Download - Demand Forecasting
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ForecastingForecasting
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Forecasting
• Predicting the future demand• Qualitative forecast methods
– Subjective• Quantitative forecast methods
– based on mathematical formulas
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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.
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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
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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
.
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Trend Component
• Persistent, overall upward or downward pattern
• Linear, exponential• Several years duration
Mo., Qtr., Yr.
Response
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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
.
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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
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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
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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
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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
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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.
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Delphi Method
• Iterative group process
• Reduces “group-think”
Answer Answer
Qu
esti
on
Qu
esti
on
Feedback
Feedback
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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.
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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).
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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).
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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
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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!
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Quantitative Forecasting Methods(Non-Naive)
QuantitativeForecasting
MultipleRegression
AssociativeModels
ExponentialSmoothing
MovingAverage
Time SeriesModels
TrendProjection
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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
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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
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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
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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
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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
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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
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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
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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
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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)
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Exponential Smoothing ExampleExponential Smoothing Example
Predicted demand = 142 Ford MustangsPredicted demand = 142 Ford MustangsActual demand = 153Actual demand = 153
Smoothing constant Smoothing constant = .20 = .20
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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)
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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
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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