Q&A Hero

Q: ________ analysis is a statistical approach that relies heavily on historical demand data to project the future size of demand, and it recognizes trends and seasonal patterns.

Q: ________ methods use historical data on independent variables to predict demand.

Q: ________ methods of forecasting translate the opinions of management, experts, consumers, or salesforce into quantitative estimates.

Q: Which one of the following statements about forecasting is true? A) The five basic patterns of demand are the horizontal, trend, seasonal, cyclical, and the subjective judgment of forecasters. B) Judgment methods are particularly appropriate for situations in which historical data are lacking. C) Casual methods are used when historical data are available and the relationship between the factor to be forecast and other external and internal factors cannot be identified. D) Focused forecasting is a technique that focuses on one particular component of demand and develops a forecast from it.

Q: When forecasting total demand for all their services or products, few companies err by more than: A) one to four percent. B) five to eight percent. C) nine to twelve percent. D) thirteen to sixteen percent.

Q: Which one of the following statements about forecasting is false? A) Causal methods of forecasting use historical data on independent variables (promotional campaigns, competitors' actions, etc.) to predict demand. B) Three general types of forecasting techniques are used for demand forecasting: time-series analysis, causal methods, and judgment methods. C) Time series express the relationship between the factor to be forecast and related factors such as promotional campaigns, economic conditions, and competitor actions. D) A time series is a list of repeated observations of a phenomenon, such as demand, arranged in the order in which they actually occurred.

Q: Aggregating products or services together generally decreases the forecast accuracy.

Q: Aggregation is the act of clustering several similar products or services.

Q: Draw a curve that represents four out of the five demand patterns for time series as discussed in this chapter. Clearly label both dependent and independent axis and the salient features of your graph that demonstrate your chosen patterns. Select a product or service and discuss what influences might cause it to exhibit each of these patterns.

Q: Nathan managed to level the customer requests for his valuable services by offering reservations, deploying some promotional pricing, and engaging in yield management, all forms of ________.

Q: Variations in demand that cannot be predicted are said to be a(n) ________ pattern.

Q: A systematic increase or decrease in the mean of the series over time is a(n) ________.

Q: In the winter, Handyman Negri repaired snowblowers and in the summer he earned extra money by repairing lawnmowers, a classic example of: A) promotional pricing. B) complementary products. C) mixed model service. D) yield management.

Q: What is the difference between a reservation and an appointment? A) There is no difference between the two terms. B) The term reservation implies that the customer has paid in advance. C) The term appointment implies that the customer has paid in advance. D) The term reservation is issued when the customer occupies the facility to receive service.

Q: "Well if you're out of Duff I'll just take my business elsewhere!" the customer shouted as he stomped out of the Quickie Mart. This unfortunate incident could be described as: A) a stockout. B) a backorder. C) a backlog. D) yield management.

Q: A weary traveler shows up at a hotel desk at midnight without a reservation. The desk clerk informs him that there is a room available, but sadly it is marked up 80% higher than the usual price. This is an example of: A) promotional pricing. B) yield management. C) backlogs. D) backorder.

Q: One aspect of demand that makes every forecast inaccurate is: A) trend variation. B) random variation. C) cyclical variation. D) seasonal variation.

Q: Which one of the following statements about the patterns of a demand series is false? A) The five basic patterns of most business demand series are the horizontal, trend, seasonal, cyclical, and random patterns. B) Estimating cyclical movement is difficult. Forecasters do not know the duration of the cycle because they cannot predict the events that cause it. C) The trend, over an extended period of time, always increases the average level of the series. D) Every demand series has at least a random component.

Q: Polly Prognosticator was the greatest quantitative forecaster in recorded history. A skillful user of all techniques in your chapter on forecasting, she knew better than to try and develop a forecast for data that exhibited a: A) random pattern. B) horizontal pattern. C) seasonal pattern. D) cyclical pattern.

Q: There are historically three 32-month periods of generally rising prices in the stock market for every one 9-month period of falling prices. This observation leads you to conclude that the stock market exhibits a: A) random pattern. B) trend pattern. C) seasonal pattern. D) cyclical pattern.

