demand - engineering economics

Theory of Demand : Meaning of Demand, Definition of Demand, Determinants of Demand, Demand Function, Demand Equation, Law of Demand, Statement of Law of Demand, Assumptions of Law of Demand, Exceptions of Law of Demand, Extension and contraction of Demand, Increase and Decrease in Demand, Reasons for change in Demand,

Demand Forecasting : Meaning, Definition, Importance of Demand Forecasting, Purpose of Short-term Forecasting, Forecasts, Steps Involved in Demand Forecasting, The Purpose of demand Forecasting differs according to the type of forecasting, Techniques of Demand Forecasting, Criteria of a Good Forecasting Method

Demand

Engineering Economics

Demand

Meaning of DemandDemand means desire/want for something, but in economics demand refers to effective demand i.e., the amount buyers are willing to purchase at a given price over a given period of time.

Demand is • Demand is desire/want backed by money (Demand=desire+ ability to pay+ will to pay)• Demand is always related to price and time (example :demand for oranges by a household at

a price of Rs.50/kg is 5kg oranges /week)• Demand may be viewed as Ex Ante (intended/potential demand)or Ex Post (amt actual

purchased/actual quantity demanded)

Definition of demandThe demand for a product refers to the amount of it which will be bought per unit of time at a particular price.

Individual demand/Market demandIndividual demand : It refers to demand from the individuals /family/house-hold. It is a single consuming entity’s demand.Market demand : It refers to the total demand of all buyers, taken together. It is the aggregate of the quantities of a product demanded by all the individuals buyers at a given price over a given period of time-it is the sum total of individual demand function. Market demand is more important from the business point of view, sales depends on market demand ,so does planning future marketing strategy Prices are determined on the basis of demand for the product etc.

The following table shows individual demands for eggs and how the market demand eggs at various prices derived from it:

ENGINEERING ECONOMICS 2013

Price /doz Rs A B C D

E Total dd for eggs

10 1 3 0 0 0 4

9 2 4 1 0 0 7

8 3 5 3 1 0 12

7 4 6 5 2 1 18

6 5 7 6 3 2 23

5 6 8 7 4 3 28

4 7 9 8 5 4 33

Demand

Determinants of Demand

INDIVIDUAL DEMAND• Price of the products• Income• Tastes, Habits, Preferences• Relative price of other goods-substitutes and complementary goods• Consumers Expectations• Advertisement Effect

MARKET DEMAND• Price of the product• Distribution of wealth and income in the community• Community’s common habits and scale of preferences• General standard of living and spending habits of the people• Growth of the population• Age structure/sex ratio of the population• Future Expectations• Level of taxation and tax structure• Fashions/inventions/innovations/customs/weather/climate• Advertisement/sales propaganda

DEMAND FUNCTIONAt any point of time, the quantity of a given product (good/service) that will be purchased by the consumers depends on a number of key variables/determinants. The most important variables are listed below:

• The ‘own price’ of the product (P)• The price of the substitute and complementary goods(Ps or Pc)• The level of disposable income(Yd) with the buyers(i.e.; income left after direct taxes)• Change in the buyers’ taste and preferences(T)• The advertisement effect measured through the level of advertising expenditure(A)• Changes in the population number or number of buyers(N)

Using the symbolic notations, the demand function can expressed as follows:D x =f (Px, Ps, Pc, Yd, T, A, N, u)

Where x –commodityDx - the amount demanded of the commodityPx- price of xu- other unspecified determinants of the demand forcommodity x

It can also be expressed asQ d= f(P,X1,X2…………..Xn)


Demand

Where, Qd –quantity demandedP –priceX1,X2………..Xn –other determinants of demand

In economics, a very simple statement of demand function is adopted where all variables that determine demand are held to be constant, expect for price.So demand function is denoted as

Dx= f(Px)This denotes that demand for commodity x is the function of its price. Demand EquationA linear demand function may be stated as

D = a – bPWhere, D - amount demandeda - is a constant parameter which signifies initial price irrespective of priceb - Denotes functional relationship b/w (P) & (D)P - Having a minus sign denotes a negative function, i.e., demand for a commodity is a decreasing function of its price.

To illustrate a demand equation & the computation of demand schedule assuming estimated demand functions, as

Dx = 20 - 2Px, whereDx = Amount demanded for the commodity XPx = Price of X

Suppose, the given prices per unit of the commodity X are: Rs.1,2,3,4 and 5 alternatively. In relation to these prices, a demand schedule may be constructed as below Demand schedule for commodity X

LAW OF DEMANDThe law of demand expresses the nature of functional relationship b/w two variables of the demand relation viz; the price and the quantity demanded. It simply states that demand varies inversely to change in price.


