G For the exclusive use of A. CAI, 2020.This document .docx

G
For the exclusive use of A. CAI, 2020.
This document is authorized for use only by AMANDA CAI in
Supply Change Management IE-GY 7993 taught by THOMAS
MAZZONE, NYU Tandon School of Engineering from Feb 2020
to
May 2020.
8031 | Core Reading: SUPPLY CHAIN MANAGEMENT 2
1 Introduction
.......................................................................................... 3
2 Essential Reading
................................................................................ 5
2.1 Types of Supply Chains
.............................................................. 5
2.2 Types of Decisions in Supply Chains
..................................... 7
2.3 Efficient or Responsive: A Framework for Supply
Chain Strategy
............................................................................... 8
2.4 Improving Efficiency: The Bullwhip Effect
........................ 10

Demand Forecast Updating ...................................................
12
Order Batching
............................................................................ 12
Price Fluctuations
....................................................................... 13
Rationing and Shortage Gaming ...........................................
13
2.5 Improving Responsiveness
..................................................... 16
Delayed Differentiation
............................................................. 17
Read-React Capability
..............................................................18
2.6 Alignment of Incentives
........................................................... 24
2.7 Supply Chain Design
.................................................................. 32
Degree of Proximity to Customers ...................................... 32
Degree of Centralization
.......................................................... 36
Degree of Flexibility
................................................................... 38
3 Supplemental Reading
..................................................................... 42

3.1 Supply Chain Risk Management
............................................ 42
4 Key
Terms.....................................................................................
........46
5 Endnotes
...............................................................................................
. 47
6 Index
...............................................................................................
......... 49
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Table of Contents

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1 INTRODUCTION
he supply chain for a product is the network of
organizations and activities involved in its production and
distribution. A car’s supply chain, for example, comprises
auto dealers, factories, component suppliers, semiconductor and
electronics producers, steel producers, plastics and chemicals
manufacturers, logistics service providers, and so on. All of
these
organizations are directly involved in the flow of materials and
services necessary for the production and distribution of a car.
Other organizations, such as information technology service
providers and supply chain analytics companies, play crucial
supporting roles.
Traditionally, organizations in a supply chain have focused on
their internal
operations without worrying about coordinating their activities
with supply chain
partners. Even within an organization, activities are often
housed in functional silos, such
as procurement, manufacturing, sales, and distribution. Each
functional manager focuses
on improving the operations within his or her scope while
taking the requirements of
other supply chain members as given. To exert control over
activities within their scope,
organizations actively buffer themselves from suppliers and
customers by establishing

rigid rules of interaction. For example, they may set long lead
times and minimum order
sizes for customers so that they can manage their factory
operations efficiently, or they
may impose penalties for nonfulfillment of procurement orders
so that their suppliers
carry sufficient inventory.
However, academic research and industry experience beginning
in the mid-1980s
have shown that organizations in a supply chain cannot exist in
isolation; they neither
have control over their costs and profits nor are they able to
manage their risk alone.
Instead, all organizations need effective supply chain
management to coordinate across
organizational and functional boundaries. The supply chain
function is responsible for
facilitating such coordination. It involves making decisions
regarding supply chain
design, sharing information about demand and product
availability with other members,
integrating production and distribution decisions, setting up
long-term supplier
relationships, writing contracts to share the risks of demand and
price uncertainty among
organizations, reducing lead time, and so on.
In recent years, various forces have heightened the importance
of supply chain
management. Increasing product variety and shortening product
life cycles have spurred
organizations to adopt new and innovative supply chain designs
that are more responsive
to customers’ needs. The sharing of information and the
emergence of new technologies

such as RFID (radio-frequency identification) have enabled
firms to collaborate with one
another and to function like an integrated entity, reducing waste
in the supply chain and
decreasing time to market. Globalization and the growth of
emerging markets, especially
China, have lengthened and fragmented supply chains, renewing
the focus on supply
chain design. The Internet is creating new methods of selling
and of configuring supply
chains, turning customers into savvy purchasers. Natural
disasters, accidents,
contamination, and global recession have turned the spotlight on
supply chain risk
T
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management, which is now managed at an organization’s most
senior levels. And
concerns about environmental sustainability and impact have
forced all organizations to
take responsibility for the entire life cycles of their products,
wherever they may be in the
supply chain.

In the Essential Reading, we discuss the principles of supply
chain management in the
context of these developments. We address questions such as:
• What are different types of supply chains? How do they fit
different product
market requirements?
• What should be the goal of a supply chain—efficiency or
responsiveness?
• How can a supply chain be coordinated across all
organizations and activities
to deliver greater value?
• What should be the supply chain footprint of an organization?
• What are the sources of supply chain risk, and how can this
risk be
managed?
In Sections 2.1 and 2.2 of this reading, we define terminology
by describing the types
of supply chains and decisions in supply chain management
(SCM). Section 2.3
introduces two broad supply chain designs—physically efficient
and market responsive—
which are distinguished by product market characteristics and
performance
requirements. Section 2.3 also describes methods to improve the
efficiency of a supply
chain by mitigating the bullwhip effect, sharing information,
and coordinating decisions
across partners, and presents methods to make a supply chain
more responsive, such as

delayed differentiation and read-react capability. In Section 2.5,
we explain how the
incentives of organizations in a supply chain can be aligned to
facilitate collaboration and
maximize total profits. Finally, in Section 2.6 we describe the
elements of supply chain
design, focusing on the trade-offs that lead to different
footprints in different situations.
In the Supplemental Reading, we explore sources of supply
chain risk and methods
for mitigating it—a topic that has gained visibility in recent
years because of increased
globalization, attention to natural disasters, and political and
terrorism-related risks.
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2 ESSENTIAL READING
Contrary to what the term suggests, a supply chain is usually a
complex network. Figures
1 through 3 show some common types of supply chain networks,
characterized by the
number of stages in each; the number of facilities, or locations,
at each stage; and their

linkages. A serial supply chain, the simplest kind, moves
products through sequential
stages, each served by a single facility. The well-known Beer
Game, played in many supply
chain management courses, is a four-stage serial supply chain
consisting of a factory, a
distributor, a wholesaler, and a retailer.a We will consider serial
supply chains in many
sections in this reading because they provide a simple context to
illustrate concepts.
In the serial supply chain in Figure 1, the factory produces
goods and sells them to
the distributor, the distributor sells to the wholesaler, the
wholesaler sells to the retailer,
and the retailer fulfills customer demand. Each location makes
decisions about how much
quantity to procure from the upstream supplier (or, in the case
of a factory, how much to
produce) in order to serve the demand from the downstream
customer at minimum cost.
Upstream and downstream are relative terms: Goods generally
flow from an upstream
location to a downstream one. Arrows in the diagram show the
flow of goods from the
factory toward the retailer. Dashed lines show the flow of
information, which can move
both upstream and downstream. For example, purchase orders
flow from the retailer
toward the factory, whereas information on production
schedules, fulfillment lead times,
and availability of inventory flows in the opposite direction.
Figure 1 Serial Supply Chain
Factors such as the nature of products and the number of

suppliers and customers
pull an organization away from a serial supply chain. A
distribution supply chain, shown
in Figure 2, has one upstream location, such as a factory or a
warehouse, which supplies
several downstream locations that serve retail customers. The
downstream supply chains
of retailers, pharmaceutical companies, and consumer packaged
goods manufacturers are
typically distribution supply chains. An assembly network,
shown in Figure 3, has many
suppliers whose products are combined into one complex
product in the downstream
stage. The procurement function of a manufacturing
organization is typically an assembly
network. Such a supply chain is useful when a buyer firm
creates a portfolio of suppliers
differentiated by cost, quality, or responsiveness. It also
represents the upstream supply
chain of a retailer that purchases different products from
specialized manufacturers.
a A variation on this kind of supply chain is when a small
supplier has a single large customer.
2.1 Types of Supply Chains
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Figure 2 Distribution Supply Chain
Figure 3 Assembly Network
Most actual supply chains are combinations of serial,
distribution, and assembly
stages. Moreover, in some supply chains, goods flow both
upstream and downstream. For
example, manufacturers that recycle their products have closed-
loop supply chains that not
only supply products to customers but also take back used
merchandise for recycling or
remanufacturing. Logistics service providers such as UPS and
FedEx, which handle
arbitrary physical flows between any pair of locations, have
streamlined their operations
by designing their supply chains according to a hub-and-spoke
model: Packages are fed
from local facilities (spokes) to centralized facilities (hubs),
where they are sorted and
forwarded to their destinations.
Supply chains are said to be differentiated or fragmented when
different stages are
owned by different organizations and to be vertically integrated
when many stages are
internal to one organization. Most supply chains are vast and
global. Multinational
corporations manage supply chains that consist of many internal
facilities as well as

