1. Tupperware only uses both qualitative and quantitative forecasting techniques, culminating in a final forecast that is the consensus of all participating managers. False (Global company profile: Tupperware Corporation, moderate) Show
2. The forecasting time horizon and the forecasting techniques used tend to vary over the life cycle of a product. True (What is forecasting? moderate) 3. Sales forecasts are an input to financial planning, while demand forecasts impact human resource decisions. True (Types of forecasts, moderate) 4. Forecasts of individual products tend to be more accurate than forecasts of product families. Don't use plagiarized sources. Get your custom essay on Get custom paperNEW! smart matching with writer False (Seven steps in the forecasting system, moderate) 5. Most forecasting techniques assume that there is some underlying stability in the system. True (Seven steps in the forecasting system, moderate) 6. The sales force composite forecasting method relies on salespersons’ estimates of expected sales. True (Forecasting approaches, easy) 7. A time-series model uses a series of past data points to make the forecast. True (Forecasting approaches, moderate) 8. The quarterly “make meeting” of Lexus dealers is an example of a sales force composite forecast. “ Ok, let me say I’m extremely satisfy with the result while it was a last minute thing. I really enjoy the effort put in. ” +84 relevant experts are online Hire writerTrue (Forecasting approaches, easy) 9. Cycles and random variations are both components of time series. True (Time-series forecasting, easy) 10. A naive forecast for September sales of a product would be equal to the sales in August. True (Time-series forecasting, easy) 11. One advantage of exponential smoothing is the limited amount of record keeping involved. True (Time-series forecasting, moderate) 12. The larger the number of periods in the simple moving average forecasting method, the greater the method’s responsiveness to changes in demand. False (Time-series forecasting, moderate) 13. Forecast including trend is an exponential smoothing technique that utilizes two smoothing constants: one for the average level of the forecast and one for its trend. Get to Know The Price Estimate For Your Paper "You must agree to out terms of services and privacy policy" True (Time-series forecasting, easy) 14. Mean Squared Error and Coefficient of Correlation are two measures of the overall error of a forecasting model. False (Time-series forecasting, easy) 15. In trend projection, the trend component is the slope of the regression equation. True (Time-series forecasting, easy) 16. In trend projection, a negative regression slope is mathematically impossible. False (Time-series forecasting, moderate) 17. Seasonal indexes adjust raw data for patterns that repeat at regular time intervals. True (Time-series forecasting, moderate) 18. If a quarterly seasonal index has been calculated at 1.55 for the October-December quarter, then raw data for that quarter must be multiplied by 1.55 so that the quarter can be fairly compared to other quarters. False (Time-series forecasting: Seasonal variation in data, moderate) 19. The best way to forecast a business cycle is by finding a leading variable. True (Time-series forecasting, moderate) 20. Linear-regression analysis is a straight-line mathematical model to describe the functional relationships between independent and dependent variables. True (Associative forecasting methods: Regression and correlation 21. The larger the standard error of the estimate, the more accurate the forecasting model. False (Associative forecasting methods: Regression and correlation analysis, easy) 22. A trend projection equation with a slope of 0.78 means that there is a 0.78 unit rise in Y for every unit of time that passes. True (Time-series forecasting: Trend projections, moderate) 23. In a regression equation where Y is demand and X is advertising, a coefficient of determination (R2) of .70 means that 70% of the variance in advertising is explained by demand. False (Associative forecasting methods: Regression and correlation analysis, moderate) 24. Tracking limits should be within ± 8 MADs for low-volume stock items. True (Monitoring and controlling forecasts, moderate) 25. If a forecast is consistently greater than (or less than) actual values, the forecast is said to be biased. True (Monitoring and controlling forecasts, moderate) 26. Focus forecasting tries a variety of computer models and selects the best one for a particular application. True (Monitoring and controlling forecasts, moderate) 27. Many service firms use point-of-sale computers to collect detailed records needed for accurate short-term forecasts. True (Forecasting in the service sector, moderate) 28. Tupperware’s use of forecasting 29. Which of the following statements regarding Tupperware’s forecasting is false? 30. Forecasts 31. One use of short-range forecasts is to determine 32. Forecasts are usually classified by time horizon into three categories 33. A forecast with a time horizon of about 3 months to 3 years is typically called a 35. The three major types of forecasts used by business organizations are 36. Which of the following is not a step in the forecasting process? 37. The two general approaches to forecasting are 38. Which of the following uses three types of participants: decision makers, staff personnel, and respondents? 