1. Which of the following general observations concerning forecasts is true? A.T
ID: 345354 • Letter: 1
Question
1. Which of the following general observations concerning forecasts is true?
A.Time series based forecasts are typically more accurate for longer term time horizons than shorter term horizons.
B. Forecasts for individual items are typically more accurate than forecasts for families of items.
C. The risk of forecast error is commonly reduced by utilizing a single forecast methodology rather than relying upon an averaged forecast from multiple forecast methods.
D. The risk of forecast error is commonly reduced by utilizing fewer observations of a time series rather than a greater number of time series observations.
E. The Delphi method of developing a forecast allows for the anonymous solicitation of individuals’ demand expectations.
2. Which of the following statements concerning forecasting is false?
A. A time series is a time ordered sequence of demand observations taken at regular intervals.
B. Decomposition is a forecast methodology that decomposes a time series into its basic elements or patterns.
C.The basic elements of a time series may include a trend, seasonal, cyclical, irregular (random) noise patterns.
D. Forecast smoothing methods rely upon a consensus of company executives.
E. Regression analysis attempts to define a cause and effect relationship between an independent variable(s) and a dependent variable.
3. Factors affecting the forecast method of choice include all of the following except:
A the length of planning horizon
B the tradeoff effect attributable to the use of either a small (e.g., 0.20) or large (e.g., 0.80) alpha value
C forecast method cost versus forecast method accuracy
D available time to collect and analyze data and then determine a forecast
E availability of demand data
For questions 4-7 use the following weekly historical demand and forecast data information.
Week Demand Week Demand
Sept. 13 - Sept. 19 24 Oct. 4 - Oct. 10 25
Sept. 20 - Sept. 26 19 Oct. 11 - Oct. 17 31
Sept. 27 - Oct. 3 27
4. What would the forecast of demand be for the week of October 25 - October 31 while using a naïve forecast approach?
5. Using a three-period simple moving average forecast model, what would the forecast of demand be for the week of October 18 - October 24?
6. Using a four-period, weighted moving average forecast model with weights of 0.40, 0.30, 0.20, and 0.10 (remember: weights decline as the demand observation gets older), what would the forecast of demand be for the week October 18 - October 24?
7. If you were using an exponential smoothing forecast model (a equal to 0.20) which generated a forecast of 25.10 units for the current week ending October 17, what would be your forecast of demand for the week October 18 - October 24? Remember: Ft+n = aDt + (1-a)Ft
8. Assume you recently determined an unbiased forecast of expected demand for next period to be 65.00 units. You informed your superior that the historical standard deviation of demand is 6.10 units so that a reasonable range within which actual demand will fall would have a lower limit of 55.00 units and an upper limit of 75.00 units. What size confidence interval were you using for this range? Remember: Z=(x-m)/s and here is a sufficient portion of a z-table.
Z Areas under the normal curve
.00 .01 .02 .03 .04 .05 .06 .07 .08 .09
1.2
0.3849
0.3869
0.3888
0.3907
0.3925
0.3944
0.3962
0.3980
0.3997
0.4015
1.3
0.4032
0.4049
0.4066
0.4082
0.4099
0.4115
0.4131
0.4147
0.4162
0.4177
1.4
0.4192
0.4207
0.4222
0.4236
0.4251
0.4265
0.4279
0.4292
0.4306
0.4319
1.5
0.4332
0.4345
0.4357
0.4370
0.4382
0.4394
0.4406
0.4418
0.4429
0.4441
1.6
0.4452
0.4463
0.4474
0.4484
0.4495
0.4505
0.4515
0.4525
0.4535
0.4545
1.7
0.4554
0.4564
0.4573
0.4582
0.4591
0.4599
0.4608
0.4616
0.4625
0.4633
1.8
0.4641
0.4649
0.4656
0.4664
0.4671
0.4678
0.4686
0.4693
0.4699
0.4706
1.9
0.4713
0.4719
0.4726
0.4732
0.4738
0.4744
0.4750
0.4756
0.4761
0.4767
9. Determine the mean absolute percent error (MAPE) for the following five-week interval of data for demand and forecast values.
Week Demand Forecast
Aug 1- Aug 7 200 197
Aug 8- Aug 14 209 202
Aug 15- Aug 21 217 220
Aug 22 - Aug 28 198 205
Aug 29- Sept 4 203 195
10. Using an exponential smoothing forecast model with alpha equal to 0.25, what would the weighting coefficient be for period 7 if the time series consists of 10 demand observations? Remember: the weighting coefficient for any period x is determined as a(1-a)N-x .
1.2
0.3849
0.3869
0.3888
0.3907
0.3925
0.3944
0.3962
0.3980
0.3997
0.4015
1.3
0.4032
0.4049
0.4066
0.4082
0.4099
0.4115
0.4131
0.4147
0.4162
0.4177
1.4
0.4192
0.4207
0.4222
0.4236
0.4251
0.4265
0.4279
0.4292
0.4306
0.4319
1.5
0.4332
0.4345
0.4357
0.4370
0.4382
0.4394
0.4406
0.4418
0.4429
0.4441
1.6
0.4452
0.4463
0.4474
0.4484
0.4495
0.4505
0.4515
0.4525
0.4535
0.4545
1.7
0.4554
0.4564
0.4573
0.4582
0.4591
0.4599
0.4608
0.4616
0.4625
0.4633
1.8
0.4641
0.4649
0.4656
0.4664
0.4671
0.4678
0.4686
0.4693
0.4699
0.4706
1.9
0.4713
0.4719
0.4726
0.4732
0.4738
0.4744
0.4750
0.4756
0.4761
0.4767
Explanation / Answer
1. option D is true. In the process of demand forecasting if we relay on one method of demand forecasting, the chances of error is higher. so, firms will chose more than one method and average it and finally estimates the demand.
2. option D is false, any forecasting methods do not relay on a consensus of company executives.
3. option B is the answer
5. three moving simple average- estimation of demand for october 18= (27+25+31)/3= 27.66 (28)
estimation of demand for October 25= (25+31+28)/3= 28
6. demand for october 18= 31*0.4+25*0.3+27*0.2+19*0.1= 12.4+7.5+5.4+1.9= 27.2
demand for october 25=27*0.4+ 31*0.3+25*0.2+27*0.1= 10.8+9.3+5+2.7= 27.8
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