Operations Management
Roy D. Shapiro, Series Editor
READING + INTERACTIVE ILLUSTRATIONS
Supply Chain
Management
VISHAL GAUR
Cornell University
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Table of Contents
1 Introduction ................................................................................................................................................ 3
2 Essential Reading ...................................................................................................................................... 5
2.1 Types of Supply Chains ..................................................................................................................... 5
2.2 Types of Decisions in Supply Chains ............................................................................................... 8
2.3 Efficient or Responsive: A Framework for Supply Chain Strategy .............................................. 9
2.4 Improving Efficiency: The Bullwhip Effect .................................................................................... 12
2.4.1 Demand Forecast Updating .................................................................................................. 15
2.4.2 Order Batching....................................................................................................................... 16
2.4.3 Price Fluctuations ................................................................................................................. 17
2.4.4 Rationing and Shortage Gaming ......................................................................................... 17
2.4.5 Steps to Alleviate the Bullwhip Effect ................................................................................. 18
2.5 Improving Responsiveness ............................................................................................................. 22
2.5.1 Delayed Differentiation ......................................................................................................... 23
2.5.2 Read-React Capability ........................................................................................................... 25
2.6 Alignment of Incentives ................................................................................................................... 33
2.6.1 Why Incentives Become Misaligned.................................................................................... 34
2.6.2 Aligning Incentives for a Buyer and Supplier .................................................................... 35
2.7 Supply Chain Design ........................................................................................................................ 44
2.7.1 Degree of Proximity to Customers ...................................................................................... 44
2.7.2 Degree of Centralization ....................................................................................................... 49
2.7.3 Degree of Flexibility .............................................................................................................. 51
2.7.4 Degree of Outsourcing Production ..................................................................................... 55
3 Supplemental Reading ............................................................................................................................ 56
3.1 Supply Chains of the Future ............................................................................................................ 56
4 Key Terms ................................................................................................................................................. 63
5 For Further Reading................................................................................................................................. 64
6 Endnotes ................................................................................................................................................... 65
7 Index .......................................................................................................................................................... 68
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Vishal Gaur, Emerson Professor of Manufacturing Management, SC Johnson School of
Business, Cornell University, developed this Core Reading.
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1 INTRODUCTION
T
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 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 mid1980s 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.
Supply chains, like banks, are engines of the world economy. 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. Globalization and the growth of markets
worldwide have lengthened and fragmented supply chains, and the occurrence
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of global disruptors, including the COVID-19 pandemic, has exposed risks,
renewing the focus on supply chain resilience. Emerging environmental, societal,
and governance regulations require all organizations to take responsibility for
the entire life cycles of their products, wherever they may be in the supply chain.
Solutions to these challenges continue to emerge: the sharing of information and
the emergence of new technologies such as radio-frequency identification
(RFID) 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; the Internet has created new methods of selling and of configuring
supply chains, turning customers into savvy purchasers; and increasing supply
chain transparency has made it possible to deliver environmentally sustainable
products.
In the Essential Reading, we discuss the principles of supply chain
management in the context of these developments. We address questions such
as these:
•
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 design of an organization?
•
How will the supply chains of the future manage disruption risk and
comply with environmental regulations?
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. Sections 2.4 and 2.5 describe 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 2.6, we explain how the
incentives of organizations in a supply chain can be aligned to facilitate
collaboration and maximize total profits. Finally, in section 2.7 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 these risks now and in the future—a topic that has
gained visibility because of the impact of globalization, increased regulation, and
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greater awareness of the immediate and long-term stresses that natural
disasters and geopolitical shifts place on supply chains.
2 ESSENTIAL READING
2.1 Types of Supply Chains
Contrary to what the term suggests, a supply chain is usually a complex network.
The exhibits in this section 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 Exhibit 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.
a A variation on this kind of supply chain is when a small supplier has a single large customer.
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EXHIBIT 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 Exhibit 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 automotive manufacturers,
pharmaceutical companies, and consumer packaged goods manufacturers are
typically distribution supply chains. An assembly network, shown in Exhibit 3,
has many suppliers whose products are combined into one complex product in
the downstream stage. The procurement functions of a manufacturing
organization are typically assembly networks, as is the upstream supply chain of
a retailer that purchases different products from specialized manufacturers.
Supply chains with many suppliers and customers at each node are typically
more complex. However, such a supply chain is useful for reducing risk when a
buyer firm creates a portfolio of suppliers differentiated by cost, quality, or
responsiveness, or a seller firm creates a portfolio of customers differentiated by
demand pattern.
EXHIBIT 2 Distribution Supply Chain
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EXHIBIT 3 Assembly Network
Most actual supply chains are combinations of serial, distribution, and
assembly stages. 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 external suppliers and customers. Different
firms in an industry can differ in their supply chain configurations. For instance,
consider clothing retailers: Gildan Activewear’s manufacturing is vertically
integrated; it produces T-shirts and knitwear in its own factories, and sells to
wholesalers and retailers. Lululemon, in contrast, subcontracts manufacturing
with suppliers all over the world, and those products are shipped to its own
stores.1
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2.2 Types of Decisions in Supply Chains
The supply chain decisions of an organization affect its logistics costs, inventory
costs, labor costs, and environmental 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;
environmental costs are incurred due to emissions and waste at different stages
of 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 reliability
and speed of delivery to customers. 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 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, business
conditions, and cost information necessary for decision making in all these
functions. Managers in different functional roles possess different types of
operational information about the areas under their control, such as production,
ordering, inventory holding costs, demand received from downstream locations,
shipments from upstream locations, forecasts of future demand, and sales
promotion activities. S&OP shares this information and coordinates future plans
across all the functional areas. The key word in S&OP processes is planning; it
encompasses short-term plans for up to two weeks at the daily level, mediumterm plans for up to two years at the weekly and monthly level, and long-term
plans for up to five years.
Supply chain decisions can be operational or strategic. Operational 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 have short-term impact on an organization and are often taken on a
daily or weekly basis.2 Strategic supply chain decisions pertain to an
organization’s physical and its soft infrastructure, and have long-term impact. In
establishing its physical infrastructure, an organization chooses upstream and
downstream partners as well as 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
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to the locations of customers and suppliers such as costs, lead time, and risk of
disruption.
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 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 its alignment with 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 needed. 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.
2.3 Efficient or Responsive:
A Framework for Supply Chain Strategy
What should a supply chain do particularly well? As we have seen so far, an
organization faces myriad choices when designing its supply chain. The supply
chain strategy of an organization can be structured according to the
characteristics of its product.
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One framework for making these decisions classifies products as either
functional or innovative.3 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 steadily increased. In
response, 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, energyefficient 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 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.
Exhibit 4 compares the characteristics of physically efficient and marketresponsive supply chains. Because 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
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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.
EXHIBIT 4 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 reacting quickly 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, 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 product design, product
launch, and replenishment. Inditex, a Spanish retail conglomerate that owns the
Zara clothing brand, is an example of a 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.
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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 Exhibit 5, 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 marketresponsive 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 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.
EXHIBIT 5 Choosing Measures to Gauge Supply Chain Performance
Performance Measure
Physically
Efficient
Supply Chain
MarketResponsive
Supply Chain
Production Cost Per Unit
Logistics Cost Per Unit
Order Fill Rate
Capacity Utilization
Amount of Excess Inventory
Estimated Lost Sales
Various Lead Times, for example:
• from design to production
• from production to launch
• replenishment lead time
2.4 Improving Efficiency: The Bullwhip Effect
During the global response to the COVID-19 pandemic, supply chains were
caught flat-footed as decisions to cut costs and preserve cash early in the
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pandemic made it harder to fulfill orders when demand rebounded. This pattern
has occurred again and again during both recessions and growth periods. In fact,
Procter & Gamble (P&G) 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 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.
Amplification factor =
variance of orders placed by a location
variance of demand received by that location
Values of this ratio greater than 1 denote amplification; values of less than 1
denote attenuation. The higher the amplification, the more severe is the
bullwhip effect. Exhibit 6 illustrates the patterns in sales and orders that are
commonly due to the bullwhip effect.
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EXHIBIT 6 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 because a
shock to the supply chain takes a long time to dissipate. The semiconductor chip
shortage may have started in late 2020 due to the pandemic, but companies still
experienced effects into 2023, with impacts anticipated years later. Even shortterm disruptions can have long-term effects. When the ship Ever Given blocked
the Suez Canal for just seven days in March 2021, the supply chain consequences
did not abate until more than six months later.
The bullwhip effect is especially costly 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—and can affect a variety of supply
chains and industries. Even products with instantaneous information flows are
not insulated from the bullwhip effect. While digital products such as
entertainment and media require no physical inventory, these products still
require semiconductor chips, data centers, internet connectivity, and other
physical products (such as Alexa or Apple TV) for their delivery.
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. Four
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common factors in supply chains contribute to the bullwhip effect: demand
forecast updating, order batching, price fluctuations, and rationing and shortage
gaming.4
2.4.1 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, a firm also generally must project demand for a nonzero lead time.
The longer the lead time, the worse 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 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. This
situation was exacerbated during the COVID-19 pandemic as global shipping
lead times became both longer and more unpredictable.
EXHIBIT 7 Company Shipment Lead Time from Europe to the United States
April 2020
April 2021
Mean Lead Time
7.8 weeks
9.9 weeks
Standard Deviation of Lead Time
1.0 weeks
1.4 weeks
Source: Author’s study of a US-based consumer products manufacturer.
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Exhibit 7 presents an example of pandemic-related delays for one consumer
products manufacturer, which lost more than two weeks of mean lead time in
shipments from Europe to ports in New Jersey from 2020 to 2021. This leads to
uncertainty because of late shipments, but it also affects planning because
uncertainty of lead times also worsened. To see the effect of these changes,
suppose that lead time follows a normal distribution with the given mean and
standard deviation (SD). Let’s compute the probability that lead time would
exceed 8.8 weeks (the 7.8-week mean lean time +1 SD) based on the company’s
data from April 2020. This lead time corresponds to 1 SD above the mean, with a
low probability (16%) that the lead time would be longer than 8.8 weeks. By
April 2021, however, the expected 8.8-week lead time corresponds to 0.79 SD
below the mean and a revised 78% probability that lead time would exceed 8.8
weeks. As a result of scenarios like these, with lead times increasing worldwide
and compounding uncertainty across industries, the bullwhip effect worsened
considerably during the pandemic.