Price per unit Rs. (Px) Units Demanded (Dx)1 182 163 144 125 10

Demand

Statement of law of demandCeteris paribus, the higher the price of a commodity the smaller is the quantity demanded

and lower the price ,larger the quantity demanded Other things remaining unchanged ,demand varies inversely with price So,

D= f (P)

Price of commodity X (in Rs)

Quantity demanded(units per week)

5 100 4 200 3 300 2 400 1 500

The schedule for commodity X, as price falls demand raises so there is an inverse relationship b/w price and quantity demanded.

Assumptions of law of demandThe law of demand is based on certain assumptions

• No change in consumer’s income• No change in consumer’s preferences• No change in fashion• No change in the price of related goods• No expectation of future price changes or shortages• No change in government policy etc.


Demand

Exceptions to the law of demandThe upward sloping curve is contrary to the law of demand, where there is a direct relationship b/w price and demand (as shown in fig-2). These exceptional cases can be listed as

• Giffen goods: In the case of certain inferior goods called Giffen goods (named after Sir Robert Giffen), in spite of price rise, demand will also rise. It was seen in Ireland in 19 th. Century people were so poor that they spent a major part of income on potatoes and a small part on meat, as price of potatoes, rose the demand also rose since they could not substitute it for meat which was very expensive. Giffen’s paradox is seen the case of inferior goods like potatoes, cheap bread etc.

• Speculation: when people speculate about prices on the commodity in the future they may not act according to the laws of demand. Speculating the prices of the commodity will further increase they will demand more of the commodity for hoarding etc. In the stock market, people tend to buy more shares when prices are rising in the hope of bull runs in anticipation of future profits.

• Article of snob appeal: Certain commodities are demanded because they happen to be expensive or prestige goods or snob value having a status symbol. So increase in price will lead to increase in demand for such goods. E.g. Diamonds ,exclusive cars etc.

• Consumer psychological Bias: when a customer is wrongly biased against quality of a commodity a fall in price may not lead to an increase in demand example clearance of stock , discounted sale , etc.


Demand

Extension and contraction of demand• A variation in demand implies extension or contraction of demand. A change in demand due

to change in price is called extension or contraction of demand.• It is a movement along the same demand curve due to changes in price.• In the following diagram, demand increases from a to b and then decreases to point c

indicating various changes to demand due to price change.

Increase and decrease in demandChanges in demand are a result of the change in the conditions / factors determining demand other than price. Change in demand thus implies an increase or decrease in demand with price remaining constant. An increase /decrease signify either more or less will be demanded at a given price. This is represented graphically by movement of the demand curve upwards (in case of increase in demand) and downward movement of demand curve in case of decrease in demand.

Reasons for change in Demand: Changes in income Changes in taste, habits and preferences Change in distribution of wealth and population Change in demand of complimentary / substitute goods Change in tax structure


Demand

Change in value of money Effect of advertisement and publicity


Demand

DEMAND FORECASTING

A forecast is a predication or estimation of a future event which is most likely to happen under given conditions.

Meaning of Demand Forecasting

Demand forecasting refers to the predication of the probable demand for a good or a service on the basis of the past events prevailing trends in the present. In other words, it tells the expected level of demand at some future date by considering the past and present behavior pattern of the related event.

Definition

According to Cundiff and Still, “Demand forecasting is an estimate of demand during a specified period. Which estimate is tied to a proposed marketing plan and which assumes a particular set of uncontrollable and competitive forces.”

The Importance of Demand Forecasting

Forecasting product demand is crucial to any supplier, manufacturer, or retailer. Forecasts of future demand will determine the quantities that should be purchased, produced, and shipped. Demand forecasts are necessary since the basic operations process, moving from the suppliers' raw materials to finished goods in the customers' hands, takes time. Most firms cannot simply wait for demand to emerge and then react to it. Instead, they must anticipate and plan for future demand so that they can react immediately to customer orders as they occur. In other words, most manufacturers "make to stock" rather than "make to order" – they plan ahead and then deploy inventories of finished goods into field locations. Thus, once a customer order materializes, it can be fulfilled immediately – since most customers are not willing to wait the time it would take to actually process their order throughout the supply chain and make the product based on their order. An order cycle could take weeks or months to go back through part suppliers and sub-assemblers, through manufacture of the product, and through to the eventual shipment of the order to the customer.

