jury of executive opinion

2-508-97 Production and Operations Management

ForecastingForecasting

Rajesh Tyagi

2 2-508-97 Production and Operations Management

ForecastingForecasting

Predicting the future demand

Qualitative forecast methods Subjective

Quantitative forecast methods based on mathematical formulas

Copyright 2006 John Wiley & Sons, Inc.


Forecasting’s ImportanceForecasting’s ImportanceForecasting is important for:

Finance uses the long term forecast to evaluate capital investment needs.

Human Resources uses forecasts to evaluate personnel needs.

IT designs and implements systems that generate forecasts.

Marketing develops sales forecasts used for mid-term to long term planning.

Operations develops and uses forecasts to make decisions such as: scheduling, inventory management and long term capacity planning.


Forecasts by Time HorizonForecasts by Time Horizon

Short-range forecast Up to 1 year; usually less than 3 months

Job scheduling, worker assignments

Medium-range forecast 3 months to 3 years

Sales & production planning, budgeting

Long-range forecast 3+ years

New product planning, facility location


Demand BehaviorsDemand Behaviors

Trend a gradual, long-term up or down movement of demand

Seasonal pattern an up-and-down repetitive movement in demand occurring periodically

(short term: often annually)

Cycle an up-and-down repetitive movement in demand (long term)

Special events promotion, stock outs

Random variations movements in demand that do not follow a pattern



Trend ComponentTrend Component

Persistent, overall upward or downward pattern

Linear, exponential

Several years duration

Mo., Qtr., Yr.

Response

© 1984-1994 T/Maker Co.

© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458


Seasonal ComponentSeasonal Component

Regular pattern of up & down fluctuations

Due to weather, habits etc.

Occurs within a predefined period: year, month, week, day

Mo., Qtr.

Response

Summer

© 1984-1994 T/Maker Co.



Cyclical ComponentCyclical Component

Repeating up & down movements

Due to interactions of factors influencing economy

Usually 2-10 years duration

Mo., Qtr., Yr.Mo., Qtr., Yr.

ResponseResponseCycle



Forecasting MethodsForecasting Methods

Judgmental (Qualitative) use management judgment, expertise, and opinion to predict future

demand

Time series statistical techniques that use historical demand data to predict future

demand

Associative models (Regression methods) attempt to develop a mathematical relationship between demand and

factors that cause its behavior



Overview of Qualitative MethodsOverview of Qualitative Methods

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

models

Delphi method Panel of experts, queried iteratively

Sales force composite Estimates from individual salespersons are reviewed for reasonableness,

then aggregated

Consumer Market Survey Ask the customer



Jury of Executive OpinionJury of Executive Opinion

Involves small group of high-level managersGroup estimates demand by working together

Combines managerial experience with statistical models

Relatively quick

“Group-think” disadvantage



Sales Force CompositeSales Force Composite

Each salesperson projects his or her sales

Combined at district & national levels

Sales reps know customers’ needs

Tends to be overly optimistic

SalesSales

© 1995 Corel Corp.



Delphi MethodDelphi Method

Iterative group process

Reduces “group-think”

Answer Answer

Qu

esti

on

Qu

esti

on

Feedback

Feedback



Consumer Market SurveyConsumer Market Survey

Ask customers about purchasing plans

What consumers say, and what they actually do are often different

Sometimes difficult to answer How many hours will you use the Internet

next week?

How many hours will you use the Internet

next week?

© 1995 Corel Corp.



Qualitative Methods : Advantages & DisadvantagesQualitative Methods : Advantages & Disadvantages

Advantages :• Take intangible factors

into consideration.

• Useful when there are little data available (new product, new market, new business unit).

Disadvantages :

• Long consultation process

• High risk of getting a biased forecast

• Expensive

• Usually not precise


Quantitative Methods : Advantages & DisadvantagesQuantitative Methods : Advantages & Disadvantages

Advantages :• Easy to use once the right

model has been developed.

• Data collection is quick and easy since most of the required information is already in the business’ systems (ex. previous sales) or readily available (ex. consumer price index).

Disadvantages :

• Do not take « new information » into consideration :

« It’s like driving a car by looking in the rear-view mirror. »


Forecasting ApproachesForecasting Approaches

Used when situation is ‘stable’ & historical data exist Existing products Current technology

Involves mathematical techniques

Quantitative Methods Used when situation is vague &

little data exist New products New technology

Involves intuition, experience

Qualitative Methods

Considerations:•Planning horizon

•Availability and value of historical data

•Needs (precision and reliability)

•Time and budget constraints


Realities of ForecastingRealities of Forecasting

1. Forecasts are seldom perfect: almost always wrong by some amount

2. Aggregated forecasts are more accurate than individual forecasts

3. More accurate for shorter time periods

4. Most forecasting methods assume that there is some underlying stability in the system: watch out for special events!


Quantitative Forecasting Methods(Non-Naive)Quantitative Forecasting Methods(Non-Naive)

QuantitativeForecasting

MultipleRegression

AssociativeModels

ExponentialSmoothing

MovingAverage

Time SeriesModels

TrendProjection


Time SeriesTime Series

Assume that what has occurred in the past will continue to occur in the future

Relate the forecast to only one factor TIMETIME

Include naive forecast

simple average

moving average

exponential smoothing

linear trend analysis


Moving AveragesMoving Averages

Naive forecast Demand of the current period is used as next period’s forecast

Simple moving average stable demand with no pronounced behavioral patterns

Weighted moving average weights are assigned to most recent data


Naive forecastNaive forecast

JanJan 120120FebFeb 9090MarMar 100100AprApr 7575MayMay 110110JuneJune 5050JulyJuly 7575AugAug 130130SeptSept 110110OctOct 9090

