Predictive modeling in trucking how critical decisions are made using data

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Predictive Modeling in TruckingHOW CRITICAL DECISIONS ARE MADE USING DATA

Photo Credit: Truck PR

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JOB OPENING

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Photo Credit: Raymond Clark

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Photo Credit: Peter Menzel

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ISSUE 1

Photo Credit: Counselor Offices

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WAGE GAP

Source Bureau of Labor Statistics

Years

Aver

age

Ann

ual W

age

(US

$)

2004 2005 2006 2007 2008 2009 2010 2011 2012 201330000

32000

34000

36000

38000

40000

42000

44000

46000

48000

Avg Driver Wage Avg US Worker Wage

13%

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AVERAGE AGE OF TRUCK DRIVER

Bureau of Labor, 18b. Employed persons by detailed industry and age, 2014

American Worker Truck Drivers39

40

41

42

43

44

45

46

47

48

42.3

47

Age

in Y

ears

(Ave

rage

)

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DRIVER SHORTAGE

2005 2006 2007 2008 2009 2010 2011 2012 2013 20141350000

1400000

1450000

1500000

1550000

1600000

1650000

1700000

1750000

0

20

40

60

80

100

120

140

Heavy & Tractor Trailer Driver Employment Truck Tonnage Index

Source: Federal Reserve of St. Louis and Bureau of Transportation Statistics

Driv

ers

Years

Truck Tonnage Index

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PROJECTED DRIVER SHORTAGE

Source: FTR Transportation Intelligence

Driv

ers

Years

2014 2015 2016 2017 2018 2019 2020 2021 20221400000

1500000

1600000

1700000

1800000

1900000

2000000

Supply Demand

Quarter Million

10 WHO ARE WE

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2004FleetRisk Advisors

HISTORY

2011Acquired By Qualcomm

2013Acquired By Vista Equity Partners

2012Rebranded as Omnitracs

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50 % 1 Million

29 GB

Photo Credit: Antonio R Villaraigosa

13 WHAT DO WE DO

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HOURS OF SERVICE DRIVER LOG

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ON DUTY NOT DRIVING

SLEEPER BERTH

ON DUTY DRIVING

OFF DUTY

4

2

3

1

DRIVER LOG – 1 DAY

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VARIABILITY IN SHIFT START

HOURS PRODUCTIVE

HOURS DRIVEN

GOOD SLEEP

LATE NIGHT DRIVING

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PREDICTING EVENTS

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DEEP DIVE

INP

UT

OU

TPU

T

VEHICLE DATA EXTERNAL DATA

DRIVER & COMPANY DATA

TelematicsLane Depart

NavigationSecurity

RFIDAnd More

CSATrafficFinancialWeatherCensusAnd More

FinanceSafetyClaims

DispatchMaintenance

ComplianceHRService LevelAnd More

DRIVING CENTER®

REMEDIATION TOOLS Sends alertsManages workflowDisplays Models ResultsTargets At-Risk DriversSuggests RemediationTracks & Reports

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HOW THE PREDICTIVE MODEL WORKS

Latest Driver Data Today

What Caused Past

Events?

Miles

Pay Stops

HoS

DoT Physical

Prior Jobs

Loads

Messages

Training

Driver Prediction Tomorrow

Predictive Model

Training / Testing

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PREDICTORS

Aggregate Data Field NamesAverage Empty Miles Total Miles Prior 6 TermsVariability in Shift Start Time Total Miles Prior YearAverage Pre-Employment Tenure Total Number of Critical EventsCount Previous Non Trucking Jobs TenureCount Trips Prior Term Total Empty MilesEmpty Percent Compared to the Fleet Average Total Loaded MilesWeekly Number of Night Sleep Opportunities Physical Evaluation Due DateMinimum Additional Payment Amount Total Time in the Sleeper Berth from 11pm-MidnightNumber of Prior Employers in the Last 10 Years Total Time in the Sleeper Berth from 1pm-2pmNumber of Prior Employers in the Last Year Total Time in the Sleeper Berth from 3pm-4pmService Failure Count Last 3 Months Total Time on Duty Not Driving from 6am-7amSum of the Total Paycheck Transaction Amount Variability in Driving Hours per WeekSum of Transaction Amount on the Paycheck Variability in Night Sleep Opportunities

POP QUIZ

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WHAT CAN BE PREDICTED?

