Analysis of turbulent market environments

EXECUTIVE SUMMARY
Analysis of Turbulent Market Environments 1

CHAPTER 1
INTRODUCTION
Besides the consumer demands and the business costs, the decisions of the firms
also depend on the number, size and behaviour of the other firms in the industry. The
strength of the competition faced by a company can profoundly affect its pricing, its
output decisions and its input purchases. Strong competitive pressures, sometimes taking
subtle forms, can severely limit the freedom pf choice by management in setting prices
and, in process, protect the interests of consumers. Giant corporations may also find
themselves under this sort of pressure, even where there are few rival domestic firms.
Industries differ dramatically in how populated they are and in the size of a typical firm.
Some industries contain a great many very small firms; others are composed of a few
industrial giants.

CHAPTER 2
MARKET AND ITS TYPES
What is a ‘market’?
Economists do not reserve the term ‘market’ to denote only an organized
exchange operating in a well defined physical location. In its more general and abstract
usage, ‘a market’ refers to a set of sellers and buyers whose activities affect the price at
which a particular commodity is sold.
With the development of transportation, communication and banking, the markets
have widened and dealings in come commodities worldwide. Therefore, the essential
feature of a market is that buyers should be able to strike bargains with sellers. According
to Wicksteed, “thus market is the characteristic phenomenon of economic life and the
constitution of markets and market prices is the central problem of Economics.”
The type of market in which the firm operates makes a great deal of difference for the
way in which it can and does behave. Under some market forms, for example, the firm
has no control over its price. In others, the firm has the power to adjust its price in a way
that adds to its profits and which, in the opinion of some, constitutes exploitation of
consumers.
Economist distinguish among different kinds of markets according to
(1) How many firms they include,
(2) Whether the products of the different firms are identical or somewhat different, and
(3) How easy it is for new firms to enter the market.
Perfect competition is at one extreme (many small firms selling an identical product),
while pure monopoly (a single firm) is at the other. In between are hybrid forms- called
monopolistic competition (many small firms selling products slightly different from the
others’) and oligopoly (a few large rival firms) - that share some of the characteristics of
perfect competition and some of the characteristics of monopoly. These kinds of markets
are explained in detail as follows.

I. Perfect Competition
A market is said to operate under perfect competition when following four
conditions are satisfied:
1. Numerous Small Firms and Customers. So many buyers and sellers that
each one constitutes a negligible portion of the market- so small, in fact,
that its decisions have no effect on the price. This requirement rules out
trade associations or other collusive arrangements strong enough to affect
price.
2. Homogeneity of product. The product offered by any seller is identical to
that supplied by any other seller. Because the product is homogeneous
product, consumers do not care from which firm they buy.
3. Freedom of entry and exit. New firms desiring to enter the market face no
impediments that the existing firms can avoid. Similarly, if production and
sale of the good proves unprofitable, there are no barriers preventing firms
from leaving the market.
4. Perfect information. Each firm and each customer is well informed about
the available products and their prices. They know whether one supplier is
selling at a price lower than another is.
Perfectly competitive industries have four characteristics:
1. The industry is fragmented. It consists of many buyers and sellers. Each buyer's
purchases are so small that they have an imperceptible effect on market price.
Each seller's output is so small in comparison to market demand that it has an
imperceptible impact on the market price. In addition, each seller’s purchases are
so small that it has an imperceptible impact on input prices
2. Firms produce undifferentiated products. That is, consumers perceive the
products to be identical no matter who produces them. When you buy fresh, cut
roses from a local flower shop, it probably does not matter to you that they were
produced by which firm. As far as you are; concerned, the roses from one grower
are just as good as the roses from any other grower. And because this is true for
you, it is also true for the flower shops and the wholesalers who buy the roses

directly from the growers. If the final consumer sees no difference in the roses
grown by the different growers then florists and wholesalers don't care who they
buy roses from either, as long as they get the best price. Roses are thus an
example of an undifferentiated product.
3. Consumers have perfect information about prices all sellers in the market charge.
This is certainly true in the rose market. The wholesalers and florists that buy
roses from the growers are keenly aware of the prevailing prices. In fact, as just
noted, these consumers need to be deeply knowledgeable about the prices because
the price is the main thing they care about when deciding which growers to buy
roses from.
4. The industry is characterized by equal access to resources. All firms-those
currently in the industry, as well as prospective entrants-have access to the same
technology and inputs. Firms can hire inputs, such as labor, capital, and materials,
as they need them, and they can release them from their employment when they
do not need them. This characteristic is generally true of the fresh-cut rose
industry: the technology for growing roses is well understood, and the key inputs
necessary to operate a rose growing firm (land, greenhouses rose bushes, and
labor) are readily available in well-functioning markets.
These characteristics have three implications for how perfectly competitive
markets work:
• The first characteristic-the market is fragmented-implies that sellers and buyers
act as price takers. That is, a firm takes the market price of the product as given
when making an output decision and a buyer takes the market price as given when
making purchase decisions. Condition 1 also implies that a firm takes input prices
as fixed when making decisions about input quantities.
• The second and third characteristics-firms produce undifferentiated products and
consumers have perfect information about prices-implies a law of one price: that
is, transactions between buyers and sellers occur at a single market price. Because
the products of all firms are perceived to be identical and the prices of all sellers
are known, a consumer will purchase at the lowest price available in the market.
No sales can be made at any higher price.

• The fourth characteristic-equal access to resources-implies that the industry
is characterized by free entry. That is, if it is profitable for new firms to enter
the industry, they will eventually do so. Free entry does not mean that a new firm
incurs no cost when it enters the industry but that it has access to the same
technology and inputs that existing firms have.
II. Pure Competition:
Economists like Chamberlin and others often make distinction between pure
competition and perfect competition. The term ‘pure competition’ is used in a restricted
sense. It is also known as atomistic competition. In order that competition be pure it
requires the fulfillment of three conditions of perfect competition, namely, the existence
of large number of buyers and sellers, homogeneity of the product, and freedom of entry
and exit. These conditions together mean that no individual firm can exert any influence
over the market price. In short, the essential feature of pure competition is the absence of
monopoly element.
But the term perfect competition is a wider concept, in the sense that it includes
the features of pure completion and some additional conditions such as perfect
knowledge on the part of buyers and sellers, perfect mobility of factors of production and
absence of transportation costs.
This means that in addition to the absence of monopoly element i.e., absence of
any control over price by an individual firm, perfect competition requires that there
should be no imperfections in the market. Such imperfections arise due to imperfect
knowledge or immobility of the factors of production.
In fact, pure competition is a part and parcel of perfect competition. American
economists prefer to use the term pure competition, while English economists prefer the
term perfect competition. However, both the terms are used to analyze the features of
perfect markets.

III. Monopoly
The definition of pure monopoly is quite stringent. First, there must be only one
firm in the industry-the monopolist must be “the only supplier in town.” Second, there
must be no close substitute for the monopolist’s product. Thus, even the sole provider of
natural gas in a city is not considered a pure monopoly, since other firms offer close
substitutes like heating oil and electricity. Third, there must be some reason why survival
of potential competitor is extremely unlikely, for otherwise monopolistic behaviour and
its excessive profits could not persist.
These rigid requirements make pure monopoly a rarity in the real world. The local
telephone company and the post office may be examples of one-firm industries that face
little or no effective competition on some of their activities. But most firms face at least a
degree of competition from substitute products. Even if only one railroad serves a
particular town, it must compete with bus lines, trucking companies and airlines.
Similarly, the producer of a particular brand of beer may be the only supplier of that
specific product but the firm is not a monopolist by our definition. Since many other
beers are close substitutes for its product, the firm will lose much of its business if it tries
to raise its price much above the prices of other brands.
And there is one further reason why the unrestrained pure monopoly of economic
theory is rarely encountered in practice. Pure monopoly can have a number of undesirable
features. As a consequence, in markets where pure monopoly might prevail, the
government has intervened to prevent monopolization or to limit the discretion of the
monopolist to set its price.
Causes of monopoly: Barriers to entry and cost advantages:
The key element in preserving a monopoly is keeping potential rivals out of the
market. One possibility is that some specific impediment prevents the establishment of a
new firm in the industry. Economists call such impediments barriers to entry. Some
examples are:
1. Legal restrictions. Local monopolies of various kinds are sometimes established
either because government grants some special privilege to a single firm (for
example, the right to operate a food concession in municipal stadium) or prevents

other firms from entering the industry (for instance, by licensing only a single
cable television supplier.)
2. Patents. A special, but important, class of legal impediments to entry are patents.
To encourage inventiveness, the government gives exclusive production rights for
a period of time to the inventor of certain products. As long as the patent is in
effect. The firm has a protected position and is a monopoly. For example, Xerox
had for many years (but no longer has) a monopoly in plain paper copying.
3. Control of a scare resource or input. If a certain commodity can be produced only
by using a rare input, a company that gains control of the source of that input can
establish a monopoly position for itself.
4. Deliberately-erected entry barriers. A firm may deliberately attempt to make
entry difficult for others. One way is to start costly lawsuits against new rivals,
sometimes on trumped-up charges. Another is to spend exorbitant amounts on
advertising, thus forcing any potential entrant to match the expenditure.
5. Large sunk costs. Entry into an industry will, obviously, be very risky if entry
requires an investment of a large amount of money and if that investment is sunk-
meaning that one cannot hope to recoup the money for a considerable period of
time. Thus, the need for large sunk investment serves to discourage entry into an
industry, and many analysts therefore consider sunk costs to be the most
important type of “naturally imposed” barrier to entry.
Obviously such barriers can keep rivals out and ensure that an industry is
monopolized. But monopoly can also occur in the absence of such barriers to entry if a
single firm has important cost advantages over its potential rivals. Two examples of this
are:
6. Technical superiority. A firm whose technological expertise vastly exceeds that of
potential competitors can, for a period of time, maintain a monopoly position.
7. Economies of scale. If mere size gives large firm a cost advantage over a smaller
rival, it is likely to be impossible for anyone to compete with the largest firm in
the industry.

