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
D1+D2~N(1+2, )
D1~N(1, 1)
Store
Store

Store
Store
D2~N(2, 2)
  12   22  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
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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
DC
The location pooling strategy:



Territory 1
Territory 2
Territory 3
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
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
Number of Pooled territory's
territories expected demand
pooled
per day
(a)
1
0.29
2
0.58
3
0.87
4
1.16
5
1.45
6
1.74
7
2.03
8
2.32

S
4
6
7
8
9
10
12
13
days-ofdemand
(b/a)
11.7
8.3
6.1
4.9
4.2
3.7
3.9
3.6
units
(b)
3.4
4.8
5.3
5.7
6.1
6.5
7.9
8.4
Pipeline inventory
units
(c)
0.29
0.58
0.87
1.16
1.45
1.74
2.03
2.32
days-ofdemand
(c/a)
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
But pooling has no impact on pipeline inventory.
6
The Inventory-Service Tradeoff Curve
16
Expected inventory (days of demand)
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.
14
12
10
1
8
6
2
4
4
2
8
0
0.96
0.97
0.98
0.99
1
In-stock probability
7
3
Why Does Location Pooling Work?
14.0
2.0
12.0
1.8
1.6
10.0
1.4
8.0
1.2
1.0
C.V.
6.0
0.8
Reduced demand uncertainty reduces the inventory needed to achieve a target service level
4.0
0.6
inventory
Coefficient of variatio

Location pooling reduces demand uncertainty as measured with the coefficient of variation. Expected inventory
in days of demand

2.2
0.4
2.0
0.2
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
Dealer1
水平轉運
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, Co is 110 – 90 = 20, Cu = 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: Q      z  6384  1670  0.77  7670

Expected profit  C u  Expected sales   C o  Expected left over inventory 
 70  6172.4  20  1497.6 
 $402,116


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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
Correlation
20
18
16
14
12
10
8
6
4
2
0
20
18
16
14
12
10
8
6
4
2
0
0

5
Correlation refers to how one random variable’s outcome tends to be related to another random variable’s outcome.
10
15
20
0
5
10
15
20
20
18
16
14
12
10
8
6
4
2
0
0
5
10
15
20
15
7
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 450000
0.40
440000
0.35
0.30
430000
Expected profit

0.25
420000
0.20
410000
0.15
400000
0.10
390000
Coefficient of variation
Key Driver of Product Pooling
0.05
380000
0.00
-1
-0.5
0
0.5
1
Correlation
COV of pooled demand  1 1  Correlation    
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% C u r r e n t s y s te m : d ir e c t fr o m
8
w e e k
le a d
S to re
1
S to re
1 0 0
..
.
S u p p lie r
P r o p o s e d
DC demand is normally distributed with mean 50 and standard deviation 15
s u p p lie r
tim e
s y s te m : c e n tr a liz e d
8
w e e k
le a d
in v e n to r y
in
a
If demands were independent across stores, then DC demand would have a standard deviation of sqrt(50) = 7.07
d is tr ib u tio n
c e n te r
1 w e e k
le a d tim e
tim e
..
.
R e ta il
D C
S u p p lie r
S to re
1
S to re
1 0 0
18
Consolidation with Centralized Inventory
C u r r e n t s y s te m : d ir e c t fr o m
8
w e e k
le a d
s u p p lie r
tim e
S to re 1
..
.
S u p p lie r
S to re 1 0 0
P r o p o s e d
s y s te m : c e n tr a liz e d
8
w e e k
le a d
in v e n to r y
in
a
d is tr ib u tio n
c e n te r
1 w e e k
le a d tim e
tim e
S to re 1
..
.
R e ta il
D C
S u p p lie r
S to re 1 0 0
Expected total inventory at the stores
Expected inventory at the DC
Pipeline inventory between
the DC and the stores
Total
Direct Centralized
delivery
inventory Location
supply chain supply chain pooling
650
300
0
0
116
116
0
650
50
466
0
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
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



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,
paint mixing,
packaging
color pigments
retail sales
retail sales,
paint mixing,
packaging
23
11
Delayed Differentiation: Process Postponement
Finished Sweaters
Dyed Yarns
Dyeing
Knitting
Finished Sweaters
White Garments
Knitting
Dyeing
24
Other Examples of Delayed Differentiation

Private label soup manufacturer

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
Black and Decker
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Problem: many different private labels Solution: Hold inventory in cans without labels, add label only when demand is realized.
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.
25
12
4. Capacity Pooling: Flexible Manufacturing

The more links in the configuration, the more flexibility constructed.
10 links:
No flexibility
11 links
Vehicle Plant
Plant
16 links
Vehicle Plant
100 links:
Total flexibility
Vehicle
A
A
1
2
B
2
B
3
C
3
C
D
4
D
4
D
E
5
E
5
E
6
F
6
F
6
F
7
G
7
G
8
H
8
H
9
I
10
J
A
1
2
B
3
C
D
4
5
E
5
6
F
A
1
2
B
3
C
4
1
Vehicle Plant
7
G
7
G
8
H
8
H
9
I
10
J
9
I
9
I
10
J
10
J
26
Heijunka: workload leveling
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
每週生產
每天生產
實際現場排程
800A、 600B、 400C
200A、 150B、 100C
40A、 30B、 20C
AAAABBBCC AAAABBBCC
mixed model production
27
13
Flexibility Increases Utilization and Sales
20 links
1000
Plant
1
A
1
2
B
2
B
3
C
4
D
5
E
20 links
Expected sales, units
12 links
900
11 links
850
No flexibility
800
80
85
90
95
Vehicle
Vehicle
Total flexibility
950
100 links:
Total flexibility
Plant
100
A
3
C
4
D
5
E
6
F
6
F
7
G
7
G

8
H
8
H
9
I
9
I
10
J
10
J
Expected capacity utilization, %
28
Flexibility vs. Cost of Capacity
20 links, 1
chain
1000
C=1500
C=1250
900

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.
C=1130
Expected sales, units
C=1000
800
C=880
No flexibility
700
C=750
600
C = total capacity of all ten plants
500
C=500
400
60
70
80
90
100
Expected capacity utilization, %
29
14

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.
30
15