Demand Forecasting for the New Products

If a firm is planning to market a new product and does not have past data on which to rely to project sales, it will have to find out and use other means in an effort to predict probable sales.

The special problem of demand forecasting for new products is that since each new product is to varying degrees, different from existing products, there are no directly relevant data available from past sales on which a forecast may be based. The more the new product is likely to be, the greater the problem. In addition, since a considerable amount of money has to be invested in developing and marketing a new product, we have to consider its sales over an extended period to be more precise over the whole of its expected commercial lifetime. It means that we need to estimate:

(i) the number of years for which the product will be sold and

(ii) the level of sales in each of those years.

There are, of course, a number of techniques available for the purpose. However, the choice of a particular technique depends on the circumstances faced by the forecaster.

1. Market Research

The forecaster can carry out market research in various forms. The marketing people may be asked to make different inquiries from the prospective ultimate buyers of the product. On the basis of such inquiries, they may try to discover whether and in what quantities customers are likely to buy the product, using various assumptions as to how, it would be marketed and at what price it would be sold.

This approach is no doubt, direct and practical. It can be applied for forecasting the demand of industrial goods because the buyers’ criteria are more precisely formulated and more stable.

However, this method is highly impracticable in the case of new consumer goods. It is so because the consumer is, for various reasons, unlikely to be able to assess reliably his own buying behavior in the hypothetical situation presented to him.

2. Test Marketing or Sales Experience Approach

It takes the form of a trial run of the product in a part of the intended market in conditions as closely similar to those that are expected to prevail if and when the product is ultimately marketed as are possible.

Frequently, a new product such as soap, toothpaste, food-stuff, etc. is put through test marketing, i.e. tested in a sample market in a bid to determine the probable demand for the product. It may be tasted at a particular price or at several different prices by seeing one or more test markets. From the test market results, a projection can be made regarding regional or national sales. The sales experience usually will give green or a red signal for the product.

This approach has been used with success for a wide range of products. However, the success of this method depends on the availability of a representative product for test marketing. This method is not applicable to the earlier stages of product development.

3. Opinion Sampling Approach

This approach is to bring together in a systematic way the informal judgments of executives, sales people, retailers and perhaps friendly customers as to the product’s probable performance.

Through use of a mail questionnaire, or by making a door survey, one can get some indication of the acceptance of new product. In this case, sampling of the potential customers may be polled directly, or there may be a poll of sources that have a feel of the actual buyer, such as retailers, wholesalers, jobbers and manufacturer representatives. This pool is likely to give some idea of market acceptance and price range.

4. Evolutionary Approach

If the product is supposed to be an improvement or has evolved out of an existing product, it can be assumed that the new product may have the same type of experience as that of an existing product. Color television sets, evolved from black and white sets, the jet engine from the propeller engine in aircraft, are the examples. In this manner, one can easily imagine what the demand for work processors will be in the office equipment industry if they became a nearly completely replacement for the electric typewriter.

5. Substitute Approach

If a new product seems to be a close substitute for a well established product, one can estimate what share of the market the new product may get by replacing some of the existing products. A new textbook may be a substitute for one of the many existing textbooks being used. After knowing the total sales, the producer of the new book may be in a position to estimate.

6. Sales Growth Approach

In the absence of the information about a new product, one may reasonably assume that the sales of the new product will simply displace those of an existing product and continue along the growth of distinct established by it. This is likely to be so when the new product is distinct, improvement on the existing one but not so radically different that buyers have learn to accept it. In the case of industrial investment, we assume that now there need to make any substantial investment in hardware or restraining. Alternatively, for consumer products, no changes in domestic habits or social attitudes and values are required.

No doubt, demand forecasting represents one of the most challenging aspects of business analysis. Continuous research has been going on in this area. The techniques of forecasting are being retired and have been improved enormously in recent years by the advent of computers. However, the use of sophisticated techniques is not enough. It is essential to exercise judgment and experience while carrying out any forecasting exercise. Techniques can only complement judgment and experience.

