1. 1 Team Buzzkill Project 2: Dell Systems Finished Goods Network Optimization Executive Summary 1

2. 2 Agenda Introduction Project Overview Findings –Parcel –Non-parcel Summary Questions 2

3. 3 Team Members Fred Wang Industrial Engineering (2 nd year) Jade Gaines Industrial Engineering (2 nd year) Previous intern at AT&T Jared Oren Operations Research (2 nd year) Army Corps of Engineers Andy Guedry Industrial Engineering (1 st year) Active duty U.S. Coast Guard Elizabeth Lowe Operations Research (2 nd year) Previous intern at U.S. Airways 3

4. 4 Project Overview Requested Deliverables: a)For all major shipping origins, calculate the average and provide a histogram for both Parcel and LTL/HWA: I.Zone (average and histogram) II.Package Weight (shipment weight for LTL), (average and histogram) III.# pcs in shipment IV.Cycle Time (Shipment to Delivery) b)Map of average and 80 th percentile cycle time for each major shipping origin for parcel ground c)Evaluate the air to ground optimization for shipments. d)How does the network capability of the 3PL (CEVA) compare with the network of the ODM origins? Where do the biggest opportunities for improvement appear to be for Parcel? For LTL? e)What opportunity exists for LTL consolidation to larger LTL shipments, or even TL, out of Mexico? In Nashville? Executive summary will focus on c, d, and e 4 Data used in analysis: Jan-Aug ‘11 Some issues: Cycle Time (identified in subsequent slides)

5. 5 Findings - Parcel 5 Frequency of Origins 95.5 # shipments (x10,000) Conclusions: 1. Largest subsets: FedEx- Nashville, Juarez 2. Most origins ship too often to high Zones (6-8) 94

6. 6 Findings - Parcel Data analysis found items were sometimes shipped from a less than optimal distribution center. For example, shipments to the west coast originating in Nashville (zone 7 and 8) potentially could ship from Torrance (zone 1, 2, 3) at a lower cost, and resulting lower CT 6 Shipping Zones – Nashville, TNShipping Zones – Torrance, CA Shipping Origins

7. 7 Findings - Parcel Evaluated a desktop, notebook separated network: (Torrance/Chicago/Nashville-notebooks) & (Juarez/El Paso - desktops) Respective inventory shipped from closer DC (notebooks) Saved $3.4M Annual (2.6% Parcel) Average CT decreases.3 days 7 Evaluated a 5 DC interchangeable network (less practical): (Torrance/Chicago/Nashville/Juarez/El Paso) All inventory shipped from nearest DC Saved $8.6M Annual (6.7% Parcel) Average cycle time decreases.9 days Minimum Zone Juarez & El Paso Minimum Zone Torrance, Chicago & Nashville Minimum Zone 5 DC’s 2 solutions recommended – Product line distribution networks::

8. 8 Findings - Parcel Bulk of savings were found along West coast with 3 DC notebook model Savings $3.4M Annual (2.6% Parcel).3 days CT reduced 3 DC notebooks model 8 Implementation: 1.Correct inventory placement initially: 2.6% savings is max if inventories match/100% avail 2.Inventory re-positioning: portion of 2.6% savings possible through LTL/TL ‘Zone skip’ to ‘correct’ DC

9. 9 Findings - Parcel 5 DC interchangeable model introduces additional savings along East coast and Great Lakes regions Savings $8.6M Annual (6.7% Parcel).9 days CT reduced 5 DC interchangeable model 9 Implementation: 1.Correct inventory placement initially: 6.7% savings is max if inventories match/100% avail 2.Inventory re-positioning: portion of 6.7% savings possible through LTL/TL ‘Zone skip’ to ‘correct’ DC

10. 10 Findings - Parcel Best: Chicago, Torrance, Juarez; Worst: El Paso, Austin, Nashville Conclusions: 3,5 DC network shipping will improve performance (Nashville, El Paso); FedEx vs. UPS: FedEx faster, meets required delivery window more often 10 * Unreliable Data in 6 month history files * * * * Grading the Network (CT)

11. 11 Findings – Non-Parcel 11 EUSA $190k SAIA $98k EUSA $0 Analysis of 9 high volume LTL routes EUSA $12k KLSA $160k SAIA $20k SAIA $90k SAIA $26k Total saving: $580k Annual (0.4%) Conclusions: 1. Estimated savings if analysis is applied to all major routes: <1% non-parcel budget 2. Savings dependent on TL discount rate (used 30% price decrease from LTL) 3. Can pair strategy with parcel 3,5 DC models to reposition parcel inventory AND conduct non-parcel consolidation

12. 12 Findings – Non-Parcel 12 Grading the Network (CT) * CT Data avail for only EUSA, ODFL Recommendation: Add a mechanism to tag shipments that were delayed due to customer unavailability

13. 13 Findings - Non-Parcel 13 Conclusions: Current HWA levels good (4% of non-parcel shipments) Few air shipments that could have shipped more cheaply (ground) El PasoNashvilleAustin Total Savings$72K$33K$10K Air to Ground2.7%2.1%8.0% Non-Parcel: Shipping Method Optimization

14. 14 Summary Significant savings for parcel shipping using closest DC shipping origin if inventories at respective DC’s match demand (6.7% - 5 DCs or 2.6% - 3 DCs) Limited opportunities for LTL consolidation for main carriers (< 1%) Little room for shipping method improvements (Air-to-ground) More detailed grading and analysis by origin/carrier included in longer report Again, much appreciation goes out to Elizabeth for all of her help and patience! Thank you to DELL for giving us the opportunity Questions? 14

15. 15 Questions / Backup 15

16. 16 Calculating Parcel Savings 16 Steps to calculate: Calculate Cost/shipment per lb for each origin Replay shipping data from all origins and determine best DC from which to ship Calculate cost savings per shipment Aggregate by destination zip for visualization Decreasing from higher zones (6-8) provides larger savings than lower zones reduction

17. 17 Example Parcel Financial 17

18. 18 Parcel Weight Ratios 18 FedEx Origins follow similar weight ratios shape, averages All major DCs max: 4.5E6

19. 19 Non-Parcel: Calculating Consolidation Savings 19 Steps: Reverse engineer LTL discount rate (Radical Tools Excel add-in) for each origin/destination/carrier combination Aggregate shipments per day (costs, weight, shipment count, etc) Obtain nearest destination LTL hub for carrier (Austin) Compute new costs in 2 parts: –Long Haul (TL discounted rate) –Short LTL delivery (Austin area, LTL rate) Aggregate savings