Demand Forecasting

Theory of Demand


2.1 Introduction

Demand theory evinces the relationship between the demand for goods and

services. Demand theory is the building block of the demand curve- a curve that establishes a relationship between consumer demand and the amount of goods available. Demand is shaped by the availability of goods, as the quantity of goods increases in the market the demand and the equilibrium price for those goods decreases as a result.

Demand theory is one of the core theories of microeconomics and consumer

behaviour. It attempts at answering questions regarding the magnitude of demand for a product or service based on its importance to human wants. It also attempts to assess how demand is impacted by changes in prices and income levels and consumers preferences/utility. Based on the perceived utility of goods and services to consumers,

companies are able to adjust the supply available and the prices charged.

In economics, demand has a specific meaning distinct from its ordinary usage. In

common language we treat ‘demand’ and ‘desire’ as synonymously. This is incongruent from its use in economics. In economics, demand refers to effective demand which implies three things:

Desire for a commodity

Sufficient money to purchase the commodity, rather the ability to pay

Willingness to spend money to acquire that commodity

This substantiates that a want or a desire does not develop into a demand unless it is supported by the ability and the willingness to acquire it. For instance, a person may desire to own a scooter but unless he has the required amount of money with him and the willingness to spend that amount on the purchase of a scooter, his desire shall not become a demand. The following should also be noted about demand:

Demand always alludes to demand at price. The term ‘demand’ has no meaning

unless it is related to price. For instance, the statement, 'the weekly demand for potatoes in city X is 10,000 kilograms' has no meaning unless we specify the price at which this quantity is demanded.

Demand always implies demand per unit of time. Therefore, it is vital to specify the period for which the commodity is demanded. For instance, the statement that demand for potatoes in city X at Rs. 8 per kilogram is 10,000 kilograms again has no meaning, unless we state the period for which the quantity is being demanded. A complete statement would therefore be as follows: 'The weekly demand for potatoes in city X at Rs. 8 per kilogram is 10,000 kilograms'. It is necessary to specify the period and the price because demand for a commodity will be different at different prices of that commodity and for different periods of time. Thus, we can define demand as follows:

“The demand for a commodity at a given price is the amount of it which will be bought per unit of time at that price”.

2.2 Theory of Demand

2.2.1 ESSENTIALS OF DEMAND

1. An Effective Need: Effective need entails that there should be a need supported by the capacity and readiness to shell out. Hence, there are three basics of an effective need:

a. The individual should have a need to acquire a specific product.

b. He should have sufficient funds to pay for that product.

c. He should be willing to part with these resources for that commodity.

2. A Specific Price: A proclamation concerning the demand of a product without

mentioning its price is worthless. For example, to state that the demand of cars is 10,000 is worthless, unless expressed that the demand of cars is 10,000 at a price of Rs. 4,00,000 each.

3. A Specific Time: Demand must be assigned specific time. For example, it is an

incomplete proclamation to state that the demand of air conditioners is 4,000 at the price of Rs. 12,800 each. The statement should be altered to say that the demand of air conditioners during summer is 4,000 at the price of Rs. 12,800 each.

4. A Specific Place: The demand must relate to a specific market as well. For example, every year in the town of Dehradun, the demand for school bags is 4,000 at a price of Rs. 200.

Hence, the demand of a product is an effective need, which demonstrates the

quantity of a product that will be bought at a specific price in a specific market at some stage in a specific period. Nevertheless, the significance of a specific market or place is not as significant as the price and time period for which demand is being measured.

2.2.2 LAW OF DEMAND

We have considered various factors that fashion the demand for a commodity. As explained the first and the most important factor that determines the demand of a commodity is its price. If all other factors (noted above) remain constant, it may be said that as the price of a commodity increases, its demand decreases and as the price of a commodity decreases its demand increases. This is a general behaviour observed in a market. This gives us the law of demand:

“The demand for a commodity increases with a fall in its price and decreases with a rise in its

price, other things remaining the same”.

The law of demand thus merely states that the price and demand of a commodity are inversely related, provided all other things remain unchanged or as economists put it ceterisparibus.

Assumptions of the Law of Demand

The above statement of the law of demand, demonstrates that that this law operates only when all other things remain constant. These are then the assumptions of the law of demand. We can state the assumptions of the law of demand as follows:

1. Income level should remain constant: The law of demand operates only when the

income level of the buyer remains constant. If the income rises while the price of the commodity does not fall, it is quite likely that the demand may increase. Therefore, stability in income is an essential condition for the operation of the law of demand.

2. Tastes of the buyer should not alter: Any alteration that takes place in the taste of the consumers will in all probability thwart the working of the law of demand. It often happens that when tastes or fashions change people revise their preferences. As a consequence, the demand for the commodity which goes down the preference scale of the consumers declines even though its price does not change.

3. Prices of other goods should remain constant: Changes in the prices of other goods often impinge on the demand for a particular commodity. If prices of commodities for which demand is inelastic rise, the demand for a commodity other than these in all probability will decline even though there may not be any change in its price. Therefore, for the law of demand to operate it is imperative that prices of other goods do not change.

4. No new substitutes for the commodity: If some new substitutes for a commodity

appear in the market, its demand generally declines. This is quite natural, because with the availability of new substitutes some buyers will be attracted towards new products and the demand for the older product will fall even though price remains unchanged.

Hence, the law of demand operates only when the market for a commodity is not

threatened by new substitutes.

5. Price rise in future should not be expected: If the buyers of a commodity expect that its price will rise in future they raise its demand in response to an initial price rise. This behaviour of buyers violates the law of demand. Therefore, for the operation of the law of demand it is necessary that there must not be any expectations of price rise in the future.

6. Advertising expenditure should remain the same: If the advertising expenditure of a firm increases, the consumers may be tempted to buy more of its product. Therefore, the advertising expenditure on the good under consideration is taken to be constant.

Desire of a person to purchase a commodity is not his demand. He must possess

adequate resources and must be willing to spend his resources to buy the commodity.

