AVAILABLE DATA TO reaching wrong conclusions about FORMULATE - - PowerPoint PPT Presentation

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AVAILABLE DATA TO reaching wrong conclusions about FORMULATE - - PowerPoint PPT Presentation

Apr 02, 2024 266 likes •684 views

Avery Vise Principal TransAdvise Profile Image 205-358-3612 avise@transadvise.com @AveryVise OPTIMIZING PUBLICLY Or how to avoid AVAILABLE DATA TO reaching wrong conclusions about FORMULATE DECISIONS the trucking industry Why FMCSA

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Profile Image

OPTIMIZING PUBLICLY AVAILABLE DATA TO FORMULATE DECISIONS

Or how to avoid reaching wrong conclusions about the trucking industry

Avery Vise

Principal TransAdvise 205-358-3612 avise@transadvise.com @AveryVise

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Why FMCSA data?

  • Easily obtained and cheap – even free
  • Operational insights, such as where a carrier operates, what

kind of freight it hauls, etc.

  • Minimal lag time, especially for pinpointing new carriers
  • Available for commercial vehicle owners in all states, sort of

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Optimizing Publicly Available Data to Formulate Decisions

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FMCSA databases

  • Census – Basic demographics, updated at least every two

years (mostly)

  • Inspections – Includes truck/trailer brand and VIN
  • Violations – Violations discovered during inspections
  • Crashes – DOT-recordable crashes – i.e., fatality, injury

requiring medical attention away from scene, or tow-away

  • SMS metrics – Activity and measures in each BASIC
  • Licensing & Insurance (L&I) – Useful in confirming for-hire

carriers remain in business. (http://li-public.fmcsa.dot.gov)

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Optimizing Publicly Available Data to Formulate Decisions

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Motor carrier annual reports

  • By law (49 USC 14123), DOT must require carriers with at

least $3 million in annual revenue to file annual financial and operations reports (Form M).

  • In practice, DOT doesn’t enforce the mandate. FMCSA’s

latest estimate: Only 308 carriers file. Most of those probably are HHG and publicly traded carriers.

  • 49 CFR 369.10 states that the data generally is publicly

available, but FMCSA insists on a FOIA request, which takes

  • months. The data hasn’t been freely available since 2003.

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Optimizing Publicly Available Data to Formulate Decisions

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Two versions of FMCSA data

Monthly free download Purchase from FMCSA

Website

https://ai.fmcsa.dot.gov/SMS/Tools/Downloads.aspx https://ask.fmcsa.dot.gov/app/mcmiscatalog/mcmishome

Census

DOT number, company name, address, phone, fax, email, vehicle mileage, carrier age, interstate versus intrastate, number of power units and drivers PLUS: County, For-hire vs. private, MC number, types of haul, Dun & Bradstreet number, detailed info on type of power unit and whether owned or leased, number of trailers and whether owned or leased, detailed info on types of driver (CDL vs. non-CDL, range of operation), names (but not titles) of company representatives, more. Cost: $23 per month.

Inspections

DOT number, date, location, type of unit (straight truck, tractor, bus, trailer, etc.), make of unit, VIN, license state, safety-related info PLUS: Violations, more granular info on inspections, GVW/GCW, shipper name. Cost: $70 per calendar year, but timing matters. Violations DOT number, detailed violation data – Vehicle, HOS, driving, etc., with SMS-related info Included in the inspection file extract

Crashes Basic info related to crash – DOT number, date, fatalities, injuries, tow-aways, weather, VIN PLUS: Greater detail regarding circumstances surrounding crash. Cost: $36 for a 24-month extract. SMS Metrics Measures and inspections in the BASICS for active motor carriers Not applicable

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Optimizing Publicly Available Data to Formulate Decisions

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Commercial data suppliers

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Optimizing Publicly Available Data to Formulate Decisions