Purpose of short-term forecasting Appropriate production scheduling so as to avoid the problem of over-production & the

problem of short-supply. Helping the firm to reducing costs of purchasing raw materials. Determining appropriate price policy. Setting sales targets & establishing controls & incentives. Evolving a suitable advertising & promotion programme.


Demand

Forecasting short-term financial requirements. Planning of a new unit or expansion of an existing unit. A multi-product firm must ascertain

not only the total demand situation, but also the demand for different items separately. Planning long-term financial requirements. As planning for raising funds requires

considerable advance notice, long –term sales forecasting are quite essential to assess long-term financial requirements.

Planning man-power requirements. Training & personnel development are long-term propositions, taking considerable time to complete.

Forecasts Short-term forecasts; short-term forecasts, involving a period up to twelve months. Medium-term forecasts; medium-term forecasts, involving a period from one to two years. Long-term forecasts; long-term forecasts, involving a period of three to ten years.

Steps Involved in Demand ForecastingFor efficient, accurate and meaningful forecast of demand, the following steps are necessary:

1. Identification of Objective: It is necessary to be clear about what does one want to get from the forecast. The purpose of the exercise may be the estimation of one or more than one aspect, like the quantity and composition of demand, price to be quoted, sales planning, Inventory control etc. The approach to the problem will accordingly differ.

2. Determining the Nature of Goods: Different category of goods such as consumer and capital goods, durable and non-durable goods, existing goods and new goods etc. have their own distinctive demand patterns. It is therefore, necessary to determine the class in which the good falls. This will help us in identifying the approach of forecast exercise.

3. Selection of Proper Method: The selection of an appropriate method of forecasting is related to the objective of forecasting, type of data available, availability of trained personnel, period for which the forecast is to be made. Then different methods may be required for short term and long term forecasting.

4. Interpretation of Results: Efficiency of a forecast depends, to a large extent, upon the efficiency in the interpretation of its results. Most of the times the forecast results are to be well supported by the background factors (like the government policy, general business environment, international economic, political and social scene etc.) which have not entered the exercise of forecasting. Further, we need to frequently revise the forecast in the light of changing circumstances because forecasts are, in the first instance, made on the assumptions of continuation of past events.

The purpose of demand forecasting differs according to the type of forecasting.

(1) The purpose of the Short term forecasting:


Demand

It is difficult to define short run for a firm because its duration may differ according to the nature of the commodity. For a highly sophisticated automatic plant 3 months time may be considered as short run, while for another plant duration may extend to 6 months or one year. Time duration may be set for demand forecasting depending upon how frequent the fluctuations in demand are, short- term forecasting can be undertaken by affirm for the following purpose;

Appropriate scheduling of production to avoid problems of over production and under- production.

Proper management of inventories Evolving suitable price strategy to maintain consistent sales Formulating a suitable sales strategy in accordance with the changing pattern of demand

and extent of competition among the firms. Forecasting financial requirements for the short period.

(2) The purpose of long- term forecasting:

The concept of demand forecasting is more relevant to the long-run that the short-run. It is comparatively easy to forecast the immediate future than to forecast the distant future. Fluctuations of a larger magnitude may take place in the distant future. In fast developing economy the duration may go up to 5 or 10 years, while in stagnant economy it may go up to 20 years. More over the time duration also depends upon the nature of the product for which demand forecasting is to be made. The purposes are;

Planning for a new project, expansion and modernization of an existing unit, diversification and technological up gradation.

Assessing long term financial needs. It takes time to raise financial resources. Arranging suitable manpower. It can help a firm to arrange for specialized labour force and

personnel. Evolving a suitable strategy for changing pattern of consumption.

Techniques of Demand Forecasting

Broadly speaking, there are two approaches to demand forecasting- one is to obtain information about the likely purchase behavior of the buyer through collecting expert’s opinion or by conducting interviews with consumers, the other is to use past experience as a guide through a set of statistical techniques. Both these methods rely on varying degrees of judgment. The first method is usually found suitable for short-term forecasting, the latter for long-term forecasting. There are specific techniques which fall under each of these broad methods.

Simple Survey Method:

For forecasting the demand for existing product, such survey methods are often employed. In this set of methods, we may undertake the following exercise.