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external suppliers and customers. Different firms in an industry
can differ in their supply
chain configurations. For instance, consider two clothing
retailers, American Apparel and
Forever 21, both U.S. chains based in Los Angeles. American
Apparel is vertically
integrated—it produces knitwear in its own factory and then
ships products to its own
stores. Forever 21, in contrast, subcontracts manufacturing with
suppliers all over the
world.1
The supply chain decisions of an organization affect its
logistics costs, inventory costs,
and labor costs. Logistics costs are incurred in the movement of
goods across locations;
inventory costs are incurred in the storage of inventory in
distribution centers,
warehouses, and retail locations; labor costs are incurred in the
handling of goods
throughout the supply chain. All these costs add up to a
substantial fraction of the total
cost of a product sold by a firm. Supply chain decisions also
have revenue implications
when they improve product availability and increase the speed
of introduction of new

products. Because of these broad cost and revenue implications,
supply chain managers
can realize many types of objectives through their decisions:
reducing cost, improving
product availability, minimizing risk, and reducing the cost to
the environment.
Supply chain decisions can have either short- or long-term
timeframes. Short-term
decisions involve procurement and production decisions, that is,
the quantities of various
products and components to procure from upstream locations
and the quantities of
finished goods, if any, to produce in order to serve demand.
Such decisions are often
taken on a daily or weekly basis. Tools such as those described
in Core Curriculum:
Managing Inventory (HBP No. 8016) are commonly used to
make those decisions.
In large organizations, procurement and production decisions
are executed through a
multifunctional process called Sales and Operations Planning
(S&OP). This process
brings together the sales, production, logistics, and finance
functions to share forecasts
and cost information necessary for decision making. Managers
in different functional
roles possess different types of operational information about
the areas under their
control, such as production, ordering, inventory holding costs,
the demand received from
downstream locations, shipments from upstream, forecasts of
future demand, and sales
promotion activities. S&OP facilitates share this information
and coordinate decisions

across functional areas and geographical locations.
Long-term supply chain decisions pertain to an organization’s
physical and its soft
infrastructure. In establishing its physical infrastructure, an
organization chooses
upstream and downstream partners as well as deciding where to
locate facilities of its
own, such as factories, warehouses, and customer service
centers. These decisions depend
on the nature of the product, the degree of demand uncertainty,
and factors related to the
locations of customers and suppliers such as costs, lead time,
and risk of disruption. The
physical infrastructure of a supply chain is also known as the
supply chain footprint.
Soft infrastructure decisions are those that determine the extent
of coordination
across locations. At one extreme is a centralized supply chain,
in which a designated
central authority makes procurement and production decisions
at all locations and
collects cost, demand, lead time, and other operational
information from all locations.
The organization playing that central coordinating role
generally seeks to maximize the
total profit of the supply chain. Vendor managed inventory
(VMI), in which a supplier
manages inventory of its product at its own as well as at its
customers’ locations, is an
2.2 Types of Decisions in Supply Chains

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example of a centralized supply chain. At the other extreme is a
decentralized supply
chain, in which each location makes independent decisions and
coordination is achieved
through contracts or incentive design. Between these two
extremes, supply chain
locations may share information about the occurrence of
demand, the availability of
inventory, production, shipments, and so on, but retain
independent decision authority.
Note that the centralization or decentralization of decisions in a
supply chain are
unrelated to the ownership of locations. A vertically integrated
organization can have a
decentralized supply chain if decision rights are assigned to the
managers at each location.
And two or more organizations in a differentiated supply chain
can choose to coordinate
their decision making.
Consideration of the soft infrastructure of a supply chain is
important because the
performance of each location depends not only on its own
decisions but also on decisions
made at other locations. For example, if an upstream supplier

does not maintain sufficient
stock, then a downstream customer may not receive the product
when it needs it.
Similarly, if a downstream location places orders that are
variable and inconsistent, the
upstream location will be forced to carry more safety stock as a
hedge against uncertainty.
Therefore, the profit of each location in a supply chain can be
improved through better
supply chain design and better coordination of actions taken by
all locations.
What should a supply chain do particularly well? As we’ve seen
so far, an organization
faces a myriad of choices when designing its supply chain. The
supply chain strategy of an
organization can be structured according to the characteristics
of its product.
One framework for making these decisions classifies products
as either functional or
innovative.2 Functional products tend to have long life cycles
of two years or more,
predictable demand with low average demand forecast error,
low profit margins, low
product variety, low rates of stockout, and small price
markdowns. Packaged foods sold in
a supermarket, personal care products, basic clothing and
accessories, and many
industrial products generally have these characteristics. In
contrast, innovative products
have short life cycles of three months to a year, unpredictable
demand with high average
demand forecast error, high profit margins, high product
variety, high rates of stockout,
and high price markdowns. Examples include products that have

significant technology
or design components, such as consumer electronics, cell
phones, fashion and seasonal
clothing, home furnishings, and toys.
In recent years, the rate of new product introduction has
increased steadily.
Correspondingly, product variety has proliferated and life
cycles have shortened.
Products that used to be functional have become innovative.
Consider light bulbs:
whereas incandescent light bulbs are a functional product,
energy-efficient versions have
the characteristics of innovative products because their
technology undergoes rapid
improvements. In industries such as consumer packaged goods,
a company with a
functional product may launch limited editions or promotional
versions, which then have
short life cycles and unpredictable demand, making them
innovative products.
The two types of products impose different costs on a supply
chain. Thus, they
require different supply chain strategies. For functional
products, physical costs—the costs
incurred in the production, distribution (transportation and
warehousing), and storage of
2.3 Efficient or Responsive:
A Framework for Supply Chain Strategy

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inventory—are the main consideration. To minimize these costs,
an organization must
improve efficiency and will therefore gravitate toward a
physically efficient supply chain
strategy.
For innovative products, market mediation costs dominate.
These arise from demand
uncertainty and the subsequent mismatch of supply with
demand, and they include the
costs of disposing of excess inventory, lost sales, and lost
customer goodwill due to a
shortage or stockout. To reduce market mediation costs, an
organization must improve
its responsiveness to fluctuations in demand and will thus
choose a market responsive
supply chain strategy.
Table 1 compares the characteristics of physically efficient and
market-responsive
supply chains. Since functional products have long life cycles,
it is possible to forecast
their demand accurately. As a result, in physically efficient
supply chains, production is
typically located in a low-cost location, such as in a foreign
country or close to the supply
base, and is often outsourced to the most efficient or specialized
suppliers. Transportation

is by low-cost means, such as sea routes, because inventory in
the pipeline carries little
risk of obsolescence or demand uncertainty. Lean production
methods are employed to
reduce inventory and capacity while increasing product
availability. As a result of those
characteristics, physically efficient supply chains tend to be
differentiated. The many
organizations in them share demand and production information
with one another and
coordinate their decisions so that costs can be lowered
throughout the supply chain.
Examples of products that have physically efficient supply
chains include industrial
commodities such as chemicals, plastics, metals, and petroleum
products, as well as
consumer packaged goods.
Table 1 Physically Efficient and Market-Responsive Supply
Chain Attributes
Physically Efficient Market Responsive
Primary Purpose Meet predictable demand at lowest
cost
Minimize excess inventory and
stockouts by responding quickly to
unpredictable demand
Manufacturing
Focus
Achieve high efficiency Have excess capacity
Inventory

Strategy
Minimize inventory throughout the
chain
Deploy sufficient inventory to
respond to uncertainty
Lead-time Focus Reduce lead time as long as cost
remains low
Aggressively reduce lead time
Supply Chain
Coordination
Collaborate; centralize; share
information to cut costs
Achieve speed and flexibility
Product Design
Strategy
Maximize performance; minimize
cost
Invest in new product development
to improve effectiveness
Reprinted by permission of Harvard Business Review. Exhibit
from Marshall L. Fisher, "What Is the Right Supply Chain for
Your
Products?" Harvard Business Review (March–April 1997).
Copyright © 1997 by the Harvard Business School Publishing
Corporation;

all rights reserved.
The primary goal of a market-responsive supply chain is quickly
reacting to changes
in demand, so short production lead times and flexibility are
valuable capabilities. To
develop them, facilities are typically located close to the
customer, excess capacity or
flexible capacity is built in so that production volume and mix
can be changed quickly,
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May 2020.
and the supply of raw material is ensured by investing in
inventory. Firms in responsive
supply chains focus on reducing various components of lead
time, such as in product
design, product launch, and replenishment. Inditex, a Spanish
retail conglomerate that
owns the Zara clothing brand, is an example of a successful
market-responsive firm. It
maintains tight control over lead times through its vertically
integrated supply chain,
which allows it to take products from design to the store in only
a few weeks. Another
example is Dell, which pioneered the direct-to-customer model

in computer
manufacturing in order to reduce lead times.
The choice of supply chain strategy should inform an
organization’s choice of
performance measures. As shown in Table 2, measures of cost,
efficiency, and fulfillment
should be emphasized in physically efficient supply chains,
whereas lead-time and
uncertainty-based measures are more appropriate for market-
responsive supply chains. It
should be noted that market mediation costs, such as lost sales,
are harder to measure
than physical costs. As a result, organizations tend to focus
excessively on physical costs
and to drive toward efficiency in their supply chains regardless
of their product
characteristics. This can result in a mismatch between supply
chain characteristics and
business requirements.
Table 2 Choosing Measures to Gauge Supply Chain
Performance
Performance Measure Physically Efficient
Supply Chain
Market-Responsive
Supply Chain