  • Resolve data hygiene issues (or at least most of them) and

simplify filtering and searching

  • Integrate with other data sources/tools (VIN decoding, SIC

codes, subscriber databases, D&B, L&I) to varying degrees

  • Fill privacy-state gaps in vehicle registration data (i.e., Polk)
  • Might not offer complete historical data
  • Don’t solve inherent limitations with underlying data

www.fleetseek.com www.rigdigbi.com www.tdsource.com

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Potential uses of FMCSA data

  • Market research and analysis
  • Fleet size by carrier type, cargo, etc.
  • Productivity/utilization
  • Age of motor carriers
  • Trailer-to-tractor ratios
  • Vehicle maintenance trends
  • ELD adoption
  • Ethnicity of ownership
  • Much more

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Equipment utilization

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Optimizing Publicly Available Data to Formulate Decisions

OTR trucking companies

*Excludes UPS and FedEx parcel operations

Source: FMCSA census data, July 2017

Fleet size Annual miles per truck

1 to 9 61,844 10 to 49 63,537 50 to 99 69,642 100 to 499 75,124 500 to 999 77,545 1,000+* 79,257 Total 70,511

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ELD adoption

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Optimizing Publicly Available Data to Formulate Decisions

OTR trucking companies

Source: FMCSA inspection and census data, July 2017

Fleet size Carriers Trucks*

1 to 9 0.7% 1.4% 10 to 49 8.5% 10.9% 50 to 99 32.0% 33.2% 100 to 499 59.8% 65.1% 500 to 999 85.2% 86.3% 1,000+ 94.5% 96.3% Total 2.2% 46.2%

*Represents trucks operated by carriers that show indicator of electronic log use. E-logs are not necessarily installed in all trucks.

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Over-the-road trucking company

  • Defined as:
  • Interstate U.S.
  • Authorized for-hire, excluding private
  • At least one truck
  • At least one CDL driver
  • At least one driver operating beyond 100 miles
  • Generally better coverage in FMCSA data than the universe
  • f USDOT-registered carriers

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Optimizing Publicly Available Data to Formulate Decisions

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Potential uses of FMCSA data

  • Market research and analysis
  • Equipment ownership insights
  • Make and type of power unit and make of trailer in raw data
  • VINs can be decoded to provide greater detail

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Optimizing Publicly Available Data to Formulate Decisions

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Potential uses of FMCSA data

  • Market research and analysis
  • Equipment ownership insights
  • Prospecting/lead generation
  • Fleet size
  • Geography (Domicile and operational footprint)
  • Type of haul
  • Brand affinity
  • Maintenance violations
  • Crash types

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Potential uses of FMCSA data

  • Market research and analysis
  • Equipment ownership insights
  • Prospecting/lead generation
  • Customer relationship management
  • Appending FMCSA data could provide useful insights
  • Examples:
  • Truck dealer could identify customers who acquire competitors’ trucks
  • Creditors and investors could monitor carriers’ shipper base for potential

revenue and freight volume changes

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Shippers and carriers

Selected carriers

  • Andrus Transportation Services Inc.
  • Big G Express Inc.
  • DPI Dedicated Logistics Inc.
  • Interstate Distributor Co.
  • ITS Logistics LLC
  • Mann Logistics LLC
  • Mile Hi Specialty Foods Inc.
  • Swift Transportation Co.

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Optimizing Publicly Available Data to Formulate Decisions

Selected shippers

  • Bridgestone
  • Goodman Manufacturing
  • Jack Daniels
  • Mars Petcare
  • Nestlé Waters North America
  • Owens Brockway Glass Container
  • Restoration Hardware
  • Starbucks
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Potential uses of FMCSA data

  • Market research and analysis
  • Equipment ownership insights
  • Prospecting/lead generation
  • Customer relationship management
  • Safety analysis

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Optimizing Publicly Available Data to Formulate Decisions

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The reality of FMCSA data

  • Census
  • Requires significant scrubbing and filtering

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July 2017 Active U.S. Entities 1,480,482

+

30,612,502 6,450,624

+ +

37,063,126 18,337,667 12,229,835 16,034,543 9,270,659

FMCSA census data

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Optimizing Publicly Available Data to Formulate Decisions

What is an active carrier?

