Capstone ProjectEnabling reduction of Driver Turnover
Advisor: Dr. Eric Ziegel
Student: Eduardo Vazquez
April 4th, 2017
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Engagement in Cemex US has been declining…
86%
76%73% 71%
2012 2013 2015 2016
Engagement Index
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… and we believe that that is pushing turnover up
11.0%11.6%
14.6%
16.0%
17.5%
6.6% 6.7%7.7%
8.9%9.4%
9.5%10.1%
12.6%
13.9%15.0%
2012 2013 2014 2015 2016
Salaried
Hourly
Average
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When talking about drivers the problem is even bigger
• Without drivers we can’t deliver our products !!
• Drivers represent 3,500 of our employees (one third of total)
• Turnover is even higher among drivers: 23% (we loose more than
800 drivers/year)
• Every lost driver represent costs on several dimensions:
‒ New drivers are more prone to an accident
‒ $2,400 on direct training costs per driver, and
‒ $185,000/year on EBITDA when unable to deliver our product
CEMEX whishes to understand why our Drivers
leave in order to take action and minimize turnover
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Driving a Ready Mix truck is much more than driving
Ready Mix Drivers have to:
• Transport a product that has a life
span of 2 hours
• Work with a “dusty product”
• Adjust water content (slump)
• Carry 40 pound chutes
• Climb ladders
• Wash their truck
• Skillfully manage truck momentum
(drum is spinning) while driving
• Start their shift at 4 am
• And all, while complying with very
high safety standards
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Frequent questions asked by managers
a. Is a new driver more prone to leave than a season one?
b. Does age influence drivers leaving (giving physical demands of work)?
c. Does the level of engagement at each location affect that a driver leave?
d. Do hourly compensation (regular and overtime affect)?
e. Do the number of hours worked (regular and OT influence turnover?
f. Are Hispanic drivers less prone to leave (given the association of the
company with Latin-America)?
g. Can we develop a model to predict if a driver is going to leave given
certain variables?
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Data availability
VARIABLES (For 2016):
• Working status (active, voluntarily, & involuntarily terminated)
• Total Salary (normal, overtime, others)
• Hourly salary (normal, overtime, others)
• Worked hours per week (normal and OT)
• Tenure before 2016
• Days worked during 2016
• Employee age
• Employee race
• Engagement level (per location at which the employee works)
• Volumes produced at each of our Ready Mix Plants
• If employee retired or had health issues during 2016
• Dates of hire and termination
• Home and work address
Target variable
Independent variable
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Variable Sampling
Main learning:
• On defining the Target Variable, two
alternatives were available:
a. Consider Involuntarily terminated as
missing data, or
b. Consider them as voluntarily
terminated (assuming they are
dissatisfied with their job to the point
of underperforming). They “seek” to be
terminated
• Model for option (b) came up with better
results
NOTE ON VARIABLE DEFINITION:
Success (=) 1. Employee resigns
Failure (=) 0. Employee stays
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Variable Exploration
OutliersOutliers
Missing
values
Not a rare
event problem
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Variable Modify (Categorical)
Main fixes:
• Simplified equivalent position titles
• Consolidated on an “others” category,
the driver titles no related with the
Ready Mix business
• Set as missing Ethnicity category that
was confusing: “Two or more races”
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Variable Modify (Interval)
Max reg
hours
in a year
Max hours
in a week
Max weeks
in a year
Max yds that
a drivers
can move
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Five models were tested
But tested many times
using different
combinations of
variables
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Partition results are consistent w/ properties requested
Proportion:
• Training (=) 70%
• Validation (=) 30%
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Best Model is the one obtained through Decision Tree
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Decision Tree results: Sensitivity
Train Data:
• Sensitivity = 454 / (454+293) = 61%
• Specificity = 2100 / (2100+119) = 95%
• Misclasification = (293+119)/(293+2100+119+454) = 14%
Validate Data:
• Sensitivity = 174 / (174+147) = 54%
• Specificity = 902 / (902+50) = 95%
• Misclasification = (147+50)/(147+902+50+174) = 15%
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Decision Tree results (partial cut view / 7 levels total)
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Decision Tree results: Model
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CONCLUSIONS (1/2)
1 Weighted earnings per hour (the division of total earnings by total worked
hours) is the most important variable to retain drivers. Review competitive
salary rates (nominal) should be a priority
2An adequate number of hours of work per week is essential. Overtime is an
important component
• Weighted earnings per hour among drivers that left: $18.79
• Weighted earnings per hour among retained drivers: $22.80
• Avg. OT hours worked among drivers that left: 5.2
• Avg. OT hours worked among retained drivers: 9.5
3 Not acting over turnover opprtunities magnifies the problem because
seasoned drivers tend to stay more than new drivers
• CX tenure among drivers that left: 1.9 years
• CX tenure among retained drivers: 7.3 years
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CONCLUSIONS (2/2)
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Drivers with health issues or on a retirement age are more prone to go
• Avg. age at which drivers retired: 63.4 years
• Avg. age of all other drivers: 46.4 years
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Hiring more drivers than required at a Ready Mix Plant (to allow sufficient
working hours) also impact retention
Ethnicity of drivers doesn’t play a significant role on retention (including
Hispanic drivers)
7 Engagement level doesn’t impact retention. This is surprising at first sight
but might be because current engagement level (metric used) is comprised
of 4 metrics of which 3 are not related with “looking for another job”
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Model Opportunities (1/3)
1 Turnover seems associated to time (the longer the employee stays, the less
prone to go). That might be a consequence of his salary that grows as he
gains experience and because we pay by performance, but also to other
variables
A time series modeling could be explored. In the mean time, a review of the
matrix salaries for the first year should be executed
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Model Opportunities (2/3)
2 It was strange that Engagement level didn’t showed up as related to
turnover. Potential reasons to encourage further analysis are:
a. Engagement level available is engagement for all personnel at a
business unit and not individual engagement (per employee)
b. Engagement level is diluted on 4 questions.
We should incorporate all other variables from the 2016 engagement survey
and not just the engagement index (including the variable: “Are you
actively looking for a job”, which is one of the questions)
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Model Opportunities (3/3)
3 We could also perform “Text Analytics” over the comments provided by
drivers at the 2016 engagement survey to identify specific issues around
turnover
Those results were provided until Janueary 2017 and I was unable to
incorporate into the model
4 Some managers have lately expressed that they think that in big cities (like
Houston), drivers also switch jobs when their commuting distance/time is
significant
I’d like to incorporate the variable: “Distance between job site and home
address” in the next version of my project
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Next Steps
1. Present results to US CEMEX Country Manager and Executive VP of
HR for their approval and back up
2. Communicate and socialize with front line managers (VP/GM’s of our
main markets) to schedule immediate actions: Review salary
matrixes (specially first year), review workload, and driver number
blue print per location.
3. Run additional analysis as stated on section “ Model opportunities”
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THANK YOU
• I can’t thank enough A&M Staff for their teaching, insight, and support.
They’ve made me not just a better professional, but also a better person
• And my project thought me that even at the so call “soft areas” like
Human Resources, Analytics can bring incredible value. I plan to push
very hard for a broader use of my acquired knowledge and tools, and
move forward my career and organization
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QUESTIONS?