ORDERSORDERSMONTHMONTH PER MONTHPER MONTH

--120120

9090100100

7575110110

50507575

130130110110

9090Nov -Nov -

FORECASTFORECAST


Simple Moving Average Simple Moving Average

MAMAnn = =

nn

ii = 1= 1 DDii

nn

wherewhere

nn ==number of periods in the number of periods in the moving averagemoving average

DDii ==demand in period demand in period ii


3-month Simple Moving Average3-month Simple Moving Average

JanJan 120120

FebFeb 9090

MarMar 100100

AprApr 7575

MayMay 110110

JuneJune 5050

JulyJuly 7575

AugAug 130130

SeptSept 110110

OctOct 9090NovNov --

ORDERSORDERS

MONTHMONTH PER PER MONTHMONTH

MAMA33 = =

33

ii = 1= 1 DDii

33

==90 + 110 + 13090 + 110 + 130

33

= 110 orders= 110 ordersfor Novfor Nov

––––––

103.3103.388.388.395.095.078.378.378.378.385.085.0

105.0105.0110.0110.0

MOVING MOVING AVERAGEAVERAGE


Weighted Moving AverageWeighted Moving Average

WMAWMAnn = = ii = 1 = 1 WWii D Dii

wherewhere

WWii = the weight for period = the weight for period ii, ,

between 0 and 100 between 0 and 100 percentpercent

WWii = 1.00= 1.00

Adjusts Adjusts moving moving average average method to method to more closely more closely reflect data reflect data fluctuationsfluctuations



Weighted Moving Average ExampleWeighted Moving Average Example

MONTH MONTH WEIGHT WEIGHT DATADATA

AugustAugust 17%17% 130130SeptemberSeptember 33%33% 110110OctoberOctober 50%50% 9090

WMAWMA33 = = 33

ii = 1 = 1 WWii D Dii

= (0.50)(90) + (0.33)(110) + (0.17)(130)= (0.50)(90) + (0.33)(110) + (0.17)(130)

= 103.4 orders= 103.4 orders

November ForecastNovember Forecast



Increasing n smooths the forecast but makes it less sensitive to changes

Do not forecast trends well

Require extensive historical data

Potential Problems WithPotential Problems With Moving Average Moving AveragePotential Problems WithPotential Problems With Moving Average Moving Average


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

Exponential SmoothingExponential SmoothingExponential SmoothingExponential Smoothing


Exponential SmoothingExponential SmoothingExponential SmoothingExponential Smoothing

New forecast =New forecast = last period’s forecastlast period’s forecast+ + (last period’s actual demand (last period’s actual demand

– – last period’s forecast)last period’s forecast)

FFtt = F = Ft – 1t – 1 + + (A(At – 1t – 1 - F - Ft – 1t – 1))

wherewhere FFtt == new forecastnew forecast

FFt – 1t – 1 == previous forecastprevious forecast

== smoothing (or weighting) smoothing (or weighting) constant (0 constant (0 1) 1)


Exponential Smoothing ExampleExponential Smoothing ExampleExponential Smoothing ExampleExponential Smoothing Example

Predicted demand = 142 Ford MustangsPredicted demand = 142 Ford MustangsActual demand = 153Actual demand = 153

Smoothing constant Smoothing constant = .20 = .20





New forecastNew forecast = 142 + .2(153 – 142)= 142 + .2(153 – 142)





New forecastNew forecast = 142 + .2(153 – 142)= 142 + .2(153 – 142)

= 142 + 2.2= 142 + 2.2

= 144.2 ≈ 144 cars= 144.2 ≈ 144 cars


Common Measures of ErrorCommon Measures of ErrorCommon Measures of ErrorCommon Measures of Error

Mean Absolute Deviation (MAD)Mean Absolute Deviation (MAD)

MAD =MAD =∑∑ |actual - forecast||actual - forecast|

nn

Mean Squared Error (MSE)Mean Squared Error (MSE)

MSE =MSE =∑∑ (forecast errors)(forecast errors)22

nn


Trend analysisTrend analysis

Many trends are possible: Linear

Exponential

Logarithmic

S-growth curve


Linear Trend AnalysisLinear Trend Analysis

Demand

0

1000

2000

3000

4000

5000

6000

0 5 10 15 20 25 30


Discussion pointsDiscussion points

Ideally, forecast should not only include point estimates, but also a range of outcomes.

Think about the cost of a wrong decision based on the forecast: You have too much production

You have not enough production

Forecasts based on previous demand versus previous sales.

Choosing a forecast horizon: Goals

Flexibility

Lead times


Elements of a good forecastElements of a good forecast

1. Appropriate forecast horizon

2. Degree of accuracy should be taken into account

3. Reliable

4. Choose meaningful units (dollars versus units)

5. Use same forecast throughout organization


Reading ListReading List

Chapter 3 Stevenson and Hojati Page 59 - 69 (including exponential smoothing)

Page 81 (explanation of associate methods)

Page 90 - 94

Learning goals-

Define forecasting behaviors

Difference between qualitative and quantitative forecasting techniques

Learn forecasting methods such as moving averages, exponential smoothing.

Learn to calculate and evaluate forecasting errors using MAD and MSE (and importance of bias)

jury of executive opinion

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