  Some Related Links:       

• Steps of Demand Forecasting

• Demand Forecasting

• Purpose of Forecasting Demand in a Business Firm

• Different methods of demand forecasting

• Non-statistical methods that is used to forecast the demand for satellite TV signal decoder

• Process of demand forecasting by the time series analysis

• Factors Determining Elasticity of Demand

• Uses of Price Elasticity of Demand in Managerial Decision-making

Merits and limitations of market studies and experimentation as a method of demand forecasting

An alternative technique for obtaining useful information about a product’s demand function involves market experiments. The firm locates one or more markets with specific characteristics, and then varies prices, packaging, advertising, and other controllable variables in the demand function, with the variations occurring either over time or between markets.

One market experiment technique entails examining consumer behavior is actual markets. The firm may also be able to sue census or survey data to determine how such demographic characteristics as income, family size, educational level and ethnic background affect demand.

Market experimentation procedure utilizes a controlled laboratory experiment where in consumers are given funds with which to shop in a simulated store. By varying prices, product packaging, displays, and other factors, the experimenters can often learn a great deal about consumer behavior. The laboratory experiment, while providing similar information as field experiments has advantages / merits because of lower cost and greater control of extraneous factors. Merits and limitations of market experiments can be shown as below:

Merits of Market Experiments

1. Market experiments are based on actual consumer behavior and not on merely their intentions to buy the commodity. 
2. They provide more accurate returns than those of consumer survey because consumers are asked to make actual decisions regarding their purchase.

Limitations of Market Experiments

1. Market experiments are costly and much time consuming. 
2. If the price rises, the consumers may switch over to the products of the rival firms. If the price reduced to the original level, it may be difficult to regain the lost customers.
3. It is also difficult to select an area, which accurately represents the potential market.
4. Firm cannot control all the factors (i.e. bad weather, economic conditions, occupation situations etc.) that influence demand for a product.
5. The changes in price or adverting to know consumer’s response may go unnoticed by them in such a short period. 
6. The selected consumers may not respond accurately when they know they are a part of an experiment being conducted and their behavior is being recorded.

  Some Related Links:      

• Demand Forecasting

• Steps of Demand Forecasting

• Human resource planning is deciding in advance the future requirements of human resource in organization

• Different methods of demand forecasting

• Meaning and Concept of HRP (Human Resource Planning)

• Purpose of Forecasting Demand in a Business Firm

• Demand Forecasting for the New Products

• Process of demand forecasting by the time series analysis

Non-statistical methods used to forecast the demand for satellite TV signal decoder

Demand forecasting means predicting demand for a product. The information regarding future demand is essential for planning and scheduling production, purchase of raw materials, acquisition of finance and advertising. In order of accuracy as well as good forecasting, we can use various methods such as survey and statistical methods.

Generally in short term forecasting, we can use survey method and statistical method can be used for long-term forecasting.

As mention above in title, satellite TV signal decoder is a short-term program. To know the peoples intention about the program, we can use survey method. Under survey method, there are various methods, such as:

1. Consumer Survey Method: Direct interview method, complete enumeration method, sample survey method.
2. Opinion Poll Method: Experts opinion method, Delphi method, Market Studies and experiments. In case of forecasting demand for satellite TV signal decoder, we can use sample survey method.

Under this method, only a few potential consumers and users are selected from the relevant market through a sampling method are surveyed. This method of survey may be direct interview or method questionnaire to these sample consumers. On the basis of the information obtained that probable demand may be estimated through the following formula,

DP = HR / HS (H. AD)

Where, DP = Probable demand forecast

H = Census number of households from the relevant market

HS = Number of households for the product

AD = Average expected consumption by the reporting households

This method is simpler, less costly and less time consuming than the comprehensive survey method. The households who plan their future purchases generally use this method to estimate short-term demand from business firms, government departments and agencies, and so on. Business firms, government departments, and such other organization budget their expenditure at least for one year in advance. It is therefore possible for them to supply a fairly reliable estimate of their future purchases. Even the households making annual or periodic budget of their expenditure can provide reliable information about their purchases.

Sample survey method is widely used to forecast demand. However, this method has some limitations similar to those of complete enumeration or exhaustive survey method. The forecast therefore should not attribute reliability to the forecast more than warranted. Besides, sample survey method can be used to verify the demand forecast made by using quantitative or statistical methods. Some authors suggest that this method for forecasting rather than to replace it, this method can be gainfully used where market area is localized.