IV. Monopolistic Competition
The two market structures viz. perfect competition and monopoly are far from
reality. The conditions of neither perfect competition nor monopoly are experienced in
practice. We hardly come across a situation in which an indefinite number of firms
produce identical or homogeneous product Similarly, a situation in which there is only
one firm regulating the entire supply of a product is equally unrealistic.
In reality, we come across a market structure in which elements of both the competition
as well as monopoly are interwoven. In other words, the real market exhibits both
monopoly and competitive elements. Such a situation is called monopolistic competition.
Thus, there are a small number of firms producing similar product. According to
Chamberlin who introduced the concept "Monopolistic competition is a challenge to the
traditional viewpoint of economists that competition and monopoly are alternative and
that individual prices are to be explained in terms of either one or the other. By contrast,
it is held that most economic situations are composites of both competition and
monopoly". This argument makes it clear that perfect competition and monopoly are not
mutually exclusive situations. The real market presents both the elements.
Characteristics:
Monopolistic competition exhibits certain unique characteristics because of which
it distinguished from other market structures.
1. Large number of firms. Monopolistic competition is characterized by large
number of sellers. In this respect it is close to perfect competition. The number
may not be as large as that under perfect competition but it is also not very small.
In fact, the firms under this market structure are "Too many too small".
Consequently no individual has any significant control over the market.
2. Absence of interdependence. Since number of firms is sufficiently large and the
size of individual firms is small enough no appreciable interdependence exists
among the different firms. No single firm can influence or is influenced by the
others in the market. It means different firms cannot produce any significant
impact on market by changing their price policies.

3. Freedom of Entry. Like perfect competition monopolistic competition also grants
unrestricted entry to rival in the market. It means there are no restrictions. This
leads to occurrence of only normal profits in the long run. However, the nature of
this feature is not the same as that under perfect competition. Under perfect
competition new firms enter the market with an identical product while under
monopolistic competition the new firm may produce only similar but not identical
product. In other words new firm can start producing tooth paste or hair oil but it
cannot produce a ‘Colgate’ or a ‘Binaca’ or a ‘Tata Hair Oil.’ What it, brings is a
different product.
4. Product Differentiation. Under monopo1istic competition different firms produce
similar (but not homogeneous) products. It means the different firms produce
what may be properly described as a differentiated product. Thus, product
differentiation is the core of monopolistic competition. The firms produce a
product belonging to a particular class, say tooth paste; but individual product is
differentiated from other rival products. It is because of such product
differentiation that firms enjoy some monopoly power, that is, the power to
control the price in a narrow circle, but in the wider circle, it faces g the
competition from the rival firms. Hence, the firms may be called as ‘competing
monopolists’ and the situation may be rightly described as monopolistic
competition.
5. Selling Costs. Another feature of monopolistic competition is the existence of
selling costs which is absent under any other market situation. Under perfect
competition and monopoly, there is no need for incurring the expenditure on
creating demand (i.e. selling costs). Under perfect competition, a firm can sell any
quantity at a given price while under monopoly, the absence of close substitutes,
there is no need for incurring the selling costs. Thus, it is only under monopolistic
competition that the selling costs find a place

V. Oligopoly
The theory of perfect competition stands only as an ideal but has lost its
significance due to non-existence of its two basic features viz. large number of firms and
homogeneous product. The real market exhibits a number of departures from the ideal
market situation. These impurities or imperfections emerge due to deliberately
differentiated products and the less than large number of firms. Under monopolistic
competition, the number of firms is sufficiently large but the product stands
differentiated. The pricing policy of any firm depends upon two factors viz., the number
of rivals and the nature and extent of product differentiation.
Oligopoly is a distinct form of imperfect market which is characterized by the
existence of few sellers producing homogeneous or differentiated products. The term
'Oligopoly' owes its origin to two Greek words, 'Oligos' meaning 'a few' and 'pollen'
meaning to sell. It differs from monopoly (single firm) and perfect competition as well as
monopolistic competition (large number of firms), as it consists of a few firms. In a broad
sense this market form is described in a variety of ways such as, limited competition,
incomplete monopoly, multiple monopoly etc. A large number of products such as
automobiles, steel, cement, heavy electricals etc. are supplied by oligopolistic markets.
Definition and Features
Stigler defines Oligopoly in the following words:
"Oligopoly is that situation in which a firm bases its market policy in part on the expected
behaviour of a few close rivals".
This clearly reveals the fact that Oligopoly consists of a few firms as a result of
which there is a close interdependence among them in respect of price output policy. The
simplest way to clearly grasp the meaning and nature of
Oligopolistic Market is to analyze and understand the essential features of the same. The
study of the unique features of Oligopoly will be very useful in understanding the price
output determination under this market form.
1. Few Sellers. It is the number of sellers or firms that distinguishes oligopoly from
other market forms such as monopoly, perfect competition and monopolistic
competition. The number of sellers under Oligopoly is very small. Naturally each

individual firm has a sizeable share of the market demand. This leads to the fact
that action of each firm in respect of price and output has a close bearing on the
market. The decision of a single firm to expand or contract the output affects the
entire market under oligopoly. His decision to expand the output leads to fall in
price and profit while contraction of output produces an opposite effect. Since the
number of firms under Oligopoly is small, it is rightly described as 'competition
among few'. The product of these sellers may be homogenous i.e. perfect
substitute or just a close substitute.
2. Interdependence. Extreme interdependence among the Oligopolistic firms is an
unique feature of this market form. Under perfect competition or monopolistic
competition, under which the number of firms is very large, the question of
interdependence does not arise. An individual firm in a competitive market has
too small a share in the total market supply to produce any significant impact on
the rivals. In case of monopoly, which implies absence of a rival, the question of
interdependence is irrelevant. It is only under Oligopoly that one witnesses a close
interdependence among the different firms. Any policy decision of an individual
firm affects and is affected by the rivals. With close substitutes offered by
different Oligopolistic firms the products have high cross elasticity of demand as
a result of which every move by any single firm receives a sharp reaction from the
others. Thus, a close interdependence based on moves and counter-moves is a
distinct feature of Oligopoly. The interdependence is so intense that every firm
has to predict and analyze the possible reaction of the rivals before taking any
decision.
3. Indeterminate Demand Curve. It is difficult to derive a definite demand curve i.e.
AR curve of an Oligopolist. This situation arises due to extreme interdependence
among the firms. Such an interdependence generates uncertainty because none
can precisely predict the possible outcome of a particular decision. Demand Curve
is derived from the knowledge of various quantities of a product that can be sold
at different prices. This knowledge i.e. a demand schedule is difficult to obtain
under Oligopoly. This is due to the fact that the effect of any decision say price
reduction by a firm, cannot be precisely predicted. Neither the rivals nor the firm

reducing the price of its product can estimate exactly what will be the effect of
such an action on the demand for their product. The price cutting firm does not
know what will be the rise in demand, nor the rivals can estimate if there would
be a decline in demand for their product and to what extend.
All this leads to an intermediate or uncertain demand curve for an Oligopolist. In
this context an oligopoly firm differs from other market structures. Under perfect
competition an individual firm faces a definiate demand curve which is horizontal
straight line at a given price. Since the product is homogeneous and the price
given and constant an individual firm under perfect competition has hardly any
decision to make. Similar is the situation under monopoly which definite and
determinate demand or AR Curve. A monopolist can take his own decision
regarding price-output without any need to wait for the reaction of the rivals. It
can thus precisely predict its sales at different prices. An Oligopolist is caught in a
peculiar situation of an indeterminate demand curve for though he knows his
decision is bound to produce a reaction, he does not know what and how strong
that reaction will be. E.g. a firm cuts its price to acquire larger share of the
market. Now whether its sales will expand and if they do to what extent are the
key questions which lack definite answers. There may be different types and
intensities of reactions by the rivals. As such a firm is not in a position to locate a
definite demand curve. Similarly, his sales are affected by the decisions of other
firms. Some of them may begin the process through change in the price of their
products. Here again what will be the effect nobody can predict. Hence the
indeterminate AR curve.
4. Conflicting attitudes of firms. The uncertainty existing under Oligopoly is
intensified due to the conflicting attitude of the firms. It is often observed that the
Oligopolistic firms sometimes resort to cooperation amongst them while on
certain other occasions they take up fights and conflicts. The entire issue revolves
round the urge for profit maximization. In some cases the Oligopolistic firms
realize the declining profits due to undesirable competition. Hence they adopt the
strategy of cooperation and therefore unite together. This is known as the
tendency towards 'Collusion' among the firms for the purpose