FMCSA definition:

  • In the past 3 years, carrier has had:
  • An inspection
  • A crash
  • A compliance review
  • A safety audit
  • An MCS-150 update
  • Vehicle registration activity
  • A Unified Carrier Registration (UCR)

system payment;

OR

  • Carrier holds current operating authority

indicated in the L&I database.

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Bad data in the FMCSA census

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Disclaimer on FMCSA website:

“FMCSA uses data filters, tailored scripts, and quality assurance checks when generating official statistics from the raw data it receives from motor carriers. The data you are receiving/purchasing are raw data, and have not been filtered or checked for accuracy.”

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Bad data in the FMCSA census

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Bad data in the FMCSA census

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FMCSA census data

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Scrubbed Active Interstate Refrigerated U.S. Total entities

44,953

Total trucks

485,617

Total buses

55

Total power

485,672

Straight trucks

71,401

Tractors

414,216

Trailers

840,122

Drivers

543,826

Scrubbed Active Interstate Property U.S. Total entities

499,539

Total trucks

3,910,200

Total buses

311

Total power

3,910,511

Straight trucks

1,922,984

Tractors

1,987,216

Trailers

4,787,532

Drivers

4,209,525

Scrubbed Active Interstate U.S. Total entities

524,110

Total trucks

4,132,547

Total buses

249,399

Total power

4,381,946

Straight trucks

2,052,027

Tractors

2,080,520

Trailers

5,055,305

Drivers

4,811,054

Bad Data Active Interstate U.S. Total entities

61

Total trucks

6,026,019

Total buses

2,020,005

Total power

8,046,024

Straight trucks

3,135,962

Tractors

2,890,057

Trailers

2,479,539

Drivers

706,996

Active Interstate U.S. Total entities

524,171

Total trucks

10,158,566

Total buses

2,269,404

Total power

12,427,970

Straight trucks

5,187,989

Tractors

4,970,577

Trailers

7,534,844

Drivers

5,518,050

July 2017 Active U.S.

Entities 1,480,482

+

30,612,502 6,450,624

+ +

37,063,126 18,337,667 12,229,835 16,034,543 9,270,659

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The reality of FMCSA data

  • Census
  • Requires significant scrubbing and filtering
  • Is “toggle-switch data”

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Optimizing Publicly Available Data to Formulate Decisions

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Multiple cargo types

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The reality of FMCSA data

  • Census
  • Requires significant scrubbing and filtering
  • Is “toggle-switch data”
  • Data fields don’t fit cleanly into industry conventions
  • No indicator for truckload and LTL
  • No distinction between owner-operator units and financial or operating leases
  • No clear indication of types of trailers operated

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The reality of FMCSA data

  • Census
  • Requires significant scrubbing and filtering
  • Is “toggle-switch data”
  • Data fields don’t fit cleanly into industry conventions
  • Intrastate carrier data is problematic
  • USDOT numbers among intrastate carriers are not universal

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Optimizing Publicly Available Data to Formulate Decisions

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17 states don’t require USDOT #

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Source: FMCSA

Optimizing Publicly Available Data to Formulate Decisions

States that require intrastate carriers to obtain USDOT numbers are shown in green.

