I.
The
different types of demand
Dependent and Independent
Demands
Independent demand refers to
demand for goods meant for final consumption; it is the demand for consumers’
goods like food items, readymade garments and houses. By contrast, dependent
demand refers to demand for goods which are needed for further production; it
is the demand for producers’ goods like industrial raw materials, machine tools
and equipments.
Thus the demand for an input or
what is called a factor of production is a dependent demand; its demand depends
on the demand for output where the input enters. In fact, the quantity of
demand for the final output as well as the degree of
substituability/complementarty between inputs would determine the dependent demand
for a given input.
For example, the demand for gas in
a fertilizer plant depends on the amount of fertilizer to be produced and
substitutability between gas and coal as the basis for fertilizer production.
However, the independent demand for a product is not contingent upon the demand
for other products.
Domestic and Industrial
Demands
The example of the refrigerator
can be restated to distinguish between the demand for domestic consumption and
the demand for industrial use. In case of certain industrial raw materials
which are also used for domestic purpose, this distinction is very meaningful.
For example, coal has both
domestic and industrial demand, and the distinction is important from the
standpoint of pricing and distribution of coal.
Autonomous and Induced
Demand
When the demand for a product is
tied to the purchase of some parent product, its demand is called induced or
derived.
For example, the demand for cement
is induced by (derived from) the demand for housing. As stated above, the
demand for all producers’ goods is derived or induced. In addition, even in the
realm of consumers’ goods, we may think of induced demand. Consider the
complementary items like tea and sugar, bread and butter etc. The demand for
butter (sugar) may be induced by the purchase of bread (tea). Autonomous
demand, on the other hand, is not derived or induced. Unless a product is
totally independent of the use of other products, it is difficult to talk about
autonomous demand. In the present world of dependence, there is hardly any
autonomous demand. Nobody today consumers just a single commodity; everybody
consumes a bundle of commodities. Even then, all direct demand may be loosely
called autonomous.
Perishable and Durable
Goods’ Demands
Both consumers’ goods and
producers’ goods are further classified into perishable/non-durable/single-use
goods and durable/non-perishable/repeated-use goods. The former refers to final
output like bread or raw material like cement which can be used only once. The
latter refers to items like shirt, car or a machine which can be used
repeatedly. In other words, we can classify goods into several categories:
single-use consumer goods, single-use producer goods, durable-use consumer
goods and durable-use producer’s goods. This distinction is useful because
durable products present more complicated problems of demand analysis than
perishable products. Non-durable items are meant for meeting immediate
(current) demand, but durable items are designed to meet current as well as
future demand as they are used over a period of time. So, when durable items
are purchased, they are considered to be an addition to stock of assets or
wealth. Because of continuous use, such assets like furniture or washing
machine, suffer depreciation and thus call for replacement. Thus durable goods
demand has two varieties – replacement of old products and expansion of total
stock. Such demands fluctuate with business conditions, speculation and price
expectations. Real wealth effect influences demand for consumer durables.
New and Replacement
Demands
This distinction follows readily
from the previous one. If the purchase or acquisition of an item is meant as an
addition to stock, it is a new demand. If the purchase of an item is meant for
maintaining the old stock of capital/asset, it is replacement demand. Such
replacement expenditure is to overcome depreciation in the existing stock.
Producers’ goods like machines.
The demand for spare parts of a machine is replacement demand, but the demand
for the latest model of a particular machine (say, the latest generation
computer) is anew demand. In course of preventive maintenance and breakdown
maintenance, the engineer and his crew often express their replacement demand,
but when a new process or a new technique or anew product is to be introduced,
there is always a new demand.
You may now argue that replacement
demand is induced by the quantity and quality of the existing stock, whereas
the new demand is of an autonomous type. However, such a distinction is more of
degree than of kind. For example, when demonstration effect operates, a new
demand may also be an induced demand. You may buy a new VCR, because your
neighbor has recently bought one. Yours is a new purchase, yet it is induced by
your neighbor’s demonstration.
Final and Intermediate
Demands
This distinction is again based on
the type of goods- final or intermediate. The demand for semi-finished
products, industrial raw materials and similar intermediate goods are all
derived demands, i.e., induced by the demand for final goods. In the context of
input-output models, such distinction is often employed.
Individual and Market
Demands
This distinction is often employed
by the economist to study the size of the buyers’ demand, individual as well as
collective. A market is visited by different consumers, consumer differences
depending on factors like income, age, sex etc. They all react differently to
the prevailing market price of a commodity. For example, when the price is very
high, a low-income buyer may not buy anything, though a high income buyer may
buy something. In such a case, we may distinguish between the demand of an
individual buyer and that of the market which is the market which is the
aggregate of individuals. You may note that both individual and market demand
schedules (and hence curves, when plotted) obey the law of demand. But the
purchasing capacity varies between individuals. For example, A is a high income
consumer, B is a middle-income consumer and C is in the low-income group. This
information is useful for personalized service or target-group-planning as a
part of sales strategy formulation.