of achieving the common objective of maximization of profits. Exactly opposite
behaviour is experienced in some other cases when they fight against each other
like enemies on the issue of distribution of profits and sharing of the markets.
Thus the two conflicting trends of cooperation and conflict are experienced in the
behaviour of the firms. This attitude enhances the unpredictable nature of this
market form.
5. Price Rigidity. Existence of price rigidity is a unique feature of Oligopoly with
product differentiation. The Oligopoly price appears to be ‘stuck up’ or ‘rigid’ or
‘fixed’ at a certain level. This implies that there is no departure in either direction
i.e., increase or decrease, from the prevailing price. No firm will resort to price
reduction as it benefits none. This is because an attempt by an individual firm of
price cutting for snatching away the customers of the rivals is immediately
followed by others. Hence hardly any benefits are received through price-cut.
What it really leads to, is a competitive price reduction, harmful to all. As against
it the policy of price-rise, for increasing the profits, is also not advantageous. This
is because the act of raising the price by one firm is not followed by the rivals. As
a result the price raising firm loses the customers to rivals and instead of
increasing the profits, faces the danger of fall in the same. Thus, no firm thinks of
changing the price from the existing level. Naturally there is price rigidity.
6. Element of Monopoly. With the existence of a few firms there is an element of
monopoly under oligopoly market. Small number of firms producing a
differentiated product naturally generates monopoly power. In its limited area
every firm enjoy monopoly as it commands an adequately large share of market.
Such monopoly power is exerted by the firm in respect of fixation of price-output.
The attachment of customers to a given product enhances the monopoly power of
the firm which enables it to have greater freedom in fixing price and output.

ATTIBUTES OF THE FIVE MARKET FORMS Table 1
Market
forms
Number of
firms in the
market
Frequency
in reality
Entry
barriers
Public
interest
results
Long-run
profit
Perfect
competition
Very many Rare (if any) None Good Zero
Pure
Competition
Many Frequent None Good Zero
Pure
Monopoly
One Rare Likely to be
high
Misallocates
resources
May be high
Monopolistic
Competition
Many Widespread Minor Inefficient Zero
Oligopoly Few Produces
large share
of GDP
Varies Varies Varies
CHAPTER 3

ENVIRONMETAL SCAN
One of the trademarks of the modern planning approach is its external orientation.
We have to address ourselves to the careful appreciation of environmental trends leading
to an understanding of the attractiveness of the industry in which the business resides. We
should be alert to all developments in our industry, especially to the behaviour of
competitors. Only a deep knowledge of the structural characteristics of the industry in
which the business operate along with a sound awareness of competitors’ actions, can
generate the high-quality strategic thinking required for the healthy long term
development of a firm.
Structural Analysis of Markets: The Five Forces Model
In order to select the desired competitive position of a business, it is necessary to
begin with the assessment of the industry to which it belongs. To accomplish this task,
we must understand the fundamental factors that determine its long-term profitability
prospects because this indicator embodies an overall measure of industry attractiveness.
By far the most influential and widely used framework for evaluating industry
attractiveness is the five forces model proposed by Michel E. Porter. Essentially, he
postulates that there are five forces that typically shape the industry structure:
1. Intensity of rivalry among competitors
2. Threat of new entrants
3. Threat of substitutes
4. Bargaining power of the buyers
5. Bargaining power of the suppliers
These five forces delimit prices, costs and investment requirements, which are the basic
factors that explain long-term profitability prospects and henceforth industry
attractiveness.

1. Intensity of rivalry among the competitors
The rivalry among the competitors is at the center of the forces contributing to industry
attractiveness. Out of the many determinants of rivalry, four of them stand out: industry
growth, the share of fixed cost to total value added to the business, the depth of product
differentiation, and the concentration and balance among competitors.
Case I: Monopolistic Competition: The Beauty Soap Industry in India
Analysis of Turbulent Market Environments
Industry
Competitors/
Intensity of RivalrySuppliersSuppliers
New Entrants
New Entrants
BuyersBuyers
Substitutes
Substitutes
Threat of
New Entrants
Bargaining
Power of
Suppliers
Bargaining
Power of
Buyers
Threat of
Substitutes
Figure 1 The Five Forces Model
17

The population of India is over one billion. The market potential is very high. The
industry life cycle of beauty soaps is in the maturity stage in the urban areas and in the
growth stage in the rural areas. The fixed costs involved in setting up a business in this
industry are high, since the entire manufacturing department would be needed to set up,
but the value added to business is also high. There are a number of different kinds of
soaps available in the market, ranging from the imported branded ones to the locally
manufactured ones. The rivalry is intense as each firm is trying hard to get a substantial
market share.
Case II: Oligopoly: The Mobile Networking Industry in India
The increased popularity of using mobile phones to keep in touch with near and
dear ones has increased the attractiveness of this industry. Hence, there is an increasing
demand for mobile services. Also, there is a wide rural market which is untapped. Hence,
the industry life cycle is in the growth. But the number of firms in the industry is limited.
This is due to the high initial fixed costs. Each firm provides with different services.
2. Threat of new entrants
on many occasions, the most critical strategic issue for a given firm does not reside in
understanding the existing set of competitors and achieving an advantage over them, but
in directing the attentions to possible and sometimes inevitable new entrants.
Case I: The threat of new entrants is high in case of the beauty soap industry. This is
because of not very high fixed costs and low switching costs. We have already seen that
the potential market is huge and hence economies of scale can be achieved if properly
directed efforts are taken. If a product, which is not available, is introduced by the new
firm then its can achieve the desired sales in a short span of time.
Case II: The threat of new entrants in the mobile networking industry is low because of
huge initial fixed costs. The demand is huge and so is the potential market but the initial
setup costs are quite high that it makes the industry less attractive.
3. Threat of Substitutes

It is not only the firms participating in the industry and the potential newcomers that are
central forces in determining industry attractiveness; we have to add firms offering
substitutes, which can either replace the industry products and services or present an
alternative to fulfill that demand. Substitutes could affect in different ways the
attractiveness of an industry. Their mere presence establishes a ceiling for profitability,
whenever there is a price threshold after which a massive transfer of demand takes place.
Case I: The threat of substitutes is very high in this case. If one firm increases its products
price beyond a certain limit, then it is likely that the consumer will switch the brand.
There is increasing popularity of body wash as against the use of soaps. Hence, the threat
of substitutes is increasing.
Case II: The threat of substitutes is very low. This is because there are no substitutes
available for mobile phones.
4. & 5. Bargaining Power of Buyers and Suppliers
Porter’s wording “bargaining power of suppliers and buyers” suggest that there is a threat
imposed on the industry by excessive use of power on the part of these two agents. Porter
can be interpreted as indicting that a proper strategy to be pursued by a business firm will
have, as a key component, the attempt to neutralize suppliers’ and buyers’ bargaining
power. Moreover, buyers are the most important constituency of the firm, to be treated
not as rivals, but as the depositories of a long-lasting, friendly relationship based on
performance and integrity.
Case I: The bargaining power of the buyer is high as compared to the supplier as the
number of suppliers is high and also there are substitutes available for change. There are
more number of firms, hence there is increased competition.
Case II: The bargaining power of the supplier is high as compared to that of the buyer as
there are no substitutes for the products and the number of players is also limited.
Case Analysis

Barriers To Entry Table 2
Case I Case II
Economies of scale Small High
Product differentiation High Little
Brand identification Low High
Switching costs Low High
Capital requirements Low High
Barriers To Exit Table 3
Case I Case II
Asset specialization Low High
One time cost of exit Low High
Government and social restrictions Low Medium
Rivalry Among Competitors Table 4
Case I Case II
Number of equally balanced competitors Large Medium
Relative industry growth Fast Fast
Diversity of competitors High Medium
Strategic stakes Low Low
Capacity increases Large increments Small increments
Power Of Buyers Table 5
Case I Case II
Number of important buyers Many Many
Availability of substitutes Few Few
Buyer switching costs Low Low
Power Of Suppliers Table 6
Case I Case II
Number of important suppliers Many Few
Availability of substitutes for supplier’s products Many Few
Supplier’s contribution to quality and service Medium High

CHAPTER 4
BUSINESS PERFORMANCE
The performance of any business is based on two kinds of elements:

1. Key Certainties
They are those elements which will be constant irrespective of the changes in the market
environment.
The certainties in the business are as follows:
2. Key Uncertainties
They are those elements which will change with the change in time, place, industry and
market environment.
The uncertainties in business are as follows:
CHAPTER 5
TURBULENT MARKET ENVIRONEMENT
Introduction
Business Performance
Key Certainties Key Uncertainties
Figure 2 Business Performance
22

Modern organizations operate in an external environment in which conditions are
often changing rapidly and unpredictably. This type of environment is called ‘turbulent
environment’ by Emery and Trist. Turbulence arises in part from changes in various
elements that make up the environment. It occurs also as a result of interaction between
organizations that have conflicting objectives and that compete with one another for
benefits in the environment. Each of these organizations is seeking to progress from its
existing stage to one that is judged to be preferable relative to its objectives. However, no
organization can be sure that it can achieve its most preferred future position in view of
the competition from others in the environment.
Those responsible for dealing with complex decision problems in modern
turbulent environment experience uncertainty with regard to future conditions and with
respect to the future actions by others. Many of the formal decision making methods that
have been developed in disciplines of operational research and decision analysis do not
take full account of these factors. The basis for most of these methods is optimization of
the benefits of a single participant in a static environment. These methods themselves
consist for a search for a uniquely rational solution in terms of that single participant.
These methods have been developed for use in decision situations that are
relatively well understood, where firm and reliable data on the characteristics of the
situation are available and where such uncertainty as exists can be represented by the use
of simple probability distributions. They are clearly not applicable in turbulent
environments, in which interaction between objectives, intentions and actions of many
participants have to be taken into account in the resolution of decision problems.
CHAPTER 6
DEMAND FORECASTING METHODS

