Capstone ProjectEnabling reduction of Driver Turnover

Advisor: Dr. Eric Ziegel

Student: Eduardo Vazquez

April 4th, 2017

2

Engagement in Cemex US has been declining…

86%

76%73% 71%

2012 2013 2015 2016

Engagement Index

3

… 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

4

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

5

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

6

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?

7

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

8

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

9

Variable Exploration

OutliersOutliers

Missing

values

Not a rare

event problem

10

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”

11

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

12

Five models were tested

But tested many times

using different

combinations of

variables

13

Partition results are consistent w/ properties requested

Proportion:

• Training (=) 70%

• Validation (=) 30%

14

Best Model is the one obtained through Decision Tree

15

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%

16

Decision Tree results (partial cut view / 7 levels total)

17

Decision Tree results: Model

18

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

19

CONCLUSIONS (2/2)

4

5

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

6

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”

20

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

21

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)

22

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

23

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”

24

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

25

QUESTIONS?