Total Market and Segmented
Market Demands
This distinction is made mostly on
the same lines as above. Different individual buyers together may represent a
given market segment; and several market segments together may represent the
total market. For example, the Hindustan Machine Tools may compute the demand
for its watches in the home and foreign markets separately; and then aggregate
them together to estimate the total market demand for its HMT watches. This
distinction takes care of different patterns of buying behavior and consumers’
preferences in different segments of the market. Such market segments may be
defined in terms of criteria like location, age, sex, income, nationality, and
so on
Company and Industry
Demands
An industry is the aggregate of
firms (companies). Thus the Company’s demand is similar to an individual
demand, whereas the industry’s demand is similar to aggregated total demand.
You may examine this distinction from the standpoint of both output and input.
For example, you may think of the
demand for cement produced by the Cement Corporation of India (i.e., a
company’s demand), or the demand for cement produced by all cement
manufacturing units including the CCI (i.e., an industry’s demand). Similarly,
there may be demand for engineers by a single firm or demand for engineers by
the industry as a whole, which is an example of demand for an input. You can
appreciate that the determinants of a company’s demand may not always be the
same as those of an industry’s. The inter-firm differences with regard to
technology, product quality, financial position, market (demand) share, market
leadership and competitiveness- all these are possible explanatory factors. In
fact, a clear understanding of the relation between company and industry
demands necessitates an understanding of different market structures.
II.
Characteristics
of Demand
If historical
data for demand are plotted against a time scale, they will show any shapes or
consistent patterns that exist. A pattern is the general shape of a time
series. Although some individual data point not fall exactly on the pattern,
they tend cluster around it.
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There are four
characteristic of the demand:
1. Trend
Graphic 1 shows
that demand increasing in a steady pattern of demand from year to year. This
graph illustrates a linear trend, but there are different shapes such as
geometric and exponential. The trend can be level, having no change from period
to period, or it can be rise and fall.
2. Seasonal
The demand
pattern in Graphic 1 shows each year’s demand fluctuating depending on the time
of year. This fluctuation may be the result of the weather, holiday seasons, or
particular events that take place on a seasonal basis. Seasonality is usually
thought of as occurring on a yearly basis, but it can also occur on a weekly or
even daily basis.
3. Random Variation
This occurs
where many factors affect demand during specific periods and occur on a random
basis. The variation may be small, with actual demand falling close to the
pattern, or it may be large, with the points widely scattered. The pattern of
variation can usually be measured.
4. Cycle
Over a span of
several years and even decades, wavelike increase and decrease in economy
influence demand.
III.
Demand Pattern
Demand takes
place for a product with deviations in the income and differentiation in the
cost of the related products. In order to increase the demand for a product,
the manufacturers follow a range of techniques to increase the marketability of
their product and employ product differentiation methods to make their product
a standout.
Various types of
demand patters are comprehensively discussed below:
·
Constant
demand: The demand for some products does not change under any circumstances.
Example, prestige goods or goods that is necessary for daily usage.
·
Cyclical
demand: Demand that differs regularly over time typically in reaction to
several effect of season or business cycle.
·
Sudden
rise: This type of demand occurs when there is a sudden lowering of factors.
For instance, lowering of tax base can encourage people to purchase more.
The demand
pattern for a particular product depends upon numerous factors that might
influence the sales of the product. The demand for a product is influenced by a
lot of internal and external factors. External factors are aplenty compared to
the internal factors. Some of the factors influencing demand include:
·
Income
level is a major factor that plays a crucial role in the demand for a product. As
the income increases, the purchasing power increases and vice versa.
·
Season
plays a considerate role. For instance, the demand for air conditioners
increases during summer time and decreases during winter period.
·
The
price of substitute goods has its own demand on a product. Because, it is
natural for people to sway towards alternate products which are low priced.
·
Anticipations
of the customer regarding the future will also change the demand.
To manage
fluctuating demand in a service business, it is imperative to have a clear
understanding of demand patterns, why they wary, and the market segments that
comprise demand at different points in time.
1.
Charting
Demand Patterns:
First, the
organization needs to chart the level of demand over relevant time periods.
Organizations that have good computerized customer information systems can do
this very accurately. The others may need to chart demand patterns more
informally.
2.
Predictable
Cycles:
In looking at
the graphic representation of demand levels, is there a predictable cycle daily
(variations occur by hours), weekly (variations occur by day), monthly
(variations occur by the month), and/or yearly (variations occur according to
months or seasons)?
If there is a
predictable cycle, what are the underlying causes? This can help a service
provider in dealing with the customers in a much better way.
3.
Random
Demand Fluctuations:
Sometimes the
patterns of demand appear to be random—there is no apparent predictable cycle.
Yet even in this case, causes can often be identified. For example, day to-day
changes in the weather may affect use of recreational, shopping, or
entertainment facilities. Although the weather cannot be predicted far in
advance, it may be possible to anticipate demand a day or two ahead.