Significant gains have been made in forecasting for marketing in the past quarter
century. Advances have occurred in the development of qualitative methods such as
Delphi, role playing, intentions and opinions surveys, and bootstrapping. They have also
occurred for quantitative methods such as extrapolation and econometrics. An attempt is
made here to build on the experience in applying these methods by researchers so
generalizations can be made about which methods would be most appropriate to forecast
demand.
In general, experts advocate the use of structured methods that avoid intuition,
unstructured meetings, focus groups, and data mining. In situations where there is
sufficient data, use of quantitative methods is encouraged, including extrapolation,
quantitative analogies, rule-based forecasting and causal methods. In other cases, use
methods that structure judgement including surveys of intentions and expectations,
judgmental bootstrapping, structured analogies, and simulated interaction. Green &
Armstrong (2005) strongly advocate the integration of Judgmental and statistical
methods. Managers’ domain knowledge should be incorporated into statistical forecasts.
Methods for combining forecasts improve accuracy.
I. Econometric methods
"Econometric methods" are defined as quantitative approaches that attempt to use
causal relationships in forecasting. In particular, they refer to models based on regression
analysis and include all methods which forecast by explicitly measuring relationships
between the dependent variable and some causal variables.
For market demand forecasting, there is empirical evidence to support the use of
econometric methods rather than subjective methods for long-range forecasts. To date,
most econometric researchers have devoted their efforts to short-term forecasting, an area
that has yielded unimpressive or contradictory results. Econometric methods would be
expected to be more useful for long-range forecasting because the changes in the
causal variables are not swamped by random error, as in the short run. In fact,
econometric methods are more accurate. Armstrong reported seven empirical
comparisons of methods used in long-range forecasting. In all comparisons econometric

methods were more accurate than extrapolations. Also, there was a 3 to 0 advantage for
econometric versus subjective forecasts. Fildes located 20 studies on long-range
forecasting; he coded them as 15 showing econometric to be more accurate, 3 ties, and 2
showing econometric to be less accurate than other methods.
Thus it may be concluded that Causal econometric methods provide more
accurate long-range forecasts. While more expensive, the methods are expected to be
the most accurate method when large changes are expected. What must however be borne
in mind is that to improve predictive capacity, these causal models need not be
complex.
II. Naive versus Causal Methods
A continuum of causality exists in forecasting models. At the naive end, no
statements are made about causality; at the causal end, the model may include many
factors.
Causal methods are more complex than naive methods. First, data must be obtained
on the causal factors. Estimates of causal relationships are obtained from these data.
These estimates of the causal relationships should be, adjusted so that they are relevant
over the forecast horizon. Next, one must forecast the changes in the causal variables.
Finally, the forecasts of the causal variables and the relationships are used to calculate the
overall forecast.
Causal methods are of more obvious value in forecasting. However, naive methods
can be used in some phases. For example, naive methods can provide forecasts of
environmental factors.
III. Intentions Surveys

Theoretical literature in psychology that suggests that a good predictor of an
individual’s future behavior is his or her stated intention. However, the psychological
literature also suggests that past behavior is an important predictor of future behavior.
With intentions surveys, people are asked how they intend to behave in specified
situations. In a similar manner, an expectations survey asks people how they expect to
behave. Expectations differ from intentions because people realize that unintended
things happen. For example, if you were asked whether you intended to purchase a
particular product you might say no. However, you realize that a problem might arise that
would necessitate such a purchase, so your expectations would be that the event had a
probability greater than zero. This distinction was proposed and tested by Juster and its
evidence on its importance was summarised by Morwitz.
Expectations and intentions can be obtained using probability scales such as
Juster’s eleven-point scale. The scale should have descriptions such as 0 = ‘No chance,
or almost no chance (1 in 100)’ to 10 = ‘Certain, or practically certain (99 in 100)’.
To forecast demand using a survey of potential consumers, the administrator
should prepare an accurate and comprehensive description of the product, its benefits and
conditions of sale. He should select a representative sample of the population of interest
and develop questions to elicit expectations from respondents.
Purchase intentions are routinely used to forecast demand of existing products and
services. While past studies have shown that intentions are predictive of sales, they have
only examined the absolute accuracy of intentions, not their accuracy relative to other
forecasting methods.
For different products and time horizons, intentions-based forecasting methods
were more accurate than an extrapolation of past sales. Combinations of these
forecasting methods using equal weights lead to even greater accuracy, with error rates
about one-third lower than extrapolations of past sales. Thus, it appears that purchase
intentions can provide better forecasts than a simple extrapolation of past sales trends.
Purchase intentions are inexpensive to acquire and easily understood, which may
account for their widespread use.
Many studies have found a positive correlation between purchase intentions and
purchase behavior.

Buyer-Intentions and expectations surveys are especially useful in forecasting
demand when demand data are not available, such as for new or specialty product
forecasts.
The theoretical literature is equivocal about whether intentions-based forecasts or
past sales trends should be more accurate. Received wisdom suggests that the best
predictor of future behavior is past behavior. On the other hand, the social psychology
literature states that a good predictor of what individuals will do is their stated intentions
to perform the behavior.
Other research suggests that intentions data are useful for predictions under
certain conditions. Armstrong summarizes these conditions:
(1) The event being predicted is important,
(2) The respondent has a plan (at least the high intenders do),
(3) The respondent can fulfill the plan,
(4) New information is unlikely to change the plan over the forecast horizon,
(5) Responses can be obtained from the decision maker, and
(6) The respondent reports correctly.
Such conditions are likely to be met for purchase intentions of “high
involvement” goods and services. Once convinced of the utility, the consumer makes a
definite plan to buy the goods at a fixed time. With the increase in purchasing power, the
consumer in a position to convert potential demand into effective demand. It is also
possible to accurately obtain intensions information from the consumers. This suggests
that intentions data could potentially improve accuracy of forecasts based solely on past
sales behavior for these products.
A variety of survey questions have been used to measure consumers purchase
intentions. Among the most commonly used measures are Juster’s 11-point purchase
probability scale and a 5-point likelihood of purchase scale. Juster’s 11-point purchase
probability scale provides substantially better predictions of purchase behavior than
other types of intentions scales.
Purchase probabilities and expectations are broader than direct intentions questions
because they refer to actions that might be unplanned as well as planned.

Assessing purchase probabilities and expectations may be advantageous in
situations where people realize that they may purchase an item even though they have no
plans at the time of the survey. Therefore, a smaller proportion of respondents reports
“zero” on purchase probability scales than report “no” on intentions scales.
For many studies, most purchases are made by those who had reported no plans to
buy. This occurs because although non-intenders seldom purchase, they are often the
largest group of respondents. Models have been developed to describe how purchase
intentions relate to purchase behavior
Two commonly used methods to forecast sales from intentions predict that the
proportion of consumers who will purchase will equal :
(1) The mean intent (transformed to lie between zero and one to represent the mean
probability of purchase), or
(2) The proportion of respondents indicating a positive purchase intent.
Intentions, by themselves, provide only a crude way to predict sales. Several
studies have shown that these methods often provide biased estimates of sales,
overstating or understating actual purchasing. Thus, when possible, sales data should be
used to adjust for the bias in intentions. The simplest way to do this is to relate an
aggregate measure of purchase intentions to an aggregate measure of sales.
For new products or new product groups, intentions are sometimes used
directly to forecast demand. However, when sales figures are available, it is sensible to
calibrate intentions against them. In other words, we look at a category of intenders and
determine what percent actually did purchase in that period. This relationship is then
extended to the period to be forecast.
Morrison developed a descriptive model of the relationship between purchase
intentions and subsequent purchasing. Morrison proposed that there are three threats to
the predictive validity of purchase intention measures. First, intentions are measured with
error. Second, respondents’ purchase intentions might change over time because of
exogenous events. Third, average stated purchase intentions might be a biased estimate of
the proportion that actually buy the product because of systematic error.

