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Milk Runs and Variability

Milk Runs and Variability. John H. Vande Vate Fall, 2002. What are Milkruns?. Daily routes Visit several suppliers Allow frequent visits by sharing vehicle capacity Reduce inventory without increasing transport Same route every day. Milkruns & Consolidation. Building Milkruns.

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Milk Runs and Variability

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  1. Milk Runsand Variability John H. Vande Vate Fall, 2002 1

  2. What are Milkruns? • Daily routes • Visit several suppliers • Allow frequent visits by sharing vehicle capacity • Reduce inventory without increasing transport • Same route every day 2

  3. Milkruns & Consolidation 3

  4. Building Milkruns • Filter out any full truckload • Decide the number of routes (may take several passes) • Using our Location/Allocation heuristic • Treat the facilities as route “anchors” • The customers assigned to the “anchor” are on the same milk run • Treat the sum of distances to the anchors as a surrogate for the route length 4

  5. Example Route Anchor Route Anchor Assembly Plant Route Anchor Route Anchor 5

  6. The Impact of Variability Plan for variability by allowing routes to use only, say, 80% of vehicle capacity on average When daily volume exceeds vehicle capacity, pay premium freight to expedite excess 6

  7. Total Cost Build routes that minimize Total Cost • Cost of planned transportation • Cost of unplanned (expedited) transportation 7

  8. Approximation • Daily Volume from supplier is normally distributed • Mean  • Variance 2 • Covariances ij • Mean on the route r = sum of Means • Variance on the route r2 = sum of variances + 2*sum of covariances 8

  9. Probability of Expediting • Depends on • how full we plan to load the vehicle • What the variance of demand on the route is • Probability we have to expedite • 1 - N((c-r)/r) (Cumulative Std Normal) • Doesn’t address the possibility of requiring more than one truck! 9

  10. Expediting • If we plan to fill the truck, 50% chance we expedite, regardless of the variance C  10

  11. Expediting • The less we plan to fill the truck the less likely we are to expedite C  11

  12. Expediting • The greater the variance the less we should plan to fill the truck C  12

  13. Tuesday • Aaron Marshall • Distribution Engineer • Peach State Integrated Technologies • Translating these kind of location models into practice – case studies, challenges. 13

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