1. Slide 1 Matching Supply with Demand: An Introduction to Operations Management Gérard Cachon ChristianTerwiesch All slides in this file are copyrighted by Gerard Cachon and Christian Terwiesch. Any instructor that adopts Matching Supply with Demand: An Introduction to Operations Management as a required text for their course is free to use and modify these slides as desired. All others must obtain explicit written permission from the authors to use these slides.

2. Slide 2 Table 15.1  Suppose each territory’s expected daily demand is 0.29, the required in- stock probability is 99.9% and the lead time is 1 day with individual territories or pooled territories.  Pooling 8 territories reduces expected inventory from 11.7 days-of-demand down to 3.6.  But pooling has no impact on pipeline inventory.

3. Slide 3 Figure 15.1  Location pooling reduces demand uncertainty as measured with the coefficient of variation.  Reduced demand uncertainty reduces the inventory needed to achieve a target service level  But there are declining marginal returns to risk pooling!  Most of the benefit can be captured by pooling only a few territories. The relationship between expected inventory (diamonds) and the coefficient of variation (squares) as territories are pooled. Daily demand in each territory is Poisson with mean 0.29 units, the target in-stock probability is 99% and the lead time is one day.

4. Slide 4 Figure15.2  Location pooling shifts the inventory-service tradeoff curve down and to the right.  For a single product, location pooling can be used to decrease inventory while holding service constant, or increase service while holding inventory cost, or a combination of inventory reduction and service increase.  Or location pooling can be used to broaden the product line. Inventory-service tradeoff curve for different levels of location pooling. The curves represent, from highest to lowest, individual territories, two pooled territories, four pooled territories, and eight pooled territories. Daily demand in each territory is Poisson with mean 0.29 and the lead time is one day.

5. Slide 5 Figure 15.3

6. Slide 6 Figure 15.4  O’Neill sells two Hammer 3/2 wetsuits that are identical except for the logo silk screened on the chest.  Instead of having two Hammer 3/2 suits, O’Neill could consolidate its product line into a single Hammer 3/2 suit, i.e., a universal design, which we will call the “Universal Hammer”. Surf Hammer 3/2 logoDive Hammer 3/2 logo

7. Slide 7 Figure 15.5  Correlation refers to how one random variable’s outcome tends to be related to another random variable’s outcome. Random demand for two products (x-axis is product 1, y-axis is product 2). In scenario 1 (upper left graph) the correlation is 0, in scenario 2 (upper right graph) the correlation is -0.9 and in scenario 3 (the lower graph) the correlation is 0.90. In all scenarios demand is Normally distributed for each product with mean 10 and standard deviation 3.

8. Slide 8 Figure 15.6

9. Slide 9 Figure 15.7

10. Slide 10 Figure 15.8

11. Slide 11 Figure 15.9  Consider the following two systems:  In each case weekly demand at each store is Poisson with mean 0.5 and the target in-stock probability at each store is 99.5% DC demand is normally distributed with mean 50 and standard deviation 15 If demands were independent across stores, then DC demand would have a standard deviation of sqrt(50) = 7.07

12. Slide 12 Table 15.4  Consolidated distribution …  reduces retail inventory by more than 50%!  is not as effective at reducing inventory as location pooling…  … but consolidated distribution keeps inventory near demand, thereby avoiding additional shipping costs (to customers) and allowing customers to look and feel the product  reduces inventory even though the total lead time increases from 8 to 9 weeks!

13. Slide 13 Figure 15.10

14. Slide 14 Figure 15.11

15. Slide 15 Figure 15.2  The more links in the configuration, the more flexibility constructed  In the 16 link configuration plant 4 is flexible enough to produce 4 products but plant 5 has no flexibility (it produces a single product).

16. Slide 16 Figure 15.13 & 15.5  A chain is a group of plants and products connected via links.  Flexibility is most effective if it is added to create long chains.  A configuration with 20 links can produce nearly the results of total flexibility as long as it constructs one large chain:  Hence, a little bit of flexibility is very useful as long as it is designed correctly

17. Slide 17 Figure 15.4  Adding flexibility increases capacity utilization and expected sales:  Note: 20 links can provide nearly the same performance as total flexibility! These data are collected via simulation

18. Slide 18 Figure 15.16  Observations:  Flexibility is most valuable when capacity approximately equals expected demand.  Flexibility is least valuable when capacity is very high or very low.  A 20 link (1 chain) configuration with 1000 units of capacity produces the same expected sales as 1250 units of capacity with no flexibility.  If flexibility is cheap relative to capacity, add flexibility.  But if flexibility is expensive relative to capacity, add capacity. C = total capacity of all ten plants

19. Slide 19 Figure 15.17