ForecastingForecasting

Forecasting

• Predicting the future demand• Qualitative forecast methods

– Subjective• Quantitative forecast methods

– based on mathematical formulas

Forecasting’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 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 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 Component

• Persistent, overall upward or downward pattern

• Linear, exponential• Several years duration

Mo., Qtr., Yr.

Response

Seasonal 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

.

Cyclical 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 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 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 Opinion• Involves small group of high-level managers

– Group estimates demand by working together

• Combines managerial experience with statistical models• Relatively quick• “Group-think” disadvantage

Sales 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 Method

• Iterative group process

• Reduces “group-think”

Answer Answer

Qu

esti

on

Qu

esti

on

Feedback

Feedback

Consumer 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 & Disadvantages

Disadvantages :• Long consultation

process

• High risk of getting a biased forecast

• Expensive• Usually not precise

Advantages :• Take intangible factors

into consideration.

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

Quantitative Methods : Advantages & Disadvantages

Disadvantages :• Do not take « new information »

into consideration :

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

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).

Forecasting 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 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)

QuantitativeForecasting

MultipleRegression

AssociativeModels

ExponentialSmoothing

MovingAverage

Time SeriesModels

TrendProjection

Time 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 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 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

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 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 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 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

Exponential SmoothingExponential Smoothing

Form of weighted moving averageWeights decline exponentiallyMost recent data weighted most

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

Involves little record keeping of past data

Exponential 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 Example

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

Smoothing constant Smoothing constant = .20 = .20

Exponential 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)

Exponential 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)

= 142 + 2.2= 142 + 2.2

= 144.2 ≈ 144 cars= 144.2 ≈ 144 cars

Common 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