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Keller and Ozment (1999)

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.

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Keller and Ozment (1999)

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  1. 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

  2. Study hypotheses • Voice sensitive • Exit sensitive • Responsiveness • Turnover

  3. Voice Responsiveness Turnover Exit

  4. Data collection • Large TL carrier • Pretest • Top 100 US carriers • 149 usable data

  5. Results Voice Responsiveness Turnover Exit

  6. 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

  7. 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?

  8. 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

  9. 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?

  10. Predicting Truck Driver Turnover Suzuki, Crum, and Pautsch (2009)

  11. 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)

  12. 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.

  13. 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?

  14. 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

  15. 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).

  16. 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.

  17. 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.

  18. Implications • Pay effect • Dispatcher effect. • Operational data effect • Personal characteristic effect. • Hire source effect • Other noticeable effects? • Demographic vs. Operational data

  19. Model Validation • Face validity • Estimation robustness • Macro-level validity • Micro-level validity • External Validity

  20. 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

  21. 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?

  22. 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%)..

  23. Suzuki (2007) (1) (2) (3) RC = driver replacement cost M = net profit per day per driver W = profit desired from each driver before exit d = target operating profit margin RPD = revenue per driver per day

  24. 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?

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