1) Experts Opinion Poll: In this method, the experts on the particular product whose demand is under study are requested to give their ‘opinion’ or ‘feel’ about the product. These experts, dealing in the same or similar product, are able to predict the likely sales of a given product in future periods


Demand

under different conditions based on their experience. If the number of such experts is large and their experience-based reactions are different, then an average-simple or weighted –is found to lead to unique forecasts. Sometimes this method is also called the ‘hunch method’ but it replaces analysis by opinions and it can thus turn out to be highly subjective in nature.

2) Reasoned Opinion-Delphi Technique: This is a variant of the opinion poll method. Here is an attempt to arrive at a consensus in an uncertain area by questioning a group of experts repeatedly until the responses appear to converge along a single line. The participants are supplied with responses to previous questions (including seasonings from others in the group by a coordinator or a leader or operator of some sort). Such feedback may result in an expert revising his earlier opinion. This may lead to a narrowing down of the divergent views (of the experts) expressed earlier. The Delphi Techniques, followed by the Greeks earlier, thus generates “reasoned opinion” in place of “unstructured opinion”; but this is still a poor proxy for market behavior of economic variables.

3) Consumers Survey- Complete Enumeration Method: Under this, the forecaster undertakes a complete survey of all consumers whose demand he intends to forecast, Once this information is collected, the sales forecasts are obtained by simply adding the probable demands of all consumers. The principle merit of this method is that the forecaster does not introduce any bias or value judgment of his own. He simply records the data and aggregates. But it is a very tedious and cumbersome process; it is not feasible where a large number of consumers are involved. Moreover if the data are wrongly recorded, this method will be totally useless.

4) Consumer Survey-Sample Survey Method: Under this method, the forecaster selects a few consuming units out of the relevant population and then collects data on their probable demands for the product during the forecast period. The total demand of sample units is finally blown up to generate the total demand forecast. Compared to the former survey, this method is less tedious and less costly, and subject to less data error; but the choice of sample is very critical. If the sample is properly chosen, then it will yield dependable results; otherwise there may be sampling error. The sampling error can decrease with every increase in sample size

5) End-user Method of Consumers Survey: Under this method, the sales of a product are projected through a survey of its end-users. A product is used for final consumption or as an intermediate product in the production of other goods in the domestic market, or it may be exported as well as imported. The demands for final consumption and exports net of imports are estimated through some other forecasting method, and its demand for intermediate use is estimated through a survey of its user industries.

Complex Statistical Methods:

We shall now move from simple to complex set of methods of demand forecasting. Such methods are taken usually from statistics. As such, you may be quite familiar with some the statistical tools and techniques, as a part of quantitative methods for business decisions.

(1) Time series analysis or trend method: Under this method, the time series data on the under forecast are used to fit a trend line or curve either graphically or through statistical method of Least Squares. The trend line is worked out by fitting a trend equation to time series data with the aid of an estimation method. The trend equation could take either a linear or any kind of non-linear form.


Demand

The trend method outlined above often yields a dependable forecast. The advantage in this method is that it does not require the formal knowledge of economic theory and the market, it only needs the time series data. The only limitation in this method is that it assumes that the past is repeated in future. Also, it is an appropriate method for long-run forecasts, but inappropriate for short-run forecasts. Sometimes the time series analysis may not reveal a significant trend of any kind. In that case, the moving average method or exponentially weighted moving average method is used to smoothen the series.

(2) Barometric Techniques or Lead-Lag indicators method: This consists in discovering a set of series of some variables which exhibit a close association in their movement over a period or time.

For example, it shows the movement of agricultural income (AY series) and the sale of tractors (ST series). The movement of AY is similar to that of ST, but the movement in ST takes place after a year’s time lag compared to the movement in AY. Thus if one knows the direction of the movement in agriculture income (AY), one can predict the direction of movement of tractors’ sale (ST) for the next year. Thus agricultural income (AY) may be used as a barometer (a leading indicator) to help the short-term forecast for the sale of tractors.

Generally, this barometric method has been used in some of the developed countries for predicting business cycles situation. For this purpose, some countries construct what are known as ‘diffusion indices’ by combining the movement of a number of leading series in the economy so that turning points in business activity could be discovered well in advance. Some of the limitations of this method may be noted however. The leading indicator method does not tell you anything about the magnitude of the change that can be expected in the lagging series, but only the direction of change. Also, the lead period itself may change overtime. Through our estimation we may find out the best-fitted lag period on the past data, but the same may not be true for the future. Finally, it may not be always possible to find out the leading, lagging or coincident indicators of the variable for which a demand forecast is being attempted.

