26 th IGHC Conference Ground Damage Database David Anderson Nancy Rockbrune
Ground Damage Database
Jan 15, 2016
Ground Damage Database. David Anderson Nancy Rockbrune. Ground Damage Database Introduction. Introductions. David Anderson GDDB Chair Head of Operational Safety ~ British Airways Nancy Rockbrune GDDB Secretary Assistant Director SMS and Operational Data Management ~ IATA. - PowerPoint PPT Presentation
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PowerPoint PresentationGround Damage Database
Introductions
Nancy Rockbrune
GDDB Secretary
*
*
History
Ground damage has been long reported to cost airlines $4b annually
Estimate
Thin margins
Collaboration to make a difference
*
*
History
Shift in IATA’s data management and analysis approach
During 2011 and 2012 new reporting protocols and requirements were developed
Updated Contract
First useable data received from 10 members (with some manipulation)
Consistently receiving useable data from 14 participants
2013 focus on expanding participation
*
Trends and analysis will feed the various TF(s)/WG(s) within IATA so they can develop and implement mitigations
The GDDB will measure the efficacy of any mitigations and feed back to the applicable TF(s)/WG(s)
Proven improvements will then drive changes to the AHM, ISAGO, etc.
Full process improvement circle ~ mirrors SMS methodology ~ mitigations identified by experts
Improved performance will significantly decrease the costs associated with ground damage
*
GDDB Coverage ~ As of December 2012
*
*
GDDB Coverage ~ As of May 1, 2013
*
23 airlines
3 GSPs
*
GDDB Coverage ~ As of May 1, 2013
*
Efforts to Support Growth
IT development
Discussions with strategic partners to develop GDDB submission extract from existing reporting systems
Common dimension tables
Participant query tool
Efforts to Support Growth
Introduction of IOSA provision ~ ISM Ed. 7
*
*
Purpose
Provide information not otherwise possible
Identify trends and contributing factors allowing for the development and assessment of effective mitigation actions
*
Trends and analysis will feed the various TF(s)/WG(s) within IATA so they can develop and implement mitigations
The GDDB will measure the efficacy of any mitigations and feed back to the applicable TF(s)/WG(s)
Proven improvements will then drive changes to the AHM, ISAGO, etc.
Full process improvement circle ~ mirrors SMS methodology ~ mitigations identified by experts
Improved performance will significantly decrease the costs associated with ground damage
*
Use of Data
Statistical analysis produces more tangible information
Measures process performance
Measure and predict process performance improvements
Provides confidence interval
Communicate findings to applicable WGs and TFs
*
Much more valuable than arrows up / down
Baseline allowa for Global, Regional and/or Individual comparisons
*
What Data?
Identifying issues
Demonstrating effectiveness of the program as a whole
Data driven decisions
*
Defensible data
*
Types of Data?
Reactive ~ wait for incidents to happen and try to understand why
Proactive ~ analyze identified risks to mitigate before they turn into an accident / incident
Predictive ~ mature system which conducts predictive analytics (statistical modeling) to identify and mitigate unknown risks
*
Defensible data
*
Confidential Reporting
Confidential reporting can be used for the following safety concerns:
Unsafe behaviors
Inadvertent errors and mistakes
Near miss occurrences (incidents that did not occur but could have easily resulted in a serious event)
Inadvertent errors or violations of aircraft handling or servicing systems
Procedures or processes that could be improved
*
Defensible data
*
Data Collection Process
Collect and risk assess reported hazards
Trend and analyze information
*
Defensible data
*
Challenge ~ Data Quality
Any airline, ground service provider, and / or airport which provide ground services are eligible to participate in the program
Variance in data received
Data integrity the utmost of importance
*
*
Data Management Principles
Information producers and knowledge workers alike must know the meaning of information; otherwise they cannot perform their work properly
Information producers must also know the business rules, valid values, and formats to create information correctly
Information definition is to data (content) what manufacturing product specifications are to the manufactured product
Quality “Information Product Specifications” are necessary for the consistent production of quality information
*
*
Data Management Principles
Managing by averages leads to flawed decision making as you are not accounting for process variation
If measurement system variation is too large there is an increased risk of:
Rejecting good data
Accepting bad data
*
MSA = Measurement System Analysis
How consistent were the answers?