Sample survey method can be of greater use in forecasting where quantification of variables (e.g. feelings, opinion, expectations etc.) is not possible and where consumer’s behavior is subject to frequent changes. Satellite TV signal decoder forecasting is also a short and non-durable program. In such a case, sample survey method is only the appropriate non-statistical method.

  Some Related Links:      

• Expert’s opinion survey differ from survey of Sales forces

• Steps of Demand Forecasting

• Different methods of demand forecasting

• Demand Forecasting

• Purpose of Forecasting Demand in a Business Firm

• Demand Forecasting for the New Products

• Process of demand forecasting by the time series analysis

Process of demand forecasting by the time series analysis

A firm, which has been in existence for some time will have accumulated/ collected considerable data on sales pertaining to different time periods. Such data when arranged chronologically yield ‘time series’. The time series relating to sales represent the past pattern of effective demand for a particular product. Such data can be presented either in a tabular form or graphically for further analysis.

The most well known method of analysis of time series is to project the trend of the time series. The trend line can be fitted through a series either visually or by means of statistical techniques such as least square method. The analyst chooses a plausible algebraic relation between sales and the independent variable, time.

The trend line is then projected into the future by extrapolation. The basic assumption of the trend method is that the past rate of change of the variable under study will continue in the future. This technique yields acceptable results so long as the time series shows a persistent tendency to move in the same direction. Whenever a turning point occurs, the trend projection breaks down. Nevertheless, a forecaster could normally expect to be right in most forecasts especially if the turning points are few and spaced at long intervals from each other.

The real challenge of forecasting is in the prediction of turning points rather than in the projection of trends. It is when turning points occur that management will have to alter and revise its sales and production strategies most drastically.

There are primarily four sets of factors, which are responsible for the characterization of time series by fluctuations and turning points in a time series, trend, seasonal variations, cyclical fluctuations, and irregular or random forces. The problem in forecasting is separate and measures each of these four factors.

The fundamental approach is to treat the original time series data (O or observed data) as composed of four parts: a secular trend (T), a seasonal factor (S), a cyclical element (C) and on irregular movement (I). It is generally assumed that these elements are bound together in a multiplicative relationship presented by the equation O = TSCI.

The usual practice is to first compute the trend from the original data. The trend values are then eliminated from observed data (TSCI/T). The next step is to calculate the seasonal index, which is used to remove the seasonal effect (SCI/S). A cycle is then fitted to the remainder, which also contains the irregular effect.

The foregoing approach of decomposition of time series data is a useful analytical device for understanding the nature of business fluctuations. The trend and seasonal factors can be forecast, but the prediction of cycle is hazardous for the simple reason that there is no regularity in the cyclical behavior.

Though, there are two assumptions underlying this approach:

• The analysis of movements would be in the order of trend, seasonal variation and cyclical charges, and
• The effects of each component are independent of each other.

For the use of economic indicators, the following steps have to be taken:

• See if a relationship exists between the demand for a product and certain economic indicators.
• Establish the relationship through the method of least squares and derive the regression equation. Assuming the relationship to be linear, the equation will be of the form y = a + bx. There can be curve-linear relationships as well.
• Once regression equation is derived, the value of Y i.e. demands, can be estimated for any given value of X.
• Past relationships may not recur, hence the need for value judgment as well. New factors may also have to be taken into consideration.

Merits and Limitations of Time Series Analysis

Merits

1. The trend method is based on least square principle of demand forecasting which is quite popular due to simplicity.
2. It provides good result, which is particularly suitable for long run.
3. It is very much simple in the sense that it doesn’t require the knowledge of economic theory and market structure.

Limitations:

1. This method is based on the assumption that future events will follow the same path, which may not be true for every time.
2. It is not suitable for short-term demand forecasting. This method cannot usually explain the turning points of the business cycle.

 SOME RELATED LINKS:    

• Demand Forecasting

• Steps of Demand Forecasting

• Different Methods of Demand Forecasting | Survey of Buyer's Intentions | Delphi Method | Time Series Analysis and Trend Projection | Market Studies and Experimentation | Regression Analysis

• Demand Forecasting for the New Products

• Purpose of Forecasting Demand in a Business Firm

• Merits and limitations of market studies and experimentation as a method of demand forecasting

Steps of Demand Forecasting

The objective of demand forecasting is achieved only when forecast is made systematically and scientifically and when it is fairly reliable. The following steps are generally taken to make systematic demand forecasting:

Related Topic: Demand Forecasting

1. Specifying the objective

The objective or the purpose of demand forecasting must be clearly specified. The objective may be specified in terms of;

(a) short-term or long-term demand,

(b) the overall demand for a product or for a firm’s own product,

(c) the whole or only a segment of the market for its product,

(d) firm’s market share.

