1 / 20

Newgistics Case Study

Newgistics Case Study. 04/19/2006 Lei Deng, Timothy Gumm, Jacqueline Jones, Adam Levin, Leihong Li, Yang Song. Agenda. Problem Overview Current vs. Proposed Situation Pre-Modeling Issues The Model Results Analysis. Problem Statement and Goal.

rhoman
Download Presentation

Newgistics Case Study

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Newgistics Case Study 04/19/2006 Lei Deng, Timothy Gumm, Jacqueline Jones, Adam Levin, Leihong Li, Yang Song

  2. Agenda • Problem Overview • Current vs. Proposed Situation • Pre-Modeling Issues • The Model • Results • Analysis

  3. Problem Statement and Goal • Problem Statement: The postal network accumulates and consolidates the return volumes at Return Delivery Units (RDU) and Return Bulk Mail Centers (RBMC). At some point, it becomes faster and less expensive to pull the consolidated volumes out of the postal stream and ship it directly to Newgistics’ returns centers. • Goal: Develop a model that Newgistics can use under changing conditions to understand where to best extract the returns from the postal network • 10, 20, and 30 million volume cases

  4. Pull from BMC: Current State RDU (i) Post Office ±70% Return Centers (k) Cost: by postal zone ±30% 10, 20, or 30 Million Packages RBMC (j) Cost: FTL cost Legend i = 1 to 6115 j = 1 to 22 k = ATL, CHI, LAX, DFW, PHY • Constraints: • maximum density • minimum pick-up frequency

  5. Pull from RDU or BMC: Possible Future State Cost: $2/parcel + DDU cost • Constraints: • Pick up at least 3 times/week • # items/bag RDU (i) 75% ±70% Post Office Return Centers (k) Cost: by postal zone ±30% 25% 10, 20, or 30 Million Packages RBMC (j) Cost: by FTL cost Legend i = 1 to 6115 j = 1 to 22 k = ATL, CHI, LAX, DFW, PHY • Constraints: • truck capacity • minimum pick-up frequency

  6. Issues Before Modeling…. • SUM(RDU dist) = 70% < 100%, where does the other 30% go? • The 30% go directly to BMC from postal office – outside of our control • So the uncontrollable volume at a single BMC is: 30%*annual volume*BMC dist? Any problems? • 30% fixed volume does not apply to an individual BMC! • The right answer is: • (BMCi dist - Sum(RDUs in BMCi dist))*annual volume

  7. Issues Before Modeling…. • Truckload cost • What if < 3 TL/week, do we still pay for 3 FTL cost? • Package and bag weight distribution • One bag can contain 1,2,3,4,or 5 packages • We are given weight distribution for packages < 10 lbs, but no distribution for packages >10 lbs • What distribution to assume? Exponential or Uniform? • What is the bag weight distribution? • Simulation needed!

  8. DDU Bag Weight Simulation Not realistic! Exponential is better estimate.

  9. DDU Bag Weight Optimization • Understand optimal bag configuration to determine the financial value in understanding cubic dimensions of packages • Developed optimization model • Results indicate that significant savings exist if bag configurations are optimized • Suggestion: Work with vendor to improve pricing contract for DDU Pickup Cost based on Bag Opt. Conclusions

  10. Excel Model GAMS Model Excel Model Transfer costs to Transfer solution to The Model • Uses Excel and GAMS (General Algebraic Modeling System) • Computed costs in Excel • Formulated problem as an Integer Program in GAMS

  11. Costs of Pulling from RDU • For 75% captured packages: • DDU cost for each RDU • Expected DDU cost/bag with i packages (i=1,2,3,4,5) • Cost(i) = sum (cost per pound * weight * P(weight)) • Total cost for each RDU • = M*cost(5)+cost(N) + 2(5M+N), M = # full bags [rounddown( # packages at RDU /5)] N = # packages in last bag [# packages at RDU – 5*M]

  12. Costs of Pulling from RDU • 25% Non-captured Packages: • Expected postal cost for each RDU • = Expected postal cost by zone * # non-captured pkgs. at each RDU • Expected postal cost for each zone = sum over all weights (weight distribution * the postal cost for each weight) • # Non-captured pkgs. at each RDU • = ROUND (RDU distribution * total annual volume * 0.25 / 52 / 3)

  13. Costs of Pulling from BMC • Expected postal cost for each RDU • = Expected postal cost by zone * # pkgs. at each RDU • Truckload cost • Includes pkgs. that go from post office  BMC and those that go from RDU  BMC • = Max (3, total weekly volume at BMC / max density of truck) * FTL cost * 52

  14. Model Formulation

  15. Total RDUs pulled: 10 million: 5276, or 86.3% 20 million: 5574, or 91.2% 30 million: 5625, or 92% Savings from proposed solution: 10 million: $8,294,499 20 million: $17,800,244 30 million: $29,618,935 Results

  16. DDU Cost Sensitivity Analysis: Detroit and Springfield

  17. Postal Cost Sensitivity Analysis: Detroit

  18. Overall Results • Pull from RDU except when volumes are very low • Very low volumes increase DDU cost  cheaper to go through BMC • As annual volume increases, pull from more RDUs • Solution is not very sensitive to small changes in DDU cost • Solution can change when postal costs decrease by at least 20%

  19. Recommendations • Start pulling packages from specified RDUs • Understand better cubic dimensions of packages reduce DDU cost • Explore possibility of paying LTL instead of FTL: may change optimal solution

  20. Questions?

More Related