Amount of Excess I
Various Lead Times:
1 from design to production
2 from production to launch
3 replenishment lead time
Procter & Gamble discovered in the 1980s that even though
consumer demand for
Pampers diapers showed little variation, there were huge
fluctuations in the orders placed
by retail chains and wholesalers. Barilla SpA discovered a
similar problem in the orders
for dry pasta received by its factories and distribution centers.
At Hewlett-Packard,
retailers’ orders for printers were more variable than retail
demand, and the variability in
orders for integrated circuits was even greater.
These three companies experienced a phenomenon known as the
bullwhip effect, in
which the variability of demand increases as one moves
upstream in a supply chain from
the retail customer to wholesalers, manufacturers, and suppliers.
The fluctuations in retail
2.4 Improving Efficiency: The Bullwhip Effect

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orders are larger than those in retail demand; the fluctuations in
wholesale orders are
larger still, and so on.
Variance (Retail Demand) ≤ Variance (Retail Orders) ≤
Variance (Wholesale Orders)
≤ … ≤ Variance (Production)
Thus, demand information becomes increasingly distorted as it
is passed along the
supply chain in the form of orders. The extent of the bullwhip
effect at a given location
can be measured by the amplification factor, defined as the ratio
of variance of orders to
variance of demand at that location.
Variance of orders placed by a locationAmplification Factor
Variance of demand received by that location
=
Values of this ratio greater than one denote amplification;
values of less than one
denote attenuation. The higher the amplification, the more
severe is the bullwhip effect.
Figure 4 illustrates the patterns in sales and orders that are

commonly due to the
bullwhip effect.
Figure 4 Bullwhip Effect in Supply Chains
Source: V. Padmanabhan, Seungjin Whang, and Hau Lee,
"Bullwhip Effect in Supply Chains," Sloan Management Review
38, no. 3
(June 1997), Figure 1. Copyright © 1997 from MIT Sloan
Management Review/Massachusetts Institute of Technology.
All rights
reserved. Distributed by Tribune Media Services. Reprinted by
permission.
The bullwhip effect is costly to all members of the supply chain
but especially to
upstream members, which receive the most distorted demand
information. Its
consequences are all-encompassing: they include excess
inventory and capacity
investments, stockouts, overtime costs, poor demand forecasts,
long lead times, and high
costs for corrections (such as expedited shipments). Thus, both
the revenue and the costs
of each firm in the supply chain are adversely affected.
A firm seeking to mitigate the bullwhip effect cannot hope to do
so by addressing its
consequences in isolation. Instead, it must confront the
underlying causes and try to
achieve better coordination in its supply chain. Because of their
far-reaching implications,
such initiatives generally require cross-functional teams and
must be championed by
senior management.

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Four common factors in supply chains contribute to the
bullwhip effect: demand
forecast updating, order batching, price fluctuations, and
rationing and shortage gaming.3
Demand Forecast Updating
Each organization in a supply chain periodically observes
demand (or procurement
orders) from its downstream customers. It uses this information
as a signal to update its
forecast of future demand and to place procurement orders with
suppliers. Those
suppliers, in turn, use those orders to update their forecasts of
demand and place orders
with their suppliers. This is how noise in demand signals
becomes amplified as it travels
upstream.
The degree of amplification depends on lead time and the
forecasting method
employed. Hypothetically, if lead times were zero—and so
information flows and
shipments from one stage of the supply chain to the next were
instantaneous—then there

would be no bullwhip effect because managers would not need
to update the demand
signals received from their customers. Instead, demand
information would be
instantaneously relayed to the upstream locations in the supply
chain.
In practice, however, a firm generally must project demand for
a nonzero lead time.
This causes the bullwhip effect. For example, if there is a four-
week lead time for a retailer
to receive new shipments from its supplier, then the retailer has
to forecast its demand for
at least the next four weeks when placing an order today. The
longer this lead time, the
longer is the forecast horizon, and the greater the amplification
of the demand signal by
the retailer. Now consider the fate of the supplier who fulfills
the retailer’s orders. If the
supplier also has a four-week lead time, then it must forecast
the retailer’s orders for the
next four weeks, which means that it has to forecast consumer
demand for about eight
weeks. Thus, lead times add up in the supply chain, leading to
progressively noisier
forecasts based on progressively noisier input.
Any time-series forecasting method, such as exponential
smoothing or moving
average, contributes to the bullwhip effect. However, the
bullwhip effect can be worsened
when managers forecast manually, using their judgment to
determine order quantities
instead of automated algorithms (such as exponential smoothing
or moving average). In
doing so, they may overreact to changes in demand or may rely

too heavily on recent
demand observations; this is called recency bias.
Order Batching
A company typically places replenishment orders with its
suppliers less frequently than it
receives demand from its customers. It maintains inventory and
thus places an order only
when the inventory runs low. This leads to ordering in batches.
There are many economic
reasons for batching:
1 The company may follow a periodic inventory control system,
so it may
place orders at fixed intervals (weekly or monthly) that coincide
with its
planning cycle, whereas demand occurs continuously. (See
Core Reading:
Managing Inventory [HBP No. 8016] for further detail on
periodic inventory
control.)
2 Companies may seek to take advantage of economies of scale
in ordering
costs and manufacturing setups. For example, the transportation
cost per
unit when using a full truckload shipment is generally lower
than when using
a less-than-full truckload shipment. Therefore, a buyer
organization may

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wait until it has enough accumulated order quantity to utilize a
full truckload
shipment. The economic rationale for batching is explained by
the economic
order quantity (EOQ) model. This model describes the total cost
of fulfilling
demand per unit time as a sum of fixed ordering costs and
variable inventory
holding costs. Those two cost components trade off against each
other. As
the order batch size increases, fixed ordering cost decreases, but
inventory
holding cost increases. Thus, the EOQ model states that this
tradeoff
determines the order batch size that minimizes the total
fulfillment cost.
3 Suppliers may impose minimum order quantity restrictions,
compelling
their customers to order infrequently in large batches.
Order batching delays the propagation of demand signals in the
supply chain. A
supplier receiving orders once a month receives no demand
information for the rest of
the month. The supplier will have to forecast orders from its
downstream customers for
longer time periods simply because those customers do not
place frequent orders.

Therefore, the uncertainty faced by the supplier will be larger,
contributing to the
bullwhip effect. Furthermore, if a product has a low demand
rate, customers may place no
orders for several months and then unpredictably place a large
order. Thus, the supplier is
forced to carry large amounts of inventory for long and
unpredictable periods and may
even incur stockouts. The cost of the bullwhip effect in the
supply chains for such
products will be large indeed.
Price Fluctuations
Suppose that the sales department of an organization offers
price discounts to customers
in order to achieve sales targets and increase market share. This
leads to a pattern in sales
called the hockey-stick effect, in which sales spike at the end of
each month if sales
incentives are tied to monthly quotas, or at the end of each
quarter if incentives are tied to
quarterly quotas.4 Even as the sales department achieves its
targets, it induces volatility in
orders and makes it harder to fulfill demand, causing stockouts
and further exacerbating
uncertainty in the supply chain. Thus, price discounts lead to a
deterioration of the
performance of the supply chain and to costs on the
organization’s manufacturing and
supply chain functions.
Rationing and Shortage Gaming
At the peak of the dot-com bubble, from 1999 to 2000, network-
equipment customers,
anticipating shortages, placed orders for Cisco equipment that
were significantly larger

than their actual needs. Cisco interpreted these orders as signals
of rising demand. To
keep up with them, Cisco in turn placed big orders with
suppliers of components, such as
chips and subassembly boards. When the bubble burst, Cisco’s
customers canceled their
orders, and the company had to take an inventory write-off of
$2.25 billion.5
Anticipated demand commonly exceeds manufacturing capacity
during the launch of
a successful new product (e.g., Harry Potter books, a new
gaming console from Microsoft
or Nintendo, a new and anticipated model of a luxury car) or
when demand is increasing
and capacity expansion is costly and time-consuming. In such
situations, manufacturers
have no alternative but to ration their production to their
customers. Customers buy into
this game and exaggerate their needs in order to get a bigger
allocation. Thus
manufacturers have difficulty determining the true needs of
each customer and may
allocate too much product to customers with less demand and
too little to those with high
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May 2020.