What if you had operational definitions?
*
Data Management Principles
Operational definitions can be:
Operational definitions should be:
Enables different people to reach the same conclusion (repeatability)
*
*
Taxonomy / Operational Definitions
Controls data inputs
*
Solution ~ Defined Fields
Representatives from Operators, GSP’s, Manufacturers and Industry groups
Identified data to be consistently reported amongst ALL members
Includes definitions / assumptions
Minimize data variance
*
*
During Marshaling or using Stand Guidance
During Deicing
Environmental
*
Activities
*
*
Field Categories
Field Categories
Phases
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
Departure ~ Time period when the anti-collision light is turned on and the brakes are off until control is handed over to
Flight Operations
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Field Categories
Phases
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
Departure ~ Time period when the anti-collision light is turned on and the brakes are off until control is handed over to
Flight Operations
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Field Categories
Phases
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
Departure ~ Time period when the anti-collision light is turned on and the brakes are off until control is handed over to
Flight Operations
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Field Categories
Phases
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
Departure ~ Time period when the anti-collision light is turned on and the brakes are off until control is handed over to
Flight Operations
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Field Categories
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
*
*
Field Categories
Phases
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
Departure ~ Time period when the anti-collision light is turned on and the brakes are off until control is handed over to
Flight Operations
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Field Categories
Phases
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
Departure ~ Time period when the anti-collision light is turned on and the brakes are off until control is handed over to
Flight Operations
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Field Categories
Phases
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
Departure ~ Time period when the anti-collision light is turned on and the brakes are off until control is handed over to
Flight Operations
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Field Categories
Phases
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
Departure ~ Time period when the anti-collision light is turned on and the brakes are off until control is handed over to
Flight Operations
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Field Categories
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Field Categories
Hours out of service ~ rounded up to the nearest hour
Note: this field is for calculation purposes only, and should not be confused with
severity
Field Categories
Field Categories
Phases
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
Departure ~ Time period when the anti-collision light is turned on and the brakes are off until control is handed over to
Flight Operations
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Assess the degree of variation
Critical to understanding the process performance
Measure process performance
Calculate separate descriptive statistics for each group, allowing to see how the groups differ
Identify “critical x’s”
Statistical Analysis
Displays data from different categories
Can compare several groups of data at once
Sample ~ Box Plot
Box = 50% of difference
· The medians (line – middle point of the data) for each sample are very similar to the means (symbol – average, influenced by outliers).
· Performance mean was greater for A than for B.
The spread of the data appears to be about the same for both samples, except that sample B has a slightly longer upper tail than sample A.
26th IGHC Conference
Displays the control of a process
In control process shows random variation
Out of control process shows unusual variation due to special causes
Help to determine where to focus problem-solving efforts by distinguishing between common and special-cause variation
Sample ~ Control Charts
A control chart for attributes consists of:
· Data points, plotted in time order, which represent a rational sample of data sampled from the process and are either
- Counts of the number of defectives or defects per sample
- Proportions of the defectives or defects per sample
· Center line , which is the average number or average proportion of defectives or defects
· Control limits , which are set at a distance of 3 s on either side of the center line and provide a visual display for the expected number or proportion of defectives or defects.
Control limits establish boundaries for the amount of variation that should exist between the samples and predict how the process should behave. The control limits are based on the actual behavior of the process, not the desired behavior - they are not specification limits. A process can be in control and yet not be capable of meeting requirements.
*
Displays defects from largest to smallest
Prioritize issues and focus improvement efforts on areas where largest gains can be made
Separates the "vital few" problems from the "trivial many”
Sample ~ Pareto Charts
*
The right Y-axis shows the percent of the total defects and the left Y-axis shows the count of defects. The red line indicates cumulative percentage , which can help you judge the added contribution of each category. The bars of the histogram show the count (and the percentage of total) for each category. The counts, percents, and cumulative percents are listed for each category.