AccidentsVoluntary TerminationsWorker’s Compensation ClaimsFatigueCritical EventsIdeal Applicants

22 CONVERSATIONS

Photo Credit: Martin Vinacur

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ADDRESSING VARIOUS ISSUESConversat ions wi th a Dr iver Predicted To Qui t

had a conversation over the phone with <driver> when i verbed him on his <client> load for tomorrow. he seems to be ok with everything, was glad to hear we were doing some <client> lanes that we had lost, meaning some better regional loads that he likes. he was concerned about the fuel solutions, so we went over that, and i think he grasps it now, said he will be looking for these. otherwise everything was ok, he's comfortable with what he's doing, just wants to make money.

<driver> has some personal issues going on, had a family emergency recently and needs to take the week off. i asked if he was ok and if there's anything i can do for him. he did not want to discuss it and said he will let me know if anything changes.

<driver> has been sick for a few days, feeling better now, heading out now. concerned with how slow things have been, but stresses that it's very important for him to make his home times.

talked to <driver> today, things seem to be going much better for him. he was happy that he already had a load back from la, and was glad to hear freight is picking up. again, did not seem like he wanted to talk about his personal stuff, and i did not push.

Photo Credit: Truck Driver Salary.com

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spoke with <driver>, we were working on plan home for sons birthday party this weekend , he is divorced and has young son - he takes his time off in woodbridge va- where wife lives with son, he does not have a place of his own, he is from georgia originally - hes running over the road and taking time off in woodbridge to see his son- he is new to trucking and liking it so far with us - we were able to get him home for the holidays and his sons birthday - he is looking to get some more experience

PERSONALConversat ions wi th a Young Dr iver predicted to Qui t

Photo Credit: TKOGraphics.com

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NOT ALWAYS OBVIOUS

Photo Credit: TruckingInfo.com

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Data From 10 October 2015 To 1 January 2016

Top 3 Managers with no Events

MANAGERS WITH AND WITHOUT EVENTSWord Cloud

Bottom 3 Managers with Multiple Events

6795 Words, 68 Drivers 3175 Words, 88 Drivers

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MEASURE QUALITY OF CONVERSATIONSHow To

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METRICSThe Scorecard is used to measure the quality of Manager’s conversation with high risk drivers based on:

Time to RemediationsDuplicate MessagesWords UsedSentimentPortal Usage

MANAGER SCORECARDMeasur ing Qual i ty of Conversat ion

Events Manager RankJ K 1SC 2TC 3RK 4PP 5SB 6NB 7ES 8BA 9TS 10SR 11KR 12GH 13

1 VB 14HW1 15HW2 16

1 RH 171 LU 185 TM 191 TD 202 MB 21

100% of all events associated to Managers who did not have good conversations

29 DOES IT WORK?

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1/3/20

15

1/31/2

015

2/28/2

015

3/28/2

015

4/25/2

015

5/23/2

015

6/20/2

015

7/18/2

015

8/15/2

015

9/12/2

015

10/10

/2015

11/7/

2015

12/5/

2015

1/2/20

160%

10%

20%

30%

40%

50%

60%

70%

80%

Never Remediated RemediatedScore Date

Volu

ntar

y A

nnua

lized

Tur

nove

r

TURNOVER RATER E M E D I AT E D V S N E V E R R E M E D I AT E D

Data From 3 January 2014 To 2 January 2016

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2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 20150

20

40

60

80

100

120

0

200

400

600

800

1000

1200

(n=6,931)

