short notes on Demand forecasting, types, ways, purpose, objective, importance and methods with the help of diagram

Demand forecasting:-

• refers to the predictionor estimation of a future situation under given constraints.
• Stuff Forecasting is the process of estimation in unknown situations and Prediction.
• Demand forecasting is the activity of estimating the quantity of a product orservice that consumers will purchase.
• Forecasting customer demand for products and services is a proactive process of determining what products are needed where, when, and in what quantities. Consequently, demand forecasting is a customer–focused activity.
• Demand forecasting is also the foundation of a company’s entire logistics process. It supports other planning activities such as capacity planning, inventory planning, and even overall business planning.
  
  
  
  
  
  
    

Types of forecasting:-

there are following types of forecasting which are below:-

1.short term forecasting:

• Short-term( 1 day to 3 months), managers are interested in forecasts for disaggregated demand( for specific product, for specific geography, etc)
• Little time to react to errors in demand forecast, so the forecasts need to be as accurate as possible.
• Time series analysis is often used.
• In absence of historical data managers use judgment methods.
  eg., clothes. Strategic decisions. Extending or reducing the limits of resources.
  
  

2. medium term forecasting:

• Time horizon for medium-term( 3 months to 24 months).
• Relates to aggregate planning(sales & operations planning).
• Medium term forecast is used to build up seasonal inventory.
• Both time-series and causal methods are used.
  
  

3. long term forecasting:

• Time horizon exceeding two years.
• Long term forecasts are used for process selection, capacity planning & location decisions.
• Judgment models & causal models are used.

eg., petroleum, paper, shipping. Tactical decisions. Within the limits of resources already available.

Forecasting could be done in the following ways:-

1) Active and Passive.

2) Total Market and Market Segmentation.

3) Company Forecast and Industry Forecast.

4) Short Term and Long Term Forecasting.

Purpose of Demand Forecasting:-

1.Better planning and allocation of resources.

2. Appropriate production scheduling.

3. Inventory control.

4. Determining appropriate pricing policies.

5. Setting s les targets and establishing controls and Incentives.

6. Planning a new unit or expanding existing one.

7. Planning long term financial requirements.

8. Planning Human Resource Development strategies.

Objective of demand forecasting:-

1. Helping for continuous production

2. Regular supply of commodities

3. Formulation of price policy

4. Arrangement of finance

5. Labor requirement.

IMPORTANCE OF DEMAND FORECASTING:-

• Planning and scheduling production
• Budgeting of costs and sales revenue
• Controlling inventories
• Making policies for long term investment
• Helps in achieving targets of the firm

Methods of demand forecasting:

1. Qualitative Methods (Survey Methods):

1. Survey method:

Survey methods constitute another important forecasting tool, especially for short-term projections.

The consumers are directly approached and are asked to give their opinions about the particular product. The questionnaire must be carefully prepared bearing in mind the qualities of a good questionnaire. It must be simple and interesting so as to evoke consumers’ response.

Consumers’ Survey may acquire three forms:

a) Complete Enumeration Survey

b) Sample Survey

c) End-Use Method.

a) Complete Enumeration Survey:

Complete Enumeration Survey covers all the consumers. It resembles the Census Data Collection which considers the entire population. In this case all the consumers are covered and information is obtained from all regarding the prospective demand for the product under consideration.in this methode consumer opinions are concerned.

We can obtain complete information by contacting every possible present, past or would be consumers of the product. No doubt it is not very easy to carry out the survey on such a large scale. Even the collected information will be difficult and too tedious to be analyzed. The reliability on such consumers’ information may be questionable, if the opinions are not authentic.

b) Sample Survey:

In case of the sample survey method, few consumers are selected to represent the entire population of the consumers of the commodity consumed. The total demand for the product in the market is then projected on the basis of the opinion collected from the sample. The most important advantage of this method is that it is less expensive and less tedious compared to the method of complete enumeration. The sample chosen should not be too small or too large. This method if applied carefully will yield reliable results especially in case of new brands and new products.

c) End –Use Method:

A given product may have different end uses. For example: milk may have different end uses such as milk powder, chocolates, sweet -meats like ‘barfi’ etc. Therefore the end users of milk are identified. A survey is planned of the end users and the estimated demands from all segments of end users are added. This method of demand forecasting is easy to manage if the number of end-users is limited. In this method the investigator expects the end- users to provide correct information well in advance of their respective production schedules.

• Delphi Method:

This is a variant of the opinion poll or survey method.

Here we invite different experts and take their opinion and they finally try to find an average of their ideas. We again intimate the experts about the average opinion, and give them an opportunity to revise their forecast. Delphi is useful when you want to have an overall subjective assessment about complex business environment. All the experts are distant and they dont know each other, therefore each one tries to give the best possible estimate.

