SCHM 2301

The city of Dallas would like to control the amount of traffic on a major tollway. One way to manage the demand is by: A) Rapid forecasting. B) Dynamic pricing. C) Signaling alternatives. D) Opening and closing lanes.

B

Given the data below, what is the bias of these forecasts? Period Actual Demand Forecast Demand Error Absolute Value of Error 1 800 1,100 300 2 1,000 200 3 1,400 −100 A) Positive. B) Negative. C) There is no bias.

B

A company has the information shown in the chart below regarding its forecast performance in the past three periods. What is the mean absolute deviation (MAD)? A) 200 B) 225 C) −66.67 D) 1,200

A

How does product design affect forecasting accuracy? A) Postponable product designs remove the need to forecast demand for final product configurations. B) A popular product design improves the demand volume and forecast. C) Forecast accuracy is not related to product design. D) None of the statements are true.

A

Long-term/strategic demand planning is typically done using what units? A) Total business unit sales B) Total product family sales C) Total product item sales D) Sales at a given location

A

Given a demand of 19 for the most recent period, use α = .2 and β =.4 to create a trend-enhanced smoothing-based forecast. Assume that FIT1 = 22 and T1 = 7.83. The correct answer is closest to: A) 29.0 B) 21.4 C) 23.2 D) 31.3

A : F2 = FIT1 + 0.2(d1 - FIT1) = 22 + 0.20 (19 - 22) = 21.4 T2 = T1 + .4(F2 - FIT1) = 7.83 + 0.4 (21.4 - 22) = 7.6 FIT2 = F2 + T2 = 21.4 + 7.6 = 29

Zanda Corp. has experienced demand in the last four years as shown below. Period (t) Demand (dt) t*dt t2 1 20 20 1 2 30 60 4 3 32 96 9 4 38 152 16 Total 10 120 328 30 Average 2.5 30 What is the trend value (b) in the data? (Choose the closest answer.) A) 5.6 units/period B) 2.5 units/period C) 2.87 units/period D) −1.25 units/period

A : b = [328 − (4)(2.5)(30)]/[30 − (4)(2.5)2] = 5.6 per period a = (30) − (5.6)(2.5) = 16

Using the data shown below, the forecast for week 5 using an exponential smoothing model is: Week 1 2 3 4 Sales 165 140 115 200 Forecast 170 168 140.1 A) 164.1 B) 146.1 C) 200.1 D) Not enough information is given to produce a forecast

A Ft+1 = α dt + (1 − α ) Ft or Ft+1 = Ft + α (dt − Ft); α = (168 − 170)/(165 − 170) = 0.4 F5 = 140.1 + 0.4 (200 − 140.1) = 164.1

Assume that the forecast for the last period is FITt = 200 units, and recent experience suggests a likely sales increase of 10 units each period. Actual sales for the last period reached 230 units. Assume a smoothing coefficient of α = 0.20 and a trend smoothing coefficient of β = 0.10. Demand in period t + 1 turned out to be 220. What is the adjusted forecast for period t + 2? (Choose the closest answer.) A) 227.3 B) 215.9 C) 217.3 D) 221.3

A Ft+2 = FITt+1 + α (dt+1 − FITt+1) = 216.6 + 0.20 (220 − 216.6) = 217.28 Tt+2 = Tt+1 + β(Ft+2 − FITt+1) = 10.6 + 0.10 (217.28 − 216.6) = 10.6 + 0.068 = 10.668 FITt+2 = Ft+2 + Tt+2 = 216.6 + 10.668 = 227.268

The table below shows quarterly sales data over four years. A regression of sales on quarters yields this equation: Sales = 18.25 + 1.55 × Quarter number. Using the regression estimate as the base, what is the seasonal index for the fourth quarter? (Pick the answer that is closest to the correct number.) Year Quarter 1 2 3 4 1 15 26 17 24 2 17 29 20 24 3 15 24 13 22 4 25 30 23 30 A) 1.10 B) 1.23 C) 1.04 D) 0.81

A Predicted sales for fourth quarter = 18.25 + 1.55 (4) = 24.45 Average fourth-quarter sales = (25 + 30 + 23 + 30)/4 = 27 Seasonal index = 27/24.45 = 1.10

Jones Corp. has noticed that sales of its product seem to be related to a variable it calls Gamma. It has developed the data shown below. Gamma Sales Gamma × Sales Gamma2 10 22 220 100 18 34 612 324 14 26 364 196 14 30 420 196 12 24 288 144 Total 1,904 960 Average 13.6 27.2 Develop a simple linear regression from the data and tell Jones what the sales forecast will be if Jones expects Gamma to be 16. (Round your forecast to the nearest number of whole units.) A) 31 B) 33 C) 28 D) 34

A You must determine the value of a and b in a simple linear regression. b = [1,904 − (5)(13.6)(27.2)]/[960 − (5)(13.6)2] = 54.4/35.2 = 1.55 a = (27.2) − (1.55)(13.6) = 27.2 − 21.08 = 6.12 Sales = 6.12 + 1.55(16) = 30.92.

