Risk Pooling Strategies to Reduce and Hedge Uncertainty Location - PDF document

Risk Pooling Strategies to Reduce and Hedge Uncertainty  Location Pooling  Product Pooling  Lead time Pooling  Capacity Pooling Risk Pooling 風險共擔：整合供應以減少因需求波動而缺貨的風險 D 1 + D 2 ~ N (  1 +  2 ,  ) D 1 ~N (  1 ,  1 ) Store Store Store Store  Store Store Store Store D 2 ~N (  2 ,  2 )          2 2 1 2 1 2 3 1

Risk Pooling Strategies  Redesign the supply chain, the production process, or the product to either reduce the uncertainty the firm faces or to hedge uncertainty so that the firm is in a better position to mitigate the consequence of uncertainty.  location pooling  product pooling  lead time pooling  consolidated distribution  delayed differentiation  capacity pooling 4 1. Location Pooling at Medtronic Current operations: Each sales representative has her own inventory  Territory 1 Lead time is 1 day from Mounds View DC  Territory 2 DC The location pooling strategy: Territory 3 A single location stores inventory used by several  sales reps. Inventory is automatically replenished at the  Territory 1 pooled location as depleted by demand. Territory 2 DC Lead time to pooled location is still 1 day.  Territory 3 5 2

The Impact of Location Pooling on Inventory 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. Expected inventory Pipeline inventory Number of Pooled territory's territories expected demand days-of- days-of- pooled per day S units demand units demand (a) (b) (b/a) (c) (c/a) 1 0.29 4 3.4 11.7 0.29 1.0 2 0.58 6 4.8 8.3 0.58 1.0 3 0.87 7 5.3 6.1 0.87 1.0 4 1.16 8 5.7 4.9 1.16 1.0 5 1.45 9 6.1 4.2 1.45 1.0 6 1.74 10 6.5 3.7 1.74 1.0 7 2.03 12 7.9 3.9 2.03 1.0 8 2.32 13 8.4 3.6 2.32 1.0 But pooling has no impact on pipeline inventory.  6 The Inventory-Service Tradeoff Curve 16 Location pooling can be used 14 Expected inventory (days of demand) to decrease inventory while 12 holding service constant, or 10 increase service while holding 1 inventory cost, or a 8 combination of inventory 6 2 reduction and service 4 4 increase. 2 8 0 0.96 0.97 0.98 0.99 1 In-stock probability 7 3

Why Does Location Pooling Work? 14.0 2.2 2.0  Location pooling reduces 12.0 1.8 demand uncertainty as 1.6 Coefficient of variatio 10.0 Expected inventory measured with the in days of demand 1.4 coefficient of variation. 8.0 1.2 1.0 C.V. 6.0 0.8  Reduced demand 4.0 0.6 uncertainty reduces the inventory 0.4 inventory needed to achieve 2.0 0.2 a target service level 0.0 0.0 0 1 2 3 4 5 6 7 8 Number of territories pooled 8 Pros and Cons of Location Pooling  Pros: Reduces demand uncertainty which allows firms such as e ‐ tailers to reduce inventory, increase service, expand the product line, or a combination of all three.  Cons: Moves inventory away from customers.  Inconvenience for the sales reps.  May create costs to ship product to customers, but may reduce inbound transportation.  Amazon is moving back to multiple inventory locations to accelerate the delivery times. 9 4

Alternatives of Location Pooling  Virtual pooling: Each Medtronic rep keeps her own inventory, but shares inventory with nearby reps if needed. 正常補貨 DC 水平轉運 Dealer1 Dealer2 Dealer3 transshipment 10 Alternatives of Location Pooling  Drop shipping: If a firm doesn’t have enough demand at each location to justify holding inventory, the firm can location pool with other firms DC1 Factory DC2 11 5

2. Product Pooling – Universal Design  O’Neill sells two Hammer 3/2 wetsuits that are identical except for the logo silk screened on the chest. Surf Hammer 3/2 logo Dive Hammer 3/2 logo  O’Neill could consolidate its product line into a single Hammer 3/2 suit, i.e., a universal design . 12 Product Pooling Analysis Assumptions  Demand for the Surf Hammer is Normally distributed with mean 3192 and standard deviation 1181. Demand for the Dive Hammer has the same distribution.  Surf and Dive demands are independent  then the Universal Hammer’s demand has mean 2 x 3192 = 6384 and std deviation = sqrt(2) x 1181 = 1670.  Price, cost and salvage value are the same:  Hence, C o is 110 – 90 = 20, C u = 180 ‐ 110 = 70  Same critical ratio = 70 /(20 + 70) = 0.7778  Same optimal z statistic, 0.77 13 6