demand. This, in turn, creates a feedback loop that exacerbates
volatility in the supply
chain. Moreover, if the manufacturer ramps up capacity to
respond to the large orders,
the capacity constraint is suddenly removed and orders drop
precipitously. This cause of
the bullwhip effect, rationing and shortage gaming, leads to
avoidable fluctuations in
upstream orders, capacity, and inventories, which are all
expensive.
The four factors discussed above can be addressed by improving
supply chain
coordination using three types of solutions, as summarized in
Table 3: information
sharing, channel alignment, and operational efficiency. For
instance, to mitigate the effect
of demand forecast updating, organizations in a supply chain
should first and foremost
share demand and inventory information by setting up an
electronic data interchange
(EDI). Information sharing reduces the information lead time in
the supply chain and
enables each organization to plan according to end demand
rather than orders placed by
organizations immediately downstream. However, EDI is just
the foundation; it increases
transparency and discipline but doesn’t change the fact that
organizations must still
respond to orders from downstream customers.
Table 3 Preventing Avoidable Fluctuations
In recent years, radio-frequency identification (RFID) has been

increasingly used to
improve information richness, increase transparency, and reduce
data errors in supply
chains. RFID tags attached to pallets (the unit of movement of
goods in factories and
warehouses), case packs, and individual items can be scanned
efficiently and cost-
effectively at various stages of the supply chain so that their
exact location is known. For
example, a retailer would know how much inventory of different
items is in shipment, a
manufacturer would know how much of its inventory is in a
retailer’s backroom and how
much is on the selling floor, and so on. Manufacturers and
retailers can then use such
information to anticipate future orders and plan their respective
inventories to reduce the
bullwhip effect.
Supply chain organizations can realize considerable additional
benefit by using
shared information to coordinate their forecasting, production,
and stocking decisions.
Frameworks for such channel alignment include vendor
managed inventory (VMI),
Information Sharing Channel Alignment Operational Efficiency
Demand
Forecast
Updating
Use electronic data
interchange (EDI)
Use point-of-sale data

Understand system
dynamics
Avoid multiple demand
forecasts
Make centralized ordering
decisions
Vendor-managed inventory
(VMI)
Discount for information
sharing
Consumer direct
Lead-time reduction
Echelon-based inventory
control
Order
Batching
EDI
Internet ordering
Mixed pallet shipments
Cross-docking
Logistics outsourcing

Reduction in fixed cost of
ordering by EDI
Price
Fluctuations
Continuous replenishment
program
Everyday low cost
Everyday low price
Activity based costing
Rationing and
Shortage
Gaming
Sharing sales, inventory,
and capacity data
Allocation based on past
sales
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May 2020.

collaborative planning, forecasting, and replenishment (CPFR),
and continuous
replenishment program (CRP). Those frameworks have been put
to use by many large
organizations, including Campbell Soup, Nestlé, M&M, P&G,
Scott Paper, and Unilever.
In VMI, a supplier has visibility and control over the inventory
at the warehouses of its
downstream (retail) customer. The supplier decides periodically
how much inventory to
replenish to these warehouses based on the rate of depletion.
The downstream customer
does not need to place orders and the supplier does not need to
forecast them. Instead, it
can integrate its production and downstream stocking decisions
through echelon-based
inventory control. Unlike VMI, CPFR does not relinquish
inventory control to the
supplier. Instead, it provides a model for sharing information
about demand forecasts and
flow of goods across supply chain partners. The planning
process is divided into common
steps, such as creating a business plan, generating sales
forecasts, and generating orders.
All supply chain partners collaborate at each step of this
process to make lock-step
decisions. CRP involves monitoring point-of-sale data
continuously and replenishing
products only for the sold amount as needed in real time. Note
that there are
commonalities across these frameworks. They seek to reveal
information and synchronize
the actions of supply chain partners in order to reduce excess
inventory and stockouts
throughout the supply chain.

Finally, since the amount of amplification caused by demand
forecast updating
depends on lead time, reduction of lead time in the supply chain
brings huge benefits to
the mitigation of bullwhip effect in the supply chain. This is
accomplished by improving
operational efficiency in the supply chain, by, for instance,
reducing ordering, production,
and shipment costs so that it becomes cost effective to order
frequently in small
quantities.
A similar framework of methods can be used to mitigate the
effect of order batching.
First, a supplier can improve its access to demand information
through EDI so that it
does not have to wait for a downstream order to estimate
demand. Instead, by
concurrently observing downstream demand and inventory
levels, it can accurately
predict when the next downstream order will be placed and
build inventory accordingly.
While this does not reduce order batching, it helps reduce
uncertainty in planning.
Second, suppliers and buyers can use methods that make it
economically feasible to
order in small batches. For instance, suppliers can set discounts
for mixed pallet
shipments or an assortment of products that fill a truck rather
than full-truck-load
shipments of single products. And they can outsource logistics
to third-party providers
such as UPS and FedEx so that full shipments can be replaced
by partially full shipments.

Finally, a supplier that produces slow-moving products (which
have low demand
rates) and so must resort to order batching to turn a profit can
focus on reducing the
fixed costs of ordering. Such a supplier may have a mismatch
between its supply chain
design and the characteristics of demand for its products. It
should consider locating its
facilities close to the customer, investing in flexible capacity,
or implementing just-in-
time production. Those changes in supply chain design will
enable the supplier to shift
production at no cost from one product to another so that
producing small batches can be
cost effective.
Reducing price fluctuations is generally a matter of channel
alignment. To reduce the
bullwhip effect caused by price fluctuations, organizations must
coordinate internally
across functions. They need to modify incentives given by the
sales department without
sacrificing the benefits of those incentives for the
competitiveness of the organization.
Organizations also need to coordinate with customers so that
they get the benefit of stable
and low prices without creating order variability. Methods such
as everyday low cost
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May 2020.
(EDLC), everyday low price (EDLP), and activity-based costing
(ABC) are commonly
used for this purpose. These methods, along with VMI, CPFR,
and CRP, are a part of a
larger initiative called Efficient Consumer Response (ECR),
which focuses on the needs of
the consumer and seeks to optimize the entire supply chain to
improve efficiency.
In the case of rationing and shortage gaming, manufacturers
employ many
mechanisms to allocate scarce stock to customers: allocating
capacity in proportion to
orders, in proportion to past sales and customer satisfaction, or
on the basis of the
priority of customers. But many of those mechanisms do not
solve the problem because
they do not induce buyers to truthfully report their requirements
to the capacity-
constrained manufacturer.6 Manufacturers can eliminate gaming
in shortage situations by
requiring customers to share sales and inventory data, imposing
stricter return and order
cancellation policies, centralizing stocking decisions in the
supply chain, or incentivizing
customers on the basis of their past ordering behavior.
As we have noted, products with short life cycles are
increasingly common. An article of
fashion clothing, for example, typically has a selling season of

two or three months but a
production lead time of nine to twelve months. Production
orders must be placed well
before the start of the season to fulfill commitments through the
complex supply chain.
Once the season starts, the firm has no recourse.
Two attributes of such products make them costly to manage:
uncertain demand
forecasts and long lead times. It is difficult to forecast demand,
and thus plan production,
for short-life-cycle products because there is typically no
historical demand or sales data
available. In these instances, the time series forecasting models
that are embedded in ERP
systems are not effective. Instead, managers must rely on their
judgment and experience.
Such “judgmental forecasts” tend to be noisy, and so the firm
loses revenue and incurs the
considerable cost of excess inventory. Long lead times
exacerbate the problems of noisy
demand forecasts by making it harder for managers to react to
changes in demand.
Managers of such products must focus on improving the speed
of the supply chain—that
is, making it more responsive.
Managers can undertake many initiatives to develop responsive
supply chains. They
can choose suppliers located close to the demand base that can
provide shorter lead time
and integrate their processes better with the buyer firm. They
can also coordinate
information sharing with suppliers, reserve production and
distribution capacity in
advance, and pre-position raw materials so that production can

be triggered at short
notice. Zara, which we mentioned earlier in discussing market-
responsive supply chains,
provides a good example of such a supply chain. The company
designs its products in-
house, maintains raw material inventories, produces in its own
factories, ships all finished
merchandise to a central distribution facility, and then allocates
merchandise to stores all
over the world several times a week. By tightly coordinating all
these activities, Zara is
able to quickly respond to changes in demand and deliver “fast
fashion.” Its supply chain
is so responsive that the total flow time of a product from
design to store can be as little as
10 days.
While Zara’s supply chain design naturally facilitates
responsiveness, many other
firms are entrenched in supply chains with long lead times. In
such cases, responsiveness
can be developed in two ways: delayed differentiation or read-
react capability.
2.5 Improving Responsiveness
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May 2020.

