ballou bab 08
TRANSCRIPT
![Page 1: Ballou Bab 08](https://reader038.vdocuments.site/reader038/viewer/2022100506/54800f145806b5ae5e8b492d/html5/thumbnails/1.jpg)
8-1
I hope you'll keep in mind that economic forecasting is far from a perfect science. If recent history's any guide, the experts have some explaining to do about what they told us had to happen but never did.
Ronald Reagan, 1984
8-2
PLA
NN
ING
OR
GA
NIZ
ING
CO
NTR
OLL
ING
Transport Strategy• Transport fundamentals• Transport decisionsCustomer
service goals• The product• Logistics service• Ord. proc. & info. sys.
Inventory Strategy• Forecasting• Inventory decisions• Purchasing and supply
scheduling decisions• Storage fundamentals• Storage decisions
Location Strategy• Location decisions• The network planning process
PLA
NN
ING
OR
GA
NIZ
ING
CO
NTR
OLL
ING
Transport Strategy• Transport fundamentals• Transport decisionsCustomer
service goals• The product• Logistics service• Ord. proc. & info. sys.
Inventory Strategy• Forecasting• Inventory decisions• Purchasing and supply
scheduling decisions• Storage fundamentals• Storage decisions
Location Strategy• Location decisions• The network planning process
8-3
What’s Forecasted in the Supply Chain?
8-4
Some Forecasting Method Choices•Historical projection
Moving averageExponential smoothing
•Causal or associativeRegression analysis
•QualitativeSurveysExpert systems or rule-based
•Collaborative
Printed with FinePrint - purchase at www.fineprint.com
![Page 2: Ballou Bab 08](https://reader038.vdocuments.site/reader038/viewer/2022100506/54800f145806b5ae5e8b492d/html5/thumbnails/2.jpg)
8-5
Typical Time Series Patterns:Random
0
50
100
150
200
250
0 5 10 15 20 25
Tim e
Actual sa lesAverage sales
8-6
Typical Time Series Patterns:Random with Trend
0
50
100
150
200
250
0 5 10 15 20 25
Time
Sale
s
Actual salesAverage sales
8-7
Typical Time Series Patterns:Random with Trend & Seasonal
01 0 02 0 03 0 04 0 05 0 06 0 07 0 08 0 0
0 1 0 2 0 3 0 4 0
T im e
A ctu a l sa le sT re n d in sa le sS m o o th e d tre n d a n d se a so n a l sa le s
8-8
Typical Time Series Patterns:Lumpy
Time
Sal
es
Printed with FinePrint - purchase at www.fineprint.com
![Page 3: Ballou Bab 08](https://reader038.vdocuments.site/reader038/viewer/2022100506/54800f145806b5ae5e8b492d/html5/thumbnails/3.jpg)
8-9
Is Time Series PatternForecastable?
Whether a time series can be reasonably forecasted often depends on the time series’degree of variability. Forecast a regular time series, but use other techniques for lumpy ones. How to tell the difference:RuleA time series is lumpy if
σ3≤Xwhere
series, of deviation standard series the of mean
==
σX
regular, otherwise.
8-10
Moving Average
Basic formula
∑−+=
=t
nti iAn
MA1
1
wherei = time periodt = current time periodn = length of moving average in periodsAi = demand in period i
8-11
Example 3-Month Moving Average Forecasting
Month, iDemand formonth, i
Total demandduring past 3months
3-monthmovingaverage
.
.
....
.
.
....
20 120 . .21 130 360/3 12022 110 380/3 126.6723 140 360/3 12024 110 380/3 126.6725 13026 ?
8-12periodcurrentinforecastperiodcurrentindemandactual
periodnextforforecast0.30to0.01usuallyconstantsmoothing
where)1(
formulasmoothingexponentialonly,levelbasic,thetoreduceswhich
)1(...)1()1(
)1(thenform,inexponentialare)(weightsIf
1
...
1
1
33
22
11
1
2211
=
=
==
−+==
−++
−+−+
−+=
=
+++=
+
+
−
−−
−
=∑
t
t
t
ttt
ntn
tt
tt
n
ii
nn
FA
F
FAFMA
A
AA
AAMAw
wwhere
AwAwAwMA
α
αα
αα
αααα
ααα
Printed with FinePrint - purchase at www.fineprint.com
![Page 4: Ballou Bab 08](https://reader038.vdocuments.site/reader038/viewer/2022100506/54800f145806b5ae5e8b492d/html5/thumbnails/4.jpg)
8-13
I. Level only
Ft+1 = αAt + (1-α)Ft
II. Level and trend
St = αAt + (1-α)(St-1 + Tt-1)
Tt = ß(St - St-1) + (1-ß)Tt-1
Ft+1 = St + Tt
III. Level, trend, and seasonality
St = α(At/It-L) + (1-α)(St-1 + Tt-1)
It = γ(At/St) + (1-γ)It-L
Tt = ß(St - St-1) + (1-ß)Tt-1
Ft+1 = (St + Tt)It-L+1
where L is the time period of one full seasonal cycle.