The objective of demand forecasting must be determined before the process of forecast is started. This has to be the first step.

2. Determining the time perspective

Depending on the firm’s objective, demand may be forecast for a short period, that is, for the next 2 to 3 years, or for a long period. In demand forecasting for a short period, 2 to 3 years, many of the demand determinants can be taken to remain constant or not to change significantly. In the long-run, however, demand determinants may change significantly. Therefore, the time perspective of demand forecasting must be specified.

3. Making choice of method for demand forecasting

There are a number of methods available for demand forecasting which we shall introduce in another section of the analysis. However, all methods are not suitable for all kinds of demand forecasting because the purpose of forecasting, data requirement and availability of data for the use of a method, and time frame of forecasting differ from method to method. Therefore, the demand forecaster has to choose a fitting method keeping in view the purpose and requirements. The choice of a forecasting method is generally based on the purpose, experience and skill/ knowledge of the forecaster. It depends also to a great extent on the availability of required data. The choice of a suitable method saves not only time and cost but also ensures the reliability of forecast to a great extent.

4. Collection of data and data adjustment

Once method of demand forecasting is decided on, the next step is to collect the required data, primary or secondary or both. The required data is often not available in the required type/ form. In that case, data needs to be adjusted – even messaged, if necessary – with the purpose of building data series consistent with data requirement. Sometimes the required data has to be generated from the secondary sources.

5. Estimation and interpretation of results

As mentioned earlier, the availability of data often determines the method, and also the potential/feasible equation to be used for demand forecasting. Once required data is collected and forecasting method is finalized, the final step in demand forecasting is to make the estimate of demand for the predetermined years or the period. Where estimates appear in the form of an equation, the result must be interpreted and presented in a usable form.

  SOME RELATED LINKS:  

• Demand Forecasting

• Non-statistical methods used to forecast the demand for satellite TV signal decoder

• Different Methods of Demand Forecasting | Survey of Buyer's Intentions | Delphi Method | Time Series Analysis and Trend Projection | Market Studies and Experimentation | Regression Analysis

• Process of demand forecasting by the time series analysis

• Demand Forecasting for the New Products

• Purpose of Forecasting Demand in a Business Firm

• Uses of Price Elasticity of Demand in Managerial Decision-making

Purpose of Demand Forecasting in a Business Firm

An accurate demand forecasting is essential for a firm to enable it to product the required quantities at the right time and arrange well in advance for the various factors of production (raw materials, equipment, machine accessories, labor, building, etc.). It helps a firm to assess the probable demand for its products and plan its production accordingly.

In fact, forecasting is an important aid in effective and efficient planning. It is also helpful in better planning and allocation of national resources.

The purpose of demand forecasting refers the estimation of volume of production; price of the commodity either short-run as well as long term forecasting.

Under the short-run, purposes of forecasting includes following headings:

 

1. Purpose of Short-Term Forecasting

• Appropriate production scheduling so as to avoid the problem of over-production and the problem of short supply. For this purpose, production schedules have to be geared expected sales.
• It helps the firm in reducing price policy so as to avoid an increase when the market conditions are expected to be weak and a reduction when the market is going to be strong.
• Determining appropriate price policy so as to avoid an increase when the market conditions are expected to be weak and a reduction when the market is going to be strong.
• Setting sales targets and establishing controls and incentives. If targets are set too high, they will be discouraging sales-man who fails to achieve them, if set too low, the targets will be achieved easily and hence incentives will prove meaningless.
• Evolving a suitable advertising and program.
• Forecasting short-term financial requirements. Cash requirements depend on sales level and production operations. It takes time to arrange for funds on reasonable terms. Sales forecasts will, therefore, enable arrangement of sufficient funds on reasonable terms well in advance.