• Nominal Group Technique:

It is similar to Delphi, but here we ask experts to sit together and explain their perspective to others so that others can also give their opinion. People frame their estimates individually, but thereafter they give justification for their opinion.

• opinion poll method :

Here we collect information about the issue from people using opinion poll. We may take interview, we may collect data using questionnaire, or we may organise meeting / conference to find opinion of people. Variants of opinion poll are : focus group discussion – which is used in marketing to know about consumer opinions.

• Expert opinion method

• PANEL OF EXPERTS IN SAME FIELD WITH EXPERIENCE & WORKING KNOWLEDGE.

• COMBINES INPUT FROM KEY INFORMATION SOURCES.

• EXCHANGE OF IDEAS AND CLAIMS.

• FINAL DECISION IS BASED ON MAJORITY OR CONSENSUS, REACHED FROM EXPERT’S FORECASTS

B. Quantitative Methods (Statistical Methods):-

1. Time Series Analysis (Trend Projection):

Time Series Analysis is used to estimate future demand. The Time Series Method is based on obtaining the historical data regarding the demand for the product.

The Time Series forecasting models are based on historical observations of the values of the variable that is being forecast.

• 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 popular method of analysis of the time series is to project the trend of the time series.a trend line can be fitted through a series either visually or by means of statistical techniques. The analyst chooses a plausible algebraic relation (linear, quadratic, logarithmic, etc.) between sales and the independent variable, time. The trend line is then projected into the future by extrapolation.
• Its Popular because: simple, inexpensive, time series data often exhibit a persistent growth trend.
• Its Disadvantage: 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, however, the trend projection breaks down.
  
  
  
  

2. Moving Averages:

• Moving averages method can be used when the forecast period is either odd or even.
• ]The method of Moving Average is useful when the market demand is assumed to remain fairly steady over time. The Moving Average for ‘n’ months is found by simply summing up the demand during the past ‘n’ months and then dividing this total by ‘n’.

Moving Average = Demand in the previous ‘n’ months/ n.

 moving average method. Here we take moving average of either 3 days or 5 days or 7 days and try to forecast using this moving average. We may also use smoothing to remove exceptional fluctuations Moving average is a case where data can be used to forecast on the basis of past trend

 Example of moving average : Period data 3 year moving average 2001 300 2002 400 400 2003 500 434 2004 400 500 2005 600 600 2006 800 700 2007 700

3. Exponential Smoothing:

Here we use the past data to smooth the data. Here we use the past data to predict the future.

In this technique more recent data are given more weight age. This is based on the argument that the more recent the observations, the more its impacton future and therefore is given relatively more weight than the earlier observations.

4. Index Numbers:

The Index Numbers offer a device to measure changes in a group of related variables over a period of time. In case of index numbers we select a Base Year which is given the value of 100 and then express all subsequent changes as a movement of this number. The most commonly used is the Laspeyres’ Price Index.

5. Regression Analysis:

This Statistical method is undertaken to measure the relationship between two variables where correlation appears to exist. For example: we can establish a relationship between the age of the air condition machine and the annual repairs expenses. However this is purely based on the availability of statistical data irrespective of the actual causes of damage for which the repair expenses have to be incurred.

6. Econometric Models:

• The Econometric Models used in forecasting takes the form of an equation or system of equation which seems best to express the most probable interrelationship between a set of economic.
• The Econometric Models can be quantitatively and qualitatively formulated.
• One of the first steps in the construction of an Econometric Model is to determine all or most of the factors influencing the series to be forecast. Then the influence of these factors is reflected in the form of an equation. These models are generally used by econometricians. One of the major limitations of Econometric Model approach is the assumption that the relationships established in the past will continue to prevail in the future. The Econometric Models have failed in many cases but this does not imply that we should abandon them. Being analytical in nature and process oriented in approach they throw more light on problems of a theoretical and statistical nature provided the statistical data are reliable.
  
  

7. Input-Output Analysis:

• The Input-Output Analysis provides perhaps the most complete examination of all the complex inter-relationships within an economic system.
• The Input-Output forecasting is based on a set of tables that explain the inter-relationship among the various components of the economy.
• The Input-Output Analysis shows how an increase or decrease in the demand for cars will lead to increase in production of steel, glass, tyres etc. The increase in demand for these materials will have second line effect. The Input-Output Analysis helps us to understand the inter-industry relationships to provide information about the total impact on all industries as a result of the original increase in demand forecast.

There is no unique method for forecasting the demand for any product

Casual method:

• Causal forecasting model show the cause for demand and its relation to other variables. Usually, regression is used for modeling the cause-and-effect behavior.

Examples: Soft drink can be related to the average summer temperature.

Rainfall can give us an estimate of crop and in turn an estimate of the estimate of the demand for consumer durables in the rural areas.