39. The forecasting model that pools the opinions of a group of experts or managers is known as the 41. Which of the following techniques uses variables such as price and promotional expenditures, which are related to product demand, to predict demand? 42. Which of the following statements about time series forecasting is true? 43. Time series data may exhibit which of the following behaviors? 44. Gradual, long-term movement in time series data is called 45. Which of the following is not present in a time series? 46. The fundamental difference between cycles and seasonality is the 47. In time series, which of the following cannot be predicted? 48. What is the approximate forecast for May using a four-month moving average? 49. Which time series model below assumes that demand in the next period will be equal to the most recent period’s demand? 50. Which of the following is not a characteristic of simple moving averages? 51. A six-month moving average forecast is better than a three-month moving average forecast if demand 52. Increasing the number of periods in a moving average will accomplish greater smoothing, but at the expense of 53. Which of the following statements comparing the weighted moving average technique and exponential smoothing is true? 54. Which time series model uses past forecasts and past demand data to generate a new forecast? 55. Which is not a characteristic of exponential smoothing? 56. Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast? 57. Given an actual demand of 103, a previous forecast value of 99, and an alpha of .4, the exponential smoothing forecast for the next period would be 58. A forecast based on the previous forecast plus a percentage of the forecast error is a(n) 59. Given an actual demand of 61, a previous forecast of 58, and an of .3, what would the forecast for the next period be using simple exponential smoothing? 60. Which of the following values of alpha would cause exponential smoothing to respond the most slowly to forecast errors? 61. A forecasting method has produced the following over the past five months. What is the mean absolute deviation? 62. The primary purpose of the mean absolute deviation (MAD) in forecasting is to 63. Given forecast errors of -1, 4, 8, and -3, what is the mean absolute deviation? 64. The last four months of sales were 8, 10, 15, and 9 units. The last four forecasts were 5, 6, 11, and 12 units. The Mean Absolute Deviation (MAD) is 65. A time series trend equation is 25.3 + 2.1 X. What is your forecast for period 7? 67. In trend-adjusted exponential smoothing, the forecast including trend (FIT) consists of 68. Which of the following is true regarding the two smoothing constants of the Forecast Including Trend (FIT) model? 69. 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? a.640 units 70. 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 over all months is 190. The approximate seasonal index for July is a.0.487 71. A fundamental distinction between trend projection and linear regression is that 72. The percent of variation in the dependent variable that is explained by the regression equation is measured by the 73. The degree or strength of a linear relationship is shown by the 74. If two variables were perfectly correlated, the correlation coefficient r would equal 75. The last four weekly values of sales were 80, 100, 105, and 90 units. The last four forecasts were 60, 80, 95, and 75 units. These forecasts illustrate 76. The tracking signal is the 77. Computer monitoring of tracking signals and self-adjustment if a signal passes a preset limit is characteristic of 78. Many services maintain records of sales noting 79. Taco Bell’s unique employee scheduling practices are partly the result of using 96. A skeptical manager asks what short-range forecasts can be used for. Give her three possible uses/purposes. Any three of: planning purchasing, job scheduling, work force levels, job assignments, production levels. (What is forecasting? moderate) 97. A skeptical manager asks what long-range forecasts can be used for. Give her three possible uses/purposes. Any three of: planning new products, capital expenditures, facility location or expansion, research and development. (What is forecasting? moderate) 98. Describe the three forecasting time horizons and their use. Forecasting time horizons are: short range—generally less than three months, used for purchasing, job scheduling, work force levels, production levels; medium range—usually from three months up to three years, used for sales planning, production planning and budgeting, cash budgeting, analyzing operating plans; long range—usually three years or more, used for new product development, capital expenditures, facility planning, and R&D. (What is forecasting? moderate) 99. List and briefly describe the three major types of forecasts. The three types are economic, technological, and demand; economic refers to macroeconomic, growth and financial variables; technological refers to forecasting amount of technological advance, or futurism; demand refers to product demand. (Types of forecasts, moderate) 100. List the seven steps involved in forecasting. 