rising Material Cost                                                                25%

Aging Assets                                                                           17%

Maturity Class Framework

Performance for Best in Class (top 20% of performers)

  • 1.7% Unscheduled Asset Downtime
  • 91% Overall Equipment Effectiveness (OEE)
  • +20% Return on Assets vs. Corporate Plan
  • -31% Reduction in Maintenance Costs

 Strategic Actions by Best in Class to Maximize RoA

  • Improve long term capital planning with better analytic tools
  • Outsource maintenance activities to third party
  • Manage energy and emissions as part of the maintenance strategy

Establishing Predictive Business Capabilities

  • Historical asset data (trends) and real-time data used as actionable intelligence for optimized decision making
  • On-demand asset lifecycle information easily accessible by all employees
  • Centralized knowledge warehouse to store asset performance data from different plants
  • Budget allocated to support reliability centered maintenance activities

 Leveraging Sustainability Data

  • Visibility into anomalies when assets exceed acceptable performance thresholds
  • Asset data utilized to minimize energy consumption
  • Benchmark the performance of each asset to determine the cost of maintaining versus replacing it with a newer, more energy efficient asset
  • Energy management integrated with overall asset management strategy

 Technology Enablers

  • Master Data Management
  • Analytics
  • Workflows
  • Dashboards
  • Spare Parts Inventory Optimization

 Key Takeaways

  • Provide centralized, real-time data
  • Utilize predictive analytics to make educated decisions about future events
  • Provide integration between business systems
  • Consider sustainability as a critical part of a maintenance strategy