Acc

iden

ts P

er 1

00 D

river

s

Driv

er C

ount

ACCIDENTS

Data From 2000 To 2015

Driver Count up 160% yet Accidents down 53%

R E S U LT S O V E R 1 5 Y E A R S

Year

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DRIVE TIME

1/5/20

14

2/1/20

14

3/1/20

14

3/29/2

014

4/26/2

014

5/24/2

014

6/21/2

014

7/19/2

014

8/16/2

014

9/13/2

014

10/11

/2014

11/8/

2014

12/6/

2014

1/3/20

15

1/31/2

015

2/28/2

015

3/28/2

015

4/25/2

015

5/23/2

015

6/20/2

015

7/18/2

015

8/15/2

015

5.9

6.0

6.1

6.2

6.3

6.4

6.5

6.6

Never Remediated Linear (Never Remediated)Remediated Linear (Remediated)

Driv

e Ti

me

(Hou

rs)

Data From 5 January 2014 To 15 August 2015

5% Difference = 2 cents per mile

R E M E D I AT E D V S N E V E R R E M E D I AT E D

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BUSINESS DECISION THROUGH DATA

Photo Credit: Raymond Clark images

ACCIDENTS

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TIME OF DAY EFFECTLoss of Contro l Accident

1 PM 2 PM 3 PM 4 PM 5 PM 6 PM 8 PM 9 PM 10 PM 11 PM 12 AM 1 AM 2 AM 3 AM 4 AM 5 AM 6 AM 7 AM 8 AM 9 AM 10 AM 11 AM 12 PM0

10

20

30

40

50

60

70

2326

19

12

35

14 3

23

51

6057

50 5148

62

5

10

46 5 6

Oct, Nov, Dec-2014 (n=535)

Num

ber o

f Acc

iden

ts

75% of Loss of Control accidents occurred between 11pm and 7am when nocturnal sleep usually occurs.

TIME OF DAY

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TURNOVER

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MEDICAL EVALUATIONDepartment of Transportat ion

24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 -10

50

100

150

200

250

300

263

191

121 121

93 87

40 47 49 4634

22

122

94

6153

6149 53

4333

6750

30

220

Months Before Next Scheduled Physical Date

Num

ber o

f Vol

unta

ry Q

uits POP

QUIZ

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RECRUITING

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CONVENTIONAL WISDOM VS DATA INSIGHTHir ing Dr ivers

No Yes0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Goo

d H

ire P

roba

bilit

y

Trucking School Indicator

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Data From 1 January 2010 To 30 November 2015

Present-Day Active Driver Percentages

MILITARY VETERANSRecrui t ing

Veteran19%

Non - Veteran81%

Military Background Non-Military0

5

10

15

20

25

13.0

19.3

Loss

es p

er M

MPresent-Day Active Driver Percentages

Military background drivers incurred 32% less non-preventable losses than non-military drivers

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EFFICIENCY

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May-14 Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Feb-150

2

4

6

8

10

12

14

16

Client1 APMM APMM Bencmark

BENCHMARKINGHow Do I Compare To My Peer Group?

Months

Acc

iden

t per

Mill

ion

Mile

s

POP QUIZ

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WORD “WAIT” AT LOCATIONGeo - Locat ions wi th

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TRENDS

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MILLENNIALS CHOOSE TO LIVE CLOSER TO WORKIndustry Trends

Generation Y Generation X Baby Boomers Maturists0

50

100

150

200

250

300

350

13

70

42

8

38

1827

175

43

85

296

92

1

48

224

62

42

184

47

2

22

132

3538

251

77

215

163

87

1

Data as of June 30 2015

0 to 2 Miles3 to 5 Miles6 to 10 Miles11 to 20 Miles21 to 30 Miles31 to 40 Miles41 to 50 Miles51 to 100 Miles101+ Miles

Driver Age on Hire Date Bucket

Mile

s B

etw

een

Hom

e Zi

p &

Ter

min

al Z

ip Age Group: Range:Maturists 70+Baby Boomers 55 to 69Generation X 35 to 54Generation Y 20 to 34

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Photo Credit: TriciaPhoto Credit: NPR.org

46 QUESTIONS?Photo Credit: Daimler Freightliner

Predictive modeling in trucking how critical decisions are made using data

Data & Analytics

Transcript of Predictive modeling in trucking how critical decisions are made using data