Delayed Differentiation
Consider a firm producing a family of products that share parts.
The production process
consists of common steps and points of differentiation. Common
steps are those that are
undertaken for more than one product, whereas differentiation
progressively determines
the identity of each product. Figure 5 depicts a manufacturing
process consisting of
common stages of production and points of differentiation. The
first differentiation
occurs after stage 1. The second differentiation occurs after
stage 2 for products A and B,
and after stage 3 for products C and D.
Delayed differentiation, also known as postponement flexibility,
postpones the point
of differentiation as late in the production-distribution supply
chain as feasible. It reduces
the need for the firm to carry inventory of differentiated
products subject to uncertain
demand. Instead, it carries inventory of undifferentiated
products, called vanilla boxes,
which are converted into finished products late in the process
when it is able to use more
accurate information about demand for each finished product.
The firm has a shorter
effective lead time. The amount of safety stock of inventory
needed by the firm decreases,
and costs of excess inventory and shortage decline.
Delayed differentiation capability can be developed by
redesigning products to share
common modules, sequencing the production process so that

points of differentiation
occur later in the process, and redesigning the supply chain so
that differentiation tasks
can be pushed closer to the customer. A classic example of
delayed differentiation is
provided by the manufacturing of knitwear, such as sweatshirts
and T-shirts. Typically,
garments of different colors are produced by first dyeing yarn
into various colors and
then knitting the yarn by a common process. By switching the
sequence of dyeing and
knitting tasks, a firm can carry inventory of undyed rather than
dyed garments and can
thus manage the uncertainty of demand for different colors with
less stock.
Figure 5 A Manufacturing Process with Common Stages of
Production and
Points of Differentiation
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May 2020.
Read-React Capability
This capability seeks to reduce procurement lead times to such

an extent that a firm can
utilize early demand signals to forecast demand and replenish
merchandise in the middle
of the selling season or life cycle of a product. Figure 6
illustrates the timeline of activities
in a firm with read-react capability. The selling season is split
into three parts. The firm
positions inventory for the first part, called the “read period,” in
advance of the season by
relying on the forecasts of experts. Upon observing demand
during this period, it updates
its demand forecast for the remaining season or product life
cycle. It then places a
replenishment order, which arrives after a short lead time. The
firm uses inventory from
the replenishment order to serve demand in the third part of the
season.
Figure 6 Read-React Timeline
Read-react capability can be developed by reserving capacity
with suppliers ahead of
time so that they will be able to produce the product on short
notice, pre-positioning raw
materials at suppliers to cut down procurement lead time, and
using algorithms to update
the demand forecast by observing initial demand during the read
period. The production
capacity that is deployed during the middle of the selling season
is called reactive
production capacity.
Read-react capability is used in many industries. A notable
example is the skiwear
manufacturer Sport Obermeyer.7 Figure 7 illustrates the impact
of read-react capability

on forecast accuracy at Sport Obermeyer. The top panel in the
figure shows actual sales
for several items plotted against initial forecasts made ahead of
the season. Note that the
forecasts have large errors. If Sport Obermeyer were to plan
inventory for the entire
season based on these forecasts, it would bear considerable
expense of excess inventory
and lost sales at the end of the season. The bottom panel shows
forecasts made during the
season by extrapolating actual demand in the first 20% of the
season. These forecasts are
remarkably more accurate. Thus, Sport Obermeyer developed
reactive production
capacity so that it could take advantage of the more accurate in-
season forecasts and thus
increase its sales revenue and profit.
to
May 2020.
Figure 7 Effect of Read-React on Forecast Accuracy
Reprinted by permission of Harvard Business Review. Exhibit
from Marshall L. Fisher, Janice H. Hammond, Walter R.
Obermeyer,
and Ananth Raman, "Making Supply Meet Demand in an

Uncertain World," Harvard Business Review (May–June 1994).
Copyright
© 1994 by the Harvard Business School Publishing Corporation;
all rights reserved.
to
May 2020.
Let’s illustrate the benefits of read-react capability through
Interactive Illustration 1.
(We shall explain the computations for this interactive
illustration later in this
discussion.) The interactive compares a firm that does not have
read-react capability with
one that does. The former firm makes a single procurement
decision before the start of
the selling season. The latter firm makes two procurement
decisions: an initial buy before
the start of the selling season and a replenishment during the
season after observing the
actual demand occurrence during the read period.
The selling price, procurement cost, and salvage value of
leftover inventory can be
varied using the sliders in the interactive. We model demand
using the normal probability
distribution.b Interactive Illustration 1 shows the mean and

standard deviation of
demand for the read period and the react period. The demand
during the react period is
correlated with the demand during the read period.
Interactive Illustration 1 Read-React
The firm that does not have read-react capability estimates the
total demand for the
season. The mean of total demand for the season is the sum of
the means of demand
during the read period and the react period. The standard
deviation of the total demand
during the season depends on the standard deviations during the
read period and the
react period, as well as on the correlation between them. For
example, if the standard
deviation of read demand is 600, the standard deviation of react
demand is 2,400, and the
correlation coefficient is 0.5, then the standard deviation of the
demand for the entire
season will be the square root of (6002 + 2,4002 + 2 ∙ 0.5 ∙ 600 ∙
2,400) = 2,750.
With this demand estimate, the nonread-react firm uses the
newsvendor model to
decide its procurement quantity. In other words, the firm
determines the optimal
inventory to buy in order to balance the costs of excess
inventory and lost sales, which
b Actual computations will be more complex and will have to be
done through simulation or
computational software packages.
Scan this QR code, click the image, or use this link to access

the interactive illustration: bit.ly/hbsp2ukeAL8
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May 2020.
https://s3.amazonaws.com/he-assets-
prod/interactives/047_read_react/Launch.html
prod/interactives/047_read_react/Launch.html
http://bit.ly/hbsp2ukeAL8
occur due to randomness of demand. The interactive shows the
resulting procurement
quantity and the average profit that the firm can expect to make.
The read-react firm places an order at the start of the season to
fulfill demand for the
read period. Unlike its nonread-react counterpart, it doesn’t
have to be precise about
optimizing this inventory. On the contrary, it should order a
little extra so that it does not
run out of stock in the first two weeks. This helps the firm
satisfy customers and get a
good reading of demand. Moreover, its inventory risk is low
because the inventory left
over after the first part can be sold off in the second part. After
observing demand during
the read period, the firm updates its forecast and places a

replenishment order according
to the newsvendor model. Let’s suppose for simplicity that the
replenishment order
arrives the next day (it has zero lead time).
The interactive shows the resulting average profit and the
average amount of
inventory bought under possible scenarios of demand for this
firm. Observe that the read-
react firm always makes a higher profit than the nonread-react
firm. Vary the parameters
of the model and explore their effect on the difference in profit.
You will observe that the
higher the magnitude of the correlation coefficient between
demand in the two periods,
the higher the percent increase in profit.
Now let us follow the details of the computations in this
interactive illustration in
order to grasp the sources of increase in profit. Suppose that
price = $10, procurement
cost = $5, and salvage value of leftover inventory = $4. For
simplicity, let us suppose that
there are no markdowns or price changes in the middle of the
season. Before the season
starts, the demand for this product is forecasted to be normally
distributed with mean =
10,000 and standard deviation = 2,750.
The newsvendor critical fractilec for the above values of price,
cost, and salvage value
is (10–5)/(10–4) = 5/6. This fractile corresponds to a z-score of
0.967 from the standard
normal distribution. If the firm does not have read-react
capability, it places a single
procurement order at the start of the season and does not plan to

place a second order
midseason. According to the newsvendor formula, the order
quantity that maximizes the
expected profit of the firm, given the above critical fractile and
demand distribution, is
given by mean demand + z∙standard deviation of demand =
10,000 + 0.967∙2,750 = 12,660
units.
This gives the following performance characteristics (Numbers
might not sum due to
rounding):
• Expected lost sales. The firm would not be able to meet the
entire possible
range of demand because it carries limited inventory. If demand
exceeds
12,660 units, the rest of the demand will be lost. For z = 0.967,
the standard
normal loss function value is L(z) = 0.0887. Thus, the firm
should expect to
lose sales of L(z)∙standard deviation = 0.0887∙2,750 = 243.9
units, on average,
due to demand uncertainty.
o Expected sales. The firm should expect, on average, to sell
Mean
Demand – Expected Lost Sales = 10,000 – 243.9 = 9,756.1 units
of the
product.
o Expected leftover inventory. The firm should expect that an
inventory
of Q* – Expected Sales = 12,660 – 9,756.1 = 2,903.9 units will
be left over
at the end of the season, on average.

c See Core Reading: Managing Inventory (HBP No. 8016) for an
in-depth description of the newsvendor
model and the critical fractile.
to
May 2020.
• Expected profit. These values will yield a total expected profit
of Price ∙
Expected Sales + Salvage Value ∙ Expected Leftover Inventory
– Cost ∙ Order
Quantity = $10 ∙ 9756.1 + 4 ∙ 2903.9 – 5 ∙ 12660 = $45,876.60.
Now suppose that the season is divided into two parts of two
and eight weeks. Let X
denote the random demand for the first part and Y the random
demand for the second
part. Suppose that the forecast of total demand is split as
follows: X has mean 2,000 and
standard deviation 600, and Y has mean 8,000 and standard
deviation 2,400. Historical
data about similar products sold in previous years tells the
company that the demand
during the second part is correlated with the demand during the
first part. That is, Y is
given by the following regression line estimated on historical

data, with an R-square of
25%:
Y = 4,000 + 2∙X + random noise
This is equivalent to saying that X and Y follow a bivariate
normal distribution with
correlation coefficient 0.5. Thus, after observing the first two
weeks of demand, the firm
will know the value of X and can apply the above regression
equation to forecast demand
for the rest of the season and order the optimal quantity
according to the newsvendor
model.
The optimal expected profit for the firm in following the above
two-part strategy
turns out to be $47,572, which represents a 3.7% improvement
over the base case. This
increase represents gross profit, which will flow to the bottom
line because none of the
fixed costs are affected. Since net profits in retailing are
typically 1% to 5% of sales, this
increase is substantial.
This increase in profit stems from a simultaneous reduction in
inventory and increase
in sales:
1 Splitting the selling season into two parts lowers the demand
uncertainty in
each one. Thus, the firm needs less safety stock and less total
inventory.
Indeed, the amount of merchandise ordered in the base case was
12,660,
whereas the total amount of merchandise ordered in the split