Any time-series forecasting method, such as exponential smoothing or moving
average, contributes to the bullwhip effect. However, the bullwhip effect can also
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).
2.4.2 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 details 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 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
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components trade off each other. As the order batch size increases, fixed
ordering cost decreases, but inventory holding cost increases. Thus, the
EOQ model states that this trade-off 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.
2.4.3 Price Fluctuations
Suppose that a sales department offers price discounts to its customers in order
to achieve sales targets and increase market share. Even as the sales department
achieves its targets, it also induces stockpiling behavior by customers, which
leads to volatility in future orders. As a result, it becomes harder to forecast and
fulfill demand, causing stockouts and further exacerbating uncertainty in the
supply chain. Thus, price discounts lead to a deterioration of the supply chain’s
performance and costs on the organization’s manufacturing and supply chain
functions.
Of course, price fluctuations can also be caused by external factors such as the
cost of ocean freight and geopolitical forces affecting commodities supply.
Regardless of the cause, when companies react to price changes by adjusting
their demand or supply, it can trigger a bullwhip effect in the supply chain.
2.4.4 Rationing and Shortage Gaming
At the peak of the dot-com bubble from 1995 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, and to keep up, 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 A similar situation occurred during the
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early phases of the COVID-19 pandemic when consumers, fearing shortages,
stocked up on toilet paper, disinfectant, and paper towels. Manufacturers
responded by increasing manufacturing capacity. But when demand declined
sharply in 2021, supply could not be adjusted as quickly, and manufacturers that
had increased capacity took a hit. This affected many large consumer products
manufacturers such as Clorox Company, Kimberly-Clark, and P&G.6
Anticipated demand commonly exceeds manufacturing capacity during the
launch of a successful new product (e.g., a new gaming console from Microsoft or
Nintendo, or 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 demand. This in
turn creates a feedback loop that exacerbates volatility in the supply chain. 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.
2.4.5 Steps to Alleviate the Bullwhip Effect
The four factors discussed above can be addressed by improving supply chain
coordination using three types of solutions: information sharing, channel
alignment, and operational efficiency (summarized in Exhibit 8).
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EXHIBIT 8 Preventing Avoidable Fluctuations
Demand Forecast
Updating
Information
Sharing
Channel
Alignment
Use electronic data
interchange (EDI)
Make centralized
ordering decisions
Collect rich inventory
data through RFID tags
Use vendor-managed
inventory (VMI)
Set up digital supply
chain control tower
Provide discount for
information sharing
Understand system
dynamics
Adopt a direct-toconsumer channel
Operational
Efficiency
Reduce lead time
Adopt echelon-based
inventory control
Use machine learning
methods (e.g.,
reinforcement learning) for
inventory management
Avoid multiple demand
forecasts
Order Batching
Use EDI
Mix pallet shipments
Use electronic ordering
aided by process
automation
Use cross-docking
Price Fluctuations
Rationing and
Shortage Gaming
Share sales, inventory,
and capacity data
Reduce the fixed cost of
ordering through EDI
Outsource logistics to
third-party logistics
and fourth-party
logistics (3PL, 4PL)
providers
Implement continuous
replenishment
Use everyday low price
(EDLP) strategy
Use everyday low cost
(EDLC) strategy
Implement activity-based
costing
Allocate inventory
based on past sales
Improving Demand Forecasts and Inventory Management
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). EDI is the computer-tocomputer sharing of business information such as orders, invoices, reports, and
forecasts from one company to another. EDI is generally one-to-one and
requires a standardized format to communicate data across different ERP
systems, which may be using different methods to represent data. Such
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.
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RFID has emerged as a technology 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 using sensors embedded with Internet of Things (IoT)
technology. The scanned data is uploaded to a cloud database and transmitted in
the supply chain via EDI. Thus, the exact location and condition of inventory
becomes known. For example, a retailer would know how much inventory of
different items is in each shipment, and a manufacturer would be able to trace
components and finished goods from source to destination. Manufacturers and
retailers can then use such information to anticipate future orders and plan their
respective inventories to reduce the bullwhip effect. They can also utilize this
information to provide traceability, aid in product recalls, manage perishable
goods and products in industries such as food and pharmaceuticals, and set up a
digital supply chain control tower.
EDI is just the foundation, however; it increases transparency and discipline,
but it doesn’t change the fact that organizations must still respond to orders
from downstream customers. 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 VMI; collaborative planning, forecasting, and replenishment
(CPFR); and continuous replenishment (CR). Those frameworks have been used
by many large brands, including Campbell Soup, Nestlé, M&M, P&G, Scott Paper,
and Unilever:
•
In VMI, a supplier has visibility of 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 lockstep
decisions.
•
CR involves monitoring point-of-sale data continuously and replenishing
products only for the sold amount as needed in real time.
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Note that there are commonalities across these three 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, because 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 the bullwhip effect. 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.
Significant advancement is being made in these solutions through innovations
such as cloud services and robotic process automation (RPA). Supply chain data
that is hosted on the cloud can be linked more easily across organizations. RPA
enables a company to automate the processes of ordering, invoice generation,
and payments processing.
Mitigating Effects of Order Batching
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 does help
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 logistics (3PL) and fourth-party logistics (4PL) providers such as
UPS, FedEx, and Fulfillment by Amazon so that full shipments can be replaced by
partially full shipments.
Finally, a supplier can redesign its supply chain and invest in manufacturing
technology to reduce fixed costs. It can locate its facilities close to the customer,
invest in flexible capacity, or implement just-in-time production. Those changes
in supply chain design enable the supplier to shift production at no cost from
one product to another so that producing small batches can be cost effective.
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Reducing Price Fluctuations
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 (EDLC), everyday low price (EDLP), and activity-based
costing (ABC) are commonly used for this purpose. These methods, along with
VMI, CPFR, and CR, are 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.
Avoiding Rationing and Shortage Gaming
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
based on the priority of customers. But many of those mechanisms do not solve
the problem because they do not induce buyers to report their requirements
truthfully to the capacity-constrained manufacturer.7 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
based on their past ordering behavior.
2.5 Improving Responsiveness
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, managers must rely
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on their judgment and experience to make forecasts. 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 times 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 has been able to respond quickly to changes in demand and
deliver “fast fashion.”8 Its supply chain has been so responsive that it has seen
total flow times of a product from design to store in 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 readreact capability.
2.5.1 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. Exhibit 9
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.
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EXHIBIT 9 A Manufacturing Process with Common Stages of Production and Points
of Differentiation
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 or semi-differentiated products, sometimes called vanilla
boxes,9 which are converted into finished products late in the process when the
firm can 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. An extreme
example of delayed differentiation is paint, where any color or hue desired by a
customer can be produced by mixing color pigments at the point of sale. In
apparel, a classic example 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.
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2.5.2 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. Exhibit
10 illustrates the timeline of activities in a firm with read-react capability. The
selling season is split into three parts:
•
Before the season starts, the firm positions inventory for the first part of
the season by relying on the forecasts of experts.
•
After observing demand during a read period in the early phase of the
season, the firm updates its demand forecast for the remaining season or
product life cycle. It then places a replenishment order, which arrives
after a short period of lead time.
•
The firm uses inventory from the replenishment order to serve demand
for the rest of the season.
EXHIBIT 10 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-position raw materials at suppliers to cut down procurement lead time, and
use 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 Obermeyer.10 Exhibit 11 illustrates the impact of readreact capability on forecast accuracy at 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 Obermeyer were to
plan inventory for the entire season based on these forecasts, it would bear
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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, Obermeyer developed reactive production capacity so that
it could take advantage of the more accurate in-season forecasts and thereby
increase its sales revenue and profit.
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EXHIBIT 11 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.
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Let’s illustrate the benefits of read-react capability through Interactive
Illustration 1. (We explain the computations for this interactive illustration
later in this discussion.) The interactive illustration 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 illustration. 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 Capability
To access the interactive illustration, click on the image or use this link. bit.ly/hbsp2ukeAL8
If the Interactive Illustration is not accessible, please see the PDF explanation of the Interactive here.
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
b Actual computations will be more complex and will have to be done through simulation or
computational software packages.
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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:
⬚
√6002 + 2,4002 + 2 ∙ 0.5 ∙ 600 ∙ 2,400 = 2,750
With this demand estimate, the non-read-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 occur due to randomness of demand. The interactive
illustration 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 non-read-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. 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). Interactive Illustration 1 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
non-read-react firm. Vary the 29 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 is
the percentage increase in profit.
Now let us follow the details of the computations in the 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
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newsvendor critical fractilec for the above values of price, cost, and salvage value
is
10 − 5
5
=
10 − 4
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 Q* that
maximizes the expected profit of the firm, given the above critical fractile and
demand distribution, is:
Mean demand + z ∙ standard deviation of demand =
10,000 + 0.967 ∙ 2,750 =
12,660 units
This gives the following performance characteristics and their values
(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 243.9 units, on average, because of demand
uncertainty.
L(z) ∙ standard deviation = 0.0887 × 2,750 = 243.9 units
•
Expected sales: The firm should expect, on average, to sell 9,756.1
units of the product.
Mean demand − expected lost sales = 10,000 − 243.9 = 9,756.1 units
•
Expected leftover inventory: The firm should expect that an inventory
of 2,903.9 units will be left over at the end of the season, on average.
Q* − expected sales = 12,660 − 9,756.1 = 2,903.9 units
c See Core Reading: Managing Inventory (HBP No. 8016) for an in-depth description of the
newsvendor model (a method to optimize the order quantity for uncertain demand) and the
newsvendor critical fractile.
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•
Expected profit: These values will yield a total expected profit of
$45,876.60.
Price × expected sales + salvage value × expected leftover inventory
– cost ∙ order quantity =
$10 × 9756.1 + 4 × 2903.9 – 5 × 12,660 = $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 + 2X + 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. Net profits in retailing
are typically 1% to 5% of sales so this increase is substantial.
There are several explanations for an increase in profit, stemming 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
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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  between demand during the two parts. You observed the effect of
varying  on the average profit in Interactive Illustration 1. In Exhibit 12, we
depict this effect by varying  while keeping X and Y fixed. The exhibit shows the
percentage increase in profit obtained from read-react capability compared to
the base case for different values of . Observe that there is an increase in profit
even when  = 0; that is, when the demand during the first period conveys no
information about demand during the second period. This increase is due to the
first two reasons described above: splitting the season into two parts reduces
inventory requirements and enables the firm to respond to demand volatility. As
 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.