Refer to the data below for Zanda Corp. What is the linear regression forecast for period 5? (Choose the nearest number of whole units.) Period (t) Demand (dt) t*dt t2 1 20 20 1 2 30 60 4 3 32 96 9 4 38 152 16 Total 10 120 328 30 Average 2.5 30 A) 44 B) 42 C) 34 D) 28

A b = [328 − (4)(2.5)(30)]/[30 − (4)(2.5)2] = 5.6 per period a = (30) − (5.6)(2.5) = 16 F5 = 16 + 5(5.6) = 44 units

Suppose your firm is about to launch a radically new product. The type of demand forecasting system you would most likely use is: A) Regression. B) Executive judgment. C) Time series. D) Moving average. E) Exponential smoothing.

B

For Platinum Nugget Hotel in Las Vegas, Saturday is the best day of the week for business. The gambling take for the hotel on Saturdays over the past four weeks was: Week $Take 1. $250,000 2. $190,000 3. $300,000 4. $280,000 Using a moving average with n = 3 terms, what would be the forecast for week 5? A) $256,667 B) $246,667 C) $255,000 D) $232,124

Answer: A Explanation: ($190,000 + $300,000 + $280,000)/3 = $256,667

An office manager forecasts demand for office stationery by exponential smoothing, with alpha = 0.4. Actual demand two weeks ago (i.e., the week before last) was 12 boxes, but the forecast for that period was only 10. Actual demand last week was seven. What was the forecast for last week? A) 10.8 B) 11.0 C) 11.2 D) 8.2 E) 8.8

Answer: A Explanation: Forecast = 10 + .4(12 − 10) = 10.8 Difficulty: 3 Hard

Over a six-month period, the demand for a product has been: June = 200, July = 210, August = 240, September = 240, October = 260, and November = 280. The three-month moving average forecast for December is: A) 240. B) 260. C) 280. D) 300.

Answer: B Explanation: (240 + 260 + 280)/3 = 260

Jones Company had sales of $100,000 last week. The company had forecasted that sales would be $120,000. Using exponential smoothing with a smoothing constant of 0.2, what is the forecast for this week's sales? A) $124,000 B) $116,000 C) $104,000 D) $112,000

Answer: B Explanation:$120,000 + .2($100,000 − $120,000) = $116,000

For Platinum Nugget Hotel in Las Vegas, Saturday is the best day of the week for business. The gambling take for the hotel on Saturdays over the past four weeks was: Week $Take 1. $250,000 2. $190,000 3. $300,000 4. $280,000 Platinum Nugget uses a three-period weighted moving average to forecast demand, with a t = 0.6, at−1 = 0.3, and at−2 = 0.1. What is the forecast for week 5? A) $232,000 B) $237,000 C) $277,000 D) $295,000

Answer: C Explanation:(.6($280,000) + .3($300,000) + .1($190,000))/3 = $277,000

Alpha Company sold 2,000 widgets yesterday. It had forecasted sales of 1,900 units. Using exponential smoothing with a smoothing constant of 0.6, what is the forecast for today's sales of widgets? A) 2,060 B) 1,940 C) 2,040 D) 1,960

Answer: D Explanation: 1,900 + 0.6(2,000 − 1,900) = 1,960

) The demand for housing is characterized by a regular pattern of increasing to a peak, then falling. When the demand reaches a low point, it then repeats the pattern. This pattern usually takes place over a three- to five-year period. This is an example of which type of demand pattern? A) Autocorrelation B) Seasonality and cycles C) Step change D) Trend

B

A company has the data shown in the chart below concerning its forecast performance over the past four time periods. Period Actual Demand Forecast Error Absolute Value of Error Absolute Percentage Error 1 345 320 25 2 320 10 3 335 350 4 340 −30 5 350 20 Complete the chart and compute the MAD. A) 2 B) 20 C) 10 D) 100

B

Apply regression to the data shown below. The forecast for next January's sales is: Sales January 100 February 200 March 150 April 400 May 300 June 200 July 250 August 350 September 400 October 350 November 400 December 500 A) 150.0 B) 477.3 C) 450.0 D) Not enough information is given to make a forecast

B

Designing postponable products has the potential to allow operations managers to: A) Ignore forecasts. B) Move from build-to-stock to assemble or make-to-order operations. C) Influence the timing of demand. D) All the items are correct.

B

The primary difference between demand management and demand forecasting is: A) Forecasting is only possible when quantitative data are available. B) Demand management is proactive, while forecasting attempts to predict. C) A firm cannot execute both approaches simultaneously. D) One approach deals with uncertainty, while the other deals with known demand.

