Forecasting Models
Everyday a shop owner thinks how many items he would be able to sell.
The florist at the roadside keeps flower thinking in mind how much he would be able to sell by the end of the evening.
Here they are applying forecasting- albeit on a miniscule scale.
So we all forecast something each and every day.
What is Forecasting?
Planning is an important function of any management which based of forecasting. Hence, Forecasting is the art and science of predicting future events. Forecasts are required throughout an organization and at all levels of decision making in order to plan for the future and make effective decisions. The principal use of forecasts in operations management is in predicting the demand for manufactured products and services for time horizons ranging from several years down to 1 day. Depending on the planning horizon, forecasting can be classified in three ways:
Short – range forecasting (up to 1 year)
Medium – range forecasting ( up to 3 years)
Long – range forecasting (more than 3 years)
Types of forecasts
In general, a contemporary business organization employs three distinct types of forecasts.
These are given under:
1. Economic forecasts
2. Technological forecasts
3. Demand forecasts
Economic forecasts address the business cycle by predicting inflation rates, money supplies, housing starts, and other planning indicators.
Technological forecasts are concerned with rates of technological progress, which can result in the birth of exciting new products, requiring new plants and equipment.
Demand forecasts are projections of demand for a company’s products or services. These forecasts, also called sales forecasts, drive a company’s production, capacity, and scheduling systems and serve as inputs to financial, marketing, and personnel planning.
What is the strategic importance of forecasting?
Forecasting plays a very important role in the following areas:
Human resource management
Hiring, training and laying-off workers all depend on anticipated demand.
Capacity planning
Everyday a shop owner thinks how many items he would be able to sell.
The florist at the roadside keeps flower thinking in mind how much he would be able to sell by the end of the evening.
Here they are applying forecasting- albeit on a miniscule scale.
So we all forecast something each and every day.
What is Forecasting?
Planning is an important function of any management which based of forecasting. Hence, Forecasting is the art and science of predicting future events. Forecasts are required throughout an organization and at all levels of decision making in order to plan for the future and make effective decisions. The principal use of forecasts in operations management is in predicting the demand for manufactured products and services for time horizons ranging from several years down to 1 day. Depending on the planning horizon, forecasting can be classified in three ways:
Short – range forecasting (up to 1 year)
Medium – range forecasting ( up to 3 years)
Long – range forecasting (more than 3 years)
Types of forecasts
In general, a contemporary business organization employs three distinct types of forecasts.
These are given under:
1. Economic forecasts
2. Technological forecasts
3. Demand forecasts
Economic forecasts address the business cycle by predicting inflation rates, money supplies, housing starts, and other planning indicators.
Technological forecasts are concerned with rates of technological progress, which can result in the birth of exciting new products, requiring new plants and equipment.
Demand forecasts are projections of demand for a company’s products or services. These forecasts, also called sales forecasts, drive a company’s production, capacity, and scheduling systems and serve as inputs to financial, marketing, and personnel planning.
What is the strategic importance of forecasting?
Forecasting plays a very important role in the following areas:
Human resource management
Hiring, training and laying-off workers all depend on anticipated demand.
Capacity planning
Quantitative Methods
The chief Quantitative methods are:
1. Moving averages
2. Exponential smoothing Time series models
3. Trend projection
4. Linear regression Causal model
The time series models of forecasting predict on the basis of the assumption that the future is a function of the past. In other words, they look at what has happened over a period of time and use a series of past data to make a forecast. If we are predicting weekly sales of washing machine, we use the past weekly sales for washing machine in making the forecast.
A causal model incorporates into the model the variables or relationships that might influence the quantity being forecast. A causal model for washing machine sales might include relationships such as new housing, advertising budget, and competitors’ prices.
Moving over to a structured approach to forecasting, let me introduce the basic steps involved in this process:-
Steps in Forecasting
There are eight steps to a forecasting system.
These are:
1. Determine the use of the forecast –
(What objectives are we trying to achieve?)
2. Select the items that are to be forecasted
3. Determine the time horizon of the forecast –
(Is it short, medium, or long – range?
4. Select the forecasting model
5. Gather the data needed to make the forecast
6. Validate the forecasting model
7. Make the forecast
8. Implement the results
We now focus our attention to one of the most widely used and effective method of forecasting.
Time Series Forecasting
A time series is based on a sequence of evenly spaced (weekly, monthly, quarterly, and so on) data points. Forecasting time series data implies that future values are predicted only from past values and that other variables, no matter how potentially valuable, are ignored.
Decomposition of a Time Series
There are four main ways of decomposing the time series:
Trend
Seasonality
Cycles
Random variations
The chief Quantitative methods are:
1. Moving averages
2. Exponential smoothing Time series models
3. Trend projection
4. Linear regression Causal model
The time series models of forecasting predict on the basis of the assumption that the future is a function of the past. In other words, they look at what has happened over a period of time and use a series of past data to make a forecast. If we are predicting weekly sales of washing machine, we use the past weekly sales for washing machine in making the forecast.
A causal model incorporates into the model the variables or relationships that might influence the quantity being forecast. A causal model for washing machine sales might include relationships such as new housing, advertising budget, and competitors’ prices.
Moving over to a structured approach to forecasting, let me introduce the basic steps involved in this process:-
Steps in Forecasting
There are eight steps to a forecasting system.
These are:
1. Determine the use of the forecast –
(What objectives are we trying to achieve?)
2. Select the items that are to be forecasted
3. Determine the time horizon of the forecast –
(Is it short, medium, or long – range?
4. Select the forecasting model
5. Gather the data needed to make the forecast
6. Validate the forecasting model
7. Make the forecast
8. Implement the results
We now focus our attention to one of the most widely used and effective method of forecasting.
Time Series Forecasting
A time series is based on a sequence of evenly spaced (weekly, monthly, quarterly, and so on) data points. Forecasting time series data implies that future values are predicted only from past values and that other variables, no matter how potentially valuable, are ignored.
Decomposition of a Time Series
There are four main ways of decomposing the time series:
Trend
Seasonality
Cycles
Random variations
QualitativeMethods:
Market Survey
Build-up forecast
Life cycle analog method
Panel consensus forecasting
Delphi method
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