This document provides an overview of forecasting. It defines forecasting as a statement about the future value of a variable of interest that is used for planning purposes. It then discusses how forecasts affect decision making across various organizational functions. The document outlines common features of forecasts, such as becoming less accurate over longer time horizons. It also describes different forecasting approaches, like judgmental, time series, and associative modeling. Time series techniques are explained in more detail, including identifying trends, seasonality, and cycles in time-ordered data. Specific time series forecasting methods like moving averages, weighted moving averages, and exponential smoothing are defined. The document concludes with a discussion and example of measuring forecast accuracy.

1. ForecastingForecasting
• Forecasting
1-1

2. FORECASTFORECAST
• A statement about the future value of a variable of
interest.
– Forecasts are the basis for budgeting, planning capacity,
sales, production and inventory, personnel, purchasing and
many more.
– Forecasts play an important role in the planning process
because they enable managers to anticipate the future so
they can plan accordingly.
• Forecasts affect decisions and activities throughout an
organization –
– Accounting, Finance, Human resource, Marketing, MIS,
Operations, Product / service design.

3. FORECASTFORECAST
Accounting Cost/profit estimates
Finance Cash flow and funding
Human Resources Hiring/recruiting/training
Marketing Pricing, promotion, strategy
MIS IT/IS systems, services
Operations Schedules, MRP, workloads
Product/service design New products and services

4. COMMON FEATURES TO ALL FORECASTSCOMMON FEATURES TO ALL FORECASTS
• Assumes causal system
past ==> future
• Forecasts rarely perfect because of randomness
• Forecasts more accurate for groups vs. individuals
• Forecast accuracy decreases as time horizon
increases
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5. • The forecast should be timely.
• The forecast should be accurate and the degree of
accuracy should be stated.
• The forecast should be reliable; it should work
consistently.
• The forecast should be expressed in meaningful
units.
• The forecast should be in writing.
• The forecasting technique should be simple to
understand and use.
• The forecast should be cost effective.
ELEMENTS OF A GOOD FORECASTELEMENTS OF A GOOD FORECAST

6. STEPS IN THE FORECASTING PROCESSSTEPS IN THE FORECASTING PROCESS
6. Check forecast
accuracy with one or
more measures
4. Select a forecast
model that seems
appropriate for data
5. Develop/compute
forecast for period of
historical data
8a. Forecast over
planning horizon
9. Adjust forecast based
on additional qualitative
information and insight
10. Monitor results and
measure forecast
accuracy
8b. Select new
forecast model or
adjust parameters of
existing model
7.
Is accuracy of
forecast
acceptable?
1. Identify the
purpose of forecast
3. Plot data and identify
patterns
2. Collect historical data
No
Yes

7. • Judgmental Forecasts
– Forecasts that use subjective inputs such as opinions
from consumer surveys, sales staff, managers,
executives and experts.
• Time - series Forecasts
– Forecasts that project patterns identified in recent
time - series observations.
• Associative Model
– Forecasting technique that uses explanatory variables
to predict future demand.
APPROACHES TO FORECASTINGAPPROACHES TO FORECASTING

8. • A time series is a time ordered sequence of
observations taken at regular intervals; e.g. –
hourly, daily, weekly, monthly etc.
– Analysis of time series data requires the analyst to
identify the underlying behavior of the series. This
can often be accomplished by merely plotting the
data and visually examining the plot.
– Different patterns are –
• Trend, Seasonality, Cycles, Irregular variations and Random
variations.
TIME - SERIES FORECASTSTIME - SERIES FORECASTS

9. • Trend - A long term upward or downward
movement in data.
• Seasonality - Short term regular variations
related to the calendar or time of day.
• Cycle - Wavelike variations lasting more than
one year.
• Irregular variation - Caused by unusual
circumstances, not reflective of typical
TIME - SERIES FORECASTS (CONTD.)TIME - SERIES FORECASTS (CONTD.)

10. TIME - SERIES FORECASTS (CONTD.)TIME - SERIES FORECASTS (CONTD.)
Trend
Irregular
variation
Seasonal variations
90
89
88
Cycles

11. • Naive Method
– The forecast for any period equals the previous
period’s value.
• The naive approach can be used with a stable series (variations
around an average), with seasonal variations or with trend.
• Techniques for Averaging
– Moving Average
– Weighted Moving Average
– Exponential Smoothing
FORECASTING METHODFORECASTING METHOD

12. • Technique that averages a number of recent
actual values, updated as new values become
available.
MOVING AVERAGEMOVING AVERAGE
MAn =
n
Aii = 1
∑
n

13. • More recent values in a series are given more
weight in computing the forecast.
WEIGHTED MOVING AVERAGEWEIGHTED MOVING AVERAGE
EXPONENTIAL SMOOTHINGEXPONENTIAL SMOOTHING
 Weighted averaging method based on previous forecast plus a
percentage of the forecast error.
Ft = Ft-1 + α(At-1 - Ft-1)

14.  Mean Absolute Deviation (MAD)
 Average absolute error
 Mean Squared Error (MSE)
 Average of squared error
 Mean Absolute Percent Error (MAPE)
 Average absolute percent error
FORECAST ACCURACYFORECAST ACCURACY
MAD=
Actual forecast−∑
n
MSE =
Actual forecast)
-1
2
−∑
n
(
MAPE=
Actual forecast−
n
/ Actual)*100%)∑((

15. EXAMPLE 1EXAMPLE 1
Period Actual Forecast (A-F) |A-F| (A-F)^2 (|A-F|/Actual)*100
1 217 215 2 2 4 0.92
2 213 216 -3 3 9 1.41
3 216 215 1 1 1 0.46
4 210 214 -4 4 16 1.90
5 213 211 2 2 4 0.94
6 219 214 5 5 25 2.28
7 216 217 -1 1 1 0.46
8 212 216 -4 4 16 1.89
-2 22 76 10.26
MAD = 2.75
MSE = 10.86
MAPE = 1.28

16. THANK YOUTHANK YOU