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The reality of FMCSA data

  • Census
  • Requires significant scrubbing and filtering
  • Is “toggle-switch data”
  • Data fields don’t fit cleanly into industry conventions
  • Intrastate carrier data is problematic
  • USDOT numbers among intrastate carriers are not universal
  • Intrastate carriers aren’t required to update USDOT registration data

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Intrastate carrier data is obsolete

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Optimizing Publicly Available Data to Formulate Decisions

Years since MCS-150 update

As of July 2017

Intrastate

2 or more 59.5% 5 or more 41.9% 10 or more 19.6%

Years since MCS-150 update

As of July 2017

Interstate

2 or more 11.6% 5 or more 2.3% 10 or more 1.1%

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The reality of FMCSA data

  • Census
  • Requires significant scrubbing and filtering
  • Is “toggle-switch data”
  • Data fields don’t match cleanly to industry conventions
  • Intrastate carrier data is problematic
  • “Active” carrier is not necessary a “live” carrier
  • Carriers with common ownership not readily grouped

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Optimizing Publicly Available Data to Formulate Decisions

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The reality of FMCSA data

  • Census
  • Inspections
  • Also require some scrubbing and filtering

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Optimizing Publicly Available Data to Formulate Decisions

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PTRB

PARO PETE PETR P B PACS PBT PB PBT PBLT PET PETBI PETBT PETER PETER BILT PETER BUIL PETERB PETERBILT PETERBUILT PETM PETR PETRB PTB PTBL PTBR PTBT PTBLT PTERB PTERBILT PTERBUILD PTERBUILT PTPTR

A myriad of code variations

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KW

KENS KENW KWDT KENT KEN KENWORTH KNNW KENWO KWTH KNCO KENSWORTH KENK KENWT KENNW KENWORTH M KNWT K1KENWORTH KNWR KENWR KAJU KENN KNBR KAYO

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The reality of FMCSA data

  • Census
  • Inspections
  • Also require some scrubbing and filtering
  • Many vehicles go uninspected

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Optimizing Publicly Available Data to Formulate Decisions

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Most vehicles are invisible

Not inspected 79.5% Inspected 20.5%

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Source: FMCSA inspection data, July 2017

Optimizing Publicly Available Data to Formulate Decisions

Unique interstate power units, 24 months ended July 2017

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OTR trucking visibility is better

Not inspected 49.7% Inspected 50.3%

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Source: FMCSA inspection data, July 2017

Optimizing Publicly Available Data to Formulate Decisions

Unique OTR trucking company trucks, 24 months ended July 2017

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Entire carriers are uninspected

Not inspected 38.4% Inspected 61.6%

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Source: FMCSA inspection data, July 2017

Optimizing Publicly Available Data to Formulate Decisions

Active U.S. motor carriers, 24 months ended July 2017

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Entire carriers are uninspected

Not inspected 23.9% Inspected 76.1%

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Source: FMCSA inspection data, July 2017

Optimizing Publicly Available Data to Formulate Decisions

OTR trucking companies, 24 months ended July 2017

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The reality of FMCSA data

  • Census
  • Inspections
  • Also require some scrubbing and filtering
  • Many vehicles go uninspected
  • Inspections are skewed geographically

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Optimizing Publicly Available Data to Formulate Decisions

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Two states = 32% of all inspections

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Source: FMCSA MCMIS data, July 2017

Optimizing Publicly Available Data to Formulate Decisions

States are shaded according to their relative level of inspections during the 24 months ended July 2017.

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Final thoughts on FMCSA data

  • Warts and all, data can be useful for prospecting/lead

generation, customer relationship management, market research and analysis – probably in that order.

  • Data has limited utility in providing insights into ownership
  • f specific makes and types of equipment.
  • Recognize that a commercial provider can do only so much

to improve the data.

  • Interpreting data requires some knowledge of how the

trucking industry is structured and operates.

  • Don’t expect precision. You must make judgments and

assumptions, but do so consistently.

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Optimizing Publicly Available Data to Formulate Decisions

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Profile Image

OPTIMIZING PUBLICLY AVAILABLE DATA TO FORMULATE DECISIONS

Or how to avoid reaching wrong conclusions about the trucking industry

Avery Vise

Principal TransAdvise 205-358-3612 avise@transadvise.com @AveryVise