EXHIBIT 12 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. And because 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
did not include real-world complications and circumstances. For an effective
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real-life implementation, our example must be refined to incorporate scenarios
such as these:
•
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 the readreact timeline in Exhibit 10. 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 Great Recession of 2007–2009, 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 difficulty of scheduling shipment
containers, labor, and factories at the last minute and the increased risk
of demand uncertainty.11
•
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.
2.6 Alignment of Incentives
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 following the development of P&G’s relationship
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with Walmart from 1987 to 2000.12 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.”13 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.
2.6.1 Why Incentives Become Misaligned
Misalignment of incentives in a supply chain can be traced to three possible
causes.14 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 other is 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 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 based on 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
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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, which is 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 and 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(s) by which compliance will be
established.15
2.6.2 Aligning Incentives for a Buyer and Supplier
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. The seller may be held liable for a penalty, for example, 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
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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 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 conflict, 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. 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.
Penelope 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 she can focus her staff members on
production quality. Let’s compare these two options to determine the best one
for Marks.
Option 1: Vertically Integrated Supply Chain
Based on tools provided by a local microbusiness MBA student club, Marks uses
the newsvendor model to determine the optimal inventory to maximize her
expected profit (numbers might not sum due to rounding):16
•
The newsvendor critical fractile for ColorCraft’s 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 (Q*) that she would produce for
this season is
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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:
•
Expected lost sales. For z = 0.5244, the standard normal loss function
value is L(z) = 0.1904. Thus, Marks should expect to lose sales of L(z) ×
standard deviation = 0.1904 × 1,500 = 285.6 cards, on average, due to
demand uncertainty.
•
Expected sales. Marks should expect to sell mean demand – expected lost
sales = 5,000 − 285.6 = 4,714.4 cards on average.
•
Expected leftover inventory. Marks 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.
•
Expected profit. Her total expected profit will be price × expected sales –
cost × inventory level = $5 × 4,714.4 − 1.5 × 5,787 = $14,892.
Option 2: Decentralized Supply Chain
ColorCraft 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
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
ColorCraft for this season is Q* = mean demand + z × standard deviation of
demand = 5,000 – 0.5244 × 1,500 = 4,213 cards.
The expected performance metrics for ColorCraft and for the retailer will be as
follows:
• ColorCraft makes a profit of $(3.50 know 1.50) × 4,213 = $8,426 because it
produces and sells 4,213 cards for $3.50 each and has a variable production
cost of $1.50 each.
• The retailer buys 4,213 cards but faces uncertain demand. We need to apply
formulas from the newsvendor model to calculate its expected profit:
o Expected lost sales. For z = −0.5244, the standard normal loss
function value is L(z) = 0.7148. Thus, the retailer should expect to lose
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•
sales of L(z) ∙ standard deviation = 0.7148 × 1,500 = 1,072.2 cards on
average.
o Expected sales. The retailer should expect to sell mean demand −
expected lost sales = 5,000 − 1,072.2 = 3,927.8 cards on average.
o Expected leftover inventory. The retailer should expect that 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.
o Expected profit. The retailer’s expected profit will be $4,894.
Price ∙ expected sales − cost × inventory level =
$5 ∙ 3,927.8 − 3.5 × 4,213 = $4,894.
The total profit of the supply chain will be $(8,426 + 4,894) = $13,320.
Observe that the decentralized supply chain stocks fewer greeting cards than
the centralized supply chain in Option 1 because the retailer’s risk-return tradeoff is worse than ColorCraft’s in the centralized supply chain. The wholesale
price contract has transferred the entire risk of demand uncertainty, but not the
entire profit, to the retailer. In particular, the retailer loses $3.50 on each card
unsold and makes a profit of $1.50 on each card sold, whereas 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
Exhibit 13. As the wholesale price increases, ColorCraft makes a higher profit on
every unit sold—but the retailer orders progressively fewer units because its
margin shrinks, as shown in Exhibit 14. The net outcome of these two opposing
forces is that there is an optimal wholesale price that maximizes the expected
profit for ColorCraft. Exhibit 13 shows that the optimal wholesale price for
ColorCraft is about $4.20 per card.
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EXHIBIT 13 Maximizing Profit in a Decentralized Supply Chain
Exhibit 13 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. Exhibit 14 shows
how the inventory stocking quantity ordered by the retailer decreases as the
wholesale price increases.
EXHIBIT 14 Inventory Stocking Quantity Versus Wholesale Price
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Contract Options and Incentives
The incentives of ColorCraft and the retailer can be aligned by redesigning the
contract between them to eliminate double marginalization and achieve the first
best total expected profit. Exhibit 15 describes the characteristics of some
common contract types.
Any of the contract types in Exhibit 15 except the wholesale price contract can
coordinate the ColorCraft supply chain. But let’s consider how a buyback
contract would work. Suppose that 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 ColorCraft, 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 so that the newsvendor critical fractile for
the retailer becomes (5 − 3.50)/(5 − 2.86) = 0.7, the same as that for the
centralized supply chain. Therefore, the retailer’s optimal order quantity is
5,787 units.
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EXHIBIT 15 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.
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.
Revenue Sharing
Contract
Supplier sells each unit to
the retailer 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.
Quantity Flexibility
Contract
Supplier sells each unit to
the retailer 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.
Supplier sells each unit to
the retailer 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.
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.
Sales Rebate
Contract
Quantity Discount
Contract
Simplest contract type; lowest administration cost.
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.
Used for contracts between movie studios and
rental firms in the video rental industry.
Retailer bears the risk of demand uncertainty above
that limit.
Useful when retailer can exert effort to increase
demand.
Cost of administering the contract is low.
Repeating the same computations as above, the retailer’s expected profit is
$6,382, ColorCraft’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.
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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−𝑤
𝑤 − 1.5
= 0.7 ⇒ 𝑏 =
5−𝑏
0.7
Exhibit 16 shows the combinations of buyback and wholesale prices that
achieve the first best order quantity (i.e., maximizes the total expected supply
chain profit).
Although this achieves coordination (that is, 100% efficiency for the total
supply chain), ColorCraft and the retailer will split the pie differently. Exhibit 17
illustrates this effect. The higher the wholesale price, the greater is the share of
supply chain profits that accrues to ColorCraft. Marks and the retailer may
bargain with each other on how to split the pie.
EXHIBIT 16 Optimal Buyback Price as a Function of Wholesale Price
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EXHIBIT 17 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 between the supplier and the retailer.
Buyback contracts are used in many settings by both brick-and-mortar and ecommerce vendors. 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 2.6 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 is 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.
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The performance of the contracts listed in Exhibit 15 is affected by these
considerations. We must also keep in mind that incentives can be misaligned for
reasons other than contract design, as discussed earlier. It is equally important
to address hidden information and hidden action.
INTERACTIVE ILLUSTRATION 2 Buyback Pricing
To access the interactive illustration, click on the image or use this link. bit.ly/hbsp2unKwOL
If the Interactive Illustration is not accessible, please see the PDF explanation of the Interactive here.
2.7 Supply Chain Design
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 decisions
involved in supply chain design and the elements of physical infrastructure that
we discussed in section 2.2.
2.7.1 Degree of Proximity to Customers
When establishing their own facilities or choosing 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
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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. Thus, 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, and Ld <
Lf. The firm follows a weekly replenishment cycle, and the weekly demandd is
normally distributed with mean  and standard deviation . The total weekly
cost of sourcing from the domestic location is
𝑐𝑑 𝜇 + ℎ𝑧𝜎√𝐿𝑑 + 1
Here, the first term denotes the cost of procurement and the second term
denotes the cost of holding inventory. The second term is a product of the
inventory holding cost (h) per unit per week with the average amount of
inventory carried by the firm. The amount of inventory depends on the lead time
(with one week added for the weekly review cycle), the mean demand, the
standard deviation of demand, and the amount of service level desired (z).
Similarly, the total weekly cost of sourcing from the foreign location is
c f  + hz L f + 1
Interactive Illustration 3 compares the cost of holding inventory across
domestic and foreign locations. The domestic lead time (Ld) is fixed at one week.
All other parameters—the foreign lead time (Lf); the standard deviation of
demand (σ); the inventory holding cost per unit (h); and the in-stock rate N(z),
where N is the cumulative normal distribution—are variable. Try different
values of these parameters and observe the effect on the cost of holding
inventory.
d We use normal distribution for demand throughout this reading because it leads to formulas that
are easier to conceptualize. While normal distribution applies to high-volume demand items, other
probability distributions, such as Poisson or Lognormal, are appropriate for low-volume or slowmoving items. The concepts described in the reading apply qualitatively to those distributions, as
well.
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INTERACTIVE ILLUSTRATION 3 Holding Costs for Domestic Versus Foreign
Sourcing
To access the interactive illustration, click on the image or use this link. bit.ly/hbsp2pHNTLD
If the Interactive Illustration is not accessible, please see the PDF explanation of the Interactive here.
Note that the foreign location provides a lower cost of procurement, whereas
the domestic location provides a lower cost of holding inventory. Thus, it is more
attractive to produce in the domestic location if
•
the cost difference between the domestic and foreign locations is low
•
demand uncertainty, , is high
•
the inventory holding cost, h, is high
•
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 illustration that, for each combination of costs, the
sourcing decision depends on the domestic and foreign lead times.
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INTERACTIVE ILLUSTRATION 4 When to Produce in a Foreign Location
To access the interactive illustration, click on the image or use this link. bit.ly/hbsp2IV9AQp
If the Interactive Illustration is not accessible, please see the PDF explanation of the Interactive here.
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 percentage of procurement cost. Therefore, varying the transportation cost
has the same type of effect as varying the procurement cost.
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INTERACTIVE ILLUSTRATION 5 Domestic Versus Foreign Sourcing
To access the interactive illustration, click on the image or use this link. bit.ly/hbsp2IXu8YA
If the Interactive Illustration is not accessible, please see the PDF explanation of the Interactive here.
The trade-off between transportation cost and procurement cost 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. Whenever
the cost of fuel rises and transportation becomes more expensive,
domestic production becomes a more attractive option.
•
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.
•
When trade tariffs offset the lower cost of foreign manufacturing and
increase the attractiveness of onshoring and nearshoring.
