What is Forecasting Answer Key Part 2

25) Which of the following is NOT a characteristic of exponential smoothing? 1. A) smoothes random variations in the data 2. B) uses an easily altered weighting scheme 3. C) weights each historical value equally 4. D) has minimal data storage requirements 5. E) uses the previous period's forecast Answer: C 26) Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast? 1. 2. 3. A) 0 B) 1 divided by the number of periods C) 0.5 4. D) 1.0 5. E) cannot be determined Answer: D 27) Given an actual demand this period of 103, a forecast value for this period of 99, and an alpha of .4, what is the exponential smoothing forecast for next period? 94. A) 94.6 95. B) 97.4 96. C) 100.6 97. D) 101.6 98. E) 103.0 Answer: C 28) A forecast based on the previous forecast plus a percentage of the forecast error is a(n): 1. 2. 3. 4. 5. A) qualitative forecast. B) naive forecast. C) moving average forecast. D) weighted moving average forecast. E) exponential smoothing forecast. Answer: E 29) Given an actual demand this period of 61, a forecast for this period of 58, and an alpha of 0.3, what would the forecast for the next period be using exponential smoothing? 45. 46. 47. A) 45.5 B) 57.1 C) 58.9 48. D) 61.0 49. E) 65.5 Answer: C 30) Which of the following values of alpha would cause exponential smoothing to respond the SLOWEST to forecast errors? 1. A) 0.10 2. B) 0.2246 3. C) 0.50 4. D) 0.90 5. E) cannot be determined Answer: A 31) A forecasting method has produced the following over the past five months. What is the mean absolute deviation? Actual 10 8 10 6 9 1. 2. 3. 4. 5. Forecast 11 10 8 6 8 A) -0.2 B) -1.0 C) 0.0 D) 1.2 E) 8.6 Error -1 -2 2 0 1 |Error| 1 2 2 0 1 Answer: D 32) The primary purpose of the mean absolute deviation (MAD) in forecasting is to: 1. A) estimate the trend line. 2. B) eliminate forecast errors. 3. C) measure forecast accuracy. 4. D) seasonally adjust the forecast. 5. E) remove random variations. Answer: C 33) Given forecast errors of -1, 4, 8, and -3, what is the mean absolute deviation? 1. A) 2 2. B) 3 3. C) 4 4. D) 8 5. E) 16 Answer: C 34) Suppose that the last four months of sales were 8, 10, 15, and 9 units, respectively. Suppose further that the last four forecasts were 5, 6, 11, and 12 units, respectively. What is the Mean Absolute Deviation (MAD) of these forecasts? 1. 2. 3. A) 2 B) -10 C) 3.5 4. D) 9 5. E) 10.5 Answer: C 35) A time-series trend equation is 25.3 + 2.1x. What is your forecast for period 7? 23. 24. 25. A) 23.2 B) 25.3 C) 27.4 26. D) 40.0 27. E) 179.2 Answer: D 36) For a given product demand, the time-series trend equation is 53 - 4x. The negative sign on the slope of the equation: 1. A) is a mathematical impossibility. 2. B) is an indication that the forecast is biased, with forecast values lower than actual values. 3. C) is an indication that product demand is declining. 4. D) implies that the coefficient of determination will also be negative. 5. E) implies that the cumulative error will be negative. Answer: C 37) Yamaha manufactures which set of products with complementary demands to address seasonal variations? 1. A) golf clubs and skis 2. B) swimming suits and winter jackets 3. C) jet skis and snowmobiles 4. D) pianos and guitars 5. E) ice skates and water skis Answer: C 38) Which of the following is TRUE regarding the two smoothing constants of the Forecast Including Trend (FIT) model? 1. A) One constant is positive, while the other is negative. 2. B) They are called MAD and cumulative error. 3. C) Alpha is always smaller than beta. 4. D) One constant smoothes the regression intercept, whereas the other smoothes the regression slope. 5. E) Their values are determined independently. Answer: E 39) Demand for a certain product is forecast to be 800 units per month, averaged over all 12 months of the year. The product follows a seasonal pattern, for which the January monthly index is 1.25. What is the seasonally-adjusted sales forecast for January? 1. 2. 