B

What is the relationship between demand management and demand forecasting? A) The two planning activities are managed independently. B) Demand management plans are usually an input to demand forecasting. C) Demand management is done by operations managers, while demand forecasting is done by marketing managers. D) All the answers are correct.

B

Which of the following statements best describes demand forecasting? A) The objective of forecasting is to develop the best statistical model. B) Better forecasts usually come from combinations of inputs. C) Executives usually make better forecasts than machines. D) Forecasting and demand planning have little to do with each other.

B

The table below shows quarterly sales data over four years. Using the average demand as the base, what is the seasonal index for the third quarter (Pick the answer that is closest to the correct number.) Year Quarter 1 2 3 4 1 15 26 17 24 2 17 29 20 24 3 15 24 13 22 4 25 30 23 30 A) 0.92 B) 0.82 C) 0.99 D) 1.20

B Average for all demands = 22.125. Average for third-quarter demands = 18.25. Seasonal index = 18.25/22.125 = 0.8249.

Assume that the forecast for the last period is FITt = 200 units, and recent experience suggests a likely sales increase of 10 units each period. Actual sales for the last period reached 230 units. Assuming a smoothing coefficient of α = 0.20 and a trend smoothing coefficient of β = 0.10, what is the BASE forecast for the next period? A) 210 B) 206 C) 236 D) 226

B Ft+1 = FITt + α (dt − FITt) = 200 + 0.20 (230 − 200) = 206

A computer program that uses algorithms to learn by analyzing many different types of models applied to large amounts of data is called: A) Historical analogy. B) Focused forecasting. C) Artificial intelligence. D) Algorithmic modeling.

C

A forecasting technique that seeks inputs from people who are in close contact with customers is known as: A) Historical analogy. B) Focused forecasting. C) Grassroots forecasting. D) Marketing research

C

Apply regression to the data shown below. The slope of the line estimated using the regression model is: Sales January 100 February 200 March 150 April 400 May 300 June 200 July 250 August 350 September 400 October 350 November 400 December 500 A) 50.0 B) 45.3 C) 27.3 D) 15.4

C

Demand planning for the intermediate term (tactical plans) would be done using dollar or unit sales for: A) A business unit B) An entire sales network C) A product family in a region D) A particular item at a given location

C

In examining the data below, the manager exclaimed that he was very happy to see no bias in the forecasts. How would you respond to the manager? Period Actual Demand Forecast Error Absolute Value of Error Absolute Percentage Error 1 345 320 25 2 320 10 3 335 350 4 340 −30 5 350 20 A) You are correct. B) I'm sorry, but there is a slight negative bias. C) I'm sorry, but there appears to be a slight positive bias. D) There is no way to estimate bias with the given information.

C

In recent years some companies have begun to work closely with their customers and/or suppliers by sharing information to develop demand plans and execute those plans. The procedure they are following is known as: A) Coordinated foreplanning of requirements. B) Joint planning of demand forecasts. C) Collaborative planning, forecasting, and replenishment. D) Conjoint analysis and forecasting.

C

Increasing the value of alpha (α) in an exponential smoothing model would produce which of the following results? A) Reduce the influence of more recent demands in computing future forecasts B) Reduce the amount of data that needs to be stored to support the forecasting process C) Increase the sensitivity of the forecast process to recent changes in demand D) Reduce the ability of the forecast process to respond to seasonality in demand

C

The tracking signal will suggest to a manager that: A) Demand for an item is changing. B) There is seasonality in demand. C) A forecast mode's parameters may need adjustment. D) All the answers are correct.

C

Using the data below, determine the MAPE. Period Actual Demand Forecast Error Absolute Value of Error Absolute Percentage Error 1 345 320 25 2 320 10 3 335 350 4 340 −30 5 350 20 A) 5.0 percent B) 7.25 percent C) 5.99 percent D) 5.41 percent

C

Using the data shown below, the forecast for week 5 using a three-period moving average model is: Week 1 2 3 4 Sales 165 140 115 200 A) 200 B) 155 C) 152 D) 158

C

Which of the following is not typically considered a component driver of demand? A) Seasonality B) Trend C) Perturbation D) Autocorrelation

C

Zanda Corp. has been testing the performance of two different forecasting models to see which it should adopt for use. It wants to choose the model that has the smaller standard deviation of the forecast errors. Zanda should compare which of the following to make its choice? A) MAD of the two models B) MAPE of the two models C) RMSE of the two models D) MFE of the two models