During the 1990s, many firms sought low-cost production locations, such as
China. While offshoring trends continued into the 2000s, it slowed significantly
because of the rising cost of labor in China, higher transportation costs, and
increased customization requirements in many industries. Instead, as demand
grew worldwide with the growth of emerging markets, many firms shifted
production to factories in emerging markets as well as in developed countries to
serve local demand in each market. By the early 2020s, the attractiveness of
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global trade had suffered due to supply chain challenges such as pandemic
response, natural disasters, and geopolitical conflict, and reflected the types of
shifts that prompt firms to rethink their supply chain strategies. The result was
increased reshoring or nearshoring by firms to seek locations closer to their
customers. (For more on onshoring versus offshoring, see Core Reading:
Strategic Sourcing [HBS No. 8037].)
2.7.2 Degree of Centralization
A decision regarding the degree of centralization determines whether a firm will
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. In contrast, in its early years, Amazon had 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.17
To illustrate this concept, suppose that a firm serves demand in N identical
regions through a facility in each location. Each region has normally distributed
demand with mean  and standard deviation . 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
𝑁(𝜇 + 𝑧𝜎) = 𝑁𝜇 + 𝑁𝑧𝜎
where z is the standard normal variable corresponding to the firm’s target instock rate.
Now suppose that the firm decides to carry inventory at a single centralized
location, similar to Amazon, 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 = 𝑁𝜇
e In-stock rate is the probability that the entirety of a customer’s request can be fulfilled immediately
from stock. Firms typically have a target in-stock rate that corresponds to their business strategy.
For example, a firm that seeks to deliver a very high level of service would have a target in-stock
rate over 90%. On the other hand, a firm whose strategy is to deliver the lowest-cost service might
target its in-stock rate nearer 70%. In-stock rate is explained in more detail in Core Reading:
Managing Inventory (HBP No. 8016).
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Standard deviation = 𝜎√𝑁
This tells us that the total inventory carried by the firm at the centralized
location is
𝑁𝜇 + 𝑧𝜎√𝑁
Note that the effect of demand uncertainty now grows in the square root of
the number of locations, N, whereas it grew linearly in N when the firm had
many locations. Thus, aggregating demand at a central location enables the firm
to reduce the cost of demand uncertainty. This statistical economy of scale can
be substantial in many situations. It enables Amazon to sell slow-moving items
whose demand is so small and uncertain that they could not be sold profitably in
a brick-and-mortar chain.
Interactive Illustration 6 enables you to visualize our comparison between
decentralized and centralized supply chains. Mean demand across the entire
geographical region is fixed. Vary the number of facilities, N, used to serve this
demand. What effect does this have on the total inventory required? In a similar
way, vary the standard deviation of demand and the in-stock rate to see how the
difference in inventories between the decentralized and centralized supply
chains increases. Interactive Illustration 6 also allows you to vary the degree to
which demand is correlated across facilities. The less positively correlated the
demand, the greater the benefit of centralization.
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INTERACTIVE ILLUSTRATION 6 Statistical Economies of Scale
To access the interactive illustration, click on the image or use this link. bit.ly/hbsp2DYwNxf
If the Interactive Illustration is not accessible, please see the PDF explanation of the Interactive here.
The benefit of statistical economies of scale increases when there is a negative
correlation in demand across locations—that is, when high demand at one
location is likely to be offset by low demand at another. On the other hand, it
decreases when demand is positively correlated across locations.
While centralization provides the advantages of statistical economies of scale
and reduced overheads, there are some costs that must be taken into account.
First, the larger the facility, the more complex it is to manage. This complexity
adds costs that erode economies of scale. Second, when a firm serves demand in
a large region from just one central location, its transportation costs can be
prohibitive, and it may not be able to respond quickly to customer requirements.
Therefore, firms sometimes set up multiple facilities as they expand. Retailers,
for example, add warehouses and distribution centers as their store network
expands. Amazon grew from one fulfillment center in 1997 to eight in 1999, and
by 2022 had 175 fulfillment centers, with more than 100 in the United States.18
2.7.3 Degree of Flexibility
Automobile plants typically produce two or more models of cars on a single
assembly line. Amazon follows a similar rule for shipping books and other
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products bought on its website. Large retailers, such as Nordstrom and Gap, ship
products from one store to another when a stockout occurs. In general, when an
organization has facilities or supply chain partners in many locations, it must
decide whether to dedicate each one to a particular product or customer region,
or to develop the flexibility to serve more than one product or region from each
location.
The ability to meet a customer’s demand from multiple locations enables an
organization to manage demand uncertainty with less capacity. For instance,
consider the scenarios shown in Exhibits 18 and 19. The firm in this example
manufactures two products, A and B, in two plants, P1 and P2. P1 has capacity
K1, and P2 has capacity K2. A and B are substitutes; a customer will buy either A
or B but not both (as with car models).
EXHIBIT 18 Scenario 1: Dedicated Supply Chains for Two Products
EXHIBIT 19 Scenario 2: Flexible Supply Chains for Two Products
In Exhibit 18, manufacturing plant P1 is dedicated to Product A, and plant P2
is dedicated to Product B. In Exhibit 19, both plants can produce both products.
Exhibit 20 shows the combinations of demand for Products A and B that can be
met in the two scenarios.
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In the dedicated supply chain in Exhibit 18, demand for Product A can be met
up to capacity K1 and for Product B up to capacity K2. The resulting sales of the
two products are represented by the part of the graph shaded gray in Exhibit 20.
In Exhibit 19, additional demand for Product A can be met from plant P2
whenever there is low demand for Product B but high demand for Product A.
Likewise, additional demand for Product B can be met from plant P1. These
additional sales are shown by the parts of the graph shaded blue and green in
Exhibit 20. Thus, the firm can achieve higher sales with the same level of
capacity when that capacity is flexible.
EXHIBIT 20 How Flexibility Increases Capacity
Flexibility comes in several varieties:
•
Capacity: The above example illustrates capacity flexibility, where
one plant can produce more than one product type by incurring
negligible switching costs.
•
Logistics: If A and B were two customer regions (rather than two
products), and P1 and P2 were different sourcing locations that could
serve both regions, then the firm would have logistical flexibility, or
dynamic routing, where the firm would choose when to serve a
customer region from location A or B depending on the availability of
the product at the two locations.
•
Lead time: If a firm can compress its lead time, it can respond to
shocks in demand while carrying less inventory. This type of
flexibility, which we discussed in section 2.5.2, is called read-react.
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•
Differentiation: Delayed differentiation, or postponement (described
in section 2.5.1), which allows a firm to produce the most stripped
down or minimally customized so-called vanilla box products through
common activities and to delay differentiation into customer- or
market-specific products as late as possible. This capability requires a
combination of lead-time reduction, capacity flexibility, and product
redesign.
•
Suppliers: A firm may achieve flexibility in its supply base by
nurturing a portfolio of suppliers. This strategy of multisourcing is
advantageous in reducing the risk of supply disruptions or other
uncertainties in product supply.
Flexibility is expensive. Depending on the type, it may require investments in
the design of products, in the technology used in production and warehousing
facilities, in transportation, and in cross-training so workers can switch
efficiently from one product to another. It may also require flexibility from
suppliers so that different products and components can be shipped to a facility
depending on its production mix. And achieving flexibility may depend on soft
infrastructure, such as scheduling systems that enable a firm to change
production and sourcing decisions as needed.
The good news is that a firm does not need all its resources to be fully flexible.
In a landmark research paper on capacity flexibility, William Jordan and Stephen
Graves showed that most of the benefit can be obtained by linking locations with
products in a chain so that each location can produce two products.19 Exhibit 21
depicts such a supply network.
EXHIBIT 21 A Shortcut to Capacity Flexibility
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If a multinational corporation can switch production from one country to
another in response to fluctuations in exchange rates, prices, and labor costs,
then this flexibility can be valued as a real option on the assets of the firm. The
firm can then get more from maximizing its flexibility than from hedging its
exchange rate risk through financial contracts.20
2.7.4 Degree of Outsourcing Production
Another decision involved in supply chain design is the degree of outsourcing,
often called the make-buy decision. This involves determining whether to
produce in-house or to outsource production to a third-party supplier. It
depends on cost considerations and ease of coordination.f On the one hand, inhouse production provides the following advantages:
•
Better integration between design and production.
•
Control over a centralized supply chain.
•
The ability to capture the profits of intermediaries.
On the other hand, outsourcing provides these advantages:
•
Specialization and access to advanced technology for complex
products.
•
Flexibility of product and volume.
•
Greater variety at lower capital investment.
The decisions described in this section establish the physical infrastructure of
the supply chain. To make these decisions, a firm must determine the
attractiveness of potential locations relative to their costs, risks, demand
projections, and availability of supply. It must also construct long-term
projections of cost competitiveness and risks. To assess the value of various
kinds of flexibility, a firm should simulate uncertainty in demand and supply and
evaluate how flexibility will be deployed. These inputs can then be used to
configure the firm’s supply chain.21
f See Core Curriculum: Strategic Sourcing (HBP No. 8037) for much more on this topic.
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3 SUPPLEMENTAL READING
3.1 Supply Chains of the Future
As an engine of global economies, supply chains must be efficient today, but they
must also anticipate future disruptions and satisfy the environmental, social, and
governance (ESG) requirements ahead. Technology and advanced analytics are
increasingly being applied to achieve these goals.
Consider some of the past events that continue to shape supply chain design
and risk management today:
•
Natural disasters: When a magnitude 9.0 earthquake struck
northeastern Japan on March 11, 2011, it caused a tsunami that
devastated the region, including damage to the Fukushima nuclear
power plant. When Renesas, a company that produced
microcontroller chips in a factory north of Tokyo, was forced to close
its plants in the area, the impact was felt by car manufacturers as far
away as Detroit, which sourced parts from various companies that in
turn sourced chips from Renesas. Similarly, the 1999 Taiwan Chi-Chi
earthquake affected the supply of semiconductors and disrupted
mobile-phone manufacturers all over the world.
•
Vendor compliance problems: In 2012, outside audits of Hon Hai
Precision Industry, a supplier of iPod, iPad, and iPhone devices to
Apple, faulted the supplier for breaches of work, health, safety, and
environmental regulations in the midst of large-scale labor unrest.