However, it was uniformly observed that for different products, time horizons, countries,
and types of intentions questions, the intentions surveys when combined with prior sales
data, were more accurate than forecasts based solely on past sales.
Understanding the ‘Intention to Try’
The Theory of Trying developed by Bagozzi and Warshaw emphasizes consumer
uncertainty when achievement of a consumption objective is not entirely within one’s
volitional control.
Impediments can take several forms: outcome uncertainty, lack of
knowledge/information, distortion of market information, unfavourable earlier
experiences, time pressure and cultural differences, need to be self-reliant and satisfaction
with current behaviour, when new solutions require efforts in terms of search costs,
transaction costs, etc.
FIGURE Basic Framework for Attitude Building for a Product

(Source : Agarwal & Agarwal, 2003)
These nine generic factors together affect the formation of an attitude towards
trying a product. Even if the consumer forms a favourable attitude towards trying the
Outcome Uncertainty
Outcome Uncertainty
Satisfaction with
current behaviour
Satisfaction with
current behaviour
Personal,
Environmental
Impediments
Personal,
Environmental
Impediments
Habits & Inertia
Habits & Inertia
Information Distortion
Information Distortion
Lack of knowledge
Lack of knowledge
Intention
to try
Intention
to try
Attitude towards
Trying
Attitude towards
Trying
Differed Gratification
Differed Gratification
Earlier
Experiences
Earlier
Experiences Recency
Recency
Being self reliant
Being self reliant
Self Expression
Self Expression
Trying
Trying
Social Stigma,
Cultural
Differences
Social Stigma,
Cultural
Differences
30

product, this might not directly translate into an intention to try. Earlier experiences and
socio-cultural norms applicable to the individual may also influence to some extent the
intention to try.
After the consumer develops an ‘intention to try’, the next step is to actually try
the product. However besides the ‘intention to try’, actual trying is also affected by
‘recency’. If the consumer has tried out a similar product in the recent past he will be
more amenable to trying out the product now. An important point to note is that earlier
experiences affect both the ‘intention to try’ and ‘actual trying’. When a person is not
clear about his intentions to try out a product, he may rely upon his past experiences to
decide whether he wants to actually try out the product.
It becomes essential therefore, while using intension surveys, to understand and
appreciate the factors that affect the formation of an attitude towards the product /
product category.
IV. Delphi Technique
Since its design at the RAND Corporation over 40 years ago, the Delphi technique
has become a widely used tool for measuring and aiding forecasting.
Delphi is not a procedure intended to challenge statistical or model-based
procedures. It is intended for use in judgment and forecasting situations in which pure
model-based statistical methods are not practical or possible because of the lack of
appropriate historical /economic/ technical data, and thus some form of human
judgmental input is necessary. Such input needs to be used as efficiently as possible, and
for this purpose Delphi technique might serve a role.
Four key features may be regarded as necessary for defining a procedure as a
‘Delphi’. These are: rounds anonymity, iteration, controlled feedback, and the statistical
aggregation of group response.
Anonymity is achieved through the use of questionnaires. By allowing the
individual group members the opportunity to express their opinions and judgments
privately, undue social pressures – as from dominant or dogmatic individuals, or from a
majority – should be avoided. Ideally, this should allow the individual group members to

consider each idea on the basis merit alone, rather than on the basis of potentially invalid
criteria (such as the status of an idea’s proponent).
Furthermore, with the iteration of the questionnaire over a number of rounds, the
individuals are given the opportunity to change their opinions and judgments without fear
of losing face in the eyes of the (anonymous) others in the group. Between each
questionnaire iteration, controlled feedback is provided through which the group
members are informed of the opinions of their anonymous colleagues.
The number of rounds is variable, though it seldom goes beyond one or two
iterations (during which time most change in panelists’ responses generally occurs).
To forecast with Delphi the administrator should recruit between five and twenty
suitable experts and poll them for their forecasts and reasons. The administrator then
provides the experts with anonymous summary statistics on the forecasts, and experts’
reasons for their forecasts. The process is repeated until there is little change in forecasts
between rounds – two or three rounds are usually sufficient. The Delphi forecast is the
median or mode of the experts’ final forecasts. Software to guide you through the
procedure is available. Rowe and Wright provide evidence on the accuracy of Delphi
forecasts. The forecasts from Delphi groups are substantially more accurate than forecasts
from unaided judgment and traditional groups, and are somewhat more accurate than
combined forecasts from unaided judgment.
V. Unaided Judgement
It is common practice to ask experts what will happen. This is a good procedure to
use when experts are unbiased, large changes are unlikely, relationships are well
understood by experts (e.g., demand goes up when prices go down), experts possess
privileged information and experts receive accurate and well-summarized feedback about
their forecasts. Unfortunately, unaided judgement is often used when the above
conditions do not hold. Green and Armstrong, for example, found that experts were no
better than chance when they use their unaided judgement to prepare forecasts in
complex situations. Considering this, unaided judgment will not be used in this study.

VI. Game Theory and Role Playing
Game theory has been touted in textbooks and research papers as a way to obtain
better forecasts in situations involving negotiations or other conflicts. A Google search
for “game theory” and “forecasting” or “prediction” identified 147,300 sites. Despite a
vast research effort, there is no research that directly tests the forecasting ability of game
theory. However, Green tested the ability of game theorists, who were urged to use game
theory in predicting the outcome of eight real (but disguised) situations. In that study,
game theorists were no more accurate than university students.
Role playing is well-suited to forecasting how people will respond to exogenous
pressures (actions of those outside the firm).
The accuracy gain of game theory over unaided judgment may be illusory, and the
advantage of role playing over game theory is likely to be greater than the 44% error
reduction found by Green. The improved accuracy of role playing over game theory was
consistent across situations. For those cases that simulated interactions among people
with conflicting roles, game theory was no better than chance (28% correct), whereas
role-playing was correct in 61% of the predictions.
VII. Bootstrapping
According to Armstrong, Brodie & McIntyre bootstrapping (including related
approaches such as expert systems and conjoint analysis) is one of the more important
advances for forecasting in marketing over the past quarter century. It was also noted as
one of the most significant advances in the field of agricultural forecasting.
Bootstrapping has been widely applied in marketing. Occasionally it has been
used with experts, but typically it is consumer intentions that are modeled. Over 1,000
marketing applications had been made by indirect bootstrapping of consumer intentions
by the early 1980s. These applications have been done under the umbrella term “conjoint
analysis.” Bootstrapping is nearly always more accurate than judgment.
VIII. Focus Groups

One popular type of survey, focus groups, violates five important principles and
they should not, therefore, be used in forecasting. First, focus groups are seldom
representative of the population of interest. Second, the responses of each participant are
influenced by the expressed opinions of others in the group. Third, a focus group is a
small sample – samples for intentions or expectations surveys typically include several
hundred people whereas a focus group will consist of between six and ten individuals.
Fourth, questions for the participants are generally not well structured. And fifth,
summaries of focus groups responses are often subject to bias. There is no evidence to
show that focus groups provide useful forecasts.
IX. Neural Nets
Neural networks are computer intensive methods that use decision processes
analogous to those of the human brain. Like the brain, they have the capability of
learning as patterns change and updating their parameter estimates. However, much data
is needed in order to estimate neural network models and to reduce the risk of over-fitting
the data. There is some evidence that neural network models can produce forecasts that
are more accurate than those from other methods. While this is encouraging, our current
advice is to avoid neural networks because the method ignores prior knowledge and
because the results are difficult to understand.
X. Data Mining
Data mining ignores theory and prior knowledge in a search for patterns. Despite
ambitious claims and much research effort, we are not aware of evidence that data mining
techniques provide benefits for forecasting. In their extensive search and reanalysis of
data from published research, Keogh and Kasetty found little evidence for that data
mining is useful.
XI. Segmentation

Segmentation involves breaking a problem down into independent parts, using data
for each part to make a forecast, and then combining the parts.
To forecast using segmentation, one must first identify important causal variables
that can be used to define the segments, and their priorities. For each variable, cut-points
are determined such that the stronger the relationship with dependent variable, the greater
the non-linearity in the relationship, and the more data that are available the more cut-
points should be used. Forecasts are made for the population of each segment and the
behaviour of the population within the segment using the best method or methods given
the information available. Population and behaviour forecasts are combined for each
segment and the segment forecasts summed.
Where there is interaction between variables, the effect of variables on demand
are non-linear, and the effects of some variables can dominate others, segmentation
has advantages over regression analysis.
Efforts at dependent segmentation have gone under the names of microsimulation,
world dynamics, and system dynamics. While the simulation approach seems
reasonable, the models are complex and hence there are many opportunities for
judgemental errors and biases. Armstrong found no evidence that these simulation
approaches provide valid forecasts and there appears no reason to change this assessment.
XII. Rule Based Forecasting
Rule-based forecasting incorporates information from experts and from prior
research. The procedure calls for the development of empirically validated and fully
disclosed rules for the selection and combination of methods.
When large changes are expected, one should draw upon methods that
incorporate causal reasoning. If the anticipated changes are unusual, judgmental
methods such as Delphi would be appropriate. If the changes are expected to be
large, the causes are well understood, and if one lacks historical data, then
judgmental bootstrapping can be used to improve forecasting.