Besides, the quantity demanded has always a reference to ‘a price’ and ‘a unity of time’. The quantity demanded referred to ‘per unit of time’ makes it a flow concept. There may be some problems in applying this flow concept to the demand for durable consumer goods like house, car, refrigerators, etc. However, this apparent difficulty may be resolved by considering the total service of a durable good is not consumed at one point of time and its utility is not exhausted in a single use. The service of a durable good is consumed over time.

At a time, only a part of its service is consumed. Therefore, the demand for the services of durable consumer goods may also be visualised as a demand per unit of time. However, this problem does not arise when the concept of demand is applied to total demand for a consumer durable. Thus, the demand for consumer goods also is a flow concept.

Demand Schedule

The law of demand can be illustrated through a demand schedule. A demand

schedule is a series of quantities, which consumers would like to buy per unit of time at

different prices. To illustrate the law of demand, an imaginary demand schedule for tea is

given in Table 2.1. It shows seven alternative prices and the corresponding quantities

(number of cups of tea) demand per day. Each price has a unique quantity demanded,

associated with it. As the price per cup of tea decreases, daily demand for tea increases, in

accordance with the law of demand.

Demand Curve


The law of demand can also be presented through a curve called demand curve.

Demand curve is a locus of points showing various alterative price-quantity combinations. It

shows the quantities of a commodity that consumers or users would buy at difference prices

per unit of time under the assumptions of the law of demand. An individual demand curve

for tea as given in Fig. 2.1 can be obtained by plotting the data give in Table 2.1.  

In Fig. 2.1, the curve from point A to point G passing through points B, C, D and F is

the demand curve DD’. Each point on the demand curve DD’ shows a unique price-quantity

combination. The combinations read in alphabetical order should decreasing price of tea

and increasing number of cups of tea demanded per day. Price quantity combinations in

reverse order of alphabets illustrate increasing price of tea per cup and decreasing number

of cups of tea per day consumed by an individual. The whole demand curve shows a

functional relationship between the alternative price of a commodity and its corresponding

quantities, which a consumer would like to buy during a specific period of item—per day,

per week, per month, per season, or per year. The demand curve shows an inverse

relationship between price and quantity demanded. This inverse relationship between price

and quantity demanded results in the demand curve sloping downward to the right.

• Why does the demand curve slope downwards

As Fig. 2.1 shows, demand curve slopes downward to the right. The downward slope

of the demand curve reads the law of demand i.e. the quantity of a commodity demanded

per unit of time increases as its price falls and vice versa.

The reasons behind the law of demand i.e. inverse relationship between price and

quantity demanded are following:


Substitution Effect: When the price of a commodity falls it becomes relatively cheaper if

price of all other related goods, particularly of substitutes, remain constant. In other

words, substitute goods become relatively costlier. Since consumers substitute cheaper

goods for costlier ones, demand for the relatively cheaper commodity increases. The

increase in demand on account of this factor is known as substitution effect.


Income Effect: As a result of fall in the price of a commodity, the real income of its

consumer increase at least in terms of this commodity. In other words, his/her

purchasing power increases since he is required to pay less for the same quantity. The

increase in real income (or purchasing power) encourages demand for the commodity

with reduced price. The increase in demand on account of increase in real income is

known as income effect. It should however be noted that the income effect is negative in

case of inferior goods. In case, price of an inferior good accounting for a considerable

proportion of the total consumption expenditure falls substantially, consumers’ real

income increases: they become relatively richer. Consequently, they substitute the

superior good for the inferior ones, i.e., they reduce the consumption of inferior goods.

Thus, the income effect on the demand for inferior goods becomes negative.   Diminishing Marginal Utility: Diminishing marginal utility as well is to be held

responsible for the rise in demand for a product when its price declines. When an

individual purchases a product, he swaps his money revenue with the product in order to

increase his satisfaction. He continues to purchase goods and services as long as the

marginal utility of money (MUm) is lesser than the marginal utility of the commodity

(MUC). Given the price of a commodity, he modifies his purchase so that MUC = MUm.

This plan works well under both Marshallian assumption of constant MUm as well as

Hicksian assumption of diminishing MUm. When price falls, (MUm = Pc) < MUC. Thus,

equilibrium state is upset. To get back his equilibrium state, i.e., MUm = PC, = MUC, he

buys more quantities of the commodity. For, when the supply of a commodity rises, its

MU falls and once again MUm = MUC. For this reason, demand for a product rises when

its price falls.

• Exceptions to the Law of Demand

The law of demand does not apply to the following cases:


Apprehensions about the future price: When consumers anticipate a constant rise in

the price of a long-lasting commodity, they purchase more of it despite the price rise.

They do so with the intention of avoiding the blow of still higher prices in the future.

Likewise, when consumers expect a substantial fall in the price in the future, they delay

their purchases and hold on for the price to decrease to the anticipated level instead of

purchasing the commodity as soon as its price decreases. These kinds of choices made by

the consumers are in contradiction of the law of demand.


Status goods: The law does not concern the commodities which function as a ‘status

symbol’, add to the social status or exhibit prosperity and opulence e.g. gold, precious

stones, rare paintings and antiques, etc. Rich people mostly purchase such goods as they

are very costly.


Giffen goods: An exception to this law is the typical case of Giffen goods named after Sir

Robert Giffen (1837-1910). 'Giffen goods' does not represent any particular commodity.

It could be any low-grade commodity which is cheap as compared to its superior

alternatives, consumed generally by the lower income group families as an important

consumer good. If price of such goods rises (price of its alternative remaining stable), its

demand escalates instead of falling. E.g. the minimum consumption of food grains by a

lower income group family per month is 30 kgs consisting of 20 kgs of bajra (a low-grade

good) at the rate of Rs 10 per kg and 10 kgs of wheat (a high quality good) at Rs. 20 per

kg. They have a fixed expenditure of Rs. 400 on these items. However, if the price of bajra rises to Rs. 12 per kg the family will be compelled to decrease the consumption of

wheat by 5 kgs and add to that of bajra by the same quantity so as to meet its minimum

consumption requisite within Rs. 400 per month. No doubt, the family's demand for

bajra rises from 20 to 25 kgs when its price rises.