Health-related events also cannot be predicted. Accidents, heart attacks, and
births all increase demand for hospital services, but the level of demand
cannot generally be determined in advance. Natural disasters such as floods,
fires, and hurricanes can dramatically increase the need for such services as
insurance, telecommunications, and health care.
4.
Demand
Patterns by Market Segment:
If an
organization has detailed records on customer transactions, it may be able to
disaggregate demand by market segment, revealing patterns within patterns. Or
the analysis may reveal that demand from one segment is predictable while
demand from another segment is relatively random.
IV.
Forecast
Forecasting is the estimation of the value of a
variable (or set of variables) at some future point in time. In this note we
will consider some methods for forecasting. A forecasting exercise is usually
carried out in order to provide an aid to decision-making and in planning the
future. Typically all such exercises work on the premise that if we can
predict what the future will be like we can modify our behaviour now to be in a
better position, than we otherwise would have been, when the future arrives.
Applications for forecasting include:
- inventory control/production planning - forecasting the
demand for a product enables us to control the stock of raw materials and
finished goods, plan the production schedule, etc
- investment policy - forecasting financial information
such as interest rates, exchange rates, share prices, the price of gold,
etc. This is an area in which no one has yet developed a reliable
(consistently accurate) forecasting technique (or at least if they have
they haven't told anybody!)
- economic policy - forecasting economic information such
as the growth in the economy, unemployment, the inflation rate, etc is
vital both to government and business in planning for the future.
Types of forecasting problems/methods
One way of classifying forecasting problems is to
consider the timescale involved in the forecast i.e. how far forward into the
future we are trying to forecast. Short, medium and long-term are the usual
categories but the actual meaning of each will vary according to the situation
that is being studied, e.g. in forecasting energy demand in order to construct
power stations 5-10 years would be short-term and 50 years would be long-term,
whilst in forecasting consumer demand in many business situations up to 6
months would be short-term and over a couple of years long-term. The table
below shows the timescale associated with business decisions.
Timescale Type
of Examples
decision
Short-term
Operating Inventory control
Up to 3-6 months
Production planning, distribution
Medium-term
Tactical Leasing of plant and equipment
3-6 months - 2 years
Employment changes
Long-term
Strategic Research and
development
Above 2 years
Acquisitions and mergers
Product changes
The basic reason for the above classification is that
different forecasting methods apply in each situation, e.g. a forecasting
method that is appropriate for forecasting sales next month (a short-term
forecast) would probably be an inappropriate method for forecasting sales in
five years time (a long-term forecast). In particular note here that the use of
numbers (data) to which quantitative techniques are applied typically varies
from very high for short-term forecasting to very low for
long-term forecasting when we are dealing with business situations.
Forecasting
Method
a.
Qualitative Method
Methods of this type are primarily used in situations where there
is judged to be no relevant past data (numbers) on which a forecast can be
based and typically concern long-term forecasting. One approach of this kind is
the Delphi technique.
b.
Regression Method
Suppose
we have k independent variables X1, X2, ..., Xk then we can fit the regression line
Y = a +
b1X1 + b2X2 + ... + bkXk
This
extension to the basic linear regression technique is known as multiple regression. Plainly
knowing the regression line enables us to forecast Y given values for the Xi i=1,2,...,k.
c.
Multiple Equation Method
Methods of this type are frequently used in economic
modelling (econometrics) where there are many dependent variables that
interact with each other via a series of equations, the form of which is given by economic
theory. This is an important point. Economic theory gives us some insight into
the basic structural relationships between variables. The precise numeric
relationship between variables must often be deduced by examining data.
d.
Time Series
Method
Methods
of this type are concerned with a variable that changes with time and which can
be said to depend only upon the current time and the previous values that it
took (i.e. not dependent on any other variables or external factors). If Yt is the value of the variable at time t
then the equation for Yt is
Yt = f(Yt-1, Yt-2,
..., Y0, t)
i.e. the
value of the variable at time t is purely some function of its previous values
and time, no other variables/factors are of relevance. The purpose of time
series analysis is to discover the nature of the function f and hence allow us
to forecast values for Yt.
Time
series methods are especially good for short-term forecasting where, within
reason, the past behaviour of a particular variable is a good indicator of its
future behaviour, at least in the short-term.
e.
Moving Average
One, very simple, method
for time series forecasting is to take a moving
average (also known as
weighted moving average).
The moving average (mt)
over the last L periods ending in period t is calculated by taking the average
of the values for the periods t-L+1, t-L+2, t-L+3, ..., t-1, t so that
mt = [Yt-L+1 + Yt-L+2 + Yt-L+3 + ... + Yt-1 + Yt]/L
To forecast using the
moving average we say that the forecast for all periods beyond t is just mt (although we usually only forecast for
one period ahead, updating the moving average as the actual observation for
that period becomes available).
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