Apple, which conducts annual supplier responsibility audits,
responded by increasing the scrutiny of its suppliers’ labor and
environmental practices and inviting third-party organizations to
audit their facilities. Similar concerns as well as other issues related
to vendors’ codes of conduct, adulteration (contaminated, counterfeit,
or substandard materials), and the use of hazardous chemicals were
discovered and addressed with many suppliers.22 A decade later,
Apple’s supplier responsibility report provides statistics on labor and
human rights, health and safety, education, and environment for 808
assessments of suppliers across more than 50 countries.23
•
Global economic recession: The COVID-19 pandemic triggered an
economic recession that sent supply chains around the globe into
turmoil. It caused severe supply shortages, including limits on
semiconductor chips, which led to long-lasting effects, as well as a
rush for medical and protective equipment.24 Similar problems have
occurred in previous recessions, whenever a cascade of credit
restrictions and financial constraints affect suppliers and customers.
For example, during the Great Recession of 2007–2009, Ariens, a
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maker of mowers and snowblowers in Wisconsin, faced a problem
when an engine supplier announced at the peak of the season that it
was halting production within 60 days because of a host of issues,
including the loss of a huge customer that represented 40% of its
business.25
•
Accidents: On March 23, 2021, one of the world’s largest ships, Ever
Given, got stuck in the Suez Canal, blocking one of the world’s busiest
shipping routes for six days.26 At the time, the ship was carrying
18,000 containers and weighed 220,000 tons. The backlog created by
the Ever Given’s grounding delayed ships in the canal for days and
created a domino effect of disrupted global supply chains for months.
It was difficult for companies to assess their own risk exposure
because of the complexity of the problem.27
Awareness of supply chain risk has increased not only because of natural
disasters, economic shifts, and geopolitical disruptions but also because of the
requirement that publicly traded US firms must report the types of supply chain
risks they face to the Securities and Exchange Commission (SEC). Clearly, supply
chains must be managed not only to improve the profits of all stakeholders but
also to mitigate their risks. It is easier for managers to deal with those risks that
occur within their organizations (and are therefore under their control) than
with external risks, such as those described at the beginning of this
Supplemental Reading.
Rare large-scale events—natural disasters such as an earthquake, a flood, or a
tsunami; accidents such as a fire in a factory or a warehouse; geopolitical risk
such as a trade embargo; worker strikes; acts of terrorism—can suddenly
disrupt supply chains by disabling supply locations or transportation links.
Supply chain risk can also arise gradually from sources such as global economic
conditions, volatility in commodity markets, and procurement strategies. For
example, the Great Recession of 2007–2009 dampened demand in many
industries, made it harder for companies to obtain financing, and put companies
out of business. In recent years, the increasing cost of fuel, cotton, and metals
has affected the cost structures of companies that depend on those commodities.
The World Economic Forum’s Global Risks Report 2023, a survey of over 1,200
experts in diverse areas, identified 32 types of global risk, classified into five
categories: economic, environmental, geopolitical, societal, and technological
(see Exhibit 22).28
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EXHIBIT 22 Ranking of Short-Term and Long-Term Global Risks Identified in the
World Economic Forum’s Global Risks Report 2023
Global Risks
Societal
Short-Term
Long-Term
1 Cost-of-living crisis
5 Large-scale involuntary migration
5 Erosion of social cohesion and
societal polarization
7 Erosion of social cohesion and societal
polarization
10 Large-scale involuntary migration
11 Misinformation and disinformation
16 Misinformation and disinformation
15 Cost-of-living crises
20 Infectious diseases
20 Chronic diseases and health conditions
23 Employment crises
Environmental
2 Natural disasters and extreme weather
events
4 Failure to mitigate climate change
Geopolitical
Technological
2 Failure of climate-change adaption
6 Large-scale involuntary migration
3 Natural disasters and extreme weather
events
7 Failure of climate-change adaption
4 Biodiversity loss and ecosystem collapse
9 Natural resource crises
5 Natural resource crises
18 Biodiversity loss and ecosystem collapse
10 Large-scale environmental damage incidents
3 Geoeconomic confrontation
9 Geoeconomic confrontation
14 Interstate conflict
12 Ineffectiveness of multilateral institutions
and international
15 Ineffectiveness of multilateral institutions
and international
Economic
1 Failure to mitigate climate change
13 Interstate conflict
21 Use of weapons of mass destruction
22 State collapse or severe instability
25 State collapse or severe instability
28 Use of weapons of mass destruction
30 Terrorist attacks
32 Terrorist attacks
11 Debt crises
14 Debt crises
12 Failure to stabilize price trajectories
19 Failure to stabilize price trajectories
13 Prolonged economic downturns
21 Prolonged economic downturns
17 Collapse of a systemically important
industry or supply chain
24 Collapse of a systemically important
industry or supply chain
22 Asset bubble bursts
29 Proliferation of illicit economic activity
28 Proliferation of illicit economic activity
31 Asset bubble bursts
8 Widespread cybercrime and cyber insecurity
8 Widespread cybercrime and cyber insecurity
24 Breakdown of critical information
infrastructure
16 Breakdown of critical information
infrastructure
29 Digital power concentration
17 Digital power concentration
31 Digital inequality and lack of access to digital
services
18 Adverse outcomes of frontier technology
32 Adverse outcomes of frontier technology
30 Digital inequality and lack of access to digital
services
Source: Adapted from World Economic Forum, “The Global Risks Report 2023, 18th Edition,” Figure E: Global risks ranked by
severity, p. 11, https://www3.weforum.org/docs/WEF_Global_Risks_Report_2023.pdf, accessed February 2023.
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In the past two decades, supply chains have become more efficient through
globalization, use of lean production principles, and specialization of tasks. This
efficiency has increased global wealth, but it has also increased the vulnerability
of all organizations in a supply chain. To exploit economies of scale, critical
inputs to an industry are now often produced in a single large facility, and
transportation is often handled through a single high-volume link, such as a
specific sea route or set of ports. And to cut costs, organizations have reduced
redundancy, waste, and inventories. As a result of both efficiency measures, the
effects of a disruption because of natural disasters and global economic
conditions are felt by distant organizations.
Regardless of the cause, supply chain disruptions can be extremely expensive.
They can lead to cost escalation, loss of revenue, loss of future business, or even
complete shutdown. And for public companies, just the announcement of a
supply chain disruption can lead immediately to large declines in stock price as
well as in profits the year of the announcement and beyond.29
The resilience of an organization in dealing with supply chain risk depends on
the structure and characteristics of its supply chain. Factors that can increase
the vulnerability of supply chain networks include the rising cost of living, labor
unrest, energy shortages, geopolitical uncertainty, and extreme weather .30 Let’s
consider several characteristics of supply chains, the risks associated with them,
and ways to mitigate those risks.
1. Single sourcing and specialization. As we have noted, lean production
and the focus on efficiency has made supply chains more vulnerable to
disruptions. To assess this risk, organizations should first and foremost
increase transparency throughout the supply chain. After its engine
supplier halted production, for example, the snowblower manufacturer
Ariens began conducting financial reviews of all its suppliers and
developing alternative sources. Organizations can decrease their reliance
on single sourcing by building redundancy and flexibility into their
supply chains. (We discussed types of flexibility in supply chains in
section 2.7 of this reading.) Some firms carry several months of
inventory for important raw materials and components to mitigate the
risk of supply chain disruption. For example, Emerson Electric carries
seven to eight months of inventory for critical parts, parts for which the
company does not have an alternative source. The company decided on
this amount using cost/benefit evaluations and its projection of how
long it would take to restore supply in the event of a disruption.
2. Extent of coordination with suppliers and customers. Organizations
can strengthen their relationships with suppliers and customers in order
to develop capabilities to recover quickly from a disruption. For
example, in February 1997, the only factory supplying brake fluid
proportioning valves to Toyota’s 20 automobile plants in Japan suffered
a major fire. This forced Toyota to shut down production in the plants,
which operated on just-in-time inventories of about four hours, and
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hundreds of tiered suppliers ground to a halt. The plants reopened
within two days of this accident, however, and Toyota recovered to full
production soon thereafter. This remarkable recovery was possible
because a large number of firms from both within and outside the
Toyota group immediately responded in a self-organized effort. Within
days, firms with no prior experience with these valves began
manufacturing them. Thus, the Toyota group was able to minimize
damage because of its long-term cooperative relationships with
suppliers.31
Consider also a fire in March 2000 at a Royal Philips Electronics radio
frequency chip manufacturing plant in Albuquerque, New Mexico.
Although plant personnel extinguished the fire within 10 minutes,
cleanup took several weeks and production of several million chips was
affected. At that time, two mobile phone manufacturers, Nokia and
Ericsson, accounted for 40% of the plant’s shipments. Nokia responded
to the disruption by intensifying its communication with Philips and
setting up alternative sources of supply—a crisis response process the
company had established over several years. Ericsson, however, did not
perceive a need for stepped-up action. As a result of these responses,
Nokia was able to expand its market share and profits, whereas Ericsson
reported a loss of $200 million because of component shortages.32
3. Complexity of product design and the supply chain network. The
design and introduction of new products often require a complex
network of specialized suppliers, which is hard to manage. They can be
subject to unanticipated disruptions and delays as well as to issues
related to vendor compliance. In the early 2000s, the woes faced by
Boeing and Airbus, the two largest commercial airplane manufacturers
in the world, exemplify this complexity and the importance of enhancing
transparency and anticipating uncertainties. For Boeing, the launch of its
787 airplane was delayed by three years because of an unanticipated
industry-wide shortage of aerospace fasteners.33 The manufacturers of
these fasteners, which constitute barely 3% of the value of an aircraft,
had consolidated and cut capacity because of a drop in aircraft demand
after the 9/11 attacks. When demand later picked up, it created a
bullwhip situation, and the manufacturers were slow to ramp up
production. They were unable to read demand signals accurately
because Boeing was using a new supply chain structure to design the
aircraft, and so demand came from many suppliers. And Boeing was
unable to assess the impact of this problem fully, leading to several delay
announcements over a period of more than two years. Thus, a seemingly
inconsequential and low-cost item brought down an entire supply chain.