3) Correlation and Regression: These involve the use of econometric methods to determine the nature and degree of association between/among a set of variables. Econometrics, you may recall, is the use of economic theory, statistical analysis and mathematical functions to determine the relationship between a dependent variable (say, sales) and one or more independent variables (like price, income, advertisement etc.). The relationship may be expressed in the form of a demand function, as we have seen earlier. Such relationships, based on past data can be used for forecasting. The analysis can be carried with varying degrees of complexity. Here we shall not get into the methods of finding out ‘correlation coefficient’ or ‘regression equation’; you must have covered those statistical techniques as a part of quantitative methods. Similarly, we shall not go into the question of economic theory. We shall concentrate simply on the use of these econometric techniques in forecasting.

We are on the realm of multiple regression and multiple correlation. The form of the equation may be:

DX = a + b1 A + b2PX + b3Py


Demand

You know that the regression coefficients b1, b2, b3 and b4 are the components of relevant elasticity of demand. For example, b1 is a component of price elasticity of demand. The reflect the direction as well as proportion of change in demand for x as a result of a change in any of its explanatory variables. For example, b2< 0 suggest that DX and PX are inversely related; b4 > 0 suggest that x and y are substitutes; b3 > 0 suggest that x is a normal commodity with commodity with positive income-effect.

Given the estimated value of and bi, you may forecast the expected sales (DX), if you know the future values of explanatory variables like own price (PX), related price (Py), income (B) and advertisement (A). Lastly, you may also recall that the statistics R2 (Co-efficient of determination) gives the measure of goodness of fit. The closer it is to unity, the better is the fit, and that way you get a more reliable forecast.

The principle advantage of this method is that it is prescriptive as well descriptive. That is, besides generating demand forecast, it explains why the demand is what it is. In other words, this technique has got both explanatory and predictive value. The regression method is neither mechanistic like the trend method nor subjective like the opinion poll method. In this method of forecasting, you may use not only time-series data but also cross section data. The only precaution you need to take is that data analysis should be based on the logic of economic theory.

(4) Simultaneous Equations Method: Here is a very sophisticated method of forecasting. It is also known as the ‘complete system approach’ or ‘econometric model building’. In your earlier units, we have made reference to such econometric models. Presently we do not intend to get into the details of this method because it is a subject by itself. Moreover, this method is normally used in macro-level forecasting for the economy as a whole; in this course, our focus is limited to micro elements only. Of course, you, as corporate managers, should know the basic elements in such an approach.

The method is indeed very complicated. However, in the days of computer, when package programmes are available, this method can be used easily to derive meaningful forecasts. The principle advantage in this method is that the forecaster needs to estimate the future values of only the exogenous variables unlike the regression method where he has to predict the future values of all, endogenous and exogenous variables affecting the variable under forecast. The values of exogenous variables are easier to predict than those of the endogenous variables. However, such econometric models have limitations, similar to that of regression method.

Criteria of a Good Forecasting Method

The following criteria can be used for choosing the suitable method for forecasting.

1. Accuracy: It is necessary to check the accuracy of past forecasts against present performance and of present forecasts against future performance. Some comparisons of the model with what actually happens and of the assumptions with what it is borne out in practice are more desirable. The accuracy of the forecast is measured by (a) the degree of deviations between forecasts and actual, and (b) the extent of success in forecasting directional changes.

2. Simplicity: Firms must be able to understand and have the confidence in the techniques used. Understanding is also needed for the proper interpretation of the results. Elaborate


Demand

mathematical and econometric procedures may be judged less desirable if firms do not really understand what the forecaster is doing and fails to understand the procedure.

3. Economy: Costs must be weighed against the importance of the forecast to the operations of the business.

4. Availability: The techniques employed should be able to produce meaningful results quickly. And be readily available and well understood. In fact, It is not the question of results achievable but results achieved by a forecasting method. For this, the persons making the decisions must fully understand the forecasting methods, their assumptions and probabilities.

5. Timeliness: There is a time gap between the occurrence of an event and its forecast–known as ‘lead’ time. Longer the lead the forecast has before the event, the greater will be its usefulness. One may even sacrifice some accuracy for gaining a ‘lead’ rather than sacrificing ‘lead’ for accuracy.

6. Effective: It is quite easy to judge the existing trend. But for a good forecast it is necessary that it should also predict deviations and turning points so that forecasts are more effective.