IV. Forecast error
MAD =|A t −
=∑ F
N
tt
N|
1
or
S(A F )
NFt t
2t 1
N
=−
=∑
and SF ≅ 1.25MAD.
Exponential Smoothing Formulas
8-14
Example Exponential Smoothing Forecasting
Time series data
1 2 3 4Last
year 1200 700 900 1100This
year 1400 1000 ?
Quarter
Getting started
Assume α = 0.2. Average first 4 quarters of data and use for previous forecast, say Fo
8-15
Example (Cont’d)
Begin forecasting9754/)11009007001200(0 =+++=F
First quarter of 2nd year
1000)975(8.0)1100(2.0
)2.01(2.0 001
=+=−+= FAF
Second quarter of 2nd year
1080)1000(8.0)1400(2.0
)2.01(2.0 112
=+=−+= FAF
8-16
Example (Cont’d)
Third quarter of 2nd year
1064)1080(8.0)1000(2.0
)2.01(2.0 023
=+=−+= FAF
Summarizing
1 2 3 4Lastyear 1200 700 900 1100
Thisyear 1400 1000 ?
Fore-cast 1000 1080 1064
Quarter
Printed with FinePrint - purchase at www.fineprint.com
![Page 5: Ballou Bab 08](https://reader038.vdocuments.site/reader038/viewer/2022100506/54800f145806b5ae5e8b492d/html5/thumbnails/5.jpg)
8-17
Example (Cont’d)
Measuring forecast error as MAD
or RMSE (std. error of forecast)
nFA
MAD
n
t tt∑ −= =1
||
1
)(1
2
−∑ −
= =n
FAS
n
t ttF
1 degree of freedom lost in level-only model, but 2 in level-trend and 3 in level-trend-seasonal models
8-18
Example (Cont’d)
Using SF and assuming n=2
40812
1080)(10001000)(1400 22
=−
−+−=FS
Note To compute a reasonable average for SF, n should range over at least one seasonal cycle in most cases.
SF = 408
Example (Cont’d)
Range of the forecast
0Bias =∑ −
= = nFAn
t tt1
F3=1064
Range
If forecast errors are normally distributed and the forecast
is at the mean of the distribution, i.e., ,
a forecast confidence band can be computed. The error distribution for the level-only model results is:
Bias should be 0 or
close to it in a model of
good fit
8-19
8-20
Example (Cont’d)
From a normal distribution table, z@95%=1.96. The actual time series value Y for quarter 3 is expected to range between:
or264 ≤ Y ≤ 1864
8001064)408(96.11064
)(3
±=±=
±=FSzFY
Printed with FinePrint - purchase at www.fineprint.com
![Page 6: Ballou Bab 08](https://reader038.vdocuments.site/reader038/viewer/2022100506/54800f145806b5ae5e8b492d/html5/thumbnails/6.jpg)
8-21
Correcting for Trend in ESThe trend-corrected model is
St = αAt + (1 – α)(St-1 + Tt-1)Tt = β(St – St-1) + (1 – β)Tt-1
Ft+1 = St + Tt
where S is the forecast without trend correction.
Assuming α = 0.2, β = 0.3, S-1 = 975, and T-1 = 0 Forecast for quarter 1 of this yearS0 = 0.2(1100) + 0.8(975 + 0) = 1000T0 = 0.3(1000 – 975) + 0.7(0) = 8F1 = 1000 + 8 = 1008
8-22
Forecast for quarter 2 of this yearS0 T0
S1 = 0.2(1400) + 0.8(1000 + 8) = 1086.4T1 = 0.3(1086.4 – 1000) + 0.7(8) = 31.5F2 = 1086.4 + 31.5 = 1117.9
Forecast for quarter 3 of this yearS2 = 0.2(1000) + 0.8(1086.4 + 31.5) = 1094.3T2 = 0.3(1094.3 – 1086.4) + 0.7(31.5) = 24.4F3 = 1094.3 + 24.4 = 1118.7, or 1119
Correcting for Trend in ES
8-23
Correcting for Trend in ES Summarizing with trend correction
1 2 3 4Lastyear 1200 700 900 1100
Thisyear 1400 1000 ?
