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© Presidion 2016 Formerly SPSS Ireland
Delivering significant efficiencies through Predictive Maintenance in the Oil & Gas
Industry
Aberdeen, 10 March 2016
© Presidion 2016
1. Significant Financial Benefits linked to Predictive Maintenance
2. What Predicitve Maintenance is about
3. How Predictive Maintenance works
4. How to make a success of Predictive Maintenance
What I will cover
2
© Presidion 2016
Predictive Maintenance – Transforming of Maintenance Business Model
3 Source: Roland Berger
2
75% Reduction in breakdown
4 More data = More accuracy = More value
1 $18 /hp p.a.
$9 /hp p.a
$13 /hp p.a.
3
15% Time spent on Predictive Maintenance only
75%
15%
Reactive a
nd
Pre
ve
ntive
© Presidion 2016
Predictive Analytics help connect data to effective action by drawing reliable conclusions about current conditions and future events
4
High
Low
Business
Value
Time Past
Business
Intelligence
Sense and Response
Future
Predictive
Analytics
Predict and Act
Techniques and Data
• Optimisation, predictive modelling, forecasting, statistical analyis
• Structured/Unstructured Data, Internal/External Data, Massive
Data Sets
Driving Questions to be answered
• What will happen next? Why?
• Why is this happening?
• What if?
• What's the optimal scenario for our business?
Techniques and Data
• Reporting, dashboarding, alerts, queries
• Structured Data, Manageable Data Sets
Driving Questions to be answered
• What happened last quarter / month /week?
• How many pumps did break down? How much did we
spend on maintenance? How much downtime on
these assets? How many preventive actions have we
completed?
• Where is the problem?
© Presidion 2016
How does Predictive Maintenance deliver?
Unearthing characteristics that lead to an increased frequency of failures
Predicting impact or consequence scores to enhance Alarms Management so that key alarm
events are prioritised
Identifying factors that increase ownership cost and downtime over the life of a system
Identifying assets at risk of failure even when they have no previous failure history
Mining free text from thousands of logs that describe the maintenance performed on systems to accurately categorise maintenance reports and identify areas of risk
Finding patterns in maintenance operations that could point to opportunities for
improvements
© Presidion 2016
What if I could tell you that a specific asset is 90% likely to fail within one week for Reasons A, B and C?
6
Data Predictive Models - Insights
Actions (Work Order)
Anomaly Detection
Diagnostic Analysis
Recommendations and
decision support
What should be done next?
Priortisation
What to attend to first
depending on fault severity?
Evaluate impact
Procurement & Supply Chain
Sensors
GIS
Data Historian
Asset Management
Maintenance Management
Automation
Change Management
Real Time
Other Sources
© Presidion 2016
How to deliver a Predictive Analytics Project succesfully
7
Determine Business
Objectives and Data Mining
Goals
1
Collect, describe, explore
and verify quality of Data
2
Select, clean, construct,
integrate and format data
3
Select, Generate, Build and
Evaluate Models
4
Evaluate how the results
help to achieve business
objectives
5
Integrate new knowledge
into your business
processes
6
Business
Understanding
Data
Understanding
Data
Preparation
Modelling
Evaluation
Deployment
© Presidion 2016
Asset Data Availability
Criticality of Failure
Trust in Predictive
Technology
When Predictive Maintenance works well
8
Actionable Insights – Return on Investment
© Presidion 2016
Challenges
9
Heterogeneous Assets
Changing Operating Conditions
Interoperability
© Presidion 2016
End-to-End approach is required
10
2.1 Tactical Audit for Advanced Analytics
2.2 Maturity Assessment for
Advanced Analytics
2.3 Roadmapping and Business Case Development
3.1 Advanced Analytics Capability Building
4.1 Performance Monitoring and
Business Benefits Realisation
2. Prepare yourself for success in Advanced Analytics
3. Improve business
performance
4. Sustain improved
performance
1.1 Advanced Analytics
Transformation Workshop
1. Learn how Advanced
Analytics can transform your
organisation
© Presidion 2016
What if you could deliver these numbers…
11 Source: us department of energy's o&m best practise guide,
Imagine if you add IoT data
4
10 x Return on Investment
1
25% Increase in production output
30% 2
Reduction in maintenance costs
45% 3
Reduction in downtime