Other Forecasting Imperatives
I. Identifying causal variables
Environmental forecasts are useful as an input to strategic planning. The
identification of possible states of the environment and a forecast of their likelihood can
provide ideas on future demand trajectories. Environmental forecasts also can help to
provide better industry forecasts (e.g. the total demand for a product class in a given
market).
It is important that the forecasting methods first identify the possible states of the
future. For this, brainstorming among a variety of experts would be useful. Particular
attention would be given to the more important of these possible states. Importance
should be judged not only by the likelihood of the environmental change, but also by its
potential impact on the Industry if it does occur. It becomes important while forecasting
long range demand to assess the likelihood of this event occurring and therefore, its
potential impact on altering demand patterns of the industry.
There is some evidence to show that the accuracy of forecasts of environmental
variables is not as important as is identifying the key variables to include in the
market forecasting model. Measurement error in the causal variables (e.g., the
environmental inputs to a market forecasting model) had little impact on the accuracy of
an econometric model.
It is important to determine which are the important factors in the
environment that might affect the industry. It is also important to predict the
direction of change in the important factors, and to then get “approximately
correct” predictions of the magnitude of the changes in these factors.
For the direction of change in environmental factors, only general trends, not
cycles, should be considered. Other than recurrent events owing to the seasons of the year
(seasonality), cycles have been of little value for improving the accuracy of forecasts.
The reason? One must also predict the phases (timing) of the cycles. If the timing is off,
large errors can occur. Organizations should have a system for scanning the environment

to be sure that they do not overlook variables that may have a large impact on their
market. These variables can be tracked through marketing information systems. Periodic
brainstorming with a heterogeneous group of experts should be sufficient to identify
which variables to track. The key is to identify the important variables and the
direction of their effects. Once identified, only crude estimates of the coefficients of
these variables are typically sufficient in order to obtain useful forecasts. When large
shocks are encountered, more sophisticated approaches may be useful.
II. Estimating Uncertainty
In addition to improving accuracy, forecasting is concerned with assessing
uncertainty. This can help manage the risk associated with the forecasts. Much work has
been done on judgmental estimates of uncertainty. One of the key findings is that judges
are typically overconfident. Fischoff and MacGregor found that 95% confidence ranges
that are estimated judgmentally typically fail to include the true value. This bias occurs
even when subjects are warned in advance about the overconfidence phenomenon.
Nevertheless, judgmental expressions of uncertainty have been found to be useful.
One way to assess uncertainty has been to examine the agreement among
judgmental forecasts. For example, Ashton, found that the agreement among the
individual judgmental forecasts was a useful proxy for accuracy.
Probably the best way to assess uncertainty is to follow the track record of a given
forecasting method in actual use.
Traditional error measures, such as the mean square error (MSE), do not provide a
reliable basis for comparison of forecasting methods. The median absolute percentage
error (MdAPE) is more appropriate because it is invariant to scale and is not influenced
by outliers. When comparing methods, especially when testing on a small number of
series, control for degree of difficulty in forecasting by using the median relative absolute
error (MdRAE), which compares the error for a given model against errors for the naive,
no change forecast.
Statisticians have relied heavily on tests of statistical significance for assessing
uncertainty. However, statistical significance is inappropriate for assessing

uncertainty in forecasting. Furthermore, its use has been attacked as being misleading.
It is difficult to find studies in marketing forecasting where statistical significance has
made an important contribution.
Instead of statistical significance, the focus should be on prediction intervals.
Chatfield summarizes research on prediction intervals. Unfortunately, prediction intervals
are not widely used in practice. Tull’s survey noted that only 25% of 16 respondent
companies said they provided confidence intervals with their forecasts. Dalrymple found
that 48% did not use confidence intervals, and only 10% ‘usually’ used them.
In a survey of experts by Yokum and Armstrong half said that it was important ‘that
your forecasting methods provide confidence bounds on the forecasts’, while 20% said
this was not important.
III. Overlooked Discontinuities
Considering the wide range of random shocks that affect an industry, there is strong
agreement about the importance of discontinuities in forecasting. This was surprising
because this topic has been largely ignored in the forecasting literature.
Identifying areas of uncertainty or disagreement among experts, or disagreements
between researchers and practitioners, could help to guide further research. Also, the
opinions might aid in the development of expert systems for forecasting.
In a study of experts by Callopy and Armstong, 92% of the experts agreed that
“abrupt changes” are an important consideration while forecasting demand. This is
surprising given that time series forecasting research and practice have largely ignored
abrupt changes. Examination of a convenience sample of indices of 28 books that discuss
time series forecasting did not include any reference to ‘abrupt changes’,
‘discontinuities’, ‘erratic fluctuations’, ‘interruptions’, ‘irregularities’, ‘ramps’, ‘shifts’,
‘steps’, and variations on these terms.
The experts agreed that seasonality and recent trend were key features. The
experts also placed a heavy emphasis on the importance of abrupt changes in the
historical data patterns. This stands in stark contrast to forecasting methods and
forecasting research which have long ignored abrupt changes. We have no explanation

for this mystery of the overlooked discontinuities. Fortunately, software developers are
responding to this problem.
IV. Combining Forecasts
Considerable literature has accumulated over the years regarding the combination
of forecasts. The primary conclusion of this line of research is that forecast accuracy
can be substantially improved through the combination of multiple individual
forecasts.
Clemen is a milestone on the topic of combining forecasts. As noted by Clemen,
past research has produced two primary conclusions, one expected and one surprising.
The expected conclusion is that combined forecasts reduce error (in comparison with the
average error of the component forecasts). The unexpected conclusion is that the simple
average performs as well as more sophisticated statistical approaches.
Combining forecasts is more useful for long-range forecasting because of the
greater uncertainty.
The level of aggregation of the data was expected to be related to the relative
accuracy of alternative extrapolation methods by 88% of the experts. We speculate that
the level of aggregation may be important because different causal factors might affect
different components. Highly aggregated data are more likely to be subject to different
causal factors than are less aggregated data. On the other hand, the reliability of data
often improves when one uses larger aggregates. 83% of the experts with an opinion
believe that combining will produce more accurate forecasts.
Clemen advises forecasters to select a set of methods that differ substantially from
one another with respect to the data used and also with respect to the procedures for
analyzing the data. The experts believed that, in general, combined forecasts are more
accurate than those based on a single method: 73% of the respondents agreed and only
15% disagreed.
Combined forecasts improve accuracy and reduce the likelihood of large errors. In a
meta-analysis, Armstrong found an average error reduction of about 12% across 30

comparisons. They are especially useful when the component methods differ
substantially from one another. For example, Blattberg and Hoch obtained improved
sales forecast by averaging managers’ judgmental forecasts and forecasts from a
quantitative model. Considerable research suggests that, lacking well-structured domain
knowledge, unweighted averages are typically as accurate as other weighting schemes.
Callopy and Armstrong favored simple methods of preparing and combining
forecasts for stable and unstable situations, with a slightly stronger preference for their
use in unstable situations. Schnaars’ results implied that simple models are most
appropriate for unstable situations. The use of a simple average has proven to do as well
as more sophisticated approaches. An alternative simple approach, the median, might
offer additional benefits. It is less likely to be affected by errors in the data. Whether the
median is superior to the mean is an empirical issue. Meta-analysis may prove useful
here. Two studies that address this issue (Larréché and Moinpour, 1983, Agnew 1985,
cited in Armstrong, 1989) suggest that the median would improve accuracy. Certainly,
there are situations where one method is more accurate than another.
V. Value of Expertise in Judgmental Forecasts
An interesting issue is how much expertise is needed for judgmental forecasting.
Surprisingly, research to date indicates that high expertise in the subject area is not
important for judgmental forecasts of change. It is, however, important for assessing
current levels. An important conclusion, then, is not to spend heavily to obtain the best
experts in the field to forecast change. But one should avoid people who clearly have no
expertise.
Extensive research over the last two decades has examined biases that occur in
judgmental forecasting. Among these biases are optimism, conservatism, anchoring, and
an overemphasis on easily available data. While some sources of bias have been
identified, little knowledge exists as to how these biases affect marketing forecasts.
When using experts, it is essential to bear in mind that people who hold viewpoints
on an issue tend to perceive the world so as to reinforce what they already believe; they
look for "confirming" evidence and avoid "disconfirming" evidence. There is much

literature on this phenomenon, commonly known as "selective perception." In cases
where disconfirming evidence is thrust upon people, they tend to remember incorrectly.
Fischhoff and Beyth, for example, found that subjects tended to remember their
predictions differently if the outcome was in conflict with their prediction.
Experts are typically overconfident. In McNee’s examination of economic
forecasts from 22 economists over 11 years, the actual values fell outside the range of
their prediction intervals about 43% of the time. This occurs even when subjects are
warned in advance against overconfidence.
Fortunately, there are procedures to improve forecasts by experts. A commonly
used technique is to ask experts to write all the reasons why their forecasts might be
wrong. Alternatively, use the devil’s advocate procedure, where someone is assigned for
a short time to raise arguments about why the forecast might be wrong. However, playing
devil’s advocate does make the person unpopular with the group. Still another way to
assess uncertainty is to examine the agreement among judgmental forecasts. For example,
Ashton, in a study of forecasts of annual advertising sales for Time magazine, found that
the agreement among the individual judgmental forecasts was a good proxy for
uncertainty.
If we take Bayes’s theorem as the standard, people tend to adjust their predictions
less than they should when they receive new information. When they consider the
likelihood of an outcome from a multistage process (Hitler invades Belgium, he succeeds,
Britain declares war, Hitler attacks Britain) people have the opposite tendency: they act
as though their best guesses of what will happen at early stages are certainties.
Stewart found that judgmental forecasts are likely to be unreliable when
(1) The task is complex,
(2) There is uncertainty about the environment,
(3) Information acquisition is subjective, or
(4) Information processing is subjective.
People are willing to pay heavily for expert advice. However, expertise beyond
a minimal level is of little value in forecasting. This conclusion is both surprising and
useful, and its implication is clear: Don't hire the best expert, hire the cheapest expert.
"Expertise … breeds an inability to accept new views." - Laski (1930)

Figure Value of Expertise in forecasting
(Source: Armstrong, 1980)
Although experts are poor at forecasting, this does not mean that judgmental
forecasting is useless. However, since all available evidence suggests that expertise
beyond an easily achieved minimum is of little value in forecasting change, the most
obvious advice is to hire inexpensive experts. Also, look for unbiased experts – those
who are not actually involved in the situation. Finally, there is safety in numbers.
Robin Hogarth has suggested using at least three independent experts and preferably six
to ten.
VI. Using Multiple Hypotheses
Green used the method of multiple hypotheses. This is an important procedure in
ensuring objectivity and accuracy in forecasting.

