forecasting techniques and their uses

1. Forecasting

2. • “A statement about the future value of a variable of interest such as
demand” It is simply can be defined as “Predictions about the future”
for variables of interest.
• Forecast effects organizations’ decisions in many ways. For
example: Forecasting the demand drives decision in many areas,
such as human resource, machine capacity, sales forces, supply
chain.
• There are two important aspects of forecasts;
• Expected level of demand(sales, cost any other variable) forecast
• Degree of accuracy
• Forecasts may be Short range (e.g., an hour, day, week, or month),
or Long range (e.g., the next six months, the next year, the next five
years, or the life of a product or service).

3. • Forecasting Forecasts affect decisions and activities throughout
an organization:
• Accounting, finance
• Human resources
• Marketing
• Management Information System
• Operations
• Product / service design

4. Use of Forecasting Method
Accounting Cost/Profit Estimates
Finance Cash flow and funding
Human Resources Hiring/recruiting/training cost
Marketing Pricing, promotion, strategy
MIS IT/IS systems, service
Operations Schedules, workloads
Product/service design New products and services

5. Elements of a Good Forecast
• Timely
• Accurate
• Reliable
• Written
• Meaningful
• Easy to Use

6. Steps in the Forecasting Process
• Forecasting follows seven basic steps. We use Disney World as an example of each step:
• 1. Determine the Purpose of the forecast: Disney uses park attendance forecasts to drive staffing, opening
time, ride availability, and food suppliers.
• 2. Select the item to be forecasted: For Disney World, there are six main parks. A forecast of daily attendance
at each is the main number that determines labor, maintenance, and scheduling.
• 3. Determine the time horizon of the forecast: is it short, medium, or long term? Disney develops daily,
weekly, monthly, annual, and 5-year forecast.
• 4. Select the forecasting Technique: Disney uses a variety of statistical models like, including moving
averages, econometrics, and regression analysis. It also employs judgmental, or nonquantitative, models.
• 5. Gather the data needed to make the forecast: Disney’s forecasting team employs 35 analysts and 70 field
personnel to survey 1 million people/businesses every year. It also uses the firm called Global Insight for travel
industry forecasts and gathers data on exchange rates, arrivals into the U.S., airline specials, Wall Street trends, and
school vacation schedules.
• 6. Make the forecast.
• 7. Validate and implement the result: At Disney, forecasts are reviewed daily at the highest levels to make
sure that the model, assumptions, and data are valid. Error measures are applied; then the forecasts are used to
schedule personnel down to 15-minutes intervals.

7. Approach in Forecasting
• Qualitative methods
• It consist mainly of subjective inputs, which often defy precise
numerical description. It involve either the projection of historical data
or the development of associative models that attempt to utilize causal
(explanatory) variables to make a forecast.
• Quantitative Methods
• It consist mainly of analyzing objective, or hard, data. They usually
avoid personal biases that sometimes contaminate qualitative
methods. In practice, either approach or a combination of both
approaches might be used to develop a forecast.

8. Qualitative Forecasting Methods
• DELPHI METHOD:
• In this method, a committee is formed. A moderator creates a
questionnaire & distributes to the participants.
• Steps involved--
• 1. Choose experts to participate from different areas.
• 2. Their questionnaire or email obtain forecasts.
• 3. Summarize the results.
• 4. Redistribute results with another new questionnaire.
• 5. Summarizes again- refining forecasts.
• 6. Carry on 3 to 6 rounds
• 7. It results in forecasts that most participants have ultimately agreed to in spite of their
initial disagreement

9. • EDUCATED GUESS
• •Judgment based on experience & intuition to estimate a sales forecast (by one person Used for short term
forecast when cost of forecast inaccuracy is low. Such forecasts have to be made very frequently.
• SURVEY OF CUSTOMERS
• •Suitable when a company has few customers e.g. Automobile/defense contractors. Estimates are gathered
from customers directly.
• EXECUTIVE COMMITTEE CONSENSUS
• •Forecast made by a committee of knowledge executive from different departments. Such forecast are
compromise forecast not reflecting the extremes. People from a lower level may not speak freely to refute
the estimates of people saving above them.

10. • SURVEY OF SALES FORCE
• Used for existing product when salespeople sell directly to customers & a good communication system exists
in an organization. Estimates of future regional sales are obtained from sales people. These are refined by
managers & total sales for all regions is estimated on its behalf.
• MARKET RESEARCH
• Suitable for new products or introduction of exiting product in new market segments. Then mail,
questionnaires, surveys, telephone interviews- hypothesis is tested.
• HISTORICAL ANALOGY
• For a new product a generic or existing product is used as a model. The analogies may be complementary
product/substitutes. Knowledge of one product sales during various stages of its product life cycle is applied
to the estimate of sales for a similar product.

11. QUANTITATIVE FORECASTING
METHODS
• Time Series Models
• Such models predict future on 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.
• Characteristics of Time Series Models
• Trend - long-term movement in data
• Seasonality - short-term regular variations in data
• Cycle – wavelike variations of more than one year’s duration
• Irregular variations - caused by unusual circumstances

13. Types of Time Series Analysis
• Moving Averages(Simple and Weighted)
• A moving average forecasts uses a number of historical actual data values to generate a forecast.
Moving averages are useful if we can assume that market demands will stay fairly steady over time.
• Simple Moving Average= Σ Demand in previous n periods/n

14. • Weighted Moving Average
• When some past periods are to be given more weightage then Weighted Moving Average is used.

15. Exponential Smoothing
• The most recent observations might have the highest predictive
value. Therefore, we should give more weight to the more
recent time periods when forecasting.
• Ft = Ft-1 + α(At-1 - Ft-1)
• α is the parameter also called the smoothing factor or smoothing
coefficient.
• This parameter controls the rate at which the influence of the observations at prior
time steps decay exponentially. Alpha is often set to a value between 0 and 1.
Large values mean that the model pays attention mainly to the most recent past
observations, whereas smaller values mean more of the history is taken into
account when making a prediction.

16. • “A value close to 1 indicates fast learning (that is, only the most recent values influence
the forecasts), whereas a value close to 0 indicates slow learning (past observations have
a large influence on forecasts).”

17. Linear Trend