Predictive Maintenance and Quality solutions access multiple data sources in real time to predict asset failure so that your organization can avoid costly downtime, reduce maintenance costs and improve operational efficiency. Driven by predictive analytics, these solutions detect even minor anomalies and failure patterns to determine the assets and operational processes that are at the greatest risk of failure or that can result to an increased number of faulty products and warranty claims. This early identification of issues helps you deploy limited maintenance resources more cost-effectively, maximise equipment uptime and enhance quality and supply chain processes, ultimately improving financial positioning, customer satisfaction and brand value.
Predictive Maintenance solutions will enable your organization:
- Predict when and where asset failures are likely to occur
- Leverage from Internet of Things (IoT) sensors readings on instrumented assets
- Combine external data with internal data, such as environmental and weather data with data from Asset Management and Supervisory Control Systems
- Avoid asset downtime and reduce maintenance cost
- Perform root-cause analyses of asset and process failures
- Mine maintenance logs to determine the most effective repair procedures and failure patterns
- Visualize maintenance and operational insights and ensure Operational Efficiency through alarms prioritization and recommendations
Predictive Quality solutions will enable your organization:
Detect emerging quality problems and reliability issues early enough- Avoid costly call backs and lost production
- Reduce scrap work, Inspections effort and Warranty claims
- Make adjustments to predictive maintenance schedules and corrective actions
- Maximise shipping rates cost-effectively
- Improve Brand Value and After Sales Support recommendations
Innovative companies consistently look for ways to achieve their goals more efficiently, quickly respond to market changes and take advantages of emerging opportunities. Predictive Maintenance and Quality solutions can help your organisation continuously improve Operational Efficiency by optimizing existing processes and turning operational data into actionable insights.
Opportunities to improve operational efficiency, reduce operational costs and maximise productivity, exist in various operational areas, including:
- Workforce and Resources Scheduling
- Equipment and Spare-Parts Inventory
- Capital and Strategic Planning
- Supply Chain Management
- Building Management
- Energy Efficiency
- Facilities Management
Predictive maintenance can be applied in:
Predictive Maintenance Resources
Videos
Infographics
What is Predictive Maintenance? - From Reactive Maintenance to Predictive Maintenance
Plan, Manage, Maximise - Be more strategic, decide faster, reduce downtime and proactively reduce risk
Case Studies
Case Study
One of the leading Oil and Gas producers in the Asia-Pacifid region, managed to save millions by deploying a predictive asset monitoring and alert system.
Santos developed predictive models that ingest data from SCADA-connected assets and many other sources, giving early warning of potential failures and opportunities to improve uptime and production.
Case Study
Automotive manufacturer increases productivity for cylinder-head production by 25 percent.
- 50 percent reduction in the time taken to ramp up the process to Daimler’s target levels.
- Enables rapid adjustments by monitoring the process in near real time.
White Papers
White Paper
Maximise asset productivity and operational performance
Discover the specific benefits predictive maintenance solutions provide asset-intensive organizations.
- Maximize asset productivity
- Optimize asset-associated processes
More Resources
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Is my data good enough for Predictive Maintenance?
Presidion’s Data Readiness Assessment (DRA) helps clients to achieve an accurate understanding of where they are in relation to data held to address their maintenance objectives.
On-Demand Webinar
Transforming Maintenance with Predictive Analytics
Deliver significant savings by leveraging data
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