If the last group in the Pareto chart is labeled "Other," then by default it will contain a count of all defects in categories with so few counts as to represent less than 5% of the total defect count.
26th IGHC Conference
Measure process improvements
If distributions are normal can estimate the performance if new procedures are put in place
Sample ~ Probability Charts
Hypothesis Test ~ P-Value
Statistically significant
Null Hypothesis = No statistical difference
Things are “on Target”, “same”, difference is due to “random variation”
Not likely to be a critical x or may require more data
Alternative Hypothesis = statistical difference
Things are NOT “on target”, “the same”, due to “random variation”
Data supports this x as a likely cause for further investigation
Hypothesis Test ~ P-Value
NO TYPE OR IMAGES CAN TOUCH THE SKY
P-Value represents the risk that we are wrong if we conclude that the null hypothesis is false
That we claim there is a difference and there isn’t one
Numerically
P-Value < 0.05 we can conclude that we found a statistical difference
We may say we have a 5% chance of being wrong when we conclude that something is “off-target”
Hypothesis Test ~ P-Value
p-value is < 0.05
Hypothesis Test ~ P-Value
Correlations: Num report, Num Sev
Pearson correlation of Num report and Num Sev = -0.334
P-Value = 0.000
Statistically Significant
Correlations: Num Org, Num report
Pearson correlation of Num Org and Num report = 0.124
P-Value = 0.040
Statistically Significant
Month of Report and Severity
Correlations: Mnth, Num Sev
P-Value = 0.292
Type of Report (found vs. reported) and Reporting Region
Correlations: Num report, Num Region
Pearson correlation of Num report and Num Region = - 0.232
P-Value = 0.000
Statistically Significant
Reporting Region and Severity
Pearson correlation of Num Region and Num Sev = 0.221
P-Value = 0.000
Statistically Significant
Month of Report and Severity
Type of Report (found vs. reported) and Reporting Region
Reporting Region and Severity
Initial Analysis ~ Some Comparisons
Severity and Organization
One-way ANOVA: Num Sev versus Num Org
Source DF SS MS F P
Num Org 8 68.510 8.564 18.30 0.000
Error 256 119.830 0.468
Initial Analysis ~ Comparison of Means
At least one organization’s mean is different
*
*
Severity and Organization
One-way ANOVA: Num Sev versus Num Org
Source DF SS MS F P
Num Org 8 68.510 8.564 18.30 0.000
Error 256 119.830 0.468
Initial Analysis ~ Comparison of Means
At least one organization’s mean is different
But how much variation does this explain?
*
*
Severity and Organization
One-way ANOVA: Num Sev versus Num Org
Source DF SS MS F P
Num Org 8 68.510 8.564 18.30 0.000
Error 256 119.830 0.468
Initial Analysis ~ Comparison of Means
But how much variation does this explain?
