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Serious Injury and Fatality (SIF) Precursor Customization Project
2
Motivation
0.00000.01000.02000.03000.04000.05000.06000.07000.08000.09000.1000
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Fata
lity
Rate
(per
100
0 w
orke
rs)
Year
Fatality Trends in Electrical Power Generation and Delivery(moving 3-year average)
3
Gregg Slintak, Consolidated Edison Tom Dyson, Ameren ServicesMatthew Hallowell, University of ColoradoTodd Gallaher, Southern California EdisonTerry Halford, ClecoDave Flener, Quanta ServicesDean Larson, KCPLJenny Bailey, Southern CompanyKathy Wilmer, Duke EnergyJoe Armatys, Bonneville Power
Bill Messner, Portland General ElectricEric Bauman, Electric Power Research InstitutePaul Mackintire, Eversource EnergyIan Wenzel, AlleteScott Lange, WEC Energy GroupMarguerite Porsch, CenterPoint EnergyRick Hoffman, American Electric PowerJames Goodnite, American Electric PowerPatrick Winkel, Consumers EnergyBob Spencer, Tennessee Valley AuthorityLen Colvin, Tennessee Valley Authority
EEI SIF Research Team
4
Key Definition
Precursor: Reasonably detectable event, condition, or action that serves as a warning sign of a serious incident or fatality
All precursors are causal factors, but not all causal factors are precursors
Use of the SIF precursor analysis does not guarantee that a SIF event will not occur
5
Objective
Customize precursor analysis for electrical generation, transmission, and distribution
What factors best distinguish success from failure?
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Mindset
7
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases
Process
Customized Method
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Brainstorm New Precursors
Specific to EEISelect Top Tier
Rate Predictive Ability of New
Precursors
Rate Generalizability
Validated General Industry
Precursors
Our Investigation Set
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
(43) (13) (16)
(29)
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1. Safe Work Procedure2. Working Alone3. Hazard Recognition4. Control Barriers5. Plan for Change6. Safety Attitudes7. Schedule Pressure8. Improvisation
Original Precursors from General Industry Study
9. Significant Overtime10. Fatigue11. Distraction12. Prior Safety Performance13. Safety Supervision14. Front-Line Supervisor15. Pre-Task Plan16. Congestion
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
10
1. Departure from Routine2. Adherence to Rules3. Familiarity with Task4. Worker Assumptions5. Multitasking6. Risk Normalization7. Use of PPE8. Equipment ID and Steps
9. Communication Barriers10. Safety Culture11. Stop Work Execution12. Worker Engagement13. Safety Devices
New Precursors to Test
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
11Create ‘case’ template
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
List of 29 Factors
• X• X• X• X• X• X
Case Template
• ???????????• ???????????• ???????????• ???????????• ???????????• ???????????
12
SIF No-SIFTD 12 12
GEN 8 8
n = 40 cases
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
13
SIF No-SIF TotalTD 12 12 24 60%
GEN 8 8 16 40%
n = 40 cases
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
14
SIF No-SIF TotalTD 12 12 24 60%
GEN 8 8 16 40%
Total 20 2050% 50%
n = 40 cases
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
15
High-energy near miss
Fatal or DisablingHigh-Energy
Success
Screen, Scrub, Randomize
Case 1 Case 2 Case 3 Case 4 Case 5 Case 6 … Case 40
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
16
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
72% of cases correctly predicted by the team
17
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
Goal: Find the precursors that best distinguish no-SIF cases from SIF cases
With this information we can shorten the engagement to something reasonable.
18
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
Simple data structure
Principal Components
Analysis (PCA)
Reduces the number of variables
Generalized Linear Modeling
Predictive equation
0 1 0 … 11 1 0 … 01 1 0 … 01 1 0 … 0
1 1 0 … 0
Case 1Case 2Case 3Case 4
Case 40
Precursors
X1 X2 X3 X29
1000
1
Y
OutcomesCases
19
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
0
2
4
6
8
10
12
14
Presence in SIF v Success
20
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
0
2
4
6
8
10
12
14
Presence in SIF v Success
Total Presence in SIF Total Presence in Success
21
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
0
2
4
6
8
10
12
14
Presence in SIF v Success
Total Presence in SIF Total Presence in Success
22
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
P(SIF) ~ B0 + B1 P1 + B2 P2 … Bn Pi Pj
What we are trying to predict: Probability of SIF
What is a generalized linear model?
23
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
P(SIF) ~ B0 + B1 P1 + B2 P2 … Bn Pi Pj
What is a generalized linear model?
Depends on (is predicted by)
24
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
P(SIF) ~ B0 + B1 P1 + B2 P2 … Bn Pi Pj
What is a generalized linear model?
What the probability when NO precursors are present (intercept)
25
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
P(SIF) ~ B0 + B1 P1 + B2 P2 … Bn Pi Pj
What is a generalized linear model?
The first precursor (present = 1, absent = 0)
26
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
P(SIF) ~ B0 + B1 P1 + B2 P2 … Bn Pi Pj
What is a generalized linear model?
