risk pooling-logistics

TTOM – Logistics TTOM – Logistics

Risk-Pooling Strategies

Francisco FurtadoFrancisco Furtado

Raul Pires

Risk-Pooling

Strategies to Reduce and Hedge Uncertainty Strategies to Reduce and Hedge Uncertainty

Redesign the supply chain, production process or product to either reduce the uncertainty a firm

faces or hedge uncertainty so that the firm is in a better position to mitigate the consequences of

uncertainty. (Cachon Gérard, et all; Matching Supply with Demand)

In other words, risk pooling consists in several pooling strategies either through centralizing

inventories (pooling inventories/stockpile), having universal designs (pooling products for

different segments), delaying time of deliveries to the client (lead time pooling) or introducing

flexibility in manufacturing (pooling different plants, with different capabilities so that they canflexibility in manufacturing (pooling different plants, with different capabilities so that they can

produce the same final product) to decrease the effects of uncertainty (reduce the coefficients

of variation - better match supply and demand).

Objectives Objectives

• Reduce Inventory while maintaining the same service;

• Increase Service while holding the same inventory;

• Combination of the above improvements.

Master’s Program on Complex Transport Infrastructure Systems (CTIS) 1

Risk-Pooling

Strategies to Reduce and Hedge Uncertainty Strategies to Reduce and Hedge Uncertainty

Topics

• Location Pooling• Location Pooling

• Virtual Pooling

• Product Pooling

• Correlation Effect

• Lead Time Pooling

• Consolidated Distribution

• Delayed Differentiation

• Capacity Pooling with Flexible Manufacturing


• Capacity Pooling with Flexible Manufacturing

Location Pooling (1/3)

In how many different locations should the firm store inventory to serve demand?In how many different locations should the firm store inventory to serve demand?

Single Distribution Center

Inventories per Store

Several Distribution Centers

by pooling regions, no store

InventoriesInventories

How can performance be improved by pooling territories having several distribution

centers for each “pooled” territory ?centers for each “pooled” territory ?

• Reducing individual stores inventories;

• By reducing demand variability, decreasing the coefficient of variation for the


demand function for the “pooled” territory.

Location Pooling – Measuring the Impact (2/3)Assumptions:

• Each single Store Demand is a Normal Distribution (we have 6 stores);

• Average daily demand is 3 items and 1 for the Standard Deviation (equal in all);

• Using order-up-to model with a target in-stock probability of 99.9%;

• Lead Time of two days (equal in all).• Lead Time of two days (equal in all).

Pooled Stores

Pooled Stores

Expected Demand

Order Up to level(S)

Units Days of Demand

Coeficient of

Variation(σ/μ)

Total (E)

Inventory

1 6 10 4,00 0,67 0,17 24

Expected Inventory

1 6 10 4,00 0,67 0,17 24

2 12 17 5,00 0,42 0,12 21

3 18 24 6,00 0,33 0,10 18

4 24 31 7,00 0,29 0,08 15

5 30 37 7,00 0,23 0,07 11

With inventories in each store total inventory would be 24, pooling them all gives us 8. It is a 66.7%

reduction in Inventory. Without reduction of level of service!

5 30 37 7,00 0,23 0,07 11

6 36 44 8,00 0,22 0,07 8

Virtual Pooling

No physical pooling of inventories. Inventory information is shared between stores so that each

store can obtain inventory from the Supplier/Central distribution center and any other store with


store can obtain inventory from the Supplier/Central distribution center and any other store with

excess inventory.

Location Pooling – Measuring the Impact (3/3)

Relation Expected Inventory - Coef. VariationComments on results:

0,10

0,12

0,14

0,16

0,18

0,50

0,60

0,70

0,80

Relation Expected Inventory - Coef. Variation

• As the mean of the Normal distribution (and sample

size) increases, it´s coefficient of variation (σ/μ) decreases,

that is, the distribution becomes less variable (because

the Mean grows faster then the Standard Deviation);

0,02

0,04

0,06

0,08

0,10

0,10

0,20

0,30

0,40

the Mean grows faster then the Standard Deviation);

• But Std Deviation decreases at a decreasing rate, each

incremental increase as a proportionally smaller impact

on the Coef. Of Variation;0,000,00

1 2 3 4 5 6

Days of Demand

Coeficient of Variation(σ/μ)

• Doesn´t affect Pipeline Inventory (depends on average

not variability);

• Reduces expected inventory investment needed to• Reduces expected inventory investment needed to

achieve a target service level.