case summed
over the two periods is an average of 12,163. This decreases the
cost of excess
inventory.
2 The order the firm places for the second part of the season
enables it to catch
up to demand volatility in the first part. If demand was high,
then more
merchandise can be produced. Otherwise, less production is
needed, and the
firm can instead focus on selling the available inventory. This
ability to
adjust to demand volatility increases revenues. In our example,
the total
expected sales in the base case was 9,830, whereas the total
expected sales in
the split case is 9,956.
3 Demand from the early part of the season provides a more
accurate forecast
of demand for the rest of the season. Thus, the firm can capture
the demand
upside when the product turns out to be hot.
In this example, we used a conservative value of 0.5 for the
correlation coefficient ρ
between demand during the two parts. You observed the effect
of varying ρ on the
average profit through Interactive Illustration 1. In Figure 8, we
depict this effect by
varying ρ while keeping X and Y fixed. The figure shows the
percentage increase in profit
obtained from read-react capability compared to the base case
for different values of ρ.
Observe that there is an increase in profit even when ρ = 0—

that is, when the demand
during the first period conveys no information about demand
during the second period.
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May 2020.
This increase is due to the first two reasons described above—
that splitting the season
into two parts reduces inventory requirements and enables the
firm to respond to
demand volatility. As ρ increases, the third reason begins to
make a difference because the
value of forecast updating becomes more and more salient,
resulting in larger increases in
profit.
Figure 8 Profit Increase Due to Implementation of Read-React
Capability
It is useful to note that the read-react capability translates into
not only higher
expected profit but also lower working capital needs. That’s
because the firm needs less
inventory and thus has better cash flow. Moreover, since
inventory levels are reduced, the
firm can provide higher variety and higher service levels to

customers without investing
in additional warehousing or retail space.
To illustrate the benefits of the read-react capability, Interactive
Illustration 1 has
not included real-world complications and circumstances. For
an effective real-life
implementation, our example must be refined to incorporate
features such as the
following:
• Orders placed midseason may not arrive immediately. Instead,
the
replenishment quantity will become available to meet demand
only after the
lead time has transpired and the shipment has been received.
Thus, the
selling season must be divided into three parts, as shown in
Figure 6. When
determining the replenishment order quantity after the read
period, we must
account for the further depletion of stock that will take place
before the order
is received.
• The supplier may charge a higher price to produce and ship
products on
short notice in the middle of the season. This would somewhat
erode the
benefit of the read-react capability. The initial and
replenishment order
quantities must be adjusted to minimize the adverse impact of
this increase
in price. The supplier would be economically justified to charge
a higher
price because, although the retailer’s risk decreases when it has

a responsive
supply chain, the supplier’s risk increases. For example, after
the recession of
2007 to 2009, apparel retailers pressured their suppliers to cut
lead times so
that the retailers could order closer to the season and thus lower
their risk of
unsold inventory. Suppliers naturally resisted this pressure
because of the
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May 2020.
difficulty of scheduling shipment containers, labor, and
factories at the last
minute and the increased risk of demand uncertainty.8
• Finally, when using historical data to estimate the regression
equation shown
above, the firm must control for other factors that influence
demand, such as
price changes during the season, promotions, and competition.
Those
variables can change from one year to the next, so we must
include variables
other than the read-period demand in the regression equation.
Doing so will

improve the accuracy of the demand forecast obtained from the
read period.
So far in this reading, we have implicitly assumed that all
organizations in a supply chain
share the objective of increasing the total profit of the supply
chain. However, the costs
and benefits of improving efficiency or responsiveness can
accrue disproportionately. For
instance, the cost of reducing the bullwhip effect or making the
supply chain more
responsive may be borne by one organization, but the benefit
may accrue to another. In
reality, organizations have different and often conflicting
objectives as they seek to
maximize their own profits. As a result, buyer-supplier
relationships in supply chains can
be adversarial rather than collaborative.
The richness of practical considerations in supply chain
coordination is exemplified
by a case study of how Procter & Gamble improved its
relationship with Walmart.9
Consider the following quote from Sam Walton, Walmart’s
founder, to Lou Pritchett,
Vice President for Sales at P&G:
Your company is just the hardest company we do business with.
It just
seems to me that if you thought of my stores as an extension of
your
company, we would have a totally different business
relationship than
we have today.10
This conversation led to multiple initiatives that increased

coordination between the
companies and their joint business over the subsequent decades.
These initiatives
addressed not only cross-firm obligations but also within-firm
incentive structures. They
involved setting up processes for periodically assessing the
impact of business conditions
and technological changes on incentives in order to avoid
misalignment and to improve
trust among supply chain partners.
Misalignment of incentives in a supply chain can be traced to
three possible causes.11
The first is hidden action. Organizations in the supply chain can
influence demand
through, for example, customer service, presentation of
products, and advertising, but
organizations often cannot observe one another’s level of effort.
If one organization in the
supply chain (say, the buyer) can make an effort to increase
demand, coordination
becomes challenging because the cost of the effort is borne by
that organization but the
benefit accrues to both the buyer and the supplier. If the effort
is visible to both
organizations or can be verified after the fact, then they can
share the cost. But if the effort
is not visible, then one organization does not know if the others
are behaving in
everyone’s best interest.
The second is hidden information about costs, demand,
capacities, and competitive
structure. Supply chain partners hide their information from one
another because of a
lack of trust and bargaining games. Such cross-company

problems are difficult to detect
2.6 Alignment of Incentives
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May 2020.
because of culture, organizational structure, personalities, and
even history. Hidden
information makes it impossible to design incentives optimally.
The third is badly designed incentives. In practice, firms set
incentives for their
suppliers and customers on the basis of sales revenue, cost,
profits, inventory shrinkage,
and so on. Too much or too little emphasis on any one variable
can lead to badly designed
incentives and erosion of profit.
To align incentives, managers should first recognize how their
suppliers’ and
customers’ decisions are affected by the incentives of their
buyers and suppliers. If there is
indeed a problem, they should determine which of the three
issues discussed above is at
its root. Hidden information, for instance, can be revealed by
capturing data on relevant

variables and incorporating that data into performance
evaluation processes. It can also
be revealed through various intermediaries; for example, third
parties collect and validate
sales data, which then enables a manufacturer to incentivize a
retailer based on sales
revenue.
The occurrence of hidden action and hidden information is
illustrated by a practice
called “markdown money,” used by department store chains to
share their risk of unsold
inventory with clothing suppliers. The chain buys products from
the supplier at a fixed
wholesale price and sells them in its stores at a fixed list price.
When the chain marks
down a product below list price, it charges a fraction of the
markdown amount (called
chargeback) to the supplier. To justify these charges,
department stores must maintain
detailed records of when the product was sold, at what price,
and what deductions were
charged from the supplier. In the absence of such records, the
supplier’s share of
markdowns cannot be determined because the actions of the
department store are not
visible to the supplier. This can lead to a situation like the one
we saw in May 2005 when
several clothing makers sued department store chains, including
Saks Fifth Avenue and
Dillard’s, for withholding payments for clothes shipped and for
deducting markdown
money from payments without authorization and without proper
recordkeeping. To
avoid such conflicts, retailers and suppliers must work closely
with one another to

determine their terms of trade as well as the mechanism by
which compliance will be
established.12
One effective way to rectify badly designed incentives is to
rewrite the contracts that
specify the decision rights for organizations in a supply chain.
For example, a contract
may specify that the supplier firm decides the final selling price
of the product, whereas
the buyer firm decides the quantity of inventory to be carried in
retail stores. Contracts set
incentives for the stakeholders, such as transfer payments,
prices for goods bought and
sold, and penalties for nonfulfillment of contractual duties. For
example, the seller may be
held liable for a penalty if it does not meet the quantity, the
quality, or the delivery
schedule for an order placed by the buyer. Contracts specify
how merchandise will be
displayed in a retail store (if it is an end product), if unsold
merchandise can be returned
to the supplier, and what compensation will be provided for it.
They specify how the costs
of advertising and promotion will be shared between the buyer
and seller. They also
describe what kind of monitoring will be conducted by
stakeholders or by a third party to
verify fulfillment of the terms of the contract. One method of
monitoring is by buyers and
sellers sharing demand, sales, or inventory information in order
to increase transparency.
Thus, contracts determine the extent of coordination in a supply
chain, the sharing of
risks and rewards, and collaboration in efforts to improve
efficiency, quality, or other