3. A) 640 units B) 798.75 units C) 801.25 units 4. D) 1000 units 5. E) 83.33 units Answer: D 40) A seasonal index for a monthly series is about to be calculated on the basis of three years' accumulation of data. The three previous July values were 110, 150, and 130. The average demand over all months during the three-year time period was 190. What is the approximate seasonal index for July? 1. 2. 3. A) 0.487 B) 0.684 C) 1.462 4. D) 2.053 5. E) cannot be calculated with the information given Answer: B 41) Suppose that the demand in period 1 was 7 units and the demand in period 2 was 9 units. Assume that the forecast for period 1 was for 5 units. If the firm uses exponential smoothing with an alpha value of .20, what should be the forecast for period 3? (Round answers to two decimal places.) 9. A) 9.00 10. B) 3.72 11. C) 9.48 12. D) 5.00 13. E) 6.12 Answer: E 42) ________ expresses the error as a percent of the actual values. 1. A) MAD 2. B) MSE 3. C) MAPE 4. D) FIT 5. E) The smoothing constant Answer: C 43) If Brandon Edward were working to develop a forecast using a moving averages approach, but he noticed a detectable trend in the historical data, he should: 1. A) use weights to place more emphasis on recent data. 2. B) use weights to minimize the importance of the trend. 3. C) change to an associative multiple regression approach. 4. D) use a simple moving average. 5. E) change to a qualitative approach. Answer: A 44) A(n) ________ forecast uses an average of the most recent periods of data to forecast the next period. Answer: moving average (or simple moving average) 45) The smoothing constant is a weighting factor used in ________. Answer: exponential smoothing 46) A measure of forecast error that does not depend upon the magnitude of the item being forecast is the ________. Answer: mean absolute percent error (or MAPE) 47) When one constant is used to smooth the forecast average and a second constant is used to smooth the trend, the forecasting method is ________. Answer: exponential smoothing with trend adjustment or trend-adjusted smoothing or second-order smoothing or double smoothing 48) ________ is a time-series forecasting method that fits a trend line to a series of historical data points and then projects the line into the future for forecasts. Answer: Trend projection 49) Simple ________ forecasts only work well if we can assume that market demands will stay fairly steady over time. Answer: moving average 50) If a barbershop operator noted that Tuesday's business was typically twice as heavy as Wednesday's, and that Friday's business was typically the busiest of the week, business at the barbershop is subject to ________. Answer: seasonal variations (or seasonality) 51) Identify the four components of a time series. Which one of these is rarely forecast? Why is this so? Answer: Trend, seasonality, cycles, and random variation. Since random variations follow no discernible pattern, they cannot be predicted, and thus are not forecast. 52) Compare seasonal effects and cyclical effects. Answer: A cycle is longer (typically several years) than a season (typically days, weeks, months, or quarters). A cycle has variable duration, while a season has fixed duration and regular repetition. Cycles include a wide variety of factors that cause the economy to go from recession to expansion to recession over a period of years. 53) Distinguish between a weighted moving average model and an exponential smoothing model. Answer: Exponential smoothing is a weighted moving average model wherein previous values are weighted in a specific manner--in particular, all previous values are weighted with a set of weights that decline exponentially. Also, exponential smoothing involves little record keeping of past data. 54) Describe three popular measures of forecast accuracy. Answer: Measures of forecast accuracy include: (a) MAD (mean absolute deviation) is a sum of the absolute values of individual errors divided by the number of periods of data; (b) MSE (mean squared error) is the average of the squared differences between the forecast and observed values; and (c) MAPE (mean absolute percent error) is the average of the absolute differences between the forecast and actual values, expressed as a percent of actual values. 