• Better scale economies (shipment and warehouses).

Downside:Downside:

• Explicit Storage costs;

• Inventory moves further away from Demand;


• transshipment between stores and decide when inventory can move from store A to B (Virtual P.)

Product Pooling (1/3)

Serve demand with fewer products. Combine different products in only one, universal design.Serve demand with fewer products. Combine different products in only one, universal design.

Example:

• 2 Products, made by the same firm (same Demand and Economics);• 2 Products, made by the same firm (same Demand and Economics);

• Demand with Normal Distribution (μ=3190; σ=1181). For each one expected Profit is $191 760,

and total profit is $383 520;

• If we combine the Product then:

• μ = 3190×2 = 6384; σ=1181×√ 2= 1670;

• Expected Profit is $402 116.

• Pooling the Product can potentially increase profit by 4.85%!

There is a decrease in demand variability, in the first scenario we have 1181/3190=0.37, with the

pooled product we have 1670/6384=0.26.

This way we reduce the mismatch cost (newsvendor model) and achieve higher expected profits.


Product Pooling – Correlation Effect (2/3)

To Pool demand we have been considering independence between different products and regions, To Pool demand we have been considering independence between different products and regions,

but is that so? What is the influence if Demands are not independent?

With a negative

correlation total

Ideal outcome is a point,

when demand for each

With the Universal product

(and negative correlation)

ideal outcome is a correlation total

demand is stable. With a

positive, total demand

varies. More variability

with positive

when demand for each

product happens to equal its

order quantity.

ideal outcome is a

downward-sloping, not a

single point.


with positive

correlations.

Product Pooling – Correlation Effect (3/3)

This effect applies also to location pooling and lead This effect applies also to location pooling and lead

time pooling.

If correlation is equal to 1 (perfect positive

correlation) there is no benefit in pooling (we do correlation) there is no benefit in pooling (we do

not decrease uncertainty/variability).

Remarks

•May not provide the needed functionality (Bike example);•May not provide the needed functionality (Bike example);

• Can be more expensive to produce (but also more cheaper, there can be scale economies);

•May eliminate some brand/price segmentation;


•May eliminate some brand/price segmentation;

• “Customer Focused” deliriums, no benefits in splitting a fixed customer base into smaller pieces.

Lead Time Pooling – Consolidated Distribution (1/2)

Address the main problem with location pooling – distance between Inventory and Customers.Address the main problem with location pooling – distance between Inventory and Customers.

Firms face two kinds of uncertainty (even with a single product): Total demand and Allocation of

that demand.

Consolidated Distribution aims at keeping inventory close to clients and at the same type reduce

allocation uncertainty.

• Instead of being directly served by the supplier, stores

are served by a Central distribution center (but still keep are served by a Central distribution center (but still keep

some storage capacity);

• Reduced lead times to replenish stores, stores are able

to reduce drastically their storage capacity (other gains to reduce drastically their storage capacity (other gains

in economies of scale);

• The more then 100 connections between Supplier and

Stores are pooled into only one between the Supplier Stores are pooled into only one between the Supplier

and the Central Distribution Center, this way uncertainty

lies only in the total demand and not in were to allocate

that total demand.


Lead Time Pooling – Consolidated Distribution (2/2)

ExampleExample

•More effective when total demand is less variable

than demand at individual stores (negative

correlation between stores);

• Lead time is bigger between before Distribution

Center, than the lead time after the distribution

center;

• Cost of a Central Distribution Center (DC);

• Extra transportation costs from DC to stores;

• Easier to store a large buy (take advantage of price

fluctuations);

• Diminishes “less-then-load” problems, more


• Diminishes “less-then-load” problems, more

frequent shipments (hub like concept).

Lead Time Pooling – Delayed Differentiation (1/2)

• Addresses the problem of uncertainty associated with product variety by making it possible to

differentiate the product in the last stages before it reaches the customers.

• Idea is to have a generic base product that can be differentiated in the end and thus still have

two different products, a strategy that can be important to achieve better sales.

• Also, differentiation is made at a point where better demand information is available.

• No inventory for finished goods but only for the generic good. • No inventory for finished goods but only for the generic good.