2. Purposes of Long-Term Forecasting

• Planning of a new unit or expansion of an existing unit, it requires an analysis of the long-term demand potential of the products in question. A multi-product firm must ascertain not only the total demand situation, but also the demand for different items separately. If the company has better knowledge that its rivals of the growth trends of the aggregate demand and of the distribution of the demand over various products, its competitive position would be much better. 
• Planning long-term financial requirements. As planning for raising funds requires considerable advance notice, long-term sales, forecasts are quite essential to assess long-term financial requirements.
• Planning manpower requirements. Training and personnel development are long-term propositions, taking considerable time to complete. They can be started well in advance only on the basis of estimates of manpower requirements assessed according to long-term sales forecasts.

Most of modern firms forecast their demand on the basis of past demand, present condition of demand. Business/ Demand forecasting is the prediction of future situation for a firm’s product in the market.

   SOME RELATED LINKS:   

• Demand Forecasting

• Steps of Demand Forecasting

• Different Methods of Demand Forecasting | Survey of Buyer's Intentions | Delphi Method | Time Series Analysis and Trend Projection | Market Studies and Experimentation | Regression Analysis

• Process of demand forecasting by the time series analysis

• Demand Forecasting for the New Products

• Merits and limitations of market studies and experimentation as a method of demand forecasting

• Factors Determining Elasticity of Demand

Demand Forecasting

Demand forecasting is the systematic method of obtaining an estimate of the future value of a commodity, usually based on the analysis of observations of its past behavior. Demand forecasting means predicting demand for a product. The information regarding future demand is essential for planning and scheduling production, purchase of raw materials, acquiring of finance and advertising. Following are the criteria of good forecasting methods. To reduce the uncertainty in the planning for future production levels demand forecasting is essential.

Forecasting demand means prediction of future demand. Forecasting of future demand is one of most important function of managers of firms. Good forecasting of demand reduces uncertainty of environment in which business decisions are made. Good forecasting of future demand is also important for calculating rate of return on capital investment. Capital investment yields returns over a number of years in future.

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 method with what actually happens and of the assumptions with what 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 and ease of comprehension: Management must be able to understand and have confidence in the techniques used. Understanding is also needed for a proper interpretation of the results. Elaborate mathematical and econometric procedures may be judged less desirable if management does not really understand what the forecaster is doing and falls to understand the procedure. 
3. Economy: Costs must be weighed against the importance of the forecast to the operations of the business. The criterion here is the economic consideration of balancing the benefits from increased accuracy against the extra cost of providing the improved forecasting.
4. Availability: The techniques employed should be able to produce meaningful results quickly; techniques, which take a long time to workout, may produce useful information too late for effective management decisions.
5. Maintenance of timeliness: The forecast should be capable of being maintained on an up-to-date basis. It has three aspects: (a) The relationships underlying the procedure should be stable so that they will carry into the future for a significant amount of time. (b) Current data required to use these underlying relationships should be available on timely basis. (c) The forecasting procedure should permit changes to be made in the relationships as they occur.

Human resource planning is deciding in advance the future requirements of human resource in organization

Human resource planning is deciding in advance the future requirements of human resource in organization

A line manager has to decide or recommend how many people and what skills they need now and at least in the short-term future. This is the process of human resource planning. If there is a personnel function it may provide the manager with data on such things as the availability of people from the labour market, the output of trainee schemes and employee turnover rates. It may even in some large organizations use computer models to make projections. But only the manger knows what he or she is doing now and will be doing the future and it is those activity levels that form the basis of human resource plans. Human resource planning aims to match human resources to meet the business need expressed in activity levels. Plans are often long-term (i.e. one year head or more) but may look at shorter-term needs as well, planning for people is concerned with

• Getting and keeping the number and quality of staff one need
• Identifying skill requirements and how to satisfy them
• Dealing with surpluses and deficits of employees
• Improving the utilization of employees.

Determining Human Resource Requirements

Human resource requirements are determined by relating the supply to the demand forecasts and identifying deficits or surpluses of human resource that will exist in the future. Table 1 shows how demand and supply forecasts can be scheduled over a period of five years. The reconciliation of demand and supply forecast give us the number of people to be recruited or made redundant as the case may be. This forms the basis for the action programme in HRP.

