Transcript Driver Shortage

Keller and Ozment (1999)
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Problems of driver turnover
Costs $3,000 to $12,000 per driver
Shipper effect
SCM impact
Tested solutions
Pay raise
Regional routes (swapping)
Newer equipment
Rewards for long stay
 Study
hypotheses
 Voice sensitive
 Exit sensitive
 Responsiveness
 Turnover
Voice
Responsiveness
Exit
Turnover
 Data
collection
 Large TL carrier
 Pretest
 Top 100 US carriers
 149 usable data
Results
Voice
Responsiveness
Exit
Turnover
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Study Implications
Significant impact of dispatcher on turnover
rate
High sensitivity to complaints and exits, and
responsiveness lead to low turnover rate
Train dispatcher for responsiveness
Assign assistants to dispatchers (n > 50)
Use inputs from exiting drivers
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Questions
1. Why drivers quit-and-hire within the industry?
2. What are the costs of losing drivers for carriers?
3. If you are the management of a trucking company,
what would you do to prevent or reduce driver turns?
4. How do you train dispatchers? What is your
strategy for hiring new dispatchers?
5. What other factors should be considered when
analyzing driver turns?
6. How does this study change the way you play
simulation game?
Min and Lambert (2002)
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Driver turnover impacts
Higher rate
Newer equipment
$ 446 billion industry
3.1 million drivers
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Study questions
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Data
Randomly selected 3000 carriers – 422
responses
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Questions
1. What kind of drivers do you want to hire or
not want to hire?
2. How does the driver turnover affect the
whole supply chain?
3. As the management, what would you do to
prevent driver turns?
4. Would giving high pays to drivers solve the
problem?
5. What other factors would you consider?
Predicting Truck Driver Turnover
Suzuki, Crum, and Pautsch (2009)
 Introduction
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Truck driver turnover is a key industry problem (TL).
Many studies have investigated driver turnover.
Limitations of past studies:
(1) Static analyses
(2) Survey data
Missing an approach that:
(1) uses time-series approach
(2) utilizes operational work variables (data)
 Advantages
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(1)
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of using new approach
Operational work data = “revealed” data.
Data collection advantage.
Can assess dynamic effect of predictor variables.
Can be used as a practical decision tool.
For these reasons several TL carriers expressed interest
in providing data for analyses
This paper reports results of two case studies and
examine the effectiveness of this new approach from a
variety of perspectives.
 Questions
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to be answered
(1) Are Operational work variables good
predictors?
(2) How do they compare against
demographic variables?
(3) Can the model be used as a practical
decision tool?
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Background (Carrier B)
One of the largest TL carrier in the US.
150% driver turnover rate
Tested almost all possible solutions
Want to develop a method to predict driver exit for each
individual driver by time
Data mining method
What else?
ISU approach
Application of the survival analysis (duration model)
Predicts death (e.g., life expectancy)
Time-series approach
Quit prediction based on statistical probability
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Data
Weekly observations of all drivers (> 5,000)
One-year data (52 weeks)
Both stationary and non-stationary variables included
Total sample = 117,874
Computation time = approx. 60 min (1.8 Ghz Pentium 4
PC).
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Background (Carrier A)
Medium TL carrier, with approx. 700 drivers.
80% driver turnover rate
Wants ISU team to analyze their data and come up
with recommendations for reducing driver turns.
ISU Model
Same model as that used for the large TL carrier.
Good opportunity for ISU team to (1) examine the
robustness of the previous estimation results, and
(2) test the validity of the approach.
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Data
Weekly observations of all drivers (9 months).
Both stationary and non-stationary included.
Slightly different set of predictor variables
Total sample size = approx. 29,000.
 Implications
Pay effect
 Dispatcher effect.
 Operational data effect
 Personal characteristic effect.
 Hire source effect
 Other noticeable effects?
 Demographic vs. Operational data
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Model Validation
Face validity
 Estimation robustness
 Macro-level validity
 Micro-level validity
 External Validity
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Actions & Results (Carrier A)
The carrier has changed its practice by using study
results
Action 1: Driver referral team
Action 2: Incentive program for dispatchers
Action 3: Improved information to dispatchers
The turnover rate has improved.
Actions & Results (Carrier B)
Outperformed data mining method
The carrier has implemented the ISU model.
Seeking to combine the model with load-assignment
model
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Questions
1. How would you utilize the proposed driver-exit
forecasting model to improve your turnover rate?
2. Does this type of model give benefits not only to
each carrier but also to the whole industry?
3. What conclusions and implications can you drive
from the two set of studies?
4. IS this type of model more helpful for large
carriers than for small carriers?
5. What other factors would you consider in future
studies?
Suzuki (2007)
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Introduction
Driver turnover rate is still high and increasing.
Many studies on this topic, but focused on how to
improve turnover rates.
By how much should the rates be reduced?
“What level of turnover rate should carriers attain to
generate desirable business results?”
Develop a method of calculating a “desirable” or
“target” turnover rates for motor carriers.
Model
Calculates the desirable rate for each individual carrier
by considering the carrier’s unique characteristics.
Based on statistical confidence (95%)..
Suzuki (2007)
RC
MRT 
M
DRT 
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RC  
M
M    MRT  RPD
M    RPD
(1)
(2)
(3)
RC = driver replacement cost
M = net profit per day per driver
 = profit desired from each driver before exit
 = target operating profit margin
RPD = revenue per driver per day
Suzuki (2007)
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Excel file with VBA
Driver heterogeneity
Tested the validity of the model for carriers with
heterogeneous drivers.
Results look promising (Table 3).
Is your company’s turnover rate higher/lower than it
should be?