Keller and Ozment (1999)  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 Exit Turnover Responsiveness

 Data collection  Large TL carrier  Pretest  Top 100 US carriers  149 usable data

Voice Exit Turnover Responsiveness Results

 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

 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)  Driver turnover impacts  Higher rate  Newer equipment  $ 446 billion industry  3.1 million drivers  Study questions  Data  Randomly selected 3000 carriers – 422 responses  Results

 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  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 of using new approach  (1) Operational work data = “revealed” data.  (2) Data collection advantage.  (3) Can assess dynamic effect of predictor variables.  (4) 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 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?

 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

 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).

 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.

 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

 Model Validation  Face validity  Estimation robustness  Macro-level validity  Micro-level validity  External Validity

 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

 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)  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) (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)  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?