36%
Initial Analysis ~ Comparison of Means
*
*
Statistical Analysis ~ In Summary
Measures process performance
Measure and predict process performance improvements
Provides confidence interval
Need trained resources
Need consistent data
Must know restrictions of data
Sample size
Practical importance will be vetted by WGs and TFs
Statistical Analysis ~ In Summary
Observations ~ Type of Report
This has been a typical observation since Q1 reporting
Consistent amongst all Regions, aircraft type (wide-body/narrow body) ~ no correlations founds
*
Observations ~ Type of Report by Severity
Source: IATA Q4 2012 GDDB
*
Good News ~ “High” severity incidents are being reported at time of instance. Only 1 “Found” with “High” severity in Q4 2012 data
Typically low (delays < 60 mins) are being “found” and not reported at time of incident
This again has been observed throughout 2012 data
Note: Unknown = Unreported
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Observations ~ Areas of Damage
Engine
7
Fuselage
46
Wings
13
Doors
81
Lights
0
High Reports ~
Observations ~ Areas of Damage
Engine
7
Fuselage
46
Wings
13
Doors
81
Lights
0
High ~ Aircraft inop ≥ 24 hours
Focus on where to look and conduct further analysis
*
Observations ~ High Severity Reports
Engine
0
Fuselage
3
Wings
3
Doors
4
Lights
0
*
High ~ Aircraft inop ≥ 24 hours
Analysis goes further into type of damage, equipment used, looking for any correlations
Further details available at workshop
*
Observations ~ High Severity Reports
*
High ~ Aircraft inop ≥ 24 hours
Analysis goes further into type of damage, equipment used, looking for any correlations
Further details available at workshop
*
GDDB Development
Numerous requests to expand the database to include entire scope of AHM and ISAGO
TF meetings planned for May (DOH) and Sep (YUL)
Establishment of a costing model
Identify reporting challenges / solutions
*
*
Gap analysis to be conducted between GDDB, AHM, IGOM and ISAGO
Expansion plan will address identified gaps
Proposed plan to be provided to ASG for comment / approval (Sep)
Sep TF meeting in YUL to coincide with ASG
26th IGHC Conference
Strategy ~ Continuous Improvement Circle
Supports the SMS methodology of continuous improvement
Once such a platform is established , IATA and/or airline community experts can use this data or produced reports to manage a continuous circle consisting of
Analysis / performance measurement (including that of corrective actions)
Framework review and adjustment if required
Implementation of new/revised operational procedures
Verification
Such a process will allow IATA – you to establish a significantly higher relevance and effectiveness in the entire aviation community
This cycle has already been developed for Ground Operations….
*
Strategy ~ Continuous Improvement Circle
Are the fixes effective?
How is Industry performing?
GSIC ODM GADM
Participant Support
IT development
Discussions with strategic partners to develop GDDB submission extract from existing reporting systems
Common dimension tables
Participant query tool
*
DoorTear / CrackFood Truck
Scratch / Dent / ScuffBridge
Tear / CrackPushback Tractor ~ Towbarless
Introductions
Nancy Rockbrune
GDDB Secretary
*
*
History
Ground damage has been long reported to cost airlines $4b annually
Estimate
Thin margins
Collaboration to make a difference
*
*
History
Shift in IATA’s data management and analysis approach
During 2011 and 2012 new reporting protocols and requirements were developed
Updated Contract
First useable data received from 10 members (with some manipulation)
Consistently receiving useable data from 14 participants
2013 focus on expanding participation
*
Trends and analysis will feed the various TF(s)/WG(s) within IATA so they can develop and implement mitigations
The GDDB will measure the efficacy of any mitigations and feed back to the applicable TF(s)/WG(s)
Proven improvements will then drive changes to the AHM, ISAGO, etc.
Full process improvement circle ~ mirrors SMS methodology ~ mitigations identified by experts
Improved performance will significantly decrease the costs associated with ground damage
*
GDDB Coverage ~ As of December 2012
*
*
GDDB Coverage ~ As of May 1, 2013
*
23 airlines
3 GSPs
*
GDDB Coverage ~ As of May 1, 2013
*
Efforts to Support Growth
IT development
Discussions with strategic partners to develop GDDB submission extract from existing reporting systems
Common dimension tables
Participant query tool
Efforts to Support Growth
Introduction of IOSA provision ~ ISM Ed. 7
*
*
Purpose
Provide information not otherwise possible
Identify trends and contributing factors allowing for the development and assessment of effective mitigation actions
*
Trends and analysis will feed the various TF(s)/WG(s) within IATA so they can develop and implement mitigations
The GDDB will measure the efficacy of any mitigations and feed back to the applicable TF(s)/WG(s)
Proven improvements will then drive changes to the AHM, ISAGO, etc.