Product Pooling Analysis Results  Performance of the two suits (Surf and Dive)  Total order quantity = 2 x 4101 = 8202  Total profit = 2 x $191,760 = $383,520  Universal Hammer  Order quantity:          6384 1670 0 . 77 7670 Q z          Expected profit C Expected sales C Expected left over inventory u o         70 6172 . 4 20 1497 . 6  $ 402 , 116  Reduces inventory by (8202 ‐ 7670)/8202 = 6.5%  Increase profit by (402116 ‐ 383520)/383520 = 4.85%  The profit increase of 4.85% = 1.45% of revenue 14 Demand 20 20 18 18 16 Correlation 16 14 14 12 12 10 10 8 8 6 6 4 4 2 2 0 0 0 5 10 15 20 0 5 10 15 20 20  Correlation refers to 18 16 how one random 14 variable’s outcome 12 10 tends to be related to 8 6 another random 4 variable’s outcome. 2 0 0 5 10 15 20 15 7

Key Driver of Product Pooling 450000 0.40  Product pooling is most 0.35 440000 effective if coefficient of variation of the Universal 0.30 Coefficient of variation 430000 product is lower. 0.25 Expected profit  COV for Surf and Dive 420000 0.20 Hammers = 1181/2192 = 410000 0.37 0.15  COV for Universal Hammer 400000 0.10 = 1670/6384 = 0.26 390000 0.05  Negative correlation in 380000 0.00 demand for the individual -1 -0.5 0 0.5 1 products is best for reducing Correlation COV    1        COV of pooled demand 1 Correlatio n  2   16 Limitations of Product Pooling  A universal design may not provide key functionality to consumers with special needs:  A universal design may be more expensive to produce because additional functionality may require additional components.  But a universal design may be less expensive to produce/procure because each component is needed in a larger volume.  A universal design may eliminate brand/price segmentation opportunities: 17 8

3. Lead time pooling: consolidated distribution  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 C u r r e n t s y s t e m : d i r e c t f r o m s u p p l i e r 8 w e e k l e a d t i m e distributed with mean 50 and S t o r e 1 . . standard deviation 15 . S u p p l i e r S t o r e 1 0 0 If demands were independent P r o p o s e d s y s t e m : c e n t r a l i z e d i n v e n t o r y i n a d i s t r i b u t i o n c e n t e r 1 w e e k 8 w e e k l e a d t i m e l e a d t i m e across stores, then DC demand S t o r e 1 . . would have a standard R e t a i l S u p p l i e r . D C deviation of sqrt(50) = 7.07 S t o r e 1 0 0 18 Consolidation with Centralized Inventory C u r r e n t s y s t e m : d i r e c t f r o m s u p p l i e r 8 w e e k l e a d t i m e S t o r e 1 . . . S u p p l i e r S t o r e 1 0 0 P r o p o s e d s y s t e m : c e n t r a l i z e d i n v e n t o r y i n a d i s t r i b u t i o n c e n t e r 1 w e e k 8 w e e k l e a d t i m e l e a d t i m e S t o r e 1 . R e t a i l . S u p p l i e r . D C S t o r e 1 0 0 Direct Centralized delivery inventory Location supply chain supply chain pooling Expected total inventory at the stores 650 300 0 Expected inventory at the DC 0 116 116 Pipeline inventory between the DC and the stores 0 50 0 Total 650 466 116 19 9

Consolidated Distribution Results  reduces retail inventory by more than 50%!  reduces inventory even though the total lead time increases from 8 to 9 weeks!  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. 20 Consolidated Distribution: other benefits  The supply chain only needs to decide the total quantity to ship from the supplier, not a total quantity and its allocation across locations. Hence, uncertainty is reduced.  Most effective if demands are negatively correlated across locations.  Most effective if the supplier lead time is long and the DC to store lead time is short.  Easier to obtain quantity discounts in purchasing.  Easier to obtain economies of scale in transportation: 21 10

Lead Time Pooling: Delayed Differentiation  Delayed differentiation is an alternative to product pooling  O’Neill stocks “generic” Hammers that have no logo.  When demand occurs O’Neill quickly silk screens on the appropriate logo, i.e., the Surf Hammer and the Dive Hammer are still offered.  When does delayed differentiation make sense:  Customers demand variety.  There is less uncertainty with total demand than demand for individual versions.  Variety can be added quickly and cheaply. 22 Delayed Differentiation with Retail Paint color pigments, retail sales paint mixing, packaging retail sales, color pigments paint mixing, packaging 23 11