55) Give an example, other than a restaurant or other food-service firm, of an organization that experiences an hourly seasonal pattern. (That is, each hour of the day has a pattern that tends to repeat day after day.) Explain. Answer: Answer will vary. However, two non-food examples would be banks and movie theaters. 56) Explain the role of regression models (time series and otherwise) in forecasting. That is, how is trend projection able to forecast? How is regression used for causal forecasting? Answer: For trend projection, the independent variable is time. The trend projection equation has a slope that is the change in demand per period. To forecast the demand for period x, perform the calculation a + bx. For causal forecasting, the independent variables are predictors of the forecast value or dependent variable. The slope of the regression equation is the change in the Y variable per unit change in the X variable. 57) Identify two advantages of the moving average forecasting model. Identify two disadvantages of the moving average forecasting model. Answer: Advantages of the model include: it uses simple calculations, it smoothes out sudden fluctuations, and it is easy for users to understand. Disadvantages include: increasing the size of n makes the method less sensitive to real changes in demand, they require extensive record keeping of past data, and they do not pick up on trends very well. 58) What does it mean to "decompose" a time series? Answer: To decompose a time series means to break past data down into components of trend, seasonality, cycles, and random variations, and then to project them forward. 59) What is the key difference between weighted moving average and simple moving average approaches to forecasting? Answer: Simple moving averages are useful where there is no identifiable trend in the historical data, i.e., demand has been fairly steady over time. If there were an identifiable trend, weighted moving averages would provide a more accurate forecast because higher weights would be put on the more recent data. 60) Weekly sales of ten-grain bread at the local organic food market are provided in the table below. Based on these data, forecast week 9 using a five-week moving average. Week Sales 1 415 2 389 3 420 4 382 5 410 6 432 7 405 8 421 Answer: (382 + 410 + 432 + 405 + 421)/5 = 410.0 61) Given the following data, calculate the three-year moving averages for years 4 through 10. Year 1 2 3 4 5 6 7 8 9 Answer: Year 1 2 3 4 5 6 7 8 9 10 Demand 74 90 59 91 140 98 110 123 99 Demand 74 90 59 91 140 98 110 123 99 3-Year Moving Ave. 74.33 80.00 96.67 109.67 116.00 110.33 110.67 62) What is the forecast for May based on a weighted moving average applied to the following past demand data and using the weights: 4, 3, 2 (largest weight is for most recent data)? Nov. 37 Dec. 36 Jan. 40 Feb. 42 Mar. 47 April 43 Answer: 2 × 42 + 3 × 47 + 4 × 43 = 84 + 141 + 172 = 397; 397/9 = 44.1 63) Weekly sales of copy paper at Cubicle Suppliers are provided in the table below. Compute a three-period moving average and a four-period moving average for weeks 5, 6, and 7. Compute the MAD for both forecasting methods. Which model is more accurate? Forecast week 8 with the more accurate method. Week 1 2 3 4 5 6 7 Answer: Week 1 2 3 4 5 6 7 8 Sales (cases) 17 21 27 31 19 17 21 Sales (cases) 3MA 17 21 27 31 19 26.3 17 25.7 21 22.3 |error| 7.3 8.7 1.3 4MA 24.0 24.5 23.5 22.0 |error| 5.0 7.5 2.5 The MAD for the 3-week moving average is (7.3 + 8.7 + 1.3) / 3 = 5.77. The MAD for the 4week moving average is (5.0 + 7.5 + 2.5) / 3 = 5.00. The four-week moving average is more accurate. The forecast with the 4-moving average is 22.0. 64) The last four weekly values of sales were 80, 100, 105, and 90 units, respectively. The last four forecasts (for the same four weeks) were 60, 80, 95, and 75 units, respectively. Calculate the MAD, MSE, and MAPE for these four weeks. Sales 80 100 105 90 Forecast 60 80 95 75 Error 20 20 10 15 Error squared 400 400 100 225 Pct. error .25 .20 .095 .167 Answer: MAD = 65/4 = 16.25; MSE = 1125/4 = 281.25; MAPE = 0.712/4 = .178 or 17.8%