Full process improvement circle ~ mirrors SMS methodology ~ mitigations identified by experts
Improved performance will significantly decrease the costs associated with ground damage
*
Use of Data
Statistical analysis produces more tangible information
Measures process performance
Measure and predict process performance improvements
Provides confidence interval
Communicate findings to applicable WGs and TFs
*
Much more valuable than arrows up / down
Baseline allowa for Global, Regional and/or Individual comparisons
*
What Data?
Identifying issues
Demonstrating effectiveness of the program as a whole
Data driven decisions
*
Defensible data
*
Types of Data?
Reactive ~ wait for incidents to happen and try to understand why
Proactive ~ analyze identified risks to mitigate before they turn into an accident / incident
Predictive ~ mature system which conducts predictive analytics (statistical modeling) to identify and mitigate unknown risks
*
Defensible data
*
Confidential Reporting
Confidential reporting can be used for the following safety concerns:
Unsafe behaviors
Inadvertent errors and mistakes
Near miss occurrences (incidents that did not occur but could have easily resulted in a serious event)
Inadvertent errors or violations of aircraft handling or servicing systems
Procedures or processes that could be improved
*
Defensible data
*
Data Collection Process
Collect and risk assess reported hazards
Trend and analyze information
*
Defensible data
*
Challenge ~ Data Quality
Any airline, ground service provider, and / or airport which provide ground services are eligible to participate in the program
Variance in data received
Data integrity the utmost of importance
*
*
Data Management Principles
Information producers and knowledge workers alike must know the meaning of information; otherwise they cannot perform their work properly
Information producers must also know the business rules, valid values, and formats to create information correctly
Information definition is to data (content) what manufacturing product specifications are to the manufactured product
Quality “Information Product Specifications” are necessary for the consistent production of quality information
*
*
Data Management Principles
Managing by averages leads to flawed decision making as you are not accounting for process variation
If measurement system variation is too large there is an increased risk of:
Rejecting good data
Accepting bad data
*
MSA = Measurement System Analysis
How consistent were the answers?
What if you had operational definitions?
*
Data Management Principles
Operational definitions can be:
Operational definitions should be:
Enables different people to reach the same conclusion (repeatability)
*
*
Taxonomy / Operational Definitions
Controls data inputs
*
Solution ~ Defined Fields
Representatives from Operators, GSP’s, Manufacturers and Industry groups
Identified data to be consistently reported amongst ALL members
Includes definitions / assumptions
Minimize data variance
*
*
During Marshaling or using Stand Guidance
During Deicing
Environmental
*
Activities
*
*
Field Categories
Field Categories
Phases
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
Departure ~ Time period when the anti-collision light is turned on and the brakes are off until control is handed over to
Flight Operations
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Field Categories
Phases
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
Departure ~ Time period when the anti-collision light is turned on and the brakes are off until control is handed over to
Flight Operations
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Field Categories
Phases
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
Departure ~ Time period when the anti-collision light is turned on and the brakes are off until control is handed over to
Flight Operations
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Field Categories
Phases
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
Departure ~ Time period when the anti-collision light is turned on and the brakes are off until control is handed over to
Flight Operations
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Field Categories
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
*
*
Field Categories
Phases
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
Departure ~ Time period when the anti-collision light is turned on and the brakes are off until control is handed over to
Flight Operations
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Field Categories
Phases
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
Departure ~ Time period when the anti-collision light is turned on and the brakes are off until control is handed over to
Flight Operations
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Field Categories
Phases
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
Departure ~ Time period when the anti-collision light is turned on and the brakes are off until control is handed over to
Flight Operations
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Field Categories
Phases
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
Departure ~ Time period when the anti-collision light is turned on and the brakes are off until control is handed over to
Flight Operations
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Field Categories
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Field Categories
Hours out of service ~ rounded up to the nearest hour
Note: this field is for calculation purposes only, and should not be confused with
severity
Field Categories
Field Categories
Phases
Arrival ~ Time period from when the aircraft nose wheel crosses onto the stand until the anti-collision light is off
Towing ~ Time period when an aircraft is being towed from one location to another
Servicing ~ Time period an aircraft is being serviced at a gate / stand
Departure ~ Time period when the anti-collision light is turned on and the brakes are off until control is handed over to
Flight Operations
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Assess the degree of variation
Critical to understanding the process performance
Measure process performance
Calculate separate descriptive statistics for each group, allowing to see how the groups differ
Identify “critical x’s”
Statistical Analysis
Displays data from different categories
Can compare several groups of data at once
Sample ~ Box Plot
Box = 50% of difference
· The medians (line – middle point of the data) for each sample are very similar to the means (symbol – average, influenced by outliers).