Human Resource Requirement

Demand forecasting: Decisions on the number of people the company need are based on demand and supply forecasts. Demand forecasting is the process of estimating the future number of people required and the skills and competencies they will need. The basis for the forecast will be present and projected activity levels in such terms as outputs or the number of items to be processed. Forecast activity levels are usually derived from the business plan and the related functional or departmental plan and budget. For example, in a manufacturing company the sales budget is translated into manufacturing plans, giving the number or types of product to be made in each period. For this information, the number of hours to be worked by each category of employee to meet the budget for each policy is computed.

1. Managerial judgment: This is the most typical method. It simply required you to sit down, think about future workloads, and decide how many people you need. Experienced managers can do this quite well but it often involves a lot of guesswork. It usually only works in the short-term and this may not present too much of a problem if increases in level of work can easily be dealt with by recruitment. Overtime, sub-contracting or outsourcing. Unforeseen decreases in activity levels may create more problems. It may not be so easy or desirable to make people redundant, lay them off or reduce their working hours. It may even be difficult to get out of a sub-contract quickly. That is why you have constantly to attempt to forecast requirements as for ahead as you can see by monitoring trends, finding out how business plans are progressing, checking on sales forecasts and results and so on. You cannot rely entirely on your own resources to plan ahead. You must get data from other people.

2. Ratio-trend analysis: Ratio trend analysis is carried out by studying past ratios. For example, the number of direct (production) workers and indirect (support) workers in a manufacturing unit. Forecast activity levels can then gives you a good idea of how many direct workers you will need (so many people to make so many bags) and the ratio of indirect to directs will tell you how many support workers you are likely to need.

3. Work study techniques: Work-study techniques can be used when it is possible to apply work measurement to calculate how long operations should take and the number of planned hours, and therefore people required. In a manufacturing company the starting point is the production budget set out as the volume of saleable products for the plant as or whole and the output volumes for each department. If standard hours for units of output have been determined by work measurement, the budgets of productive (planned hours for the year) are then compiled by multiplying standard hours per unit of output by the planned output in units. This is decided by the number of actual working hours for an individual worker, having allowed for absenteeism and forecast idle or down time to show the number of operators required. The following are a simplified example of this process.

Planned output for year                        : 2000 units standard hours
Standard hours per unit                        : 5 hours
Planned hours for year                         : 100, 000 hours
Net productive hours per person year: 2000 hours
Number of direct workers required     : 50 hours
(Planned hours divided by productive hours per person)


Work-study techniques for direct workers can be combined with ratio trend analysis to calculate the number of indirect workers needed.

4. Forecasting skill requirements: Forecasting of skill requirements that from an analysis of the current range of skills required and the analyze the impact of forecast product-market developments, the acquisition of new or different types of business, projected changes in work methods and the effect of the new technology. The latter would include information technology, computerized production methods such as manufacturing requirements planning (MRP/ or some form of automation or robotics). Skill forecasts are largely a matter of judgment based on experience and a thorough analysis of likely trends in the type of work to be carried out and working methods.

5. Regression Models: Regression analysis is used to measure the relationship between one or more independent variables to explain a dependent variable. In HRP regression analysis can be used to correlate personnel requirements with output, revenue etc. as in trend-ratio and time-series analysis. The analysis is based more on historical patterns. The real value of regression analysis in HRP is in generating alternative scenarios in personnel needs. It is important to bear in mind that the relationships are not always liner and the purpose of the analysis is not present the management with precise quantitative forecasts.

6. Supply forecasting estimate: Supply forecasting estimates the number of people likely to be available from within and outside the organization (i.e. what are sometimes called the internal and external labour market). The supply analysis ever

• Existing people available-how many have you got and what skills do they posses? 
• Potential losses to existing resource through employee wastage-what are the present and forecast employee turnover figures? 
• Effect of absenteeism-what is the present rate and what it will be in the future? 
• Sources of supply from within the organization – who have we got now with the right skills? Who are going to be available in the future, and when? 
• Sources of supply from outside the organization-what are the chances of being able to recruit the sort of people we need? 
• Defining sources of future supply and reaching satisfactory conclusions on availability may be difficult. It depends in a number of factors out of one’s control. 
• Demand and supply forecasts indicate the extent to which there are likely to be surpluses or deficits of people.