Forecasting methods by Neeraj Bhandari ( Surkhet.Nepal )

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FORECASTING METHODS

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Transcript of Forecasting methods by Neeraj Bhandari ( Surkhet.Nepal )

Page 1: Forecasting methods by Neeraj Bhandari ( Surkhet.Nepal )

FORECASTING METHODS

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Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed.

Prediction is a similar, but more general term.

Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts.

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Forecasting

Marketing: Forecasts sales for new and existing products.

Production: uses sales forecasts to plan production and operations; sometimes involves in generating sales forecasts

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Time Horizon for ForecastingThe key factor in choosing a proper

forecastingapproach is the time horizon for the

decision requiring forecasting. Forecasts can be made for various timeframes:

1. Short- term2. Mid- term3. Long- term

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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 judgement methods.

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Medium- term Forecasting

Time horizon for medium term (3 months to 24 months).

Relates to aggregate planning (sales & operations planning).

Medium term forecasts is used to build up seasonal inventory

Both time-series and causal methods are used.

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Long-term-Forecasting

Time horizon exceeds two years Long term forecasts are used for

process selection, capacity planning & location decisions.

Judgement models & causal models are used.

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Forecasting (Objective & Subjective)

The various categories of forecasting methods that are available to businesses:

  Forecasting methods can be either objective (using quantitative approaches) or;

Subjective (using more intuitive or qualitative approaches), depending on what data is available and the distance into the future for which a forecast is desired.  

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Types of Forecasting Methods1. Subjective Methods

Sales force composites Customer Survey Jury of executive opinion Delphi Methods

2. Objective Methods Casual Methods Time-Series Methods

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Qualitative Forecasting

Qualitative forecasting techniques are subjective, based on the opinion and judgment of consumers, experts

They are appropriate when past data are not available.

They are usually applied to intermediate- or long-range decisions.

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Quantitative Forecasting

Quantitative forecasting models are used to forecast future data as a function of past data; they are appropriate when past data are available.

These methods are usually applied to short- or intermediate-range decisions.

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Subjective Methods

1. Sales force composite: Marketers have sales managers or

representatives at different sales territories (districts/region) and marketers believe that sales managers know their territory better than anybody else.

Managers ask respective sales manager to forecast expected sales in their own territories. The total of all these estimates basically gives company’s sales/demand forecast for next period.

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2. Customer Survey: Marketers ask buyers about how many units that they would like to purchase from ABC company’s products for coming period of time.

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3. Jury of Executive Opinion Method: In the Jury of executive opinion method

of Sales Forecasting, appropriate managers within the organization assemble to discuss their opinions on what will happen to sales in the future.

Since these discussion sessions usually resolve around experienced guesses, the resulting forecast is a blend of informed opinions.

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4. Delphi Method also gathers, evaluates, and summarizes expert opinions as the basis for a forecast, but the procedure is more formal than that for the jury of executive opinion method.

The Delphi Method has the following steps:STEP 1 – Various Experts are asked to answer,

independently and in writing, a  series of questions about the future of sales or whatever other area is being forecasted.                                                      

STEP 2 – A summary of all the answers is then prepared. No expert knows, how any other expert answered the questions.       

STEP 3 – Copies of summary are given to the individual experts with the request that they modify their original answers if they think it necessary.  

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STEP 4 – Another summary is made of these modifications, and copies again are distributed to the experts. This time, however, expert opinions that deviate significantly from the norm must be justified in writing.  

STEP 5 – A third summary is made of the opinions and justifications, and copies are once again distributed to the experts. Justification in writing for all answers is now required.   

STEP 6 – The forecast is generated from all of the opinions and justifications that arise from step 5.

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Objective Methods1. Casual Methods: Some forecasting methods use the

assumption that it is possible to identify the underlying factors that might influence the variable that is being forecast. (Cause and Effect)

For example, including information about climate patterns might improve the ability of a model to predict umbrella sales.

This is a model of seasonality which shows a regular pattern of up and down fluctuations.

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2. Time SERIES ANALYSIS METHOD: The time series analysis method

predicts the future sales by analyzing the historical relationship between sales and time.

Although the actual number of years included in a time series analysis will vary from company to company, as a general rule, managers should include as many years as possible to ensure that important sales trends do not get undetected.

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The three most common historical data used are: Seasonality: A seasonal pattern (eg., quarter of the

year, month of the year, week of the month, day of the week) exists when the demand is influenced by seasonal factors.

Trend: During the growth and decline stages of the product-life cycle, a consistent trend pattern in terms of demand growth or demand decline can be observed.

Level: It is difficult to capture short term patterns that are not repetitive in nature. In short run, sometimes there is a swing, which could be in either direction, upward or downward, and is usually has momentum that lasts for a few periods.