· Performance mean was greater for A than for B.
The spread of the data appears to be about the same for both samples, except that sample B has a slightly longer upper tail than sample A.
26th IGHC Conference
Displays the control of a process
In control process shows random variation
Out of control process shows unusual variation due to special causes
Help to determine where to focus problem-solving efforts by distinguishing between common and special-cause variation
Sample ~ Control Charts
A control chart for attributes consists of:
· Data points, plotted in time order, which represent a rational sample of data sampled from the process and are either
- Counts of the number of defectives or defects per sample
- Proportions of the defectives or defects per sample
· Center line , which is the average number or average proportion of defectives or defects
· Control limits , which are set at a distance of 3 s on either side of the center line and provide a visual display for the expected number or proportion of defectives or defects.
Control limits establish boundaries for the amount of variation that should exist between the samples and predict how the process should behave. The control limits are based on the actual behavior of the process, not the desired behavior - they are not specification limits. A process can be in control and yet not be capable of meeting requirements.
*
Displays defects from largest to smallest
Prioritize issues and focus improvement efforts on areas where largest gains can be made
Separates the "vital few" problems from the "trivial many”
Sample ~ Pareto Charts
*
The right Y-axis shows the percent of the total defects and the left Y-axis shows the count of defects. The red line indicates cumulative percentage , which can help you judge the added contribution of each category. The bars of the histogram show the count (and the percentage of total) for each category. The counts, percents, and cumulative percents are listed for each category.
If the last group in the Pareto chart is labeled "Other," then by default it will contain a count of all defects in categories with so few counts as to represent less than 5% of the total defect count.
26th IGHC Conference
Measure process improvements
If distributions are normal can estimate the performance if new procedures are put in place
Sample ~ Probability Charts
Hypothesis Test ~ P-Value
Statistically significant
Null Hypothesis = No statistical difference
Things are “on Target”, “same”, difference is due to “random variation”
Not likely to be a critical x or may require more data
Alternative Hypothesis = statistical difference
Things are NOT “on target”, “the same”, due to “random variation”
Data supports this x as a likely cause for further investigation
Hypothesis Test ~ P-Value
NO TYPE OR IMAGES CAN TOUCH THE SKY
P-Value represents the risk that we are wrong if we conclude that the null hypothesis is false
That we claim there is a difference and there isn’t one
Numerically
P-Value < 0.05 we can conclude that we found a statistical difference
We may say we have a 5% chance of being wrong when we conclude that something is “off-target”
Hypothesis Test ~ P-Value
p-value is < 0.05
Hypothesis Test ~ P-Value
Correlations: Num report, Num Sev
Pearson correlation of Num report and Num Sev = -0.334
P-Value = 0.000
Statistically Significant
Correlations: Num Org, Num report
Pearson correlation of Num Org and Num report = 0.124
P-Value = 0.040
Statistically Significant
Month of Report and Severity
Correlations: Mnth, Num Sev
P-Value = 0.292
Type of Report (found vs. reported) and Reporting Region
Correlations: Num report, Num Region
Pearson correlation of Num report and Num Region = - 0.232
P-Value = 0.000
Statistically Significant
Reporting Region and Severity
Pearson correlation of Num Region and Num Sev = 0.221
P-Value = 0.000
Statistically Significant
Month of Report and Severity
Type of Report (found vs. reported) and Reporting Region
Reporting Region and Severity
Initial Analysis ~ Some Comparisons
Severity and Organization
One-way ANOVA: Num Sev versus Num Org
Source DF SS MS F P
Num Org 8 68.510 8.564 18.30 0.000
Error 256 119.830 0.468
Initial Analysis ~ Comparison of Means
At least one organization’s mean is different
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Severity and Organization
One-way ANOVA: Num Sev versus Num Org
Source DF SS MS F P
Num Org 8 68.510 8.564 18.30 0.000
Error 256 119.830 0.468
Initial Analysis ~ Comparison of Means
At least one organization’s mean is different
But how much variation does this explain?
