Predictive modeling in trucking how critical decisions are made using data
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Transcript of Predictive modeling in trucking how critical decisions are made using data
1
Predictive Modeling in TruckingHOW CRITICAL DECISIONS ARE MADE USING DATA
Photo Credit: Truck PR
2
JOB OPENING
3
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
21
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
31
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
32
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
35
BUSINESS DECISION THROUGH DATA
Photo Credit: Raymond Clark images
TURNOVER
36
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
37
BUSINESS DECISION THROUGH DATA
Photo Credit: Raymond Clark images
RECRUITING
38
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
39
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
40
BUSINESS DECISION THROUGH DATA
Photo Credit: Raymond Clark images
EFFICIENCY
41
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
42
WORD “WAIT” AT LOCATIONGeo - Locat ions wi th
43
BUSINESS DECISION THROUGH DATA
Photo Credit: Raymond Clark images
TRENDS
44
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
45
Photo Credit: TriciaPhoto Credit: NPR.org
46 QUESTIONS?Photo Credit: Daimler Freightliner