101. What are the realities of forecasting that companies face? First, forecasts are seldom perfect. Second, most forecasting techniques assume that there is some underlying stability in the system. Finally, both product family and aggregated forecasts are more accurate than individual product forecasts. (Seven steps in the forecasting system, moderate) 102. What are the differences between quantitative and qualitative forecasting methods? Quantitative methods use mathematical models to analyze historical data. Qualitative methods incorporate such factors as the decision maker’s intuition, emotions, personal experiences, and value systems in determining the forecast. (Forecasting approaches, moderate) 103. List four quantitative forecasting methods. 104. What is a time-series forecasting model? 105. What is the difference between an associative model and a time-series model? A time series model uses only historical values of the quantity of 106. Name and discuss three qualitative forecasting methods. Qualitative forecasting methods include: jury of executive opinion, where high-level managers arrive at a group estimate of demand; sales force composite, where salespersons’ estimates are aggregated; Delphi method, where respondents provide inputs to a group of decision makers; the group of decision makers, often experts, then make the actual forecast; consumer market survey, where consumers are queried about their future purchase plans. (Forecasting approaches, moderate) 107. List the four components of a time series. Which one of these is rarely forecast? Why is this so? Trend, seasonality, cycles, and random variation. Since random variations follow no discernible pattern, they cannot be predicted, and thus are not forecast. (Time-series forecasting, moderate) 108. Compare seasonal effects and cyclical effects. 109. Distinguish between a moving average model and an exponential smoothing model. 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. (Time-series forecasting, moderate) 110. Describe three popular measures of forecast accuracy. 111. 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 will vary. However, two non-food examples would be banks and movie theaters. (Time-series forecasting, moderate) 112. 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? 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 t, perform the calculation a + bt. 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. (Time-series forecasting, difficult) 113. List three advantages of the moving average forecasting model. List three disadvantages of the moving average forecasting model. Two advantages of the model are that it uses simple calculations, it smoothes out sudden fluctuations, and it is easy for users to understand. The disadvantages are that the averages always stay within past ranges, that they require extensive record keeping of past data, and that they do not pick up on trends very well. (Time-series forecasting, moderate) 114. What does it mean to “decompose” a time series? 115. Distinguish a dependent variable from an independent variable. The independent variable causes some behavior in the dependent variable; the dependent variable shows the effect of changes in the independent variable. (Associative forecasting methods: Regression and correlation, moderate) 116. Explain, in your own words, the meaning of the coefficient of determination. The coefficient of determination measures the amount (percent) of total variation in the data that is explained by the model. (Associative forecasting methods: Regression and correlation, moderate) 117. What is a tracking signal? How is it calculated? Explain the connection between adaptive smoothing and tracking signals. A tracking signal is a measure of how well the forecast actually predicts. Its calculation is the ratio of RSFE to MAD. The larger the absolute tracking signal, the worse the forecast is performing. Adaptive smoothing sets limits to the tracking signal, and makes changes to its forecasting models when the tracking signal goes beyond those limits. (Monitoring and controlling forecasts, moderate) 118. What is focus forecasting? 124. A management analyst is using exponential smoothing to predict merchandise returns at an upscale branch of a department store chain. Given an actual number of returns of 154 items in the most recent period completed, a forecast of 172 items for that period, and a smoothing constant of 0.3, what is the forecast for the next period? How would the forecast be changed if the smoothing constant were 0.6? Explain the difference in terms of alpha and responsiveness. 166.6; 161.2 The larger the smoothing constant in an exponentially smoothed forecast, the more responsive the forecast. (Time-series forecasting, easy) 126. The following trend projection is used to predict quarterly demand: Y = 250 – 2.5t, where t = 1 in the first quarter of 2004. Seasonal (quarterly) relatives are Quarter 1 = 1.5; Quarter 2 = 0.8; Quarter 3 = 1.1; and Quarter 4 = 0.6. What is the seasonally adjusted forecast for the four quarters of 2006? PeriodProjectionAdjusted 127. Jim’s department at a local department store has tracked the sales of a product over the last ten weeks. Forecast demand using exponential smoothing with an alpha of 0.4, and an initial forecast of 28.0. Calculate MAD and the tracking signal. What do you recommend? 