Around the same time, but for different reasons, Airbus faced problems
in the manufacture of the A380, whose size and passenger capacity
required a bottom-up evaluation of every aspect of aircraft and airport
operations. Deep coordination was needed among internal and external
teams to ensure readiness for the new airplane. But Airbus encountered
problems in executing such coordination, such as design and
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manufacturing issues in integrating subassemblies produced by many
factories dispersed throughout Europe. The result was frequent delay
announcements, an increase in production cost, nonfulfillment penalties
of several million dollars, and a significant loss of shareholder value. 34
Both Boeing and Airbus have complex product designs and vast supply
chains. Companies in such situations need to collaborate closely with
their supply chain partners, conduct detailed mapping of risks, subject
their supply chains to stress tests, and apply large-scale scenario
planning to assess the impact of failures on the development and
production schedules of new products.
In addition to building resilience under disruptions, supply chains must also
comply with ESG regulations. In pharmaceuticals, the US Drug Supply Chain
Security Act (DSCSA) of 2013 is designed to protect consumers from exposure to
contaminated, stolen, counterfeit, or otherwise harmful drugs by improving
traceability in the distribution of prescription drugs. The act outlines steps to
build an electronic, interoperable system to improve the detection of such
defective drugs and their removal from the supply chain. A similar regulation,
called the Falsified Medicines Directive, was introduced in the European Union
(EU) in 2011 to combat fake medicines and tampering.
The US Congress enacted the Food Safety Modernization Act (FSMA) in 2011
to improve the prevention of foodborne illnesses in the consumer packaged
goods and food industries. Recognizing that food safety is a shared responsibility
of the participants of the global food supply chain, the Food and Drug
Administration (FDA) stipulates rules to implement FSMA, including a
requirement that firms in the food supply chain should maintain traceability
records of key data elements and critical events related to the growing,
receiving, transformation, creation, and shipping of food.
Environmental regulations, such as the EU’s Sustainable Finance Disclosure
Regulation, seek to improve transparency of sustainable products and prevent
greenwashing. The European Union Deforestation Regulation (EUDR) also
prohibits companies from placing commodities linked with deforestation and
forest degradation on the EU market and requires them to issue due-diligence
statements that their products do not come from deforested land.
To comply with regulations such as these, companies must invest in new
capabilities. One of these is supply chain traceability, that is, the ability to track
each item from its source through the stages of the supply chain and finally to
the consumer. Supply chain traceability provides the following advantages:
•
Enables a company to control the risk of counterfeit and adulterated
products because a contaminated product can be fully traced in the
supply chain.
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•
Provides the means to execute a product recall faster because the
inventory associated with the recall can be identified and located
more quickly when there is traceability.
•
Makes it possible to certify sustainably sourced and environmentally
friendly products and provide economic returns to farmers for
sustainable farming.
Traceability requires investments in inventory tagging and sensor-based
technology, as discussed in section 2.4, and in shared or centralized database
systems such as blockchain, where data and transactions can be verified,
recorded, and secured.35 Track-and-trace solutions for the pharmaceutical
industry support serialization of inventory and maintaining quality control and
compliance. Similar track-and-trace and blockchain-based provenance solutions
are being introduced for commodities such as palm oil, coffee, and aquaculture.
Another capability that supports compliance applies Design for the
Environment (DfE) standards and methodologies to the supply chain. DfE
incorporates sustainability goals in the design of new products. It is required
because manufactured products consist of many inputs, and a manufacturer
would need to identify ESG-compatible inputs during product design to ensure
that a compliant supply chain is set up for its products. The US Environmental
Protection Agency (EPA) recognizes companies with U.S EPA DfE certification,
for example, and specialists now support manufacturers in maintaining supplier
and component databases to achieve DfE goals.
A third capability is carbon accounting, which involves measuring the amount
of greenhouse gas (GHG) emissions in various activities across the stages of a
supply chain and allocating it to products. Specifically, Scope 3 GHG emissions36
pertain to emissions from the suppliers and customers of an organization rather
than GHG emissions generated by the organization itself. While these emissions
are not produced by the company, they carry supply chain implications. These
emissions from suppliers need to be tracked as do those from users of the
company’s end product. The standards and tools for carbon accounting in supply
chains are developing rapidly and are complementary to track-and-trace and
DfE capabilities.37 Supply chain managers are embracing digital technologies to
make their organizations more resilient and more environmentally and socially
responsible. These trends are leading to significant advancement in what is
often referred to as digital supply chains.
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4 KEY TERMS
assembly network A supply chain in which the number of locations decreases
as one moves downstream. Each location assembles products from many
suppliers, then fulfills demand from a single downstream location.
bullwhip effect The phenomenon by which the variance of demand increases as
one moves upstream in a supply chain.
buyback contract A procurement contract between a buyer and a seller in
which the seller offers the product to the buyer at a fixed wholesale price and
accepts returns of unsold inventory at a buyback price. The buyback price is less
than the wholesale price.
centralized supply chain A supply chain in which all decisions, including
procurement, production, and distribution, are made centrally.
decentralized supply chain A supply chain in which different organizations
make decisions independently for the activities they manage.
differentiated supply chain A supply chain consisting of many organizations
that own different stages of production and distribution.
distribution supply chain A supply chain in which the number of locations
increases as one moves downstream. Each location receives product from a
single supplier, then fulfills demand from many downstream locations.
double marginalization The phenomenon by which profit margin is split
between buyer and seller organizations in a decentralized supply chain. Double
marginalization leads to a decline in the total supply chain profit when each
organization tries to maximize its own profit.
make-buy decision The choice between producing a product in-house and
buying it from an external supplier.
multisourcing Sourcing from a portfolio of suppliers with varying cost, quality,
and fulfillment capabilities. Multisourcing enables a firm to adjust procurement
quantities sourced from each supplier to manage uncertainty and reduce risk.
postponement flexibility The capability by which an organization can delay
the time of differentiation of its product to be closer to the time the demand
occurs. Also known as delayed differentiation.
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reactive production capacity Production capacity that is deployed during the
middle of a selling season to replenish product with a short lead time in reaction
to the demand observed during the read period.
sales and operations planning (S&OP) An integrated process involving all
functions of an organization to share forecasts, past performance, and cost
information, and to use this information to make plans for sales, production,
procurement, inventory, new product launch, and resulting financial goals.
serial supply chain A supply chain in which product flows in a single sequence
through locations that are arranged in series.
statistical economies of scale The reduction in the cost of managing
uncertainty that occurs when demand from multiple customer locations is
pooled and served from a single location.
supply chain coordination The alignment of decisions across different stages
of a supply chain in order to maximize total profit. Coordination takes place
through centralization of decision making, sharing of information, and the
design of incentives.
vanilla boxes Undifferentiated products that can be converted into finished
products late in the process when more accurate forecasts of demand are
available.
vertically integrated supply chain A supply chain in which all stages are
owned by a single organization.
wholesale price contract A procurement contract between a buyer and a
seller in which the seller offers the product to the buyer at a fixed wholesale
price and does not accept any returns.
5 FOR FURTHER READING
Fisher, Marshall L. “What Is the Right Supply Chain for Your Products?” Harvard
Business Review 75, no. 2 (March–April 1997): 105–116.
Gaur, Vishal. “Bringing Blockchain, IoT, and Analytics to Supply Chains.” HBS No.
H06RVC (Boston: Harvard Business School Publishing, December 21, 2021).
Gaur, Vishal, and Abhinav Gaiha. “Building a Transparent Supply Chain:
Blockchain Can Enhance Trust, Efficiency, and Speed.” Harvard Business
Review 98, no. 3 (2020): 94–103.
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Gaur, Vishal, Nikolay Osadchiy, and Maximiliano Udenio. “Research: Why It's So
Hard to Map Global Supply Chains.” HBS No. H07B8E. (Boston: Harvard
Business School Publishing, October 2022).
Lacity, Mary, and Remko Van Hoek. “What We've Learned So Far About
Blockchain for Business.” MIT Sloan Management Review 62, no. 3 (Spring
2021): 48–54.
Lee, Hau, V. Padmanabhan, and Seungjin Whang. “The Bullwhip Effect in Supply
Chains.” Sloan Management Review 38, no. 3 (Spring 1997): 93.
Lumineau, Fabrice, Wenqian Wang, Oliver Schilke, and Laura Huang. “How
Blockchain Can Simplify Partnerships.” HBS No. H06A6F (Boston: Harvard
Business School Publishing, April 9, 2021).
Saénz, Maria Jesús, Elena Revilla, and Inma Borrella. “Digital Transformation Is
Changing Supply Chain Relationships.” HBS No. H0744L (Boston: Harvard
Business School Publishing, July 7, 2022).
Schuh, Christian, Wolfgang Schnellbächer, and Daniel Weise. “3 Steps to Prepare
Your Supply Chain for the Next Crisis.” HBS No. H07CK2 (Boston: Harvard
Business School Publishing, November 9, 2022).
6 ENDNOTES
1 Gildan, “Our business” https://gildancorp.com/en/company/our-business/, accessed May 2023;
Lululemon, “Operations,” https://corporate.lululemon.com/our-business/operations, accessed May
2023.
2 Tools such as those described in Core Curriculum: Managing Inventory (HBP No. 8016) are commonly
used to make those decisions.
3 Marshall L. Fisher, “What Is the Right Supply Chain for Your Products?” Harvard Business Review 75, no.
2 (March–April 1997): 105–116.
4 Hau Lee, V. Padmanabhan, and Seungjin Whang, “The Bullwhip Effect in Supply Chains,” Sloan
Management Review 38, no. 3 (Spring 1997): 93.
5 Mor Armony and Erica L. Plambeck, “The Impact of Duplicate Orders on Demand Estimation and
Capacity Investment,” Management Science 51, no. 10 (October 2005): 1505–1518.
6 Sharon Terlep, “Drop in Toilet-Paper Demand Prompts Kimberly-Clark’s Worst Sales Decline in a
Decade” April 23, 2021, https://www.wsj.com/articles/drop-in-toilet-paper-demand-promptskimberly-clarks-worst-sales-drop-in-a-decade-11619195039, accessed May 2023; Jaewon Kang and
Sharon Terlep, “Americans Are Stocking Up on Toilet Paper Again” Wall Street Journal, August 31,
2021, https://www.wsj.com/articles/americans-are-stocking-up-on-toilet-paper-again11630431042, accessed May 2023.
7 Gérard P. Cachon and Martin A. Lariviere, “Capacity Choice and Allocation: Strategic Behavior and
Supply Chain Performance,” Management Science 45, no 8 (August 1999): 1091–1108.