C

The data below show the forecasted probability of rain on Election Day and the actual number of people who voted in the election for each of the past eight years in a given city. If tomorrow is Election Day and the weather forecast shows a 50 percent chance of rain, how many voters do you expect to turn out? Years in the past 8 7 6 5 4 3 2 1 probability of rain (%) 10 15 0 80 15 25 0 90 Number of voters (100s) 20 25 40 15 20 10 35 10 A) 28.3 B) 20.0 C) 17.4 D) 75.2

C : Regression equation from formulas: Voters = 28.3 − 0.22 (probability of rain); 28.3 − 0.22(50) = 17.4

Assume that the forecast for the last period is FITt = 200 units, and recent experience suggests a likely sales increase of 10 units each period. Actual sales for the last period reached 230 units. Assume a smoothing coefficient of α = 0.20 and a trend smoothing coefficient of β = 0.10. What is the ADJUSTED forecast for the next period? A) 210.0 B) 210.6 C) 216.6 D) 216.0

C Tt+1 = Tt + β(Ft+1 − FITt) = 10 + 0.10 (206 − 200) = 10 + 0.6 = 10.6. FITt+1 = Ft+1 + Tt+1 = 206 + 10.6 = 216.6.

) Which of the following statements most accurately describes the outcome of using a simple moving average model to forecast demand that has a strong trend? A) Using more periods in the moving average calculation will produce better forecasts. B) Moving average models require less historical data than exponential smoothing models. C) Moving averages must be weighted in order to accurately predict a trend. D) Changes in forecasts produced by a moving average model will lag behind changes in demand.

D

Convex Computer Company makes many different forecasts. Which of the following forecasts is probably the least accurate? A) Total number of computers (laptops and desktops) to be sold next month. B) Total number of laptops to be sold next month. C) Total number of desktops to be sold next year. D) Total number of laptops with 2 gigabyte RAM, 80 gigabyte hard drive, and 16x DVD drive to be sold next year.

D

Using an exponential smoothing model, the forecast for next January's sales is: Sales January 100 February 200 March 150 April 400 May 300 June 200 July 250 August 350 September 400 October 350 November 400 December 500 A) 150.0 B) 477.3 C) 450.0 D) Not enough information is given to make a forecast

D

Which of the following factors should be considered when one designs a forecasting process? A) Time horizon for planning. B) Level of detail for planning. C) Availability of data. D) All the answers are correct.

D

A company uses actual demand data to develop its seasonal indices. It has the data shown below for each quarter of the previous two years. Quarter 1 2 3 4 1 2 3 4 Demand 50 60 90 80 60 70 100 90 What is the seasonal index for Quarter 1? A) 0.714 B) 0.750 C) 1.25 D) 0.732

D Year 1 average demand = (50 + 60 + 90 + 80)/4 = 70. Year 2 average demand = (60 + 70 + 100 + 90)/4 = 80. Quarter 1 SI for year 1 = 50/70 = 0.714. Q1 SI for year 2 = 60/80 = 0.75. Q1 average SI = 0.732.

Use the data below. Quarter 1 2 3 4 1 2 3 4 Demand 50 60 90 80 60 70 100 90 A company has forecasted next year's demand to be 400. What is the seasonally adjusted forecast for Quarter 1? (Choose the closest answer.) A) 75 B) 71 C) 125 D) 73

D Year 1 average demand = (50 + 60 + 90 + 80)/4 = 70. Year 2 average demand = (60 + 70 + 100 + 90)/4 = 80. Quarter 1 SI for year 1 = 50/70 = 0.714. Q1 SI for year 2 = 60/80 = 0.75. Q1 average SI = 0.732. Average/quarter = 100, seasonal adjustment for Q1 = 100(0.732) = 73.2.

Use the data below and the regression model. Gamma Sales Gamma × Sales Gamma2 10 22 220 100 18 34 612 324 14 26 364 196 14 30 420 196 12 24 288 144 Total 1,904 960 Average 13.6 27.2 What is the sales forecast if Gamma is expected to be 21? (Round your forecast to the nearest number of whole units.) A) 42 B) 36 C) 48 D) 39

D You must determine the value of a and b in a simple linear regression. b = [1904 − (5)(13.6)(27.2)]/[960 − (5)(13.6)2] = 54.4/35.2 = 1.55 a = (27.2) − (1.55)(13.6) = 27.2 − 21.08 = 6.12 Sales = 6.12 + 1.55(16) = 30.92. Sales = 6.12 + 1.55(21) = 6.12 + 32.55 = 38.67

A forecasting system that changes the value of the alpha parameter in response to the level of forecast error is known as: A) A tracking signal. B) A trend-enhanced exponential smoothing model. C) A causal regression. D) A time series model. E) An adaptive model

E

Strategic demand planning would best be utilized: A) To direct day-to-day operations in a manufacturing plant. B) To determine plans for employee overtime. C) To decide whether or not to close a manufacturing plant. D) To determine plans for hiring or laying off employees. E) All answers are correct.

c