Fore-cast 1008 1118 1119
Quarter
α0 1
Fore-casterror
Optimizing α for ES
Minimize averageforecast error
8-24
Printed with FinePrint - purchase at www.fineprint.com
![Page 7: Ballou Bab 08](https://reader038.vdocuments.site/reader038/viewer/2022100506/54800f145806b5ae5e8b492d/html5/thumbnails/7.jpg)
Controlling Model Fit in ES
MSEFA tt −= signal Tracking
Tracking signal monitors the fit of the model to detect when the model no longer accurately represents the data
where the Mean Squared Error (MSE) is
ntFtA
MSE
n
t∑ −
= =12)(
If tracking signal exceeds a specified value (control limit), revise smoothing constant(s).
n is a reasonable numberof past periods depending
on the application
8-25
8-26
Classic Time Series Decomposition Model
Basic formulationF = T × S × C × R
whereF = forecastT = trendS = seasonal indexC = cyclical index (usually 1)R = residual index (usually 1)
Some time series data
1 2 3 4Last year 1200 700 900 1100This year 1400 1000 ?
Quarter
8-27
Classic Time Series Decomposition Model
Trend estimation
Use simple regression analysis to find the trend equation of the form T = a + bt. Recall the basic formulas:
22 tnt
tYnYtb−∑
−∑=
and
tbYa −=
8-28
Classic Time Series Decomposition Model
Redisplaying the data for ease of computation.
t Y Yt t2
1 1200 1200 12 700 1400 43 900 2700 94 1100 4400 165 1400 7000 256 1000 6000 36
∑t=21 ∑Y=6300 ∑Yt=22700 ∑t2=91
Printed with FinePrint - purchase at www.fineprint.com
![Page 8: Ballou Bab 08](https://reader038.vdocuments.site/reader038/viewer/2022100506/54800f145806b5ae5e8b492d/html5/thumbnails/8.jpg)
8-29
Classic Time Series Decomposition Model
Hence,
and
then
26(21/6)9100/6)6(21/6)(6322700
−−=b
920.01)37.14(21/66
6300 =−=a
T = 920.01 + 27.14t
Forecast for 3rd quarter of this year is:
T = 920.01 + 37.14(7) = 1179.99
8-30
Classic Time Series Decomposition Model
Compute seasonal indices
The procedure is to form a ratio of actual demand to the estimated demand for a full seasonal cycle (4 quarters). One way is as follows.
t Y TSeasonalIndex, St
1 1200 957.15* 1.25**2 700 994.29 0.703 900 1031.43 0.874 1100 1068.57 1.03
*T=920.01 + 37.14(1)=957.15**St=1200/957.15=1.25
8-31
Classic Time Series Decomposition Model
Compute seasonal indices
Since C and R index values are usually 1, the adjusted seasonal forecast for the 3rd quarter of this year would be:
F7 = 1179.99 x 0.87 = 1026.59
Forecast rangeThe standard error of the forecast is:
2
)(1
2
−
∑ −= =
n
FYS
n
ttt
F
A degree of freedom is lost for the aand b values in forecast equation
8-32
Classic Time Series Decomposition Model
Qtr t Yt Tt St Ft
1 1 1200 957.15 1.252 2 700 994.29 0.703 3 900 1031.43 0.874 4 1100 1068.57 1.031 5 1400 1105.71 1.27 1404.25*2 6 1000 1142.85 0.88 1005.71**3 7 1179.99 1026.59
*1105.71x1.27=1404.25**1142.85x0.88=1005.71
Tabled computations
Printed with FinePrint - purchase at www.fineprint.com
![Page 9: Ballou Bab 08](https://reader038.vdocuments.site/reader038/viewer/2022100506/54800f145806b5ae5e8b492d/html5/thumbnails/9.jpg)
8-33
Classic Time Series Decomposition Model
There is inadequate data to make a meaningful estimate of SF. However, we would proceed as follows:
infinity 22
1005.71)(10001404.25)(1400 22
=−
−+−=FS
Then,
Ft − z(SF) ≤ Y ≤ Ft + z(SF)
Normally, a larger sample size would be used giving
a positive value for SF
8-34
Regression AnalysisBasic formulation
F = βo + β1X1 + β2X2 + … + βnXn
Example
Bobbie Brooks, a manufacturer of teenage women’s clothes, was able to forecast seasonal sales from the following relationship
F = constant + β1(no. nonvendor accounts) + β2(consumer debt ratio)
Sales period
(1)
Timeperiod, t
(2)
Sales (Dt )($000s)
(3)
Dt × t
(4)
t2
(5)
Trend value(Tt )
(6)=(2)/(5)
Seasonalindex
Forecast($000s)