• The Market Demand Curve

The quantity of a commodity which an individual is willing to buy at a particular price

of the commodity during a specific time period, given his money income, his taste and prices

of substitutes and complements, is known as individual demand for a commodity. The total

quantity which all the consumers of a commodity are willing to buy at a given price per time

unit, other things remaining the same, is known as market demand for the commodity. In

other words, the market demand for a commodity is the sum of individual demands by all

the consumers (or buyers) of the commodity, per time unit and at a given price, other

factors remaining the same. For instance, suppose there are three consumers (viz., A, B, C)

of a commodity X and their individual demand at different prices is of X as given in Table 2.2.

The last column presents the market demand i.e. the aggregate of individual demand by

three consumers at different prices.  

Graphically, market demand curve is the horizontal summation of individual demand

curves. The individual demand schedules plotted graphically and summed up horizontally

gives the market demand curve as shown in Fig. 2.2. The individual demands for commodity X are given by DA, DB and Dc, respectively. The

horizontal summation of these individual demand curves results into the market demand

curve (DM) for the commodity X. The curve DM represents the market demand curve for

commodity X when there are only three consumers of the commodity.  

Demand Function

The functional relationship between the demand for a commodity and its various

determinants may be expressed mathematically in terms of a demand function, thus:

Dx = f (Px, Py, M, T, A, U) where,

Dx = Quantity demanded for commodity X.

f = functional relation.

Px = The price of commodity X.

Py = The price of substitutes and complementary goods.

M = The money income of the consumer.

T = The taste of the consumer.

A = The advertisement effects.

U = Unknown variables or influences.

The above-stated demand function is a complicated one. Again, factors like tastes

and unknown influences are not quantifiable. Economists, therefore, adopt a very simple

statement of demand function, assuming all other variables, except price, to be constant.

Thus, an over-simplified and the most commonly stated demand function is: Dx = f (Px),

which connotes that the demand for commodity X is the function of its price. The traditional

demand theory deals with this demand function specifically.

It must be noted that by demand function, economists mean the entire functional

relationship i.e. the whole range of price-quantity relationship and not just the quantity

demanded at a given price per unit of time. In other words, the statement, 'the quantity

demanded is a function of price' implies that for every price there is a corresponding

quantity demanded.

To put it differently, demand for a commodity means the entire demand schedule,

which shows the varying amounts of goods purchased at alternative prices at a given time.     Shift in Demand Curve

When demand curve changes its position retaining its shape (though not necessarily),

the change is known as shift in demand curve.

Let’s suppose that the demand curve D2 in Fig. 2.3 is the original demand curve for

commodity X. As shown in the figure, at price OP2 consumer buys OQ2 units of X, other

factors remaining constant. If any of the other factors (e.g., consumer’s income) changes, it

will change the consumer’s ability and willingness to buy commodity X. For example, if

consumer’s disposable income decreases, say, due to increase in income tax, he may be able

to buy only OQ1 units of X instead of OQ2 at price OP2 (This is true for the whole range of

price of X) the consumers would be able to buy less of commodity X at all other prices. This

will cause a downward shift in demand curve from D2 to D1. Similarly, increase in disposable

income of the consumer due to reduction in taxes may cause an upward shift from D2 to D3.

Such changes in the position of the demand curve are known as shifts in demand curve.


Reasons for Shift in Demand Curve

Shifts in a price-demand curve may take place owing to the change in one or more of

other determinants of demand. Consider, for example, the decrease in demand for

commodity X by Q1Q2 in Fig 2.3. Given the price OP1, the demand for X might have fallen

from OQ2 to OQ1 (i.e., by Q1Q2) for any of the following reasons:

• Fall in the consumer’s income so that he can buy only OQ1 of X at price OP2—

it is income effect.

• Price of X’s substitute falls so that the consumers find it beneficial to substitute Q1Q2 of X with its substitute—it is substitution effect.

• Advertisement made by the producer of the substitute, changes consumer’s taste or

preference against commodity X so much that they replace Q1Q2 of X with its substitute,

again a substitution effect.

• Price of complement of X increases so much that they can now afford only OQX of X

• Also for such reasons as commodity X is going out of fashion; its quality has deteriorated;

consumer’s technology has so changed that only OQ1 of X can be used and due to

change in season if commodity X has only seasonal use.     Elasticity of Demand

While the law of demand establishes a relationship between price and quantity

demanded for a product, it does not tell us exactly as how strong or weak the relationship

happens to be. This relation, as already discussed, is inverse baring some rare exceptions.

However, a manager needs an exact measure of this relationship for appropriate business

decisions. Elasticity of demand is a measure, which comes to the rescue of a manager here.

It measures the responsiveness of demand to changes in prices as well as changes in income.

A manager can determine almost exactly how the demand for his product would change

when he changes his price or when his rivals alter prices of their products. He can also

determine how the demand for his product would change if incomes of his consumers go up

or down. Elasticity of demand concept and its measurements are therefore very important

tools of managerial decision making.

From decision-making point of view, however, the knowledge of only the nature of

relationships is not sufficient. What is more important is the extent of relationship or the

degree of responsiveness of demand to changes in its determinants. The responsiveness of

demand for a good to the change in its determinants is called the elasticity of demand. The

concept of elasticity of demand was introduced into the economic theory by Alfred Marshall.

The elasticity concept plays an important role in various business decisions and government

policies. In this unit, we will discuss the following kinds of demand elasticity.

• Price Elasticity: Elasticity of demand for a commodity with respect to change in its price.

• Cross Elasticity: Elasticity of demand for a commodity with respect to change in the price

of its substitutes.

• Income Elasticity: Elasticity of demand with respect to change in consumer’s income.

• Price Expectation Elasticity of Demand: Elasticity of demand with respect to consumer’s

expectations regarding future price of the commodity.


PRICE ELASTICITY OF DEMAND

The price elasticity of demand is delineated as the degree of responsiveness or

sensitiveness of demand for a commodity to the changes in its price. More precisely,

elasticity of demand is the percentage change in the quantity demanded of a commodity as

a result of a certain percentage change in its price. A formal definition of price elasticity of

demand (e) is given below:    The measure of price elasticity (e) is called co-efficient of price elasticity. The

measure of price elasticity is converted into a more general formula for calculating

coefficient of price elasticity given as  

Where QO = original quantity demanded, PO = original price,
Q = change in quantity

demanded and
P = change in price.

