Horizon Newsletter • September 7, 2022
Trucking No-Show Uncertainty Management Cheat Sheet

With trucking capacity tight, trucking no-show risk is elevated.

Follow these tips to minimize the impact of trucking no-shows on your operation:

  • Estimate total number of no-show trucks for a given lineup
    • Use statistics!
    • Factor in causal variables (day/night, holiday proximity, mobilization distance)
  • Categorize drivers by no-show probability
    • Keep it simple - high, low to start
  • Estimate impact of no-show trucks by job
    • Number of trucks (lower = more impact due to relative production impact)
    • Mobilization distance (higher = more impact due to difficulty of replacement)
    • Planned duration (longer = higher impact due to higher marginal cost)
    • Other consequences (additional days, customer satisfaction, damages)
  • Assign high-risk drivers to jobs with the lowest no-show impact
    • Distribute risk through trucker assignment as possible
    • Communicate to truckers re: assignment strategy given no-show probability
      • Don’t assign flakey drivers to high-impact jobs
      • Don’t assign flakey driver together
      • Assign flakey drivers to later shifts in the rotation
  • Follow-up with high-risk drivers post assignment
    • Contact them directly to reinforce assignment and encourage communication
  • Dispatch an insurance pool based the expected number of no-show trucks
    • The ideal insurance truck is flexible, centrally-located, and versatile
    • Consider starting insurance trucks on stockpiling if centrally located and flexible
    • Communicate to the trucker and driver about their role
  • Communicate to planners re: insurance strategy and need for accurate plans
    • No local insurance decisions
    • No local rationing decisions
  • Schedule time to assess the impact of no-shows and re-dispatch accordingly
    • Leverage no-show impact estimates
    • Consider mobilization costs
    • Consider updated job production plan details
  • Assess no-show management performance each day and adjust accordingly
    • How accurate were no-show estimates?
    • Was risk distributed to the lowest cost places?
    • Were replacements managed optimally?

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