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Severity and Organization
One-way ANOVA: Num Sev versus Num Org
Source DF SS MS F P
Num Org 8 68.510 8.564 18.30 0.000
Error 256 119.830 0.468
Initial Analysis ~ Comparison of Means
But how much variation does this explain?
36%
Initial Analysis ~ Comparison of Means
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Statistical Analysis ~ In Summary
Measures process performance
Measure and predict process performance improvements
Provides confidence interval
Need trained resources
Need consistent data
Must know restrictions of data
Sample size
Practical importance will be vetted by WGs and TFs
Statistical Analysis ~ In Summary
Observations ~ Type of Report
This has been a typical observation since Q1 reporting
Consistent amongst all Regions, aircraft type (wide-body/narrow body) ~ no correlations founds
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Observations ~ Type of Report by Severity
Source: IATA Q4 2012 GDDB
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Good News ~ “High” severity incidents are being reported at time of instance. Only 1 “Found” with “High” severity in Q4 2012 data
Typically low (delays < 60 mins) are being “found” and not reported at time of incident
This again has been observed throughout 2012 data
Note: Unknown = Unreported
Moderate ~ Aircraft inop ≥60 minutes <24 hours
High ~ Aircraft inop ≥ 24 hours
Catastrophic ~ Hull loss
Observations ~ Areas of Damage
Engine
7
Fuselage
46
Wings
13
Doors
81
Lights
0
High Reports ~
Observations ~ Areas of Damage
Engine
7
Fuselage
46
Wings
13
Doors
81
Lights
0
High ~ Aircraft inop ≥ 24 hours
Focus on where to look and conduct further analysis
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Observations ~ High Severity Reports
Engine
0
Fuselage
3
Wings
3
Doors
4
Lights
0
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High ~ Aircraft inop ≥ 24 hours
Analysis goes further into type of damage, equipment used, looking for any correlations
Further details available at workshop
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Observations ~ High Severity Reports
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High ~ Aircraft inop ≥ 24 hours
Analysis goes further into type of damage, equipment used, looking for any correlations
Further details available at workshop
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GDDB Development
Numerous requests to expand the database to include entire scope of AHM and ISAGO
TF meetings planned for May (DOH) and Sep (YUL)
Establishment of a costing model
Identify reporting challenges / solutions
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Gap analysis to be conducted between GDDB, AHM, IGOM and ISAGO
Expansion plan will address identified gaps
Proposed plan to be provided to ASG for comment / approval (Sep)
Sep TF meeting in YUL to coincide with ASG
26th IGHC Conference
Strategy ~ Continuous Improvement Circle
Supports the SMS methodology of continuous improvement
Once such a platform is established , IATA and/or airline community experts can use this data or produced reports to manage a continuous circle consisting of
Analysis / performance measurement (including that of corrective actions)
Framework review and adjustment if required
Implementation of new/revised operational procedures
Verification
Such a process will allow IATA – you to establish a significantly higher relevance and effectiveness in the entire aviation community
This cycle has already been developed for Ground Operations….
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Strategy ~ Continuous Improvement Circle
Are the fixes effective?
How is Industry performing?
GSIC ODM GADM
Participant Support
IT development
Discussions with strategic partners to develop GDDB submission extract from existing reporting systems
Common dimension tables
Participant query tool
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DoorTear / CrackFood Truck
Scratch / Dent / ScuffBridge
Tear / CrackPushback Tractor ~ Towbarless
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