Note that a minus sign (-) is generally inserted in the formula before the fraction with

a view to making elasticity coefficient a non-negative value.

2.4.2 POINT AND ARC ELASTICITY OF DEMAND

The elasticity of demand is conventionally measured either at a finite point or

between any two finite points, on the demand curve. The elasticity measured on a finite

point of a demand curve is called point elasticity and the elasticity measured between any

two finite points is called arc elasticity. Let us now look into the methods of measuring point

and arc elasticity and their relative usefulness.

(A) POINT ELASTICITY

The point elasticity of demand is defined as the proportionate change in quantity

demanded in response to a very small proportionate change in price. The concept of point

elasticity is useful where change in price and the consequent change in quantity demanded

are very small.

The point elasticity may be symbolically expressed as

Measuring Point Elasticity on a Linear Demand Curve   To illustrate the measurement of point elasticity of a linear demand curve, let us

suppose that a linear demand curve is given by MN in Fig. 2.4 and that we want to measure

elasticity at point P.

Let us now substitute the values from Fig. 2.4 in eq. II. As it is obvious from the

figure, P = PQ and Q = OQ. What we need now is to find the values for δQ and δP. These

values can be obtained by assuming a very small decrease in the price. However, it will be

difficult to depict these changes in the figure as and hence Q –O. There is however an easier

way to find the value for δQ/δP. In derivative given the slope of the demand curve MN. The

slope of demand curve MN, at point P is geometrically given by QN/PQ. That is, may be

proved as follows. If we draw a horizontal line from P and to the vertical -.here will be three

triangles.

Since at point P, P=PQ and Q=OQ, substituting these values in eq. II, (ignoring

the minus sign), we get

Geometrically,

MON,
MRP and
PQN (Fig. 3.1) in which
MON and
PQN are right angles.

Therefore, the other corresponding angles of the triangles will always be equal and hence,


MON,
MRP and
PQN are similar triangles. According to geometrical properties of similar triangles, the ratio of any two sides of

similar triangle is always equal to the ratio of corresponding sides of the other sides.

Therefore, in
PQN and
MRP,

It follows that at mid-point of a linear demand curve, e = 1, as shown at point P in Fig.

2.6, because both lower and upper segments are equal (i.e., PN = PM) at any other point to

the left of point P, e > I and at any point to the right of point.

Price Elasticity at Terminal Points

The price elasticity at terminal point N equals 0 i.e. at point N, e = 0. At terminal

point M, however, price-elasticity is undefined, though most texts show that at terminal

point M, e = ∞. According to William J. Baumol, a Nobel Prize winner, price elasticity at

upper terminal point of the demand curve is undefined. It is undefined because measuring

elasticity at terminal point (M) involves division of zero and division by-zero is undefined. In

his own words, “Here the elasticity is not even defined because an attempt to evaluate the fraction p/x at that point forces us to commit the sign of dividing by zero. The reader who

has forgotten why division by zero is immoral may recall that division is the reverse

operation of multiplication. Hence, in seeking the quotient c = a/b we look for a number, c,

which when multiplied by b gives us the number a, i.e., for which cb = a. But if a is not zero,

say a = 5 and b is zero, there is no such number because there is no c such that c x 0 = 5”.

(B) MEASURING ARC ELASTICITY

The concept of point elasticity is pertinent where change in price and the resulting

change in quantity are infinite or small. However, where change in price and the consequent

hunger in demand is substantial, the concept of arc elasticity is the relevant concept. Arc

elasticity is a measure of the average of responsiveness of the quantity demanded to a

substantial change in the price. In other words, the measure of price elasticity of demand

between two finite points on a demand curve is known as arc activity. For example, the

measure of elasticity between points J and K (Fig. 2.7) is: the measure of arc elasticity. The

movement from point J to K along the demand curve D) shows a fall in price from Rs 25 to Rs

10 so that AP = 25 - 10 = 15. The consequent increase in demand, AQ = 30 - 50 = - 20. The arc

elasticity between point J and K and (moving from J to K) can be obtained by substituting

these values in the elasticity formula.

method does not give one measure of elasticity.     Determinants of Demand

Price elasticity of demand fluctuates from commodity to commodity. While the

demand of some commodities is highly elastic, the demand for others is highly inelastic. In

this section, we will describe the main determinants of the price elasticity of demand.

1. Availability of Substitutes

One of the most significant determinants of elasticity of demand for a commodity is

the availability of its substitutes. Closer the substitute, greater is the elasticity of demand for

the commodity. For instance, coffee and tea could be regarded as close substitutes for one

another. Thus, if price of one of these goods rises, its demand reduces more than the

proportionate rise in its price as consumers switch over to the relatively lower-priced

substitute. Moreover, broader the choice of the substitutes, greater is the elasticity. E.g.

soaps, washing powder, toothpastes, shampoos, etc. are available in several brands; each

brand is a close substitute for the other. Thus, the price-elasticity of demand for each brand

will be to a large extent greater than the general commodity. In contrast, sugar and salt do

not have their close substitute and for this reason their price-elasticity is lower.

2. Nature of Commodity

The nature of a commodity as well has an effect on the price elasticity of its demand.

Commodities can be categorised as luxuries, comforts and necessities, on the basis of their

nature. Demand for luxury goods (e.g., luxury cars, decorative items, etc.) are more elastic

than the demand for other types of goods as consumption of luxury goods can be set aside

or delayed when their prices increase. In contrast, consumption of essential goods, (e.g.,

sugar, clothes, vegetables, etc.) cannot be delayed and for this reason their demand is

inelastic. Demand for comforts is usually more elastic than that for necessities and less elastic than the demand for luxuries. Commodities may also be categorised as durable goods

and perishable or non-durable goods. Demand for durable goods is more elastic than that

for non-durable goods, as when the prices of the former rises, people either get the old one

fixed rather than substituting it or buy ‘second hand’ goods.

