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

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Risk Pooling Strategies to Reduce and Hedge Uncertainty

 Location Pooling  Product Pooling  Lead time Pooling  Capacity Pooling

Risk Pooling

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

Store Store Store Store Store Store Store Store D1~N(1, 1) D2~N(2, 2) D1+D2~N(1+2, )

2 1 2 2 2 1

        

風險共擔：整合供應以減少因需求波動而缺貨的風險

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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

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• 1. Location Pooling at Medtronic

Current operations:



Each sales representative has her own inventory



Lead time is 1 day from Mounds View DC

The location pooling strategy:



A single location stores inventory used by several sales reps.



Inventory is automatically replenished at the pooled location as depleted by demand.



Lead time to pooled location is still 1 day.

DC Territory 1 Territory 2 Territory 3 DC Territory 1 Territory 2 Territory 3

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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.



But pooling has no impact on pipeline inventory.

Number of territories pooled Pooled territory's expected demand per day (a) S units (b) days-of- demand (b/a) units (c) days-of- demand (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 Expected inventory Pipeline inventory 6

The Inventory-Service Tradeoff Curve

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.

2 4 6 8 10 12 14 16 0.96 0.97 0.98 0.99 1 In-stock probability Expected inventory (days of demand)

1 2 4 8

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Why Does Location Pooling Work?

 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

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 1 2 3 4 5 6 7 8 Number of territories pooled Expected inventory in days of demand 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 Coefficient of variatio

inventory C.V.

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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.

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Alternatives of Location Pooling

 Virtual pooling: Each Medtronic rep keeps her own

inventory, but shares inventory with nearby reps if needed.

Dealer1

DC

正常補貨 水平轉運

Dealer3 Dealer2

transshipment

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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

DC2 DC1

Factory

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• 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.

 O’Neill could consolidate its product line into a single

Hammer 3/2 suit, i.e., a universal design.

Surf Hammer 3/2 logo Dive Hammer 3/2 logo

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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, Co is 110 – 90 = 20, Cu = 180‐110 = 70  Same critical ratio = 70 /(20 + 70) = 0.7778  Same optimal z statistic, 0.77

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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:   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

7670 77 . 1670 6384        z Q  

       

116 , 402 $ 6 . 1497 20 4 . 6172 70          inventory

• ver

left Expected C sales Expected C profit Expected

• u

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Demand Correlation

 Correlation refers to

how one random variable’s outcome tends to be related to another random variable’s outcome.

2 4 6 8 10 12 14 16 18 20 5 10 15 20 2 4 6 8 10 12 14 16 18 20 5 10 15 20 2 4 6 8 10 12 14 16 18 20 5 10 15 20

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380000 390000 400000 410000 420000 430000 440000 450000

• 1
• 0.5

0.5 1 Correlation 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 Coefficient of variation Expected profit  Product pooling is most

effective if coefficient of variation of the Universal product is lower.

 COV for Surf and Dive

Hammers = 1181/2192 = 0.37

 COV for Universal Hammer

= 1670/6384 = 0.26

 Negative correlation in

demand for the individual products is best for reducing COV

 

           n Correlatio 1 2 1 demand pooled

• f

COV

Key Driver of Product Pooling

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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

• pportunities:

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• 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%

S u p p l i e r S t o r e 1 S t o r e 1 0 0

. . .

8 w e e k l e a d t i m e 8 w e e k l e a d t i m e R e t a i l D C 1 w e e k l e a d t i m e 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 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 S u p p l i e r S t o r e 1 S t o r e 1 0 0

. . .

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

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Consolidation with Centralized Inventory

S u p p l i e r S t o r e 1 S t o r e 1 0 0

. . .

8 w e e k l e a d t i m e 8 w e e k l e a d t i m e R e t a i l D C 1 w e e k l e a d t i m e 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 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 S u p p l i e r S t o r e 1 S t o r e 1 0 0

. . .

Direct delivery supply chain Centralized inventory supply chain Location pooling Expected total inventory at the stores 650 300 Expected inventory at the DC 116 116 Pipeline inventory between the DC and the stores 50 Total 650 466 116

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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.

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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:

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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

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color pigments retail sales, paint mixing, packaging color pigments, paint mixing, packaging retail sales

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Delayed Differentiation: Process Postponement

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Dyed Yarns Knitting Finished Sweaters Dyeing Finished Sweaters Knitting Dyeing White Garments

Other Examples of Delayed Differentiation

 Private label soup manufacturer

 Problem: many different private labels  Solution: Hold inventory in cans without labels, add

label only when demand is realized.

 Black and Decker

 Sell the same drill to different retailers that want

different packaging.

 Store drills and package only when demand is realized.

 Nokia

 Customers want different color phones.  Design the product so that color plates can be

added quickly and locally.

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• 4. Capacity Pooling: Flexible Manufacturing

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 The more links in the configuration, the more flexibility constructed.

Plant 7 6 5 4 3 2 1 8 9 10 Vehicle G F E D C B A H I J 10 links: No flexibility Plant 7 6 5 4 3 2 1 8 9 10 Vehicle G F E D C B A H I J 11 links Plant 7 6 5 4 3 2 1 8 9 10 Vehicle G F E D C B A H I J 16 links Plant 7 6 5 4 3 2 1 8 9 10 Vehicle G F E D C B A H I J 100 links: Total flexibility

Heijunka: workload leveling

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根據實際需求安排生產計劃，使生產量與生產內容平準化

當月計劃生產 800A、 600B、 400C 每週生產 200A、 150B、 100C 每天生產 40A、 30B、 20C 實際現場排程 AAAABBBCC AAAABBBCC mixed model production

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Flexibility Increases Utilization and Sales

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800 850 900 950 1000 80 85 90 95 100 Expected capacity utilization, % Expected sales, units No flexibility Total flexibility 20 links 11 links 12 links

Plant 7 6 5 4 3 2 1 8 9 10 Vehicle G F E D C B A H I J 20 links Plant 7 6 5 4 3 2 1 8 9 10 Vehicle G F E D C B A H I J 100 links: Total flexibility

 Flexibility vs. Cost of Capacity

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Flexibility is most valuable when capacity approximately equals expected demand.



Flexibility is least valuable when capacity is very high or very low.



If flexibility is cheap relative to capacity, add flexibility.



But if flexibility is expensive relative to capacity, add capacity.

400 500 600 700 800 900 1000 60 70 80 90 100 Expected capacity utilization, % Expected sales, units No flexibility 20 links, 1 chain C=500 C=750 C=880 C=1000 C=1130 C=1250 C=1500

C = total capacity of all ten plants

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 Risk pooling strategies are most effective when

demands are negatively correlated.

 With location pooling, the biggest bang is from

pooling only a few locations

 With capacity pooling, a little flexibility

performs almost as good as full flexibility.

 Risk pooling allows a firm to lower inventory

and increase service simultaneously.

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