Post on 13-Apr-2017
Proactive Risk Leadership – using data analytics to prevent serious process
safety incidents
Coen van Driel
Robert Kauer
TÜV SÜD and Kienbaum Management Consulting October 6th, 2016
Process Safety Transformation Management
Technical Process Safety Expertise &
2016 European Conference on Process Safety and Big Data
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Big Data is “in” and everyone wants to get into it but most don’t understand it ... Big Data, Big Expectations, and a lot of opinions
Big data the holy grail for the industry….
…or is it of great value for risk management/ proactive risk leadership?
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Our reference when we are talking about big data in the context of risk
management
Understanding the business
Data Analytics
The use of algorithms and statistical modeling to draw meaning and insights from data
Supporting the decision making circle
Big data
Data generated from various sources in different formats in an ever increasing speed
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Big Data defined by the 5 V’s Big Data Definition
» Volume
» The quantity of generated and stored data. The size of the data determines the value and potential insight- and whether it can actually be considered big data or not.
» Variety
» The type and nature of the data. This helps people who analyze it to effectively use the resulting insight.
» Velocity
» In this context, the speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development.
» Variability
» Inconsistency of the data set can hamper processes to handle and manage it.
» Veracity
» The quality of captured data can vary greatly, affecting accurate analysis.
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The transformation process to Data analytics Excellence takes an multidiciplinary
approach Five levels of data analytics
Level 1: Basic analytics » Standard RCA and application of PDCA
» Firefighting to deal with problems
Level 2: Descriptive analytics » Applying structural lean six sigma on single source data
» Descriptive statistics and regression analyses to understand past trends
Level 3: Predictive analytics
Level 4: Prescriptive analytics
» Start modeling future outcomes with past data
» R-analyses on multiple sources
» Converting trends into future scenarios to make decisions
» Complex algorithms using multiple source of data to convert into action
Level 5: Data Analytics excellence
Action to take
Looking Ahead
Looking Back
Average position of most companies in chemical sector.
Compliant
Tran
sfo
rmat
ion
Jo
urn
ey
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Using the data analytics it will be possible to grow from static audit type
evaluation to continuous information and real time decision making Technological developments
Audit type results Real time Process Safety decision making
Process Safety Performance Measurement
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To transform to real time decision making using data analytics various sources
need to be combined based on a risk management approach Integrated risk process safety management system
Linking risk based thinking with process safety performance based on big
data preventing serious process safety incidents from happening Proactive risk management
Hazard analysis
Risk based thinking Process Safety performance
Select High Impact
scenarios
Review with the work
teams
Integrate in work
process
Visual board shop floor
Leadership rounds
Performance monitoring
Performance Site/Europe
• Risk identification through
efficient and tailored hazard
analysis (PHA/PHR process)
• Aggregation of the
performance to develop
policy and strategy
• Highest Consequence
Scenarios
• Extract key scenarios for each
section
• Use the KPI’s to set priorities
and show the developments
• Prevention activities & reduce
consequences
• Review in control room and
other operational area’s
• Develop risk awareness with
personal
• Verification of risk awareness
of personal in the
organization
• Verify with management system
elements and element owner
• Integrate in Shifttour
maintenance / production,
Inspections, Alarms
• Visualize near misses based
on the shift tours, process
deviations, etc.
• Discuss findings in shifts and
meeting structure
Risks need to be clear to define leading KPIs on near miss level of the API
pyramid (level 3) and controlling the barriers (work processes)
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Big data
DADA
DADA Data – Analyze – Decision – Action
The decision making process based on the risk based management thinking
enable proactive risk leadership
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Big data based risk based performance management
Data
Analysis
Decision
Action
Business Understanding
Deployment
Evaluation
Modeling
Data Understanding
Data Preparation
Data
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Example From standard inspection to multi source risk based decision making
Level 1: Basic analytics
Level 2: Descriptive analytics
Level 3: Predictive analytics
Level 4: Prescriptive analytics
Level 5: Big data excellence
OK 5y
OK 5y
» isolated approach » subjective, relying on expertice » not transparent
CML
Limit State » single source » frozen until next inspection results » isolated approach
CML on-stream monitoring » multi source » multi-disciplinary » more flexible decision making
screening in combination with hot spots » risk-oriented and focused » interlinked multi-sources » „real- time decision making and
links to other initiatives » learning about relations
Big data is not the holy grail for the industry….
But the opportunity to enable proactive risk leadership at all level using prescriptive analytics