3. Proportion of Income Spent on a Commodity

Another aspect that has an impact on the elasticity of demand for a commodity is the

proportion of income, which consumers use up on a specific commodity. If proportion of

income spent on a commodity is extremely little, its demand will be less elastic and vice

versa. Characteristic examples of such commodities are sugar, matches, books, washing

powder etc., which use a very tiny proportion of the consumer’s income. Demand for these

goods is usually inelastic as a rise in the price of such goods does not largely have an effect

on the consumer’s consumption pattern and the overall purchasing power. Thus, people

continue to buy approximately the same quantity even at the time their price rises.

4. Time Factor

Price-elasticity of demand relies moreover on the time which consumers take to

amend to a new price: longer the time taken, greater is the elasticity. As each year passes,

consumers are capable of altering their spending pattern to price changes. For instance, if

the price of bikes falls, demand may not rise instantaneously unless people acquire surplus

buying capacity. In the end nevertheless people can alter their spending pattern so that they

can purchase a car at a (new) lower price.

5. Range of Alternative Uses of a Commodity

Broader the range of alternative uses of a commodity, higher the price elasticity of its

demand intended for the fall in price however less elastic for the increase in price. As the

price of a versatile commodity falls, people broaden their consumption to its other uses.

Thus, the demand for such a commodity usually rises more than the proportionate fall in its

price. E.g., milk can be consumed as it is, it could be transformed into curd, cheese, ghee and

buttermilk. The demand for milk will thus be extremely elastic for fall in their price. Likewise,

electricity can be utilised for lighting, cooking, heating, as well as for industrial purposes.

Thus, demand for electricity is extremely price elastic for fall in its price. For this reason,

nevertheless, demand for such goods is inelastic for the increase in their price.

6. The Proportion of Market Supplied

Price elasticity of market demand furthermore relies on the proportion of the market

supplied at the determined price. If less than half of the market is supplied at the determined price, elasticity of demand will be higher if more than half of the market is

supplied. i.e. demand curve is more elastic at the upper half than at the lower half.   Demand Forecasting

Demand forecasting entails forecasting and estimating the quantity of a product or

service that consumers will purchase in future. It tries to evaluate the magnitude and

significance of forces that will affect future operating conditions in an enterprise. Demand

forecasting involves use of various formal and informal forecast techniques such as informed

guesses, use of historical sales data or current field data gathered from representative

markets. Demand forecasting may be used in making pricing decisions, in assessing future

capacity requirements, or in making decisions on whether to enter a new market. Thus,

demand forecasting is estimation of future demand. According to Cardiff and Still, “Demand

forecasting is an estimate of sales during a specified future period based on a proposed


marketing plan and a set of particular uncontrollable and competitive forces". As such,

demand forecasting is a projection of firm’s expected future demands.

2.6.1 DEMAND FORECAST AND SALES FORECAST

Due to the dynamic and complex nature of marketing phenomenon, demand

forecasting has become an important and regular business exercise. It is essential for profit

maximisation and the survival and expansion of a business. However, before selecting any

vendor a retailer should well understand the requirement and the importance of demand

forecasting. In management circles, demand forecasting and sales forecasting are used

interchangeably. Sales forecasts are first approximations in production and forward

planning. These provide a platform upon which plans could be prepared and amendments

may be made. According to American Marketing Association, “Sales forecast is an estimate

of sales in monetary or physical units for a specified future period under a proposed

business plan or programmer or under an assumed set of ‘economic and other environment

forces, planning premises, outside business/ antiquate which the forecast or-estimate is

made”.   2.6.2 COMPONENTS OF DEMAND FORECASTING SYSTEM

• Market research operations to procure relevant and reliable information about the

trends in market.

• A data processing and analysing system to estimate and evaluate the sales performance

in various markets.

• Proper co-ordination of steps (i) and (ii) above

• Placing the findings before the top management for making final decisions.

2.6.3 OBJECTIVES OF DEMAND FORECAST

1. Short Term Objectives

a. Drafting of Production Policy: Demand forecasts facilitate in drafting appropriate

production policy so that there may not be any space between future demand and

supply of a product. This can in addition ensure:

• Routine Supply of Materials: Demand forecasting assists in figuring out the

preferred volume of production. The essential prerequisite of raw materials in

future can be calculated on the basis of such forecasts. This guarantees regular

and continuous supply of the materials in addition to managing the amount of

supply at the economic level.

62 Managerial Economics

• Best Possible Use of Machines: Demand forecasting in addition expedites cutting

down inactive capacity because only the necessary amount of machines and

equipments are set up to meet future demands.

• Regular Availability of Labour: As soon as demand forecasts are made, supplies

of the necessary amount of skilled and unskilled workers can be organised well

beforehand to meet the future production plans.

b. Drafting of Price Policy: Demand forecasts facilitate the management to prepare a

few suitable pricing systems, so that the level of price does not rise and fall to a great

extent during depression or inflation.

c. Appropriate Management of Sales: Demand forecasts are made area wise and after

that the sales targets for different regions are set in view of that. This abets the

calculation of sales performances.

d. Organising Funds: On the basis of demand forecast, an individual can find out the

monetary requirements of the organisation in order to bring about the desired

output. This can make it possible to cut down on the expenditure of acquiring funds.

2. Long Term Goals: If the demand forecast period is more than a year, in that case it is

termed as long term forecast. The following are the key goals of such forecasts:

a. To settle on the production capacity: Long term decisions are entwined with

capacity variations by adding or discarding capacity in the form of capital assets -

manufacturing plants, new technology implementation etc. Size of the organisation

should such that output matches with the sales requirements. Organisations that are

extremely small or large in size might not be in the financial interest of the company.

Inadequate capability can hasten declining delivery performance, needless rise in

work-in-process and disturb sales personnel and those in the production unit.

Nevertheless, surplus capacity can be expensive and pointless. The incompetence to

appropriately deal with capacity can be an obstacle to attaining the best possible

performance. By examining the demand pattern for the product as well as the

forecasts for the future, the company can prepare for a company's output of the

desired capacity.

b. Labour Requirements: Spending on labour is one of the most vital elements of cost

of production. Dependable and correct demand forecasts can facilitate the

management to evaluate suitable labour requirements. This can ensure finest labour

supply and uninterrupted production procedures.


c. Production Planning: Long term production planning can aid the management in

organising long term finances on practical terms and conditions.

