FORECASTING

CHAPTER

3

FORECASTING

Forecasts serve as a basis for planning--capacity, budgeting, sales, production, inventory, personnel

Successful forecasting requires a skillful blending of both art and science

Two uses of forecasts:Planning the system--Long RangePlanning the use of the system--Short Range

Forecasting

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

I see that you willget an A this semester.

Elements of a Good Forecast

Timely

AccurateReliable

Mea

ningf

ul Written

Easy t

o us

e

Steps in the Forecasting Process

Step 1 Determine purpose of forecast

Step 2 Establish a time horizon

Step 3 Select a forecasting technique

Step 4 Gather and analyze data

Step 5 Prepare the forecast

Step 6 Monitor the forecast

“The forecast”

APPROACHES TO FORECASTING

QUALITATIVE--based on subjective inputs, soft data

judgmental forecasts, opinions, hunches, experience, etc.

QUANTITATIVE--based on historical data

--project past experience into the future

--uncover relationships between variables that can be used to predict the future

Types of Forecasts

Judgmental - uses subjective inputs Time series - uses historical data

assuming the future will be like the past Associative models - uses explanatory

variables to predict the future

Judgmental Forecasts

Executive opinions Sales force composite Consumer surveys Outside opinion Opinions of managers and staff

– Delphi technique

QUANTITATIVE FORECASTS

Time-Series techniques

--Naïve

--Moving Average models

--Exponential Smoothing models

--Classical Decomposition

--Box-Jenkins ARIMA models

--Neural Networks

QUANTITATIVE FORECASTS

Causal or Associative techniques

--Simple linear regression

--Multiple linear regression

--Nonlinear regression

FORECASTING DATA

“time-series”

--time-ordered sequence of observations taken at regular intervals over a period of time

Annual, Quarterly, Monthly, Weekly,

Daily, Hourly, etc.

UNDERLYING BEHAVIOR

Trend - long-term movement in data Seasonality - short-term, regular, periodic variations in

data Cycles - wave-like variations of more than one year’s

duration Irregular variations - caused by unusual circumstances Random variations - caused by chance

Forecast Variations

Trend

Irregularvariation

Cycles

Seasonal variations

908988

Naive Forecasts

Uh, give me a minute.... We sold 250 wheels lastweek.... Now, next weekwe should sell.…

“the latest observation in a sequence is used as the forecast for the next period”

Ft = At-1

Simple Moving Average

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12 13

Actual

MA3

MA5

MAn Ft = n

Ai “an average that is

repeatedly updated”

i = 1

n

Exponential Smoothing

Premise--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 + (At-1 - Ft-1)

Forecast Accuracy

Error – difference between actual value and predicted value

Mean absolute deviation (MAD) - Average absolute error Mean squared error (MSE) - Average of squared error Mean absolute percent error (MAPE) - Average absolute percent error Tracking Signal - Ratio of cumulative error and MAD

MAD,MSE, & MAPE

MAD = Actual forecast

n

MSE = Actual forecast)

-1

2

n

(

MAPE = Actual - forecastActual

X 100

n

Tracking Signal

Tracking signal = (Actual-forecast)

MAD

Tracking signal = (Actual-forecast)Actual-forecast

n