ATA Antitrust Statement - American Trucking Associations...
Transcript of ATA Antitrust Statement - American Trucking Associations...
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2015 ANNUAL CONFERENCE In conjunction with ATA’s
Exploring Trucking’s Connected World
October 18‐20, 2015
Philadelphia Marriott Downtown,
Philadelphia, PA
Jeremy Clopton, CPA, CFE, ACDA, CIDABKD, LLP
Transportation Data Mining: The Case for Data Analytics
ATA Antitrust Statement
All ATA meetings are held in strict compliance with applicable state and federal laws and ATA’s antitrust policies that prohibit the exchange of information among competitors regarding matters pertaining to price, refusals to deal, markets division, tying relationships and other topics which might infringe upon antitrust regulations.
No such exchange or discussion will be tolerated during this meeting.
As an attendee it is your duty to avoid improper conversations.
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Analytics Foundations
Applications in Transportation
Application Framework
Closing Thoughts
Presentation Roadmap
Analytics Foundations
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Big DataInformation of extreme size, diversity and complexity.
‐ Gartner, Inc.Source: http://www.gartner.com/technology/topics/big‐data.jsp
Data Analytics…processes and activities designed to obtain and evaluate data to extract useful information and answer strategic questions...
Definitions
• ACL
• IDEA
• SQL
• SAS
• Arbutus
• IBM Cognos
• Tableau
• Spotfire
• Qlik
Common Tools
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Accounts Payable
Corporate Credit Cards
General Ledger
Payroll
Common Financial Applications
MarketingOperational Decision Making
Business Intelligence
Risk Assessment
Common Non‐Financial Applications
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Textual Analytics
Relationship Mapping
Named Entity
Extraction
Predictive Coding
Topic Mapping
Digital Forensics
Tone Detection
Textual Analytics
Network Relationship Analysis
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Applications in Transportation
Four Major Areas of Application
Fraud Prevention & Detection
Fleet Management
Recruiting & Retention
Internal Audit
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Fraud Prevention & Detection
• Performance metrics
• Predictive maintenance analytics
• Repairs‐based analytics
• Safety analysis
• Asset existence
• Fuel purchases
Fleet Management
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• Approach to recruiting – marketing to candidates
• Analytics related to invites/shows/hires
• Costs/leads per hire
• Turnover analytics
– Causation
– Prevention
Recruiting & Retention
• Internal audit
• Operational efficiencies
• Resource allocation
Other Applications
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Application Framework
Application Framework
Data Analytics
Strategic Question
Define Objectives
Obtain Data
Develop Procedures
Analyze Results
Manage Results
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Ask a Strategic Question
• The more specific the better.
• Must have data available to answer question.
• Question can address data.
• Consider existing questions.
Define Objectives
• Steps required to answer question.
• Sub‐questions of the broader strategic question.
• Should be specific and attainable.
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Obtain Data
Work directly with IT department.
Begin communications early.
Consider cross‐training personnel.
Required data: Data for analysis
Data for follow‐up
Develop Procedures
Start simple and expand:
Single ad hoc procedure
Automated single procedure
Automated groups of procedures
Scheduled analytics
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Analyze Results
Are there false positives in the results?
What do the results tell us?
Have we found causation or correlation?
What can we do to verify/further research these results?
Have we met our objectives and/or answered the strategic question?
Manage Results
Develop a plan for follow‐up.
Consider how routinely procedures should occur.
Use results to facilitate change.
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Application Example: Fleet Management
What can we do to minimize fleet downtime due to repairs & maintenance?
Strategic Question
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• Determine impact of vehicle make/model on downtime.
• Identify common repairs and required parts.
• Identify causes of extended maintenance time.
• Determine proactive maintenance that improves downtime.
Define Objectives
• Basic vehicle information:– Make, model, year, driver, use, etc.
• Repairs & maintenance history.
• Diagnostic information.
• Other relevant data.
Obtain Data
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• Correlation between downtime and age of vehicle.
• Average downtime by repair type.
• Average downtime by maintenance procedure.
• Correlation between repairs and maintenance schedules.
Develop Procedures
• Are there other relevant variables?
• Does the data contain false positives due to data quality?
• Have we met our objectives?
• Did we forget anything in our analysis?
Analyze Results
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• Develop a preventative maintenance plan for each vehicle make/model/year.
• Educate drivers on impact of delayed maintenance.
• Assess other operational impacts of analysis results.
Manage Results
Application Framework
Data Analytics
Strategic Question
Define Objectives
Obtain Data
Develop Procedures
Analyze Results
Manage Results
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Closing Thoughts
“ ”We’re discovering in nature that simplicity often lies on the other side of complexity. So for any problem, the more you can zoom out and embrace complexity, the better chance you have of zooming in on the simple details that matter most.
‐Eric Berlow, TED Talk July 2010
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• Data Fluency
– Zach Gemignani, Chris Gemignani
• Forensic Analytics
– Mark Nigrini
• Data Points
– Nathan Yau
Resources
Jeremy Clopton, CPA, CFE, ACDA, CIDADirectorBKD, LLP, Forensics & Valuation ServicesPhone: 417.865.8701Email: [email protected]
Social MediaBlog: bkdforensics.comTwitter: @j313LinkedIn: http://www.linkedin.com/in/jeremyclopton/
Contact Information
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Thank You ForYour Attention!
Jeremy Clopton, CPA, CFE, ACDA, CIDABKD, LLP
[email protected]@j313http://linkedin.com/in/jeremyclopton
Questions?