The study of long term sales is accorded greater importance as compared with shortterm

sales. Long term sales forecast facilitates the management to take a few policy

decisions of huge importance and any mistake carried out in this could be extremely

different or costly to be corrected.

Therefore, the complete success of an organisation usually is contingent upon the

quality and authenticity of sales forecasting methods.

2.6.4 IMPORTANCE OF DEMAND FORECAST

1. Management Decisions: An effective demand forecast facilitates the management to

take appropriate steps in factors that are pertinent to decision making such as plant

capacity, raw-material requisites, space and building requirements and availability of

labour and capital. Manufacturing schedules can be drafted in compliance with the

demand requisites; in this manner cutting down on the inventory, production and other

related costs.

2. Evaluation: Demand forecasting furthermore smoothes the process of evaluating the

efficiency of the sales department.

3. Quality and Quantity Controls: Demand forecasting is an essential and valuable

instrument in the control of the management of an organisation to provide finished

goods of correct quality and quantity at the correct time with the least amount of

expenditure.

4. Financial Estimates: As per the sales level as well as production functions, the financial

requirements of an organisation can be calculated using various techniques of demand

forecasting. In addition, it needs a little time to acquire revenue on practical terms. Sales

forecasts will, as a result, make it possible for arranging adequate resources on practical

terms and in advance as well.

5. Avoiding Surplus and Inadequate Production: Demand forecasting is necessary for the

old and new organisations. It is somewhat essential if an organisation is engaged in large

scale production of goods and the development period is extremely time-consuming in

the course of production. In such situations, an estimate regarding the future demand is

essential to avoid inadequate and surplus production.

6. Recommendations for the future: Demand forecast for a specific commodity

furthermore provides recommendations for demand forecast of associated industries.


E.g. the demand forecast for the vehicle industry aids the tyre industry in calculating the

demand for two wheelers, three wheelers and four wheelers.

7. Significance for the government: At the macro-level, demand forecasting is valuable to

the government as it aids in determining targets of imports as well as exports for various

products and preparing for the international business.

2.6.5 METHODS OF DEMAND FORECAST

No demand forecasting method is 100% precise. Collective forecasts develop

precision and decrease the probability of huge mistakes.

1. Methods that relay on Qualitative Assessment:

Forecasting demand based on expert opinion. Some of the types in this method are:

• Unaided judgment

• Prediction market

• Delphi technique

• Game theory

• Judgmental bootstrapping

• Simulated interaction

• Intentions and expectations surveys

• Conjoint analysis

2. Methods that rely on quantitative data:

• Discrete event simulation

• Extrapolation

• Quantitative analogies

• Rule-based forecasting

• Neural networks

• Data mining

• Causal models

• Segmentation


2.6.6 SOME DEMAND FORECASTING METHODS

A. QUALITATIVE ASSESSMENT

1. Prediction markets: These are speculative markets fashioned with the intention of

making predictions. Assets that are produced possess an ultimate cash worth bound to a

specific event (e.g. who will win the next election) or situation (e.g., total sales next

quarter). The present market prices can then be described as forecasts of the likelihood

of the event or the estimated value of the situation. Prediction markets are as a result

planned as betting exchanges, without any kind of compromise for the bookmaker.

People who buy low and sell high are rewarded for improving the market prediction,

while those who buy high and sell low are punished for degrading the market prediction.

Evidence so far suggests that prediction markets are at least as accurate as other

institutions predicting the same events with a similar pool of participants.

Many prediction markets are open to the public. Betfair is the world's biggest

prediction exchange, with around $28 billion traded in 2007. Intrade is a for-profit company

with a large variety of contracts not including sports. The Iowa Electronic Markets is an

academic market examining elections where positions are limited to $500. Trade Sports are

prediction markets for sporting events.

2. Delphi method: This is a systematic, interactive forecasting method which relies on a

panel of experts. The experts answer questionnaires in two or more rounds. After each

round, a facilitator provides an anonymous summary of the experts’ forecasts from the

previous round as well as the reasons they provided for their judgments. Thus, experts

are encouraged to revise their earlier answers in light of the replies of other members of

their panel. It is believed that during this process the range of the answers will decrease

and the group will converge towards the 'correct' answer. Finally, the process is stopped

after a pre-defined stop criterion (e.g. number of rounds, achievement of consensus,

stability of results) and the mean or median scores of the final rounds determine the

results.

3. Game theory: Game theory is a branch of applied mathematics that is used in the social

sciences, most notably in economics, as well as in biology (particularly evolutionary

biology and ecology), engineering, political science, international relations, computer

science and philosophy. Game theory attempts at mathematically capturing behaviour in

strategic situations or games in which an individual's success in making choices depends

on the choices of others. While initially developed to analyse competitions in which one

individual does better at another's expense (zero sum games), it has been expanded to


treat a wide class of interactions, which are classified according to several criteria.

Today, "game theory is a sort of umbrella or 'unified field' theory for the rational side of

social science, where 'social' is interpreted broadly, to include human as well as nonhuman

players (computers, animals, plants)" (Aumann 1987).

Traditional applications of game theory aim at finding equilibrium in these games. In

equilibrium, each player of the game has adopted a strategy that they are unlikely to

change. Many equilibrium concepts have been developed (most famously the Nash

equilibrium) in an endeavor to capture this idea. These equilibrium concepts are differently

motivated depending on the field of application, although they often overlap or coincide.

This methodology is not without criticism and debates continue over the appropriateness of

particular equilibrium concepts, the appropriateness of equilibrium altogether and the

usefulness of mathematical models more generally.

Although, some developments occurred before it, the field of game theory came into

being with Émile Borel's researches in his 1938 book Applications aux Jeux des Hazard and

was followed by the 1944 book Theory of Games and Economic Behavior by John von

Neumann and Oskar Morgenstern. This theory was developed extensively in the 1950s by

many scholars. Game theory was later explicitly applied to biology in the 1970s, although

similar developments go back at least as far as the 1930s. Game theory has been widely

recognised as an important tool in many fields. Eight game theorists have won the Nobel

Memorial Prize in Economic Sciences and John Maynard Smith was awarded the Crafoord

Prize for his application of game theory to biology.