The first precursor (present = 1, absent = 0)
The weight of that precursor (coefficient)
27
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
P(SIF) ~ B0 + B1 P1 + B2 P2 … Bn Pi Pj
What is a generalized linear model?
The second precursor
28
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
P(SIF) ~ B0 + B1 P1 + B2 P2 … Bn Pi Pj
What is a generalized linear model?
The second precursor
Its weight
29
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
P(SIF) ~ B0 + B1 P1 + B2 P2 … Bn Pi Pj
What is a generalized linear model?
Interaction of two precursors
30
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
P(SIF) ~ B0 + B1 P1 + B2 P2 … Bn Pi Pj
What is a generalized linear model?
Interaction of two precursors
Weight of the interaction
31
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
Precursors Present in SIF Cases
Present in no-SIF Cases
Difference (SIF-no SIF) 𝜷𝜷𝟎𝟎
Rules and Procedures 27% 2% 24% 4.12
Departure from Routine 24% 5% 20% 3.57
Hazard Recognition 22% 5% 17% 0.79
Safety Attitudes 22% 5% 17% 0.02
Workers Inactive in Safety 17% 2% 15% 0.73
Risk Normalization 32% 20% 12% 0.91
Safe Work Procedure 12% 2% 10% 0.92
Familiar with the Task 17% 7% 10% 0.58
Stop Work Execution 12% 2% 10% 0.58
Perceived Safety Culture 15% 7% 7% 1.11
Pre-Task Plan 20% 12% 7% 1.56
Plan to Address Change 22% 17% 5% 0.49
Productivity Pressure 20% 15% 5% 1.06
32
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
Precursors 𝜷𝜷𝟎𝟎 Weights
Rules and Procedures 4.12 3
Departure from Routine 3.57 3
Pre-Task Plan 1.56 3
Perceived Safety Culture 1.11 3
Productivity Pressure 1.06 3
Safe Work Procedure 0.92 2
Risk Normalization 0.91 2
Hazard Recognition 0.79 2
Workers Inactive in Safety 0.73 2
Familiar with the Task 0.58 2
Stop Work Execution 0.58 2
Plan to Address Change 0.49 2
Safety Attitudes 0.02 1
33
11% 14% 17%21%
26%32%
38%45%
52%58%
65%71%
76%81%
85%
97%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
POTE
NTI
AL F
OR
SIF
WEIGHTED PRECURSOR SCORE
SIF Potential
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
34
Identify Precursors
Experiment to Test
Precursors
Objective Statistics
Collect Cases Customized Method
35
What is a field safety engagement? When performing the engagement… Potential pitfalls
Field Safety Engagements to Complete the SIF Precursor Scorecard
36
• 27 Questions and 11
Observations• Validated, Research, and
Customization Process
• List of Questions
• Peel the onion
Field Safety Engagements to Complete the SIF Precursor Scorecard
37
Setting up for Success!For a Strong and Positive Engagement
Déjà vu- Add strategy and Intentional Focus- Framework to engage field workers
Let the engagement begin- Minimize disruption of work- Participate in Job Briefing
Present on not?- Knowledge of the work & workers- Precursors are rare occurrences
Coaching
2 Way Communication
Respect
Comfortable ConversationFeedback
Positive Tone
38
Using the SIF Precursor Scorecard
1
2
3SUM
Check the precursors that were present before any intervention was made.
Find the sum of all the weights for the selected precursors.
Interpret the total weighted score.
40
How did you score?
41
Putting Precursor Analysis into Practice
43
The product of individual and group values, attitudes, competencies, and patterns of behavior that determines the commitment to safetyIt’s the way we do things!
Safety Culture determines: Personal Responsibility Trust Communication Behavior Lessons learned
Safety Culture
44
Empowerment
Empower individuals to successfully fulfill their safety responsibilities to themselves, their family, and their coworkers.
Encourage everyone to: Hold themselves and each other accountable for safety! Exercise authority to stop unsafe behavior without fear of negative
repercussions. Correct unsafe conditions as soon as possible Provide multiple options for your team to report unsafe conditions
and/or behaviors
45
Communication
Build TRUST! Do what you say you will do, when you say you will do it!
Ensure timely and appropriate responses to identified hazards and have an action plan in place to address and remove the hazards.
Reinforce current safety practices through regular coaching
Celebrate the successes along the way!
46
Organization - All Employees“Perceived” Safety Culture, Stop work and Productivity
pressures .
Planning and PreparationRules and procedures, Working Together/Communication
PerceptionHazard Recognition, risk normalization and familiarity with
the work.
EngagementSafety Attitudes and Ownership
IMPLEMENTATION
How it all fits together
47
The presence of precursors like schedule pressure, risk normalization, and poor attitudes compromise readiness and may increase the potential for events.
When strong pre-task planning is performed to manage hazards and precursor analysis is used to check worker readiness, BOTH the demands of the work and readiness of the worker are considered.
Integration with Existing Programs
48
Executive Summary Implementation Guide Project Report Engagement Video Conference Video
Available Resources