• Different from Product pooling:

• eliminates variety by creating a single product, with no differentiation in the final outcome.

• does not require a significant modification in the production process.


Lead Time Pooling – Delayed Differentiation (2/2)

• To be successful :

• Resolve technical issues regarding last stage differentiation

• No substantial delay between request and shipment process

• It is an ideal strategy when:

1. Customers demand many versions, variety is important

2. There is less uncertainty for total demand than there is for individual versions

3. Variety is created in the late stages of the production process3. Variety is created in the late stages of the production process

4. Variety can be added cheaply and quickly

5. The components that create variety are inexpensive compared to the generic

component

• Examples:

• Packaging differences of Black&Decker drill between Wal-Mart and Home Depot

• Retail paint colors

• Fast/Slow printers with no additional processing• Fast/Slow printers with no additional processing

• Resembles make-to-order strategy

•Main difference concerns production steps required to a finished unit


Capacity Pooling with Flexible Manufacturing (1/3)

•What degree of flexibility should the manufacturing process of a factory have so that

shortage of production of fast selling products of a company can be overcome by other

factories?

• If no flexibility exists, then if the demand for a given product is higher than the capacity to

produce it, the excess demand sales will be lost. This becomes even more evident if, for

instance, the same company also produces a certain product whose demand is lower than the

capacity of the factory that produces it. capacity of the factory that produces it.

• So, there can be a compensation between them by the introduction of flexibility so that the

one whose capacity is not fulfilled can produce the product for which the capacity is not

available.available.

• The problem is that with flexibility comes higher costs in the manufacturing process, as it is

more expensive to have flexible processes and equipment instead of just dedicated ones.

• Therefore, how do we decide the level of flexibility and where? And when does it make

sense to invest in it instead of in increasing capacity?



• Auto car maker GM example.

• Each plant can produce 100 vehicles

• Results produced by simulation

• Demand 20 to 180 vehicles for each model• Demand 20 to 180 vehicles for each model

• As we add links, capacity utilization rate

increases as well as expected sales.

• But, as with location pooling, the difference • But, as with location pooling, the difference

between total flexibility and 20 links flexibility

only increases capacity utilization rate (and

expected sales) by around 1%!

• Links configuration -> Chaining effect



• Pooled capacity flexibility also affected by 2 important issues:

• Correlation • Uncertainty in total demand is more important than

individual demand which implies that negative correlation individual demand which implies that negative correlation

of demand increases success of flexibility since increase in

one product means decrease in another and so

compensation can be accomplished.

• It is not mandatory that a plant can produce two

negatively correlated products, it is enough that they are in negatively correlated products, it is enough that they are in

the same chain.

• Vital information that can reduce costs because, instead

of having a plant capable of producing these two different

products, we can just connect them through the chain.products, we can just connect them through the chain.

• Total Capacity• If plants capacity is either very large or very small then no

use in having flexibility because either they are already at

full capacity or they have more then enough capacity to full capacity or they have more then enough capacity to

cover more demand.

Flexibility is more valuable at intermediate levels of capacity and they are substitutes because capacity

increase can cover less flexibility and vice versa. Depends on costs of each.


increase can cover less flexibility and vice versa. Depends on costs of each.

Conclusions

• There are different risk pooling strategies to better match supply with demand, each one

with its own strengths and limitations.

• Risk Pooling should be carefully measured. Substantial improvements are obtained in the • Risk Pooling should be carefully measured. Substantial improvements are obtained in the

first iterations and in most cases yield nearly the same level of gains as using risk pooling to its

maximum.

• Risk Pooling strategies are more effective in the existence of negative correlated demands • Risk Pooling strategies are more effective in the existence of negative correlated demands

due to the fact that uncertainty with total demand is less than with individual.

• They can be used with 3 objectives:

1. reduce inventory while maintaining same level of service1. reduce inventory while maintaining same level of service

2. Increase level of service while maintaining same inventory

3. Combination of 1. and 2.

• Ford, Chrysler, Nokia use some kind of risk pooling in their processes• Ford, Chrysler, Nokia use some kind of risk pooling in their processes

• Contract Manufacturers is an emerging business.


risk pooling-logistics

Documents

risk pooling

pooling territories

client lead time pooling

hedge uncertainty strategies

firm store inventory

single store demand

effects of uncertainty

consequences of uncertainty