performance goals.
From the perspective of an organization in a supply chain,
contracts serve two broad
purposes. First, they determine the organization’s profit and
risk. Second, they determine
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May 2020.
whether the incentives of other organizations in the supply
chain are aligned with it. If a
contract is not designed well, these two objectives would be in
conflict with each other,
which could hurt the performance of the entire supply chain.
That is, the higher your
share of profits, the less the decisions of the other organizations
in the supply chain would
be aligned with your interests. To maximize the profit of the
entire supply chain, it is not
sufficient that each organization seeks to maximize its own
profit. Instead, the profits of
each can be improved only if the incentives of all are aligned
and contractual terms are
chosen properly.
Let’s explore the implications of contract design on the

alignment of incentives in a
supply chain through a simple hypothetical example of contracts
between a single buyer
and a single supplier.
Suppose that ColorCraft is a producer of artistic greeting cards
in a small town in
upstate New York. The company uses a special papermaking
process with a long
production lead time. Cards for a holiday season must be
designed and ordered weeks in
advance. Each card has a variable production cost of $1.50 and
sells for $5, and unsold
cards have no residual value. Using historical data, the company
forecasts that demand
for its greeting cards in the coming holiday season will be
normally distributed with a
mean of 5,000 and standard deviation of 1,500 cards.
Ms. Marks, the owner of ColorCraft, has been running a
vertically integrated
operation, making and selling cards from her shop. This year,
she is interested in selling
through a retailer so that she can focus her staff on production
quality. Let’s compare
these two options to determine the best one for Ms. Marks.
Vertically integrated supply chain: Based on tools provided by a
local microbusiness
MBA student club, she uses the newsvendor model to determine
the optimal inventory to
maximize her expected profit (Numbers might not sum due to
rounding):13
• The newsvendor critical fractile for her price and cost values
is (5 – 1.5)/5 =

0.7.
• This fractile corresponds to a z-score of 0.5244 from the
standard normal
distribution.
• Thus, the optimal amount of inventory that she would produce
for this
season is Q* = Mean Demand + z ∙ Standard Deviation of
Demand = 5,000 +
0.5244∙1,500 = 5,787 cards.
• Her expected performance metrics will be as follows:
o Expected lost sales. For z = 0.5244, the standard normal loss
function
value is L(z) = 0.1904. Thus, she should expect to lose sales of
L(z) ∙
Standard Deviation = 0.1904∙1,500 = 285.6 cards, on average,
due to
demand uncertainty.
o Expected sales. She should expect to sell Mean Demand –
Expected Lost
Sales = 5,000 – 285.6 = 4,714.4 cards on average.
o Expected leftover inventory. She should expect that an
inventory of Q*
– Expected Sales = 5,787 – 4,714.4 = 1,072.6 will be left over
at the end of
the season on average.
o Expected profit. Her total expected profit will be
Price∙Expected Sales –
Cost ∙ Inventory Level = $5 ∙ 4,714.4 – 1.5 ∙ 5,787 = $14,892.

Differentiated supply chain: Ms. Marks sells greeting cards to a
local arts and crafts
retailer at a wholesale price of $3.50 each, and the retailer then
sells them to customers for
$5 each. The retailer has the same demand forecast and decides
ahead of the season how
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May 2020.
many greeting cards to procure in order to maximize its own
expected profit. Any leftover
cards have no residual value. This type of contract is called the
wholesale price contract.
Let’s apply the same method we used for the centralized chain
to assess the
performance of this decentralized chain (Numbers might not
sum due to rounding):
• The newsvendor critical fractile for the retailer is (5 – 3.5)/5 =
0.3.
• This fractile corresponds to a z-score of – 0.5244 from the
standard normal
distribution.
• Thus, the optimal amount of inventory that the retailer would

order from
Ms. Marks for this season is Q* = Mean Demand + z ∙ Standard
Deviation of
Demand = 5000 – 0.5244 ∙ 1,500 = 4,213 cards.
• The expected performance metrics for Ms. Marks and for the
retailer will be
as follows:
o Ms. Marks makes a profit of $(3.50 – 1.50) ∙ 4213 = $8,426
because she
produces and sells 4,213 cards for $3.50 each and has a variable
production cost of $1.50 each.
o The retailer buys 4,213 cards but faces uncertain demand. We
need to
apply formulas from the newsvendor model to calculate its
expected
profit:
– 0.5244, the standard normal
loss
function value is L(z) = 0.7148. Thus, the retailer should expect
to lose sales of L(z) ∙ Standard Deviation = 0.7148∙1,500 =
1,072.2 cards on average.
etailer should expect to sell Mean
Demand
– Expected Lost Sales = 5,000 – 1,072.2 = 3,927.8 cards on
average.
an
inventory of Q* – Expected Sales = 4,213 – 3,927.8 = 285.2
will

be left over at the end of the season on average.
Sales –
Cost ∙ Inventory Level = $5∙3,927.8 – 3.5∙4,213 = $4,894.
o Total profit of the supply chain will be equal to $(8,426 +
4,894) =
$13,320.
Observe that the decentralized supply chain stocks fewer
greeting cards than the
centralized supply chain because the retailer’s risk-return trade-
off is worse than Ms.
Marks’s in the centralized supply chain. The wholesale price
contract has transferred the
entire risk of demand uncertainty to the retailer but not the
entire profit. In particular, the
retailer loses $3.50 on each card unsold and makes a profit of
$1.50 on each card sold,
whereas Ms. Marks was losing $1.50 on each card unsold and
making a profit of $3.50 on
each card sold.
The stocking quantity in the centralized supply chain is called
the first best solution
because it yields the highest possible expected profit. The
decentralized supply chain
makes lower total profit than the centralized supply chain. This
phenomenon, in which
the profit margin is split into two parts in the decentralized
chain and each party tries to
maximize its own profit, is called double marginalization.
Is there a particular wholesale price that would maximize the
expected profit for

ColorCraft in the decentralized supply chain? The answer is,
“Yes,” as shown in Figure 9.
As the wholesale price increases, Ms. Marks makes a higher
profit on every unit sold. But
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May 2020.
the retailer orders progressively fewer units because its margin
shrinks (Figure 10). The
net outcome of these two opposing forces is that there is an
optimal wholesale price that
maximizes the expected profit for ColorCraft. Figure 9 shows
that the optimal wholesale
price for Ms. Marks is about $4.20 per card.
Figure 9 Maximizing Profit in a Decentralized Supply Chain
Figure 9 also shows that the total profit of the decentralized
supply chain decreases as
the wholesale price increases. Recall that the supply chain
profit under the first best
solution was $14,892, which occurs when the wholesale price is
exactly equal to
ColorCraft’s production cost, because it induces the retailer to
order the first best
inventory quantity. As the wholesale price increases, the retailer

orders less. Thus, the
supply chain profit decreases. The supply chain profit at a
wholesale price of $4.20 is
$11,648. Figure 10 shows how the inventory stocking quantity
ordered by the retailer
decreases in the wholesale price.
Figure 10 Inventory Stocking Quantity vs. Wholesale Price
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May 2020.
The incentives of Ms. Marks and the retailer can be aligned by
redesigning the
contract between them to eliminate double marginalization and
achieve the first best total
expected profit. Table 4 describes the characteristics of some
common contract types.
Any of these except the wholesale price contract can coordinate
the ColorCraft supply
chain. But let’s consider how a buyback contract would work.
Table 4 Contract Characteristics
Contract
Type

Description Characteristics
Wholesale
Price
Contract
Supplier (upstream firm) offers a fixed
wholesale price w to retailer
(downstream firm).
Risk of demand uncertainty is borne
by retailer.
Simplest contract type; lowest
administration cost.
Buyback
Contract
Supplier sells each unit to the retailer
at a fixed wholesale price w. Retailer
returns unsold units to the supplier
and receives a buyback price b for
each unsold unit.
Risk of demand uncertainty is shared.
Used in book publishing and apparel
retailing industries.
It is not necessary that unsold units be
returned to supplier. Retailer may
salvage them and share the cost with
the supplier.
Revenue
Sharing
Contract

at a fixed wholesale price w. Retailer
gives a fixed fraction p of the total
revenue to the supplier.
Risk of demand uncertainty is shared.
Used for contracts between movie
studios and rental firms in the video
rental industry.
Quantity
Flexibility
Contract
at a fixed wholesale price w. Supplier
compensates the retailer for all its
losses on unsold inventory up to an
upper limit.
Retailer is fully protected from the risk
of demand uncertainty up to a limit.
Retailer bears the risk of demand
uncertainty above that limit.
Sales
Rebate
Contract
at a fixed wholesale price w. Supplier
gives a rebate r to the retailer for each
unit sold above a threshold t.