Summer 1 $9,458 9,458 1 $12,053 0.78Trans-season 2 11,542 23,084 4 12,539 0.92Fall 3 14,489 43,467 9 13,025 1.11Holiday 4 15,754 63,016 16 13,512 1.17Spring 5 17,269 86,345 25 13,998 1.23
Summer 6 11,514 69,084 36 14,484 0.79Trans-season 7 12,623 88,361 49 14,970 0.84Fall 8 16,086 128,688 64 15,456 1.04Holiday 9 18,098 162,882 81 15,942 1.14Spring 10 21,030 210,300 100 16,428 1.28
Summer 11 12,788 140,668 121 16,915 0.76Trans-season 12 16,072 192,864 144 17,401 0.92Fall 13 ? 17,887* $18,602Holiday 14 ? 18,373* 20,945
Totals 78 176,723 1,218,217 650
Regression Forecasting Using Bobbie Brooks Sales Data
N = 12 ∑Dt × t = 1,218,217 ∑t2 = 650 = =( , / ) , .176 723 12 14 726 92 = =78 12 6 5/ .Regression equation is: Tt = 11,567.08 + 486.13t*Forecasted values
D t
8-35
8-36
Combined Model Forecasting
Combines the results of several models to improve overall accuracy. Consider the seasonal forecasting problem of Bobbie Brooks. Four models were used. Three of them were two forms of exponential smoothing and a regression model. The fourth was managerial judgement used by a vice president of marketing using experience. Each forecast is then weighted according to its respective error as shown below.
Calculation of forecast weights
Modeltype
(1)
Forecasterror
(2)
Percentof totalerror
(3)=1.0/(2)
Inverse oferror
proportion
(4)=(3)/48.09
Modelweights
MJ 9.0 0.466 2.15 0.04R 0.7 0.036 27.77 0.58ES1 1.2 0.063 15.87 0.33ES2 8.4 0.435 2.30 0.05
Total 19.3 1.000 48.09 1.00
Printed with FinePrint - purchase at www.fineprint.com
![Page 10: Ballou Bab 08](https://reader038.vdocuments.site/reader038/viewer/2022100506/54800f145806b5ae5e8b492d/html5/thumbnails/10.jpg)
8-37
Combined Model Forecasting (Cont’d)
Forecasttype
(1)
Modelforecast
(2)
Weightingfactor
(3)=(1) × (2)
Weightedproportion
Regressionmodel (R) $20,367,000 0.58 $11,813,000ExponentialSmoothingES1 20,400,000 0.33 6,732,000Combinedexponentialsmoothing--regressionmodel(ES2)
17,660,000 0.05 883,000
Managerialjudgment(MJ) 19,500,000 0.04 780,000
Weighted average forecast $20,208,000
8-38
8-39
•Seek information directly from customers
•Collaborate with other channel members
•Apply forecasting methods with caution (may work where forecast accuracy is not critical)
•Delay supply response until demand becomes clear
•Shift demand to other periods for better supply response
•Develop quick response and flexible supply systems
8-40
Demand is lumpy or highly uncertainInvolves multiple participants each with a unique perspective—“two heads are better than one”Goal is to reduce forecast errorThe forecasting process is inherently unstable
Printed with FinePrint - purchase at www.fineprint.com
![Page 11: Ballou Bab 08](https://reader038.vdocuments.site/reader038/viewer/2022100506/54800f145806b5ae5e8b492d/html5/thumbnails/11.jpg)
8-41
•Establish a process champion•Identify the needed Information and collection processes•Establish methods for processing information from multiple
sources and the weights assigned to multiple forecasts•Create methods for translating forecast into form needed by
each party•Establish process for revising and updating forecast in real
time•Create methods for appraising the forecast•Show that the benefits of collaborative forecasting are obvious
and real
8-42
•Delay forecasting as long as possible
•Prioritize supply by product’s degree of uncertainty (supply to the more certain products first)
•Apply the principle of postponement to the most uncertain products (delay committing to a final product form until an order is received)
•Create flexible supply to changing demand (alter capacity and output rates through subcontracting, computer technology, multi-purpose processes, etc.)
•Be able to respond quickly to uncertain demand levels
Printed with FinePrint - purchase at www.fineprint.com