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A breakthrough research integrated human factors concepts into safety management system, and proved quantitatively the causality between human factors and safety
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A Human Factors-Based Safety Management Model for Aviation Maintenance Safety
Yu-Lin Hsiao, PhD
Department of Industrial and Systems Engineering
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How do we manage safety?
• The way we currently do– Reactive way: Accident & Incident Investigation
– Proactive way
• Daily- or Periodic-Based: Audit, LOSA, FOQA
• Behavior-Oriented: ASAP, LOSA
• Event- or Consequence-Based: MEDA, Risk Matrix
• Each system has its own advantages, but also probably goes its own and unique way
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How do we manage safety?• If there is a integrated method to manage safety from
a macro-system viewpoint?
– According to the purpose and philosophy of the ICAO Safety Management System (SMS) as well
• Can we integrate all these safety programs or methods by using the same language?
– Based on the same common concepts
– Quantitative and Data-driven Method
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How do we manage safety?• Furthermore, can we use these safety data to
evaluate the safety status and manage the risk?
– To assist upper-level management and decision-making
– For long-term and continued safety management
– But not just focus on single event analysis or case by case
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Answer: Human Factors (HF)
• It is the major cause of flight accidents– Implicated in most accidents and incidents
• Most safety programs are related to human factors in some way
• Connected with current risk and safety management concepts– To eliminate or mitigate specific human error
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Steps to establish the HF model
• 1. Data Transformation– From Qualitative Documents
• audit, investigation, or voluntary reports
– To Quantitative Data
• Human Factors Rates
• Use the Human Factors Analysis and Classification System – Maintenance Audit (HFACS-MA)
– Set up an internal review board
• To analyze data sourced from various systems
• Extract consensus results to calculate the quantitative rates
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UnsafeSupervision
SupervisionDysfunction
SupervisionDisobedience
PlanningOrganizing
ControllingCorrecting
Routine ExceptionalLeading
Coordinating
OrganizationalInfluences
OrganizationalFunctionality
OrganizationalSafety Climate
OperationsProcedure
ExecutionSafetyCulture
ResourceManagement
SafetyPolicies
SafetyOversight
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UnsafeActs
Errors Disobedience
Skill-basedErrors
DecisionErrors
Routine Exceptional
PreconditionsFor
Unsafe Acts
Conditions ofOperators
Conditions ofTask / Environment
AdverseStates
TeamworkTask
DemandsHardware &
SoftwareLimitations
PhysicalEnvironment
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Human Factors Rates• Each error type was accumulated for monthly period
• Multiply by the weights of severity degree– Weights were developed by aviation authority– Based on Analytic Hierarchy Process (AHP) method
• Human Factors Rate =
W : The highest weight of severity degree (W = 11, the designated weight of Finding ) wi : The weight of the severity degree, i={I, R, C, F} n : The sum of the human failures with all severity degree per month ni : The sum of the human failures with specific severity degree, i={I, R, C, F}
nW
nwi
ii
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Future Incident Rate
Incident Rate =
Incident : The number of incidents per month
Departure: The number of flight departure per month
000,1Departure
Incident
Airline DepartureTimes
Accident Incident AccidentRate
Incident Rate
A 81,448 1 73 0.012 0.90
B 134,814 2 192 0.015 1.42
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Steps to establish the HF model
• 2. Develop the mathematical model– Use Neural Network method
– Verify the prediction performance of the model
• 3. Start using the model to evaluate safety status– Output: Future Incident Rate
– Different company might have different prediction performance or time range (month or quarter)
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Steps to establish the HF model
• 4. Detect the uprising trend of future incident rate – Self- or Expert-decided warning threshold
• 5. Find out the root causes and the original data sources– Based on real and reliable data collected by different
safety systems or programs
– To support the corresponding safety management activities
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• Current Achievement: – Succeed in the prediction of future incident rate using
human factors analysis
– Based on real safety audit data from aviation authority (data-driven & practical)
– Prove the causality of human error and safety
– General Accuracy
• Correlation Coefficient: 0.6 ~ 0.75
• R2: 0.35 ~ 0.58
HF-Based Safety Management Model
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Prediction Result
0
2
4
6
8
10
Months
Inc
ide
nt
Ra
tes
Actual Simulate
Airline A
Airline B
60
0
1
2
3
4
5
Month
Inc
ide
nt
Ra
tes
Actual Simulate
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• Advantages– Can integrate various data sources from different safety
programs using the same standard
• Human Factors concept
• As an integrated part of SMS
– Can become a safety management tool to detect risk associated with uprising incident rate from a systematic perspective
• Find out the root causes related to incident rate
• Conduct corresponding management activities to control the risk
HF-Based Safety Management Model
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HF-Based Safety Management Model
• Improvement to safety management system– Help manager’s decision making regarding the safety
management priority
– Integrate and utilize various safety data to improve safety management in a quantitative way
– Focus on both active and latent human factors such as safety climate which could affect the safety performance
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