1 / 21

S upply Chain analysis at

S upply Chain analysis at . Purdue University Team Name: Panda Elites Xian Zhu Junming Liu Yu He Yangon Chen. Agenda (Xian Zhu). Objectives (Xian Zhu). Short-term T o balance the performance between the west & east coasts

alida
Télécharger la présentation

S upply Chain analysis at

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. Supply Chain analysis at Purdue University Team Name: Panda Elites Xian Zhu Junming Liu Yu He Yangon Chen

  2. Agenda(Xian Zhu)

  3. Objectives (Xian Zhu) • Short-term • To balance the performance between the west & east coasts • Lower the Transportation Cost in the western sites; • Lower the Inventory Cost in the western sites • Long-term • Support the growth of the whole Eaton Power Distribution Systems • Lean operation

  4. Concerns(Xian Zhu) • From the Data • High Turnover rate of Dallas SVC • The huge monthly variation of demand • The frequent usage of premium freight • The limited capacity of W87 & DBN

  5. Data Analysis (Junming Liu) • Cost of Good Sold

  6. Data Analysis (Junming Liu) • Turnover • Annualize COGS Extreme case of Dallas-SVT

  7. Data Analysis (Junming Liu) • Highlight on DIO • Atlanta-SAT April DIO = 1537 days • Chicago-SVT May DIO = 3862 days • Houston-SVC Feb. DIO = 1052 days

  8. Data Analysis (Junming Liu) • Days of Inventory Outstanding Mean 49.11 days SD = 7.65 days

  9. Data Analysis (Junming Liu) • Trend on COGS (Sales)

  10. Data Analysis (Junming Liu) • High COGS Fluctuation • No Pattern on Demand • Low Responsiveness to the Change of Demand

  11. Data Analysis (Junming Liu) • Overall Trend

  12. Data Analysis (Junming Liu) • Premium Ship Percentage • Chicago, Dallas, San Francisco: High Percentage

  13. Data Analysis (Junming Liu) • Reasons • Distance with Suppliers • Demand Varies • Local Economy • Unemployment • New Construction Extreme Case for Dallas-SVC

  14. Data Analysis (Junming Liu) • Percentage of Order by Source

  15. Data Analysis (Junming Liu) • Los Angeles (Best Case) 1 2 3 Order from Closer Sources Average Premium Shipping Percentage Balanced Order Sources Highly Utilization of W87 Highest Purchases High COGS (Demand) 4

  16. Data Analysis (Junming Liu) • In Addition, Low Capacity of Warehouse

  17. Proposals(Yu He) We suggest to build a major warehouse to enhance the whole supply chain system. Our Reasons: 1. W87 is relatively useless to Electrical Sector. 2. Electrical Sector has no priority. 3. Relieve W34 and W87’s pressure. 4. Shorten distance of supply to some CMSC sites. 5. Increasing trend of demand in the future.

  18. Proposals(Yu He) • Adjust three-day rotation ABC Classifications

  19. Recommendations(Yanjun Chen) • Kanban card- It’s time to change! • LTL Problem • Demand Forecasting • Aggregate Planning -- Level strategy • Looking for more external suppliers in West Coast

  20. Summary(Yanjun Chen)

  21. Thank you!

More Related