Student ID 5671020 Master of science (Management)Email: wichiasr@hotmail.com, Tel.+66810716437

Quantitative Analysis for Management

ForecastingBy

Wichian Srichaipanya

Contents

• Measure of Forecast Accuracy

• Monitoring and Controlling Forecast

• Moving Average

• Exponential Smoothing

2

• Introduction

FORECAST:• A statement about the future value of a variable of

interest such as demand.• Forecasting is used to make informed decisions.

Introduction

Uses of Forecasts

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

3-4

Features of 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

3-5

Steps in the Forecasting Process

3-6

Step 1 Determine purpose of forecastStep 2 Establish a time horizon

Step 3 Select a forecasting techniqueStep 4 Obtain, clean and analyze data

Step 5 Make the forecastStep 6 Monitor the forecast

“The forecast”

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

3-7

Judgmental Forecasts

• Executive opinions• Sales force opinions• Consumer surveys• Outside opinion• Delphi method

– Opinions of managers and staff– Achieves a consensus forecast

3-8

Time Series Forecasts

• 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• Random variations - caused by chance

3-9

Forecast Variations

3-10

Trend

Irregularvariation

Seasonal variations

908988

Cycles

Moving average – A technique that averages a number of recent actual values, updated as new values become available.

where Ft+1 = Forecast for time period t+1 Yt = actual value in time period t n = number of periods to average

3-11

Ft+1 = n

Yt+Yt-1+ …+Yt-n+1

Moving Average

• Weighted moving average – More recent values in a series are given more weight in computing the forecast.

3-12

Ft+1 = n

w1Yt+w2Yt-1+ …+wnYt-n+1

where wi = weight for ith observation

Simple Moving Average

3-13

35373941434547

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

Actual

MA3

MA5

3-14

Example : Moving AverageA 3-month moving average for some company according to actual sales volume and estimated sales volume.

3-15

Example : Weighted Moving AverageForecast with weights of 3 for the most recent observation, 2 for the next observation, and 1 for the most distant observation.

• 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.

3-16

Ft+1 = Ft + (Yt - Ft)

Exponential Smoothing

• Weighted averaging method based on previous forecast plus a percentage of the forecast error

• Y-F is the error term, is the % feedback

3-17

Period Actual Alpha = 0.1 Error Alpha = 0.4 Error1 422 40 42 -2.00 42 -23 43 41.8 1.20 41.2 1.84 40 41.92 -1.92 41.92 -1.925 41 41.73 -0.73 41.15 -0.156 39 41.66 -2.66 41.09 -2.097 46 41.39 4.61 40.25 5.758 44 41.85 2.15 42.55 1.459 45 42.07 2.93 43.13 1.87

10 38 42.36 -4.36 43.88 -5.8811 40 41.92 -1.92 41.53 -1.5312 41.73 40.92

Example - Exponential SmoothingExample - Exponential Smoothing

Example : Exponential Smoothing graph

3-18

35

40

45

50

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

Period

Dem

and .1

.4

Actual

3-19

Measure of 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

MAD, MSE and MAPE

• MAD– Easy to compute– Weights errors linearly

• MSE– Squares error– More weight to large errors

• MAPE– Puts errors in perspective

3-20

MAD, MSE, and MAPE

3-21

MAD = Actual forecast

n

MSE = Actual forecast)

-1

2

n

(

MAPE = Actual forecast

n

/ Actual*100)

Example : MAD, MSE, MAPE

3-22

Period Actual Forecast (A-F) |A-F| (A-F)^2 (|A-F|/Actual)*1001 217 215 2 2 4 0.922 213 216 -3 3 9 1.413 216 215 1 1 1 0.464 210 214 -4 4 16 1.905 213 211 2 2 4 0.946 219 214 5 5 25 2.287 216 217 -1 1 1 0.468 212 216 -4 4 16 1.89

-2 22 76 10.26

MAD= 2.75MSE= 10.86

MAPE= 1.28

3-23

Monitoring and Controlling Forecast

After a forecast has been completed, it is important that it is not be forgotten.

No manager wants to be reminded when his or her forecast is horribly in accurate,

but the firm needs to determine why the actual demand differed from that projected.

Tracking Signal

3-24

Tracking signal = (Actual- forecast)MAD

• Running sum of the forecast error(RSFE) divided by the mean absolute deviation (MAD)

Upper and Lower limit– Persistent tendency for forecasts to be Greater or less than actual values.

One way to monitor forecasts to ensure that they are performing well

Upper and Lower Limit

3-25

There is no single answer, but they try to find reasonable values.

Example : Tracking signals

3-26

The objective is to compute the tracking signal and determine whether forecasts are performing adequately.

In period 6, this tracking signal is within acceptable limits from -2.0 MADs to +2.5 MADs.

Any Question?

Thank You