The games studied in game theory are well-defined mathematical objects. A game

consists of a set of players, a set of moves (or strategies) available to those players and a

specification of payoffs for each combination of strategies. Most cooperative games are

presented in the characteristic function form, while the extensive and the normal forms are

used to define non-cooperative games.

QUANTITATIVE DATA

1. Discrete-event simulation: The operation of a system is represented as a chronological

sequence of events. Each event occurs at an instant in time and marks a change of state

in the system. For example, if an elevator is simulated, an event could be "level 6 button

pressed", with the resulting system state of "lift moving" and eventually (unless one

chooses to simulate the failure of the lift) "lift at level 6".

A common exercise in learning how to build discrete-event simulations is to model a

queue, such as customers arriving at a bank to be served by a teller. In this example, the


system entities are CUSTOMER-QUEUE and TELLERS. The system events are CUSTOMERARRIVAL

and CUSTOMER-DEPARTURE. (The event of TELLER-BEGINS-SERVICE can be part of

the logic of the arrival and departure events.) The system states, which are changed by these

events, are NUMBER-OF-CUSTOMERS-IN-THE-QUEUE (an integer from 0 to n) and TELLERSTATUS

(busy or idle). The random variables that need to be characterised to model this

system stochastically are CUSTOMER-INTERARRIVAL-TIME and TELLER-SERVICE-TIME.

2. Rule based forecasting: Rule-based forecasting (RBF) is a proficient method that

incorporates judgment as well as statistical techniques to merge forecasts. It involves

condition-action statements (rules) where conditions are based on the aspects of the

past progress and upon knowledge of that specific area. These rules give in to the load

suitable to the forecasting condition as described by the circumstances. In fact, RBF uses

structured judgment as well as statistical analysis to modify predictive techniques to the

condition. Practical outcomes on several sets of the past progress indicate that RBF

generates forecasts that are more precise than those generated by the conventional

predictive techniques or by an equal-load amalgamation of predictions.

3. Data mining: Data mining is the process of extracting patterns from data. Data mining is

seen as an increasingly important tool by modern business to transform data into an

informational advantage. It is currently used in a wide range of profiling practices, such

as marketing, surveillance and scientific discovery.

Data mining commonly involves four classes of tasks:

• Clustering - is the task of discovering groups and structures in the data that are in some

way or another "similar", without using known structures in the data.

• Classification - is the task of generalising known structure to apply to new data. For

example, an email program might attempt to classify an email as legitimate or spam.

Common algorithms include decision tree learning, nearest neighbor, naive Bayesian

classification, neural networks and support vector machines.

• Regression - Attempts to find a function which models the data with the least error.

• Association rule learning - Searches for relationships between variables. For example a

supermarket might gather data on customer purchasing habits. Using association rule

learning, the supermarket can determine which products are frequently bought together

and use this information for marketing purposes. This is sometimes referred to as

market basket analysis.


2.6.7 METHODS OF ESTIMATION

1. Regression analysis: Regression analysis is the statistical technique that identifies the

relationship between two or more quantitative variables: a dependent variable whose

value is to be predicted and an independent or explanatory variable (or variables), about

which knowledge is available. The technique is used to find the equation that represents

the relationship between the variables. A simple regression analysis can show that the

relation between an independent variable X and a dependent variable Y is linear, using

the simple linear regression equation Y= a + bX (where a and b are constants). Multiple

regression will provide an equation that predicts one variable from two or more

independent variables, Y= a + bX1+ cX2+ dX3.

The steps in regression analysis are:

a. Construction of the causal model: The construction of an explanatory model is a

crucial step in the regression analysis. It must be defined with reference to the action

theory of the intervention. It is likely that several kinds of variable exist. In some

cases, they may be specially created, for example to take account of the fact that an

individual has benefited from support or not (a dummy variable, taking values 0 or

1). A variable may also represent an observable characteristic (having a job or not) or

an unobservable one (probability of having a job). The model may presume that a

particular variable evolves in a linear, logarithmic, exponential or other way. All the

explanatory models are constructed on the basis of a model, such as the following,

for linear regression:

Y = β0 + β1X1 + β2X2 + …. + β kXk + ε, where

Y is the change that the programme is mainly supposed to produce (e.g. employment

of trainees)

X1-k are independent variables likely to explain the change.

β0-k are constants and

ε is the error term

Phenomena of co-linearity weaken the explanatory power. For example, when

questioning women about unemployment, if they have experienced periods of previous

unemployment which are systematically longer than those of men, it will not be possible to

separate the influence of the two explanatory factors: gender and duration of previous

unemployment.


b. Construction of a sample: To apply multiple regression, a large sample is usually

required (ideally between 2,000 to 15,000 individuals). Note that for time series data,

much less is needed.

c. Data collection: Reliable data must be collected, either from a monitoring system,

from a questionnaire survey or from a combination of both.

d. Calculation of coefficients: Coefficients can be calculated relatively easily, using

statistical software that is both affordable and accessible to PC users.

e. Test of the model: The model aims to explain as much of the variability of the

observed changes as possible. To check how useful a linear regression equation is,

tests can be performed on the square of the correlation coefficient r. This tells us

what percentage of the variability in the y variable can be explained by the x variable.

A correlation coefficient of 0.9 would show that 81% of the variability in Y is captured

by the variables X1-k used in the equation. The part that remains unexplained

represents the residue (ε). Thus, the smaller the residue better is the quality of the

model and its adjustment. The analysis of residues is a very important step: it is at

this stage that one sees the degree to which the model has been adapted to the

phenomena one wants to explain. It is the residue analysis that also enables one to

tell whether the tool has made it possible to estimate the effects in a plausible way

or not. If significant anomalies are detected, the regression model should not be

used to estimate effects and the original causal model should be re-examined, to see

if further predictive variables can be introduced.

