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