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8 Pankaj Ghemawat and Jose Luis Nueno Iniesta, “ZARA: Fast Fashion” (Boston: Harvard Business
School, 2006), HBS No. 703497; Daniel Doiron, “What Business Is Zara In? (Revised)” (Boston:
Harvard Business School Publishing, 2019), HBS No. W19157.
9 See, for example, Jayashankar M. Swaminathan and Sridhar R. Tayur’s "Managing Broader Product
Lines Through Delayed Differentiation Using Vanilla Boxes," Management Science 44, no. 12 (1998):
S161–S172.
10 Marshall L. Fisher, Janice H. Hammond, Walter R. Obermeyer, and Ananth Raman, “Making Supply
Meet Demand in an Uncertain World,” Harvard Business Review 72, no. 3 (May–June 1994): 83–93.
11 See Elizabeth Holmes, “Tug-of-War in Apparel World,” Wall Street Journal, July 16, 2010,
http://online.wsj.com/article/SB10001424052748703722804575369392983459752.html,
accessed May 2023.
12 James K. Sebenius and Ellen Knebel, “Tom Muccio: Negotiating the P&G Relationship with Wal-Mart
(A),” HBS No. 907-013 (Boston: Harvard Business School Publishing, 2010).
13 James K. Sebenius and Ellen Knebel, “Tom Muccio: Negotiating the P&G Relationship with Wal-Mart
(A)” HBS No. 907-013 (Boston: Harvard Business School Publishing, 2010).
14 V. G. Narayanan and Ananth Raman, “Aligning Incentives in Supply Chains,” Harvard Business Review
82, no. 11 (November 2004): 94–102.
15 Tracie Rozhon, “Saks Suppliers Want Records of Clothing Transactions,” New York Times, July 15,
2005, https://www.nytimes.com/2005/07/15/business/saks-suppliers-want-records-of-clothingtransactions.html, accessed May 2023; Tracie Rozhon, “Stores and Vendors Take Their Haggling Over
Payment to Court,” New York Times, May 17, 2005,
https://www.nytimes.com/2005/05/17/business/stores-and-vendors-take-their-haggling-overpayment-to-court.html, accessed May 2023; Tracie Rozhon, “Clothier's Suit Says Saks Abused
Markdown Deductions,” New York Times, May 18, 2005,
https://www.nytimes.com/2005/05/18/business/clothiers-suit-says-saks-abused-markdowndeductions.html, accessed May 2023.
16 See Core Reading: Inventory Management (HBP No. 8016) for an in-depth description of the
newsvendor model and the critical fractile.
17 Gary D. Eppen, “The Effects of Centralization on Expected Costs in a Multi-Location Newsboy
Problem,” Management Science 25, no. 5 (May 1979).
18 Securities and Exchange Commission, “Amazon.com Form 10-K” for fiscal years 1997–1999; Amazon,
“Bots by the Numbers: Facts and figures About Robotics at Amazon,”
https://www.aboutamazon.com/news/innovation-at-amazon/bots-by-the-numbers-facts-andfigures-about-robotics-at-amazon, accessed March 2023.
19 William C. Jordan and Stephen C. Graves, “Principles on the Benefits of Manufacturing Process
Flexibility,” Management Science 41, no. 4 (April 1995).
20 Bruce Kogut and Nalin Kulatilaka, “Operating Flexibility, Global Manufacturing, and the Option Value
of a Multinational Network,” Management Science 40, no. 1 (January 1994): 000–000; Arnd
Huchzermeier and Morris A. Cohen, “Valuing Operational Flexibility Under Exchange Rate Risk,”
Operations Research 44, no. 1 (January–February 1996): 100.
21 See Core Reading: Strategic Sourcing (HBS No. 8037) for more on the factors behind make-buy
decisions.
22 Paul Mozur, “Apple Supplier Foxconn Says Fight at Plant Spread Into Larger Unrest,” Wall Street
Journal, September 24, 2012, http://online.wsj.com/article/
SB10000872396390444180004578015170427352146.html;
Jessica E. Vascellaro, “Audit Faults Apple Supplier,” Wall Street Journal, March 30, 2012,
http://online.wsj.com/article/
SB10001424052702303404704577311943943416560.html;
“Apple to Audit Supplier’s Pollution Management,” Wall Street Journal, April 16, 2012,
http://online.wsj.com/article/
SB10001424052702304299304577347294151002440.html;
“Foxconn to Raise Salaries,” Wall Street Journal, April 5, 2012, http://online.wsj.com/article/
SB10001424052702303302504577324780116867816.html.
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23 Apple, “People and Environment in Our Supply Chain: 2023 Annual Progress Report,”
https://www.apple.com/supplier-responsibility/pdf/Apple_SR_2023_Progress_Report.pdf, accessed
May 2023.
24 William Boston, Asa Fitch, Mike Colias, and Ben Foldy, “How Car Makers Collided With a Global Chip
Shortage,” Wall Street Journal, February 12, 2021, https://www.wsj.com/articles/car-chip-shortageford-vw-gm-11613152294, accessed May 2023; “Chips Are Difficult to Make, and It's Even Harder
During a Supply Crunch,” Wall Street Journal, June 3, 2021, https://www.wsj.com/video/series/wsjexplains/chips-are-difficult-to-make-and-it-even-harder-during-a-supply-crunch/654DC874-952042A2-9D02-C47AB60E2DCD, accessed May 2023.
25 Timothy Aeppel, “A Snowblower Maker Braces for Slump's Blizzard of Woe,” Wall Street Journal,
November 7, 2008. http://online.wsj.com/article/SB122602502818007621.html, accessed May
2023.
26 Rory Jones, Summer Said, and Jared Malsin, “Inside the Suez Canal Race to Free the Ever Given Ship,”
Wall Street Journal, April 2, 2021, https://www.wsj.com/articles/inside-the-suez-canal-race-to-freethe-ever-given-11617372257, accessed May 2023.
27 Elisabeth Braw, “What the Ever Given Taught the World,” Foreign Policy, November 11, 2021,
https://foreignpolicy.com/2021/11/10/what-the-ever-given-taught-the-world/, accessed May
2023.
28 World Economic Forum, Global Risks Report 2023, 18th Edition, Figure E: Global risks ranked by
severity, p. 11, https://www3.weforum.org/docs/WEF_Global_Risks_Report_2023.pdf, accessed
February 2023.
29 K. B. Hendricks and V. R. Singhal, “An Empirical Analysis of the Effect of Supply Chain Disruptions on
Long-Run Stock Price Performance and Risk of the Firm,” Production and Operations Management 14
(2005): 35–52; K. B. Hendricks and V. R. Singhal, “Association Between Supply Chain Glitches and
Operating Performance, Management Science 51 (2005), 695–711.
30 For example, see World Economic Forum, “5 Challenges Facing Global Supply Chains,” September 7,
2022, https://www.weforum.org/agenda/2022/09/5-challenges-global-supply-chains-trade.
31 Toshihiro Nishiguchi, Alexandre Beaudet. “The Toyota Group and the Aisin Fire,” Sloan Management
Review 40, no. 1 (Fall 1998): 49–59.
32 Amit S. Mukherjee, “The Fire That Changed an Industry: A Case Study on Thriving in a Networked
World,” informIT.com, October 1, 2008, https://www.informit.com/articles/article.aspx?p=1244469,
accessed May 2023.
33 Ravi Anupindi, “Boeing: The Fight for Fasteners,” William Davidson Institute at the University of
Michigan, Case No. 1-428-787, November 17, 2009.
34 William Schmidt, Ananth Raman, and Vishal Gaur, “Airbus A380: Turbulence Ahead,” HBS No. 609-041
(Boston: Harvard Business School, 2010).
35 Vishal Gaur, Abhinav Gaiha, “Building a Transparent Supply Chain: Blockchain Can Enhance Trust,
Efficiency, and Speed,” Harvard Business Review (May−June 2020).
36 For an introduction to Scope 1, 2, and 3 emissions, see the World Economic Forum’s overview, “What
Is the Difference Between Scope 1, 2 and 3 Emissions, and What Are Companies Doing to Cut all
Three?,” September 20, 2022, https://www.weforum.org/agenda/2022/09/scope-emissionsclimate-greenhouse-business, accessed June 2023.
37 Robert S. Kaplan and Karthik Ramanna, “Accounting for Climate Change,” Harvard Business Review,
November–December 2021. Robert S. Kaplan and Karthik Ramanna, “We Need Better Carbon
Accounting. Here’s How to Get There” Harvard Business Review, April 12, 2022,
https://hbr.org/2022/04/we-need-better-carbon-accounting-heres-how-to-get-there.
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7 INDEX
Note: Page numbers followed by f refer to figures. Page numbers followed by i refer
to interactive illustrations. Page numbers followed by t refer to tables.