2. Time series analysis: An analysis of the relationship between variables over a period of

time. Time-series analysis is useful in assessing how an economic or other variable

changes over time. For example, one may conduct a time-series analysis on a stock, sales

volumes, interest rates and quality measurements etc.

Methods for time series analyses may be divided into two classes: frequency-domain

methods (spectral analysis and recently wavelet analysis) and time-domain methods

(auto-correlation and cross-correlation).

a. Frequency domain: Frequency domain is a term used to describe the domain for

analysis of mathematical functions or signals with respect to frequency, rather than

time. A time-domain graph shows how a signal changes over time. Whereas a

frequency-domain graph shows how much of the signal lies within each given

frequency band over a range of frequencies. A frequency-domain representation can

also include information on the phase shift that must be applied to each sinusoid in


order to be able to recombine the frequency components to recover the original

time signal.

b. Time domain: Time domain is a term used to describe the analysis of mathematical

functions, or physical signals, with respect to time. In the time domain, the signal or

function's value is known for all real numbers, for the case of continuous time, or at

various separate instants in the case of discrete time. An oscilloscope is a tool

commonly used to visualise real-world signals in the time domain.

3. Utility analysis: A subset of consumer demand theory that analysis consumer behavior

and market demand using total utility and marginal utility. The key principle of utility

analysis is the law of diminishing marginal utility, which offers an explanation for the law

of demand and the negative slope of the demand curve. The main focus of utility analysis

is on the fulfillment of wants and needs acquired by the utilization of goods. It

additionally facilitates in getting the knowledge of market demand as well as the law of

demand. The law of demand by way of utility analysis states that consumers buy goods

that fulfill their wants and needs, i.e., create utility. Those goods that create more utility

are more important to consumers and therefore buyers are prepared to pay a higher

price. The main aspect to the law of demand is that the utility created falls when the

quantity consumed rises. As such, the demand price that buyers are prepared to pay falls

when the quantity demanded rises.

The law of diminishing marginal utility asserts that marginal utility or the

extra utility acquired from consuming a good, falls as the quantity consumed rises.

Basically, each extra good consumed is less fulfilling as compared to the previous one.

This law is mostly significant for awareness into market demand as well as the law of

demand.

a. Cardinal utility: A measure of utility, or satisfaction derived from the consumption of

goods and services that can be measured using an absolute scale. Cardinal utility

exists if the utility derived from consumption is measurable in the same way that

other physical characteristics--height and weight--are measured using a scale that is

comparable between people. There is little or no evidence to suggest that such

measurement is possible and is not even needed for modern consumer demand

theory and indifference curve analysis. Cardinal utility, however, is often employed

as a convenient teaching device for discussing such concepts as marginal utility and

utility maximisation.

b. Ordinal utility: A method of analysing utility, or satisfaction derived from the

consumption of goods and services, based on a relative ranking of the goods and

services consumed. With ordinal utility, goods are only ranked only in terms of more

or less preferred, there is no attempt to determine how much more one good is

preferred to another. Ordinal utility is the underlying assumption used in the analysis

of indifference curves and should be compared with cardinal utility, which

(hypothetically) measures utility using a quantitative scale.


Summary


Demand: "The demand for a commodity at a given price is the amount of it which

will be bought per unit of time at that price”.

Law of Demand: “The demand for a commodity increases with a fall in its price and

decreases with a rise in its price, other things remaining the same”. The Law of demand thus

merely states that the price and demand of a commodity are inversely related, provided all

other things remain unchanged or as economists put it ceteris paribus.

Assumptions to the Law of Demand: We can state the assumptions of the law of

demand as follows: (1) Income level should remain constant, (2) Tastes of the buyer should

not change, (3) Prices of other goods should remain constant, (4) No new substitutes for the

commodity, (5) Price rise in future should not be expected and (6) Advertising expenditure

should remain the same.

Why Demand Curve Slopes Downwards: The reasons behind the law of demand, i.e.,

inverse relationship between price and quantity demanded are following: (i) substitution

effect, (ii) income effect, (iii) diminishing marginal utility.

Market Demand: The total quantity which all the consumers of a commodity are

willing to buy at a given price per time unit, other things remaining the same, is known as

market demand for the commodity. In other words, the market demand for a commodity is

the sum of individual demands by all the consumers (or buyers) of the commodity, per time

unit and at a given price, other factors remaining the same.

Individual demand: The individual demand means the quantity of a product that an

individual can buy given its price. It implies that the individual has the ability and willingness

to pay.

Demand Function: Demand function is a mathematical expression of the law of

demand in quantitative terms. A demand function may produce a linear or curvilinear

demand curve depending on the nature of relationship between the price and quantity

demanded. The functional relationship between the demand for a commodity and its various determinants may be expressed mathematically as:

Dx = f (Px, Py, M, T, A, U) where, Dx = Quantity demanded for commodity X, f =

functional relation, Px = The price of commodity X, Py = The price of substitutes and

complementary goods, M = The money income of the consumer, T = The taste of the

consumer, A = The advertisement effects, U = Unknown variables or influences

Elasticity of Demand: The concept of elasticity of demand can be defined as the

degree of responsiveness of demand to given change in price of the commodity.

Methods of Measurement of Elasticity of Demand: By using three different

methods, elasticity of demand is measured.

• Ratio Method

• Expenditure Method

• Point Method

Demand Forecasting: According to Cardiff and Still, “Demand forecasting is an

estimate of sales during a specified future period based on a proposed marketing plan and a

set of particular uncontrollable and competitive forces’’.

Objectives of Demand Forecast: Following are the objectives of demand forecasting:

• Formulation of production policy

• Price policy formulation

• Proper control of sales

• Arrangement of finance

• To decide about the production capacity

• Labour requirements

• Production planning

GAME THEORY

Game theory is a branch of applied mathematics that is used in the social sciences,

most notably in economics, as well as in biology (particularly evolutionary biology and

ecology), engineering, political science, international relations, computer science and

philosophy. It attempts to capture behaviour mathematically in strategic situations or games

in which an individual's success in making choices depends on the choices of others.

While initially developed to analyse competitions in which one individual does better

at another's expense (zero sum games), it has been expanded to include a wide class of

interactions, which are classified according to several criteria.