130. A small family-owned restaurant uses a seven-day moving average model to determine manpower requirements. These forecasts need to be seasonalized because each day of the week has its own demand pattern. The seasonal relatives for each day of the week are: Monday, 0.445; Tuesday, 0.791; Wednesday, 0.927; Thursday, 1.033; Friday, 1.422; Saturday, 1.478; and Sunday 0.903. Average daily demand based on the most recent moving average is 194 patrons. What is the seasonalized forecast for each day of next week? The average value multiplied by each day’s seasonal index. Monday: 194 x .445 = 86; Tuesday: 194 x .791 = 153; Wednesday: 194 x .927 = 180; Thursday: 194 x 1.033 = 200; Friday: 194 x 1.422 = 276; Saturday: 194 x 1.478 = 287; and Sunday: 194 x .903 = 175. (Associative forecasting methods: Regression and correlation, moderate) 131. A restaurant has tracked the number of meals served at lunch over the last four weeks. The data shows little in terms of trends, but does display substantial variation by day of the week. Use the following information to determine the seasonal (daily) index for this restaurant. 132. A firm has modeled its experience with industrial accidents and found that the number of accidents per year (Y) is related to the number of employees (X) by the regression equation Y = 3.3 + 0.049*X. R-Square is 0.68. The regression is based on 20 annual observations. The firm intends to employ 480 workers next year. How many accidents do you project? How much confidence do you have in that forecast? Y = 3.3 + 0.049 * 480 = 3.3 + 23.52 = 26.52 accidents. This is not a time series, so next year = year 21 is of no relevance. Confidence comes from the coefficient of determination; the model explains 68% of the variation in number of accidents, which seems respectable. (Associative forecasting methods: Regression and correlation, moderate) 133. Demand for a certain product is forecast to be 8,000 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? 8,000 x 1.25 = 10,000 (Time-series forecasting, easy) 134. 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, 135, and 130. The average over all months is 160. The approximate seasonal index for July is (110 + 135 + 130)/3 = 125; 125/160 = 0.781 (Time-series forecasting, moderate) 135. Marie Bain is the production manager at a company that manufactures hot water heaters. Marie needs a demand forecast for the next few years to help decide whether to add new production capacity. The company’s sales history (in thousands of units) is shown in the table below. Use exponential smoothing with trend adjustment, to forecast demand for period 6. The initial forecast for period 1 was 11 units; the initial estimate of trend was 0. The smoothing constants are = .3 and · = .3 136. The quarterly sales for specific educational software over the past three years are given in the following table. Compute the four seasonal factors. 137. An innovative restaurateur owns and operates a dozen “Ultimate Low-Carb” restaurants in northern Arkansas. His signature item is a cheese-encrusted beef medallion wrapped in lettuce. Sales (X, in millions of dollars) is related to Profits (Y, in hundreds of thousands of dollars) by the regression equation Y = 8.21 + 0.76 X. What is your forecast of profit for a store with sales of $40 million? $50 million? Students must recognize that sales is the independent variable and profits is dependent; the problem is not a time series. A store with $40 million in sales: 40 x 0.76 = 30.4; 30.4 + 8.21 = 38.61, or $3,861,000 in profit; $50 million in sales is estimated to profit 46.21 or $4,621,000. (Associative forecasting methods: Regression and correlation, moderate) 138. Arnold Tofu owns and operates a chain of 12 vegetable protein “hamburger” restaurants in northern Louisiana. Sales figures and profits for the stores are in the table below. Sales are given in millions of dollars; profits are in hundreds of thousands of dollars. Calculate a regression line for the data. What is your forecast of profit for a store with sales of $24 million? $30 million? Students must recognize that “sales” is the independent variable and profits is dependent. Store number is not a variable, and the problem is not a time series. The regression equation is Y = 5.936 + 1.421 X (Y = profit, X = sales). A store with $24 million in sales is estimated to profit 40.04 or $4,004,000; $30 million in sales should yield 48.566 or $4,856,600 in profit. (Associative forecasting methods: Regression and correlation, moderate) What kind of forecast is more accurate than individual forecasts?Aggregate forecasts are more accurate than individual forecasts. Longer term forecasts are more accurate because a business has more time to adjust the forecast. Seasonality and cycles are examples of forecasting data types. A horizontal data pattern typically occurs with demand patterns for a new product.
What is the most accurate definition of forecasting quizlet?Definition. 1 / 30. The study of historical data to discover their underlying tendencies and patterns and the use of this knowledge to project the data into future time periods. Tap the card to flip 👆
What are the three major types of forecasts in planning the future?Organizations use three major types of forecasting (economic, technological and demand forecasting) in planning the future of their operations. All forecasts lead to demand forecasting.
What is a forecast that is consistently higher or consistently lower than the actual values of a time series called?A consistent tendency for forecasts to be greater or less than the actual values is called bias error.
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