Evaluating the utility of the Forecasting Model
The usefulness of a quantitative model depends on both "acceptability" and
"quality." Acceptability refers to approval by those who would actually use the model,
while quality refers to the ability to provide better predictions or decisions. A model must
score well on both characteristics if it is to be judged useful. A high-quality model that is
not accepted is of no value. Usually, some trade-offs must be made between quality and
acceptability.
A model is said to be "good" if it is better than alternative models. Quality and
acceptability are characteristics that may depend not only upon the model but also upon
the situation.
Research in forecasting has commonly assumed that accuracy is the primary
criterion in selecting among forecasting techniques. In fact, it has been used as the sole
criterion in many studies. In the sixteen 1992 International Journal of Forecasting papers
that compared the results of different techniques and series, only one used criteria other
than accuracy.
When asked ‘Relative to other considerations (e.g. cost, ease of interpretation,
cost/time, ease of use), how important is the accuracy of the forecasting methods that you
use?’ 29% of the experts said that accuracy was ‘extremely important’ and an additional
56% said that it was ‘important’. These results are similar to the opinions of practitioners
and researchers as reported in Carbone and Armstrong and with those of practitioners as
reported by Mentzer and Cox.
Table Rankings of criteria from previous studies (number of respondents)

(Source : Yokum and Armstrong, 1995)
However, this single-minded focus on accuracy is not completely reasonable.
To encourage diffusion, new techniques should be evaluated, not only in terms of
comparative accuracy, but also in terms of the "ease of use,” "ease of interpretation,” and
"flexibility.” "Cost savings" varied in rank depending upon its framing from a top
criterion if related to savings from improved decisions to a lower criterion if linked to
savings from technique development and maintenance.
Witt and Witt found that "speed" was most important for short-range forecasts,
while "accuracy" was most important for medium- and long-term forecasts.
The evaluation of overall quality of the model calls for an examination of four key
stages.
The first stage relates the "real world" to the assumptions of the model: Are the
assumptions reasonable and comprehensive? A review of written documents must be
carried out in order to develop an explicit listing of the key assumptions. This list may be
checked by conducting interviews with the advocates of the model. The assumptions are
then tested for reasonableness against:
(1) Empirical evidence,
(2) Judgments of managers, and
(3) Assessments by the evaluator.
Admittedly, this procedure is rather crude; however, the objective at this stage is
merely to identify “highly unreasonable" assumptions. Their appeal was strictly one of
face validity—that is, the assumptions seem reasonable.

The second stage relates the model's assumptions to the final form of the model.
Does the model follow logically from the assumptions? This is an examination of the
logical structure of the model. This stage of analysis is generally the most important one
for assessing the quality of a model. One possible approach is to assess the total costs
associated with the model [Initial development (money and time), Maintenance (money
and time), User (ease in understanding, time to get results, need for expert assistance)]
versus the total benefits derived [Predictive accuracy, Ability to assess uncertainty,
Identification of improved policies, Learning (the model improves as experience is
gained), Ability to assess effects of alternative policies, Adaptability (can adapt as the
environment changes)]
The third stage relates the model and its outputs: Given the same input data, can
the outputs be replicated?
And the fourth stage relates the outputs to the real world: Do the benefits of the
model (e.g., better predictions, better assessments of risk, or better decision making)
justify the costs of the model?
Based on the foregoing sections that review empirical forecasting literature, a
summary of general principles to be used while developing forecasting procedures is
summarized below:
• Domain knowledge should be incorporated into forecasting methods.
• When making forecasts in highly uncertain situations, be conservative. For
example, the trend should be dampened over the forecast horizon.
• Complex methods have not proven to be more accurate than relatively simple
methods. Given their added cost and the reduced understanding among users,
highly complex procedures cannot be justified.
• In case data on actual behaviour is unavailable, forecasts based on judgments or
intentions, may be used to predict behaviour.
• Methods that integrate judgmental and statistical data and procedures (e.g., rule-
based forecasting) can improve forecast accuracy.
• When making forecasts in situations with high uncertainty, use more than one
method and combine the forecasts, generally using simple averages.

CHAPTER
THE NEW COMPETITION

A profound, but silent, transformation of our society is afoot. Our industrial
system is generating more goods and services than at any point in history, delivered
through an ever-growing number of channels. Superstores, boutiques, online retailers and
discount stores proliferate, offering thousands of distinct products and services. This
product variety is overwhelming to consumers. Simultaneously, thanks to the propagation
of cell phones, Web sites, and media channels, consumers have increased access to more
information, at greater speed and lower cost than ever before. But who has the leisure and
the proficiency needed to sort through and evaluate all these products and services? The
burgeoning complexity of offerings, as well as the associated risks and rewards,
confounds and frustrates most time-starved consumers. Product variety has not
necessarily resulted in better consumer experiences.
For senior management, the situation is no better. Advances in digitization,
biotechnology, and smart materials are increasing opportunities to create fundamentally
new products and services and transform businesses. Major discontinuities in the
competitive landscape –ubiquitous connectivity, globalization, industry deregulation, and
technology convergence- are blurring industry boundaries and product definitions. These
discontinuities are releasing worldwide flows of information, capital, products, and ideas,
allowing nontraditional competitors to upend the status quo. At the same time,
competition is intensifying and profit margins are shrinking. Managers can no longer
focus solely on costs, product and process quality, speed, and efficiency. For profitable
growth, managers must also strive for new sources of innovation and creativity.
Thus, the paradox of the twenty-first-century economy: Consumers have more
choices that yield less satisfaction. Top management has more strategic options that yield
less value. Are we on the cusp of a new industrial system with characteristics different
from those we now take for granted? The emerging reality is forcing us to reexamine the
traditional system of company-centric value creation that has served us so well over the
past hundred years. We now need a new frame of reference for value creation. The
answer, we believe, lies in a different premise centered on co-creation of value. It begins
with the changing role of the consumer in the industrial system.

CHAPTER
THE INDUSTRY LIFE CYCLE

The nature of new competition leads us to one conclusion. The industry life cycles
are getting shorter. Every firm in every industry is trying to get a competitive advantage
over the other. It tries this in various ways- publicity, differentiated products, low prices,
huge advertising budgets, unique benefits of the products, degrading the competitors’
product, etc.
But one must realize that all these are ways to survive in the market. A firm needs
to give a product which is different from its competitor or else its products will not be
accepted. The consumer wants to know the marginal benefit he will receive by using
Company X’s product as against Company Y’s. He wants the product at low price with
additional features and services. He wants value for his money.
All these aspects have resulted in shorter industry life cycles. And hence, shorter
company life cycles. Then profit margins are reducing, the managers try to reduce the
cost of production yet not compromising on the quality of the product.
It is traditional to categorize enterprises as business-to-business (B2B) or
business-to-consumer (B2C), decidedly putting “business” first and taking a firm centric
view of the economy. But these conventions are challenged in today’s dynamic economy.
What if the individual consumer (whether an enterprise or a household) were at the center
and not the firm? What if we spoke of “consumer-to-business-to-consumer” (C2B2C)
patterns of economic activity?
Consequently, we challenge the traditional notion of value an its creation, namely
that firms create and exchange value with customers. The joint efforts of the consumer
and the firm-the firm’s extended network and consumer communities together-are co-
creating value through personalized experiences that are unique to each individual
consumer. This proposition challenges the fundamental assumptions about our industrial
system-assumptions about value itself, the value creation process, and the nature of the
relationship between the firm and the consumer. In this new paradigm, the firm and the
consumer co-create value at points of interaction. Firms cannot think and act unilaterally.
We will now analyze how the consumer’s role had changed in the new
environment and how a firm can operate in the using the new emerging managerial
principles and hence increase the company life cycles.

CHAPTER
THE CHANGING ROLE OF CONSUMER AND VALUE CREATION

The most basic change has been a shift in the role of the consumer-from isolated to
connected, from unaware to informed, from passive to active. The impact of the
connected, informed, and active consumer is manifest in many ways. Let us examine
some of them.
1. Information Access
With access to unprecedented amounts of information, knowledgeable consumers can
make more informed decisions. For companies accustomed to restricting the flow of
information to consumers, this shift is radical. Millions of networked consumers are now
collectively challenging the traditions of industries as varied as entertainment, financial
services, and health care.
2. Global View
Consumers can also access information on firms, products, technologies, performance,
prices, and consumer actions and reactions from around the world. Twenty years ago, the
two car dealerships (General Motors and Ford) in small towns in North America would
probably have influenced the driving aspirations of a local teenager. Today, a teen
anywhere can dream about owning one of more than seven hundred car models listed on
the Internet, creating a serious gap between what is immediately available in the
neighborhood and what is most desirable. Geographical limits on information still exist,
but they are eroding fast, changing the rules of business competition. For example,
broader consumer scrutiny of product range, price, and performance across geographic
borders is limiting multinational firms’ freedom to vary the price or quality of products
from one location to another.
3. Networking
Human beings have a natural desire to coalesce around common interests, needs, and
experiences. The explosion of the Internet and advances in messaging and telephony-the
number of mobile phone users is already over one billion-is fueling this desire, creating
an unparalleled ease and openness of communication among consumers. Consequently,
"thematic consumer communities," in which individuals share ideas and feelings without
regard for geographic or social barriers, are revolutionizing emerging markets and
transforming established ones. The power of consumer communities comes from their
independence from the firm.