Q: Professor Willis noted that the popularity of his office hours mysteriously rose in the middle and the end of each semester, falling off to virtually no visitors throughout the rest of the year. The demand pattern at work is: A) cyclical. B) random. C) seasonal. D) trend.

Q: A regression equation with a coefficient of determination near one would be most likely to occur when the data demonstrated a: A) seasonal demand pattern. B) trend demand pattern. C) cyclical demand pattern. D) random demand pattern.

Q: The electricity bill at Padco was driven solely by the lights throughout the office; everything else was driven by alternative energy sources. The office was open roughly 8 hours a day, five days a week and the cleaning crew spent about the same amount of time in the offices each week night. The kilowatt hour usage for the office was best described as a: A) horizontal demand pattern. B) random demand pattern. C) seasonal demand pattern. D) cyclical demand pattern.

Q: Which one of the following basic patterns of demand is difficult to predict because it is affected by national or international events or because of a lack of demand history reflecting the stages of demand from product development to decline? A) horizontal B) seasonal C) random D) cyclical

Q: A weary traveler shows up at a hotel desk at midnight without a reservation. The desk clerk informs him that there is a room available, but sadly it is marked up 80% higher than the usual price. This is an example of promotional pricing.

Q: A water ski manufacturer believes they can double their sales by producing snow skis during the other half of the year. This approach to demand management is an example of complementary products.

Q: One of the basic time series patterns is random.

Q: The repeated observations of demand for a product or service in their order of occurrence form a pattern known as a time series.

Q: 8.1 Managing Demand

Q: Describe some of the managerial considerations required to utilize big data effectively.

Q: What are some of the principles organizations can observe to improve their forecasting process?

Q: What are the steps of the forecasting process as described in the text?

Q: Describe the combination forecast techniques and discuss how they have been shown to perform in recent studies.

Q: How is a typical forecasting process similar to the Plan-Do-Study-Act (PDSA) cycle? (See Chapter 5 for more information on PDSA)

Q: Pho Bulous, a Vietnamese restaurant in the bustling metropolis of Edmond, has had great success using forecasting techniques to predict demand for their main menu items ever since they opened their doors. Their forecast for last month was grossly inaccurate and so far this month, their forecast appears to be just as bad as last month's. It's already time to prepare the forecast for next month, what should they do about their model?

Q: ________ is a collection of data from traditional and digital sources and is characterized by volume, variety, and velocity.

Q: ________ are produced by averaging independent forecasts based on different methods or different data, or both.

Q: Which of the following statements about bid data is not true? A) Data technicians must be the ones to identify problems to be tackled with big data. B) Companies employing data-driven decisions tend to be more successful than others. C) Data scientists and skilled professionals are a necessity to execute big data projects. D) Public cloud providers are an option for hosting bid data projects that may swamp single servers.

Q: Table 8.8 The manager of a pizza shop must forecast weekly demand for special pizzas so that he can order pizza shells weekly. Recent demand has been: WEEK No. Special Pizzas 1 30 2 45 3 33 4 36 5 35 6 40 Use the information from Table 8.8. The pizza shop manager is looking for a forecasting approach that will forecast her demand within 0.5 pizzas. If the actual demand for week #7 was 39 pizzas, which of the combination forecasts came closest to predicting this demand? A) simple moving average and weighted moving average forecast B) simple moving average and exponentially smoothed forecast C) weighted moving average and exponentially smoothed forecast D) week #7 demand of 39 is within 0.5 pizzas for all three of these combination forecasts, and thus all of them are appropriate

Q: Andy took what he liked to call "the sheriff without a gun" approach to forecasting. Every period he tried a number of different forecasting approaches and simply averaged the predictions for all of the techniques. This overall average was the official forecast for the period. The more formal name for this technique is: A) grand averaging. B) focus forecasting. C) simple average. D) combination forecasting.

Q: Barney took what he liked to call "the shotgun approach" to forecasting. Every period he tried a number of different forecasting approaches and at the end of the period he reviewed all of the forecasts