Retailer bears a higher proportion of
the risk of demand uncertainty for
demand below the threshold than for
demand above the threshold.
Useful when retailer can exert effort
to increase demand.
Quantity
Discount
Contract
Supplier offers the retailer a wholesale
price that is decreasing in the number
of units ordered by the retailer.
Retailer bears the risk of demand
uncertainty.
Cost of administering the contract is
low.
Suppose that Ms. Marks offers to buy back unsold cards from
the retailer for $2.86
each. The cost of production, wholesale price, and selling price
are the same as before.
The buyback price transfers a part of the risk of unsold
inventory from the retailer to Ms.
Marks, reducing the cost of unsold cards for the retailer. Thus,
the retailer’s optimal order
quantity increases. In fact, we have set the buyback price in
such a way that the
newsvendor critical fractile for the retailer becomes (5 –
3.50)/(5 – 2.86) = 0.7, the same as

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May 2020.
that for the centralized supply chain. Therefore, the retailer’s
optimal order quantity is
5,787 units.
Repeating the same computations as above, the retailer’s
expected profit is $6,382,
Ms. Marks’s profit is $8,510, and the total expected profit of
the decentralized supply
chain is $14,892. Thus, both parties’ expected profits increase,
and the supply chain
achieves the first best order quantity and expected profit. We
say that this supply chain is
coordinated.
Many combinations of wholesale price and buyback price can
achieve coordination.
For example, if the wholesale price is $3.00 and the buyback
price is $2.14, then the
retailer’s critical fractile is again 0.7, which leads to the first
best order quantity and first
best total supply chain profit. In fact, for any wholesale price
(w) between $1.50 and $5.00,
a buyback price (b) achieves coordination if it satisfies the
following condition:

5 1.50.7
5 0.7
w wb
b
− −= ⇒ =
−
Figure 11 shows combinations of buyback and wholesale prices.
Although they
achieve coordination (that is, they achieve 100% efficiency),
they split the pie differently
between Ms. Marks and the retailer. The higher the wholesale
price, the greater the share
of supply chain profits that accrues to Ms. Marks. Figure 12
illustrates this effect. She and
the retailer may bargain with each other on how to split the pie.
Figure 11 Optimal Buyback Price as a Function of Wholesale
Price
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May 2020.

Figure 12 Expected Profit as a Function of a Wholesale Price
Under
Coordinating Buyback Contracts
Interactive Illustration 2 enables you to see the effect of
different choices of
wholesale price and buyback price on the retailer’s inventory
order quantity, expected
sales, and split of profits between the supplier and retailer. First
set the buyback price to
zero in order to mimic the wholesale price contract. Vary the
wholesale price and observe
the effect on the order quantity and profits. Then fix the
wholesale price to any value and
vary the buyback price to see the effect on the order quantity
and the price. Note how
there are many combinations of wholesale price and buyback
price that coordinate the
channel, but allow profits to be split in different proportions
across the supplier and the
retailer.
Buyback contracts are used in many settings. For example,
luxury goods
manufacturers may prefer unsold merchandise to be returned
rather than sold at a
discount so that they control pricing and brand. Book publishers
take back unsold
merchandise so that it can be reallocated to other retailers or
sold at a future date.
Buyback doesn’t necessarily have to involve the return of
merchandise to the supplier.
The practice of “markdown money” described in Section 2.5 is
also equivalent to a
buyback contract. In this practice, excess inventory is marked
down and sold by the

retailer, but the cost of the markdown is shared with the
supplier.
The above example shows how a poor choice of contract or
price can reduce the
profits of both ColorCraft and the retailer. By choosing the
buyback contract and setting
prices appropriately, it’s possible to coordinate ColorCraft’s
supply chain. In practice,
contract design can be more complex because different firms
may not agree on the
forecast of demand, there is competition, prices vary over time,
and firms engage in sales
promotion or advertising to increase demand. The performance
of the contracts listed in
Table 4 is affected by these considerations. Moreover we must
keep in mind that
incentives can be misaligned for reasons other than contract
design, as we discussed
earlier. It’s equally important to address hidden information and
hidden action.
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May 2020.
Interactive Illustration 2 Buyback Pricing

The previous sections of this reading have looked at the
decisions managers face as they
manage supply chains that already exist. We now turn to the
decisions involved in supply
chain design, which involve the elements of physical
infrastructure, or footprint, we
discussed in Section 2.2.
Degree of Proximity to Customers
As they establish their own facilities or choose external supply
chain partners, firms
commonly must choose between locating close to the customer
and farther from the
customer—in a foreign country, perhaps. Proximity to the
customer shortens lead time,
which improves responsiveness and reduces inventory holding
costs. But it often results
in higher production costs because it limits a firm’s sources of
supply.
When proximity to the customer is not essential, the firm can
choose a location that
provides a lower production cost but may entail a longer lead
time and less
responsiveness. So, the location decision depends on differences
in production costs and
lead time and the extent of demand uncertainty. The following
example illustrates the
trade-off among these parameters.
Suppose that the per-unit cost for domestic production is cd and
for production in a
foreign country is cf, with cd > cf. The replenishment lead time
is Ld weeks for the domestic
location and Lf weeks for the foreign location, Ld < Lf. The
firm follows a weekly

2.7 Supply Chain Design
the interactive illustration: bit.ly/hbsp2unKwOL
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May 2020.
prod/interactives/014_buyback_pricing/Launch.html
http://bit.ly/hbsp2unKwOL
prod/interactives/014_buyback_pricing/Launch.html
Ld
Lf
the interactive illustration: bit.ly/hbsp2pHNTLD

to
May 2020.
prod/interactives/035_holding_costs_local_vs_faraway/Launch.
html
http://bit.ly/hbsp2pHNTLD
prod/interactives/035_holding_costs_local_vs_faraway/Launch.
html
locations is low, if demand uncertainty, σ, is high, if the
inventory holding cost, h, is high,
or if the targeted service level, z, is high. Interactive
Illustration 4 offers an intuitive way
to explore how holding costs, the cost of production, the in-
stock rate, and demand (and
variance in demand) affect the decision to produce in a
domestic or foreign location.
Observe from this interactive that for each combination of
costs, the sourcing decision
depends on the domestic and foreign lead times.
Interactive Illustration 4 When to Produce in a Foreign Location
Interactive Illustration 5 shows the computation of total costs
that are involved in
the comparison of domestic and foreign sourcing. It fixes the
mean weekly demand and

the domestic lead time, and allows you to see the cost effect of
changing any of the
remaining parameters. Besides procurement cost and holding
cost, we have also included
transportation cost, which is often expressed as a percent of
procurement cost. Therefore,
varying the transportation cost has the same type of effect as
varying procurement cost.
the interactive illustration: bit.ly/hbsp2IV9AQp
to
May 2020.
prod/interactives/036_when_to_produce_faraway/Launch.html
http://bit.ly/hbsp2IV9AQp
prod/interactives/036_when_to_produce_faraway/Launch.html
Interactive Illustration 5 Domestic vs. Foreign Sourcing
The above trade-off is modulated by additional factors, such as
supply chain risk. For
example, domestic production becomes more attractive in the
following situations:

• When the exchange rate is volatile and the cost attractiveness
of a foreign
sourcing facility is lower.
• When transportation cost rises or becomes more volatile. In
recent years, the
cost of fuel has risen; as a result, transportation has become
more expensive,
which has made domestic production more attractive.
• When products require a great deal of customization or have a
significant
service component.
• When a firm wants to maintain control over its intellectual
property.
To return to an earlier example, American Apparel finds it
beneficial to produce
domestically because it focuses primarily on knitwear, which
has a highly automated
manufacturing process that can be located in Los Angeles
despite high labor costs. In
contrast, Forever 21 is able to provide a broad assortment of
labor-intensive woven
garments, such as dress shirts and cashmeres, by locating in
low-cost countries.
During the 1990s, many firms sought low-cost production
locations in emerging
economies such as China. While this trend of offshoring
continued into the 2000s, it
slowed significantly because of the rising cost of labor in
China, higher transportation
costs, and increases in customization requirements in many
industries. This resulted in

reshoring by firms that sought locations closer to their
customers. Now, as demand grows
worldwide, many firms are deploying factories in emerging
markets as well as in
developed countries to serve local demand in each market.
the interactive illustration: bit.ly/hbsp2IXu8YA
to
May 2020.
prod/interactives/020_local_vs/Launch.html
http://bit.ly/hbsp2IXu8YA
prod/interactives/020_local_vs/Launch.html
Degree of Centralization
This decision regarding degree of centralization will determine
whether to have many
small facilities or one large one. A brick-and-mortar retail chain
has hundreds of stores
spread throughout its market to serve customers in different
regions. But the Internet
retailer Amazon.com had in its early years a single distribution
center to fulfill demand
received through its website from all over the United States. A

single, large location offers
benefits of economies of scale from two sources: lower
overhead costs, and the pooling of
demand uncertainty across many locations. In 1979, Gary Eppen
termed the second
source of benefit statistical economies of scale. 14
To illustrate this, suppose that a firm serves demand in N
identical regions through a
facility in each location. Each region has normally distributed
demand with mean μ and
standard deviation σ. The firm has identical costs of excess
inventory or shortage at each
facility and thus wishes to provide the same in-stock rate.e The
total inventory carried by
the firm is
( )μ σ μ σ+ = +N z N Nz
where z is the standard normal variable corresponding to the
firm’s target in-stock rate.
Now suppose that the firm decides to carry inventory at a single
centralized location,
similar to Amazon.com, and serve demand in all N regions from
that location. To keep
things simple, suppose that demand is independent across the N
regions—that is, there is
zero correlation between the demand in any two regions. (We
will later explore the effect
of correlation through interactive illustrations.) The total
demand at the centralized
location has
mean =

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