4. Experimentation
Consumers can also use the Internet to experiment with and develop products, especially
digital ones. Consider MP3, the compression standard for encoding digital audio
developed by a student Karlheinz Brandenburg and released to the public by the
Fraunhofer Institute in Germany. Once technology-savvy consumers began
experimenting with MP3, a veritable audio-file-sharing movement surged to challenge
the music industry. The collective genius of software users the world over has similarly
enabled the co-development of such popular products as the Apache Web server software
and the Linux operating system.
Of course, the Internet facilitates consumer sharing in nondigital spheres as well:
Aspiring chefs swap recipes, gardening enthusiasts share tips on growing organic
vegetables, and homeowners share in- sights into home improvements. More crucial,
consumer networks allow proxy experimentation-that is, learning from the experiences of
others. The diversity of informed consumers around the world creates a wide base of
skills, sophistication, and interests that any individual can tap into.
5. Activism
As people learn, they can better discriminate when making choices;
and, as they network, they embolden each other to act and speak out. Consumers
increasingly provide unsolicited feedback to companies and to each other. Already,
hundreds of Web sites are perpetuating consumer activism, many targeting specific
companies and brands. The Web has also become a powerful tool by which groups
focused on issues such as child labor and environmental protection seek corporate and
governmental attention and promote reforms. Consumer advocacy through online groups
may have even greater impact than company marketing. When Novartis AG launched
clinical trials of a promising leukemia drug, Gleevec, word spread so fast on the Internet
that the company was inundated by demand from patients wanting to participate.
Activism by leukemia patients who were on the early clinical trials for this drug led to a
highly effective lobbying effort via Inter- net support groups to speed up its production,
and even get the Food & Drug Administration (FDA) to expedite its approval.

What is the net result of the changing role of consumers? Companies can no
longer act autonomously, designing products, developing production processes, crafting
marketing messages, and controlling sales channels with little or no interference from
consumers. Consumers now seek to exercise their influence in every part of the business
system. Armed with new tools and dissatisfied with available choices, consumers want to
interact with firms and thereby co-create value. The use of interaction as a basis for co-
creation is at the crux of our emerging reality.
Consumer- Company Interactions: The Emerging Reality of Value Creation
Consider the evolution of the health care industry. Innovations in
pharmaceuticals, biotechnology, nutrition, cosmetics, and alternative therapies are
creating various treatment modalities and transforming our concepts of health. As both
consumers and technologies advance, traditional medicine ("curing sickness"), preventive
medicine, and improvements in the quality of life are rapidly merging into a “wellness
space.” Let us examine the changing dynamics of interaction between a consumer and the
firms that participate in the wellness space.
Twenty years ago, when I was feeling ill and visited my doctor, I might have
undergone a battery of tests that would have informed my doctor’s diagnosis, which he
would explain to me only if he had to. He would then choose a treatment modality,
prescribe some medications, and schedule a follow-up examination. Health care back
then was generally doctor-centric, just as commerce was company-centric. Doctors
thought that they knew how to treat me, and since I wasn’t a physician I probably agreed.
Similarly, most businesses figured that they knew how to create customer value-and most
customers agreed.
Now, health care process is far more complex. As soon as I feel ill, I will tap into
the expertise and experience of other patients and :health care professionals. I can access
an abundance of information, some of it reliable, some not. I can learn what I want about
breast cancer or high cholesterol or liposuction. I can investigate alternative treatments
for any condition and develop an opinion about what might and might not work for me.
Ultimately, I can cut my own path through the wellness space, thereby
constructing a personal wellness portfolio. If I'm grappling with high cholesterol, then I

can include pharmaceuticals for blood pressure and cholesterol approved by the FDA,
health supplements not approved by the FDA, a fitness regimen developed with an
instructor, and genetic screening for hereditary heart disease.
Notice that my wellness portfolio does not fit neatly into any traditional industry
classification. Yes, I visit my doctor. I get tests and medications and submit the bills to
my medical insurance, provided through my employer. But other services in my wellness
portfolio fall outside the conventional doctor-based health care, pharmaceutical, or
insurance industries. My wellness space springs from my view of wellness, my biases,
values, expertise, preferences, expectations, experiences, and financial wherewithal. My
spouse, meanwhile, can construct her own wellness portfolio. Rather than rely solely on
my doctors’ expertise, I can seek experts among my peers-other health care consumers-
organized into thematic communities, such as a high-cholesterol group. This networked
knowledge encompasses not just the medical aspects pertinent to my condition but its
sociology, psychology, and likely impact on me, my family, and the community at large.
Thus, my next visit to the doctor can differ dramatically from the conventional
checkup. I can ask, Why did you prescribe this treatment? Why not the alternative that I
found through my exploration with other consumers and the Web? My doctor probably
won't enjoy my challenging his expertise and authority. After all, I’m asking him to
explain and defend his approach, which takes time and energy. What’s more, I’m testing
the depth, breadth, and currency of his knowledge. What if I’m experimenting with
alternatives-herbs, dietary supplements, and so on-that he may not yet understand? Will
he know of any complex interactions between these treatments modalities? Should he?
Of course, health care consumers have always shaped their own treatment to a certain
extend. Remember Grandma's prescribing a remedy such as chicken soup for a cold? But
with today's access to information, consumer war stories, and advice from an experienced
peer group, consumers are far more likely to network and experiment than ever before.
As a health care consumer, I can more actively determine the "value bundle" that is
appropriate for me, cutting across customary industry boundaries.
Now position yourself as a manager in a pharmaceutical firm. The commingling
of traditional industries into a complex, evolving wellness space challenges deeply
entrenched and implicit assumptions in managerial tradition, which have evolved over

decades. For starters, what constitutes or defines a product or service? Is an anti-wrinkle
cream with Retinol a cosmetic, a fashion, or a pharmaceutical product? With unclear
industry boundaries, how do we identify the nature of our competitive advantage?
More important, what value does the pharmaceutical firm provide in wellness
space of an active, involved consumer? How does the consumer’s increasing desire to
interact with both the providers and their provisions affect the various parties involved in
that consumer’s wellness space? Who bears the risk-the doctor, the hospital, or the
patient? Patients will likely hold doctors, as experts, accountable.
Let’s move beyond doctors and patients. What if consumers inappropriately use
or modify your products and then hold you responsible for any resulting damage?
Increasingly, consumers seem to want power without accountability. They want to
choose for themselves but not be liable for the consequences of their choice. Are you as a
manager responsible for the product's performance even though you cannot control the
consumer’s usage? How do you protect yourself? Is this risk a new cost of doing
business? No matter how the future unfolds in terms of the roles, rights and
responsibilities of companies and consumers, companies will have to engage consumers
in co-creation of value.
Thus we scrutinize consumer-company interactions and amplify the weak signals
reverberating in the wellness space, we glimpse the emerging reality of the active
involvement of consumers whether as thematic communities or as informed individuals.
This fundamentally challenges two deeply embedded, traditional business assumptions:
(1) that any given company or industry can create value unilaterally
(2) that value resides exclusively in the company's or industry's products and services.
Escaping the Past: The Traditional System of Value Creation

The traditional belief structure that has served business leaders so well for the past
hundred years is shown in the figure.
The relationships between the rows and the columns in the chart depict the
internal consistency of the traditional logic of value creation. Let us start with the
premises in the top row of the figure. Traditional business thinking starts with the

premise that the firm creates value. A firm autonomously determines the value that it will
provide through its choice of products and services. Consumers represent demand for the
firm's offerings.
The implications for business follow from these premises. The firm needs an
interface with consumers-an exchange process-to move its goods and services. This firm-
customer interface has long been the locus of the producer's extracting economic value
from the consumer. Firms have developed multiple approaches to extracting this value-by
increasing the variety of offerings, by efficiently delivering and servicing those offerings,
by customizing them for individual consumers, or by wrapping contexts around them and
staging the value creation process, as themed restaurants do.
These premises and implications manifest themselves in the perspectives and
practices of firms in the industrial system. Managers focus on the “value chain” that
captures the flow of products and services through operations that the firm controls or
influences. This value chain system essentially represents the “linear cost build” of
products and services. Decisions on what to make, what to buy from suppliers, where to
assemble and service products, and a host of other supply and logistics decisions all
emanate from this perspective. Employees focus on the quality of the firm’s products and
processes, potentially enhanced through internal disciplines such as Six Sigma and Total
Quality Management. Innovation involves technology, products, and processes.
Thus, we have a coherent system for value creation. The rows and columns are internally
consistent. If the firm creates value, then the value creation process is separate from the
market, where various parties simply exchange this value. The importance of efficiently
matching supply from the firm’s value chain with demand from consumers becomes
obvious. In fact, matching supply and demand has long been the bedrock of the value
creation process.
Consider the shifts in thinking identified thus far. Consumers are overwhelmed
and dissatisfied by the product variety available today. Armed with new connective tools,
consumers want to interact and co-create value, not just with one firm but with whole
communities of professionals, service providers, and other consumers. The co-creation
experience depends highly on individuals. Each person’s uniqueness affects the co-
creation process as well as the co-creation experience. A firm cannot create anything of

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