activity-based costing (ABC), 21–22
advertising, 33, 34, 42
Airbus, 61–62
aircraft manufacturing, 61
Amazon.com, 48, 49, 51
amplification factor, 13, 15
assembly networks, 6, 7f
automated manufacturing process, 21
Barilla SpA, 13
Boeing, 61
book publishing and retailing, 42
bullwhip effect, 12–15, 14f
buyback contracts, 39, 42
buyback price, 40, 41f, 42, 43i
capacity allocation, 18, 21–22
capacity flexibility, 53, 53f, 54f
capacity utilization, 16, 24–25, 52–54
carbon accounting, 63
centralized decision making, 9, 36
centralized facilities, 7
centralized supply chains, 9, 48–51, 52f
channel alignment, 18, 20
chargebacks, 34
China, 47
Cisco, 17–18
closed-loop supply chains, 7
clothing manufacturers, 11, 34
clothing retailers, 7, 34, 51
clothing suppliers, 11, 34
codes of conduct, 56
collaborative planning forecasting and
replenishment (CPFR), 20
ColorCraft, 35–39
competition, 32, 42
competitiveness, 21, 55
compliance issues, 62–63
consumer packaged goods, 6, 10–11, 62
continuous replenishment program (CRP),
20
contracts, 34–35, 40f. See also buyback
contracts; wholesale price contracts
coordinated supply chain, 40, 58
COVID-19, 12–13, 15–16, 18
cross-company initiatives, 33
cross-functional teams, 14–15
8031 | Core Reading: SUPPLY CHAIN MANAGEMENT
cross-training, 54
culture of company, 33
customer decisions, 33
customer interaction rules, 3
customer needs and requirements, 3, 18, 22,
51
customer proximity, 43–48
customer satisfaction, 22
customer service, 8, 33
customization, 47
decentralized decision making, 9, 38, 38f
decentralized supply chain facilities, 50
decentralized supply chains, 9, 36–37, 38f
decision making, 8–11
decision making coordination, 18–22. See
also supply chain coordination
decision rights, 9, 34
delayed differentiation, 23–24, 23f
Dell, 11
demand, 5, 8–21
demand data (demand signals), 15, 24, 61
demand fluctuations, 18, 19f
demand forecast updating, 15–16
demand forecasts, 19–21
demand hidden information, 33
demand modeling, 11, 27
demand uncertainty, 8–9, 29, 30, 32, 36, 37,
44, 48, 49, 52
demand variability (bullwhip effect), 12–15,
14f
demand volatility, 30–31
department store chains, 34
design. See product design; supply chain
design
Design for the Environment (DfE), 63
differentiated supply chains, 7
differentiation delayed, 24
Dillard’s, 34
direct-to-customer model, 11
discounts, 17, 21
disruption risk, 8–9
disruptions, 58, 61
distribution centers, 8, 13, 48, 51
distribution costs, 10
distribution decisions, 3
distribution function, 10
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distribution supply chains, 6, 6f
distributors, 5
domestic centralized supply chain facilities,
9, 48–51, 52f
domestic production facilities, 44–47, 45i,
47i
double marginalization, 37
downstream, 5
dynamic routing, 53
economic order quantity (EOQ) model, 16–
17
economic recessions, 56
economic risk, 32–35, 55
economies of scale, 16
efficiency, 9–22
Efficient Consumer Response (ECR), 21–22
electronic data interchange (EDI), 19–20, 21
emerging economies, 47
environmental impact, 56, 62–63
Ericsson, 61
everyday low cost (EDLC), 21–22
everyday low price (EDLP), 21–22
excess inventory, 10, 14, 20, 22, 24–25, 28,
42
exchange rate, 47, 47i, 54–55
expected leftover inventory, 29, 36, 37
expected lost sales, 29, 36–37
expected profit, 30, 36, 37, 41f
expected sales, 29, 36, 37
exponential smoothing, 16
facilities centralized location, 7, 48
facilities flexibility, 11, 21
factories, 3, 8, 19, 23, 47, 62
FedEx, 21
finance function, 8
first best solution, 37
flexibility, 51–55, 52f
floods, 57
fluctuations, 13, 17, 19f, 21
forecast updating, 15–16
forecasts, 22, 24–26
foreign production facilities, 44–47, 45i, 46i,
47i
foreign supply chain facilities, 10–11, 43–47
fourth-party logistics (4PL), 21
fragmented supply chains, 3, 7
fuel costs, 47, 57
fulfillment centers, 21, 51
functional managers, 3
functional products, 10
functional silos, 3
gaming in shortage situations, 17
Gap (retailer), 51
geopolitical risk, 48, 57–58
Gildan Activewear, 7
global economic conditions, 56–57
global risks, 57–58, 60f
globalization, 3, 4, 57
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Graves Stephen, 54
greenhouse gas (GHG), 63
Hewlett-Packard, 13
hidden action, 33–34
hidden information, 33–34
holding costs, 45i
hub-and-spoke supply chains, 7
incentives, 32–43
Inditex, 11
information flow, 14–15
information sharing, 18
infrastructure, Soft infrastructure. See
physical infrastructure
innovative products, 10
intellectual property, 47
Internet of Things (IoT), 19–20
inventory, 8–11, 14, 16–17, 21–22, 24–25,
27–28, 29–30, 31–32, 34, 36–38, 42, 48–
50, 58, 63
inventory control system, 16
inventory costs, 8
inventory data, 22
inventory holding costs, 8, 16–17, 43–45
inventory management, 19–21
inventory risk, 28
inventory shortages, 10, 17–18, 22, 24, 48,
56, 61
inventory shrinkage, 33
inventory storage, 8
inventory strategy, 8
inventory transportation, 8
inventory, vendor managed (VMI), 9, 20, 21
Jordan, William, 54
just-in-time inventories, 58
just-in-time production, 21
labor costs, 8, 47, 54
labor practices, 55
labor scheduling, 32, 54
labor unrest, 56, 58
lead times, 15f, 16, 53
lean production methods, 10, 57, 58
life cycles of products, 3–4, 10, 22, 24
list prices, 34
location decisions, 43–54
location flexibility, 11, 54
logistical flexibility, 53
logistics costs, 8
logistics function, 8
logistics outsourcing, 21
logistics service providers, 3, 7
long-term decisions, 8
Lululemon, 7
make-buy decisions, 55
managers, 8, 22–23, 33, 57, 63
manufacturing costs, 17
manufacturing disruptions, 9, 58, 61
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manufacturing location decisions, 43–54
markdown money, 34, 42
markdowns, 10, 28, 34
market mediation costs, 10
market share, 17, 61
market-responsive supply chains, 4, 10, 11,
11f
Marks, Penelope, 35
minimum order quantity restrictions, 3, 17
moving average, 16
multinational companies, 7, 54
multisourcing, 54
natural disasters, 56
nearshoring, 48
new product introductions, 10, 18, 61, 62
newsvendor critical fractile, 29
newsvendor model, 28, 35–36
Nokia, 61
nonfulfillment penalties, 3, 34, 62
Nordstrom, 51
Obermeyer, 25
offshoring, 47–48
operational decisions, 8
operational efficiency, 18, 20
order batching, 16–17, 21
organizational culture, 33
organizational structure, 33
outsourcing, 55
penalties for nonfulfillment, 3, 34, 62
performance evaluation processes, 12f
periodic inventory control, 16
pharmaceutical companies, 6, 20, 62, 63
Philips, 61
physical costs, 10
physical infrastructure, 8, 43, 55
physically efficient supply chains, 4, 10, 11f
point-of-sale data, 20
postponement flexibility, 24
price discounts, 17, 21
price fluctuations, 17, 19f, 21–22
price markdowns, 10, 28
price uncertainty, 3
Pritchett Lou, 33
Procter & Gamble (P&G), 12–13, 33
procurement costs, 28, 47
procurement decisions, 8, 9, 27
procurement function, 6
product availability, 3, 8, 11
product design, 61
product life cycles, 3–4, 10, 22, 24
production capacity, 25
production costs, 35, 36, 38, 43, 62
production data, 11
production decisions, 8–9
production disruptions, 9, 14, 54, 58, 61
production flexibility, 51–55, 53f
production function, 8
production lead times, 11, 14–16
8031 | Core Reading: SUPPLY CHAIN MANAGEMENT
production location, 5–9, 47
production process with delayed
differentiation, 23–24, 53
production rationing of new products, 17–
18, 22
production responsiveness, 22–32
production technology investment, 21, 54
profit margins, 37
profit maximization, 38f
promotions, 8, 10, 32, 34, 42
proximity to customers, 43–48
quantity discount contract, 30, 34
quantity flexibility contract, 32, 34
radio-frequency identification (RFID), 4, 19–
20
rationing, 17–18, 22
raw material inventories, 11, 23
react period, 27–28, 27t
reactive production capacity, 25
read period, 24
read-react capability, 24–32, 26f, 27i, 31f
read-react timeline, 25f
recency bias, 16
recessions, 32, 56–57
recycling, 7
regional facilities, 48, 49, 51
remanufacturing, 7
replenishment lead times, 11, 11t, 16, 20,
24, 28, 44
reshoring, 48
responsiveness, 22–32. See also marketresponsive supply chains
retailers, 13
risk management, 56–57
robotic process automation (RPA), 21
Royal Philips Electronics, 61
safety rules, 56
Saks Fifth Avenue, 34
sales and operations planning (S&OP), 8
sales data, 22, 34
sales department (sales function), 17, 21
sales forecasts, 20
sales promotions, 8, 10, 32, 34, 42
salvage value of leftover inventory, 28
Scope 3 GHG emissions, 63
Securities and Exchange Commission (SEC),
57
selling price, 27, 27i, 34, 39
senior management, 14
serial supply chains, 5, 6f
shipping, 15f
short-term decisions, 8
shortage gaming, 17–18, 22
shortages, 17–18
single sourcing, 58
societal risk, 4, 57
soft infrastructure, 8, 9, 54
sourcing decisions, 54
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This document is authorized for use only in Prof. Arun Kumar Biswal; Prof. Pushpesh Pant; Prof. Sricharan 's Supply Chain Management -III-2025 at Institute of Management Technology Hyderabad from Jan 2025 to Jul 2025.
specialization, 58
specialized manufacturers, 6, 10
statistical economies of scale, 48–50, 50i,
51i
stocking decisions, 20, 22
stocking quantity, 37, 38f
stockouts, 14, 17, 20
subcontracting, 7
suppliers’ decisions, 8–9
supply chain coordination, 18–22
supply chain decisions, 8–9
supply chain design, 43–55, 56–57
supply chain disruptions, 58
supply chain footprint, 4
supply chain function, 3
supply chain organizations, 20
supply chain risk management, 4, 47, 57, 58
supply chain types, 3
sustainability, 4, 62–63
uncertainties, 3, 8, 10, 16, 17, 29, 54, 61. See
also demand uncertainty; price
uncertainty
unsold merchandise, 32, 34, 39, 42
UPS, 11
upstream, 5, 14
technological risk, 57
third-party logistics (3PL), 21
time frames of decisions, 8–9
Toyota, 58–59
training, 54
transportation costs, 16, 47
transportation disruptions, 10, 58
transportation investment, 54
Zara clothing brand, 11, 23
8031 | Core Reading: SUPPLY CHAIN MANAGEMENT
vanilla boxes, 24
vendor compliance, 56
vendor managed inventory (VMI), 9, 20, 21
vertically integrated supply chains, 7, 35–36
volatility, 17, 30, 31, 57
Walmart, 33
Walton Sam, 33
warehouses, 8, 19, 20, 51
wholesale price, 37, 38f, 40, 41f
wholesale price contracts, 36
wholesalers, 5, 13
71
This document is authorized for use only in Prof. Arun Kumar Biswal; Prof. Pushpesh Pant; Prof. Sricharan 's Supply Chain Management -III-2025 at Institute of Management Technology Hyderabad from Jan 2025 to Jul 2025.