Chapter 4 - Forecasting Mr. David P. Blain. C.Q.E. Management Department UNLV

Chapter 4 - Forecasting

Mr. David P. Blain. C.Q.E.

Management Department

UNLV

(Principles of Operations Management, Heizer & Render, 5th Edition)

Outline

Global Company Profile: TupperwareWhat is Forecasting?Types of ForecastsSeven Steps in the Forecasting SystemForecasting Approaches

Overview of Qualitative & Quantitative MethodsTime-Series ForecastingMonitoring and Controlling Forecasts


Forecasting at Tupperware

Each of 50 profit centers around the world is responsible for computerized monthly, quarterly, and 12-month sales projections

These projections are aggregated by region, then globally, at Tupperware’s World Headquarters

Tupperware uses techniques discussed in text


Three Key Factors for Tupperware

The number of registered “consultants” or sales representatives

The percentage of currently “active” dealers (this number changes each week and month)

Sales per active dealer, on a weekly basis


Tupperware - Forecast by Consensus

Although inputs come from sales, marketing, finance, and production, final forecasts are the consensus of all participating managers.

The final step is Tupperware’s version of the “jury of executive opinion”


What is Forecasting?

Process of predicting a future event

Underlying basis of all business decisions Production Inventory Personnel Facilities

Sales will be $200 Million!


Forecasting Time Horizons

Process of predicting a future eventShort-range forecast

Job scheduling, worker assignments

Medium-range forecast Sales & production planning, budgeting

Long-range forecast New product planning, facility location


Types of Forecasts

Economic forecasts Address business cycle, e.g., inflation rate,

money supply, etc.Technological forecasts

Predict technological change Predict new product sales

Demand forecasts Predict existing product sales


Forecasting Approaches

Used when situation is stable & historical data existExisting productsCurrent technology

Involves mathematical techniquese.g., forecasting sales of

color televisions

Quantitative Methods Used when situation is

vague & little data existNew productsNew technology

Involves intuition, experiencee.g., forecasting sales

on Internet

Qualitative Methods


Overview of Qualitative Methods

Jury of executive opinion Pool opinions of high-level executives, sometimes

augment by statistical modelsSales force composite

Estimates from individual salespersons are reviewed for reasonableness, then aggregated

Delphi method Panel of experts, queried iteratively

Consumer Market Survey Ask the customer


Overview of Quantitative Approaches

Naïve approachMoving averagesExponential smoothingTrend projection

Linear regression

Time-series models

Associative models


TrendTrend

SeasonalSeasonal

CyclicalCyclical

RandomRandom

Time Series Forecasting


Naive Approach

Assumes demand in next period is the same as demand in most recent period If May sales were 48, then June sales will

be 48Sometimes can be cost effective &

efficient


Moving Average Method

MA is a series of arithmetic means Used if little or no trendUsed often for smoothing

Provides overall impression of data over time

Equation

MAn

n Demand in previous periods


Weighted Moving Average Method

Used when trend is present Older data usually less important

Weights based on intuition Ranges between 0 & 1, & sum to 1.0

Equation

WMA =Σ((Weight for period n) () (Demand in period n))

ΣWeights


Exponential Smoothing Method

Form of weighted moving average Weights decline exponentially Most recent data weighted most

Requires smoothing constant () Ranges from 0 to 1 Subjectively chosen

Involves little record keeping of past data


Ft = Ft-1 + (At-1 - Ft-1) Use for computing forecast

Ft = At - 1 + (1-)At - 2 + (1- )2·At - 3

+ (1- )3At - 4 + ... + (1- )t-1·A0

Ft = Forecast value

At = Actual value

= Smoothing constant

Exponential Smoothing Equations


^XY i i= +a b

Shows linear relationship between dependent & explanatory variables Example: Sales & advertising (not time)

Dependent (response) variable

Independent (explanatory) variable

SlopeY-intercept

Trend & Linear Regression Model


Linear Regression Equations

Equation: ii bxaY

Slope:

xnx

yxnyxb

i

n

i

ii

n

i

Y-Intercept: xbya


Slope (b) Estimated Y changes by b for each 1 unit

increase in X• If b = 2, then sales (Y) is expected to increase by 2

for each 1 unit increase in advertising (X)

Y-intercept (a) Average value of Y when X = 0

• If a = 4, then average sales (Y) is expected to be 4 when advertising (X) is 0

Interpretation of Coefficients


You want to achieve: No pattern or direction in forecast error

• Error = (Yi - Yi) = (Actual - Forecast)

• Seen in plots of errors over time

Smallest forecast error• Mean square error (MSE)

• Mean absolute deviation (MAD)

Selecting a Forecasting Model


Mean Square Error (MSE)

Mean Absolute Deviation (MAD)

Forecast Error Equations

2

n

1i

2ii

n

errorsforecast

n

)y(yMSE

n|errorsforecast |

n

|yy|MAD

n

iii


Measures how well the forecast is predicting actual values

Ratio of running sum of forecast errors (RSFE) to mean absolute deviation (MAD) Good tracking signal has low values

Should be within upper and lower control limits

Tracking Signal


Chap 4 ForecastingLearning Objectives

Ability to Identify or Define:Forecasting and types of forecastsTime horizonsApproaches to forecasts

Ability to Describe or Explain:Moving averagesExponential smoothingTrend projections and regressionMeasures of forecast accuracyTracking signal


Ch 4 Forecasting

4.6, 4.7


Sum 218 78 650 1474

Average 18.17 6.5

Period (x) x 2 xy

1 1 20

2 4 42

3 9 45

4 16 56

5 25 65

6 36 96

7 49119

8 64144

9 81180

10 100200

11 121231

12 144276

Monthly Sales for Telco Batteries were as follows:Month Sales (y)

January 20

February 21

March 15

April 14

May 13

June 16

July 17

August 16

September 20

October 20

November 21

December 23

Problem 4.6 Forecasting methodsCh 4

Forecasting


Sales

0

5

10

15

20

25

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

Month

Dolla

rsP 4.6 a) Plotting this data:

Ch 4 Forecasting


P 4.6 b) Forecast January Sales using each of the following :

2) 3 month Moving Average

Using the last 3 months Oct, Nov, Dec = (20 + 21 + 23)/3 =21 .33

We would forecast January at 21 based on smoothing of 3 month average

1) Naive Method

Demand in next period same as last period

Therefore January will be 23 since those were December’s sales

Ch 4 Forecasting


3) 6-month weighted average

Using weighting of .1, .1, .1, .2, .2 and .3;

with the heavier weights applied to the most recent months

( Oct, Nov, Dec)

(0.1 * 17) + (0.1 * 18) + (0.1 * 20) + (0.2 * 20) + ( 0.2 * 21) + (0.3 * 23) = 21.33 which gives us a January forecast of 21

Ch 4 Forecasting


4) Exponential smoothing

sophisticated weighted moving average forecasting method New forecast = last periods forecast + alpha *

(last period's actual demand - last period's forecast)

Therefore with alpha of .3 and September’s forecast of 18

October forecast = Sept forecast + alpha times (Sept. actual - Sept. forecast)

October forecast = 18 + 0.3 (20-18) = 18.6November forecast = 18.6 + 0.3 (20-18.6) = 19.02December forecast = 19.02 + 0.3 (21-19.02) = 19.6January forecast = 19.6 + 0.3 (23-19.6) = 20.6

Ch 4 Forecasting


5) Trend projection is a forecasting method that fits a trend line to a series of historical data points and then projects the line into the future for forecasting:

Ŷ = a + b*x (equation of the line)

Calculate slope and y-intecept

b = ∑xy – n x y a = y – bx

∑x² - nx²∑ = summation sign

Ŷ = (“y hat”) computed value of the variable to be predicted

(dependant variable)

a = y-axis intercept

b = slope of regression line (rate of change in y given change in x)

x = the independent variable

X = known values of the independent variable

Y = known values of the dependant variable

x = average of value of x’s

y = average of value of y’s

n = number of data points or observations

Ch 4 Forecasting


5) Trend projection: Based on earlier data supplied: ∑ x = 78

X = 6.5∑ y = 218Ŷ = 18.2

Calculate slope

b = ∑xy – n x y

∑x² - nx²

b = 1474-(12*6.5*18.2) = 54.4 = 0.38

650 – (12*(6.5)2 ) 143

and y-intercept

a = y – bx

a = 18.2 – (0.38*6.5) = 15.73

Forecast is Ŷ = a + b*x = 15.73 + ( .38*13) = 20.76 , where January is 13th month

Ch 4 Forecasting


P 4.7 Doug Moodie is the president of Garden Products Limited. Over the last 5 tears, he has asked both his vice president of marketing and his vice president of operations to provide sales forecasts. The actual sales and the forecasts are given below.

VP VP

YEAR SALES Marketing Operations

1 167,325 170,000 160,000

2 167,325 170,000 160,000

3 167,325 170,000 160,000

4 167,325 170,000 160,000

5 176,325 165,000 165,000

Using MAD which vice president is better at forecasting?

Mkt VP Oper VP

Error Error

2,675 7,325 5,362 10,322 7,464 2,536 23,268 18,268

11,325 11,325Totals 50,094 49,816

MAD VP Marketing = 50,094 /5 = 10,019

Ch 4 Forecasting

MAD VP Operations = 49,816 /5 = 9.963

Therefore, based on past data, the VP of operations has been presenting better forecasts.

Documents

Chapter 4 - Forecasting Mr. David P. Blain. C.Q.E. Management Department UNLV