A Review and Application of Quantitative Sales Forecasting ...
Quantitative Forecasting Methods (Non-Naive)
-
Upload
ambrose-stevens -
Category
Documents
-
view
238 -
download
0
description
Transcript of Quantitative Forecasting Methods (Non-Naive)
1 2-508-97 Production and Operations Management
Quantitative Forecasting Methods(Non-Naive)
QuantitativeForecasting
MultipleRegression
AssociativeModels
ExponentialSmoothing
MovingAverage
Time SeriesModels
TrendProjection
2 2-508-97 Production and Operations Management
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
3 2-508-97 Production and Operations Management
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
4 2-508-97 Production and Operations Management
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
5 2-508-97 Production and Operations Management
Simple Moving Average
MAMAnn = =
nn
ii = 1= 1 DDii
nnwherewhere
nn ==number of periods in the number of periods in the moving averagemoving average
DDii ==demand in period demand in period ii
6 2-508-97 Production and Operations Management
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
7 2-508-97 Production and Operations Management
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
Copyright 2006 John Wiley & Sons, Inc.
8 2-508-97 Production and Operations Management
Weighted Moving Average Example
MONTH MONTH WEIGHT WEIGHT DATADATAAugustAugust 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
Copyright 2006 John Wiley & Sons, Inc.
9 2-508-97 Production and Operations Management
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 Average
10 2-508-97 Production and Operations Management
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 Smoothing
11 2-508-97 Production and Operations Management
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)
12 2-508-97 Production and Operations Management
Exponential Smoothing ExampleExponential Smoothing Example
Predicted demand = 142 Ford MustangsPredicted demand = 142 Ford MustangsActual demand = 153Actual demand = 153
Smoothing constant Smoothing constant = .20 = .20
13 2-508-97 Production and Operations Management
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)
14 2-508-97 Production and Operations Management
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
15 2-508-97 Production and Operations Management
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
16 2-508-97 Production and Operations Management
Trend analysis Many trends are possible:
Linear Exponential Logarithmic S-growth curve
17 2-508-97 Production and Operations Management
Linear Trend AnalysisDemand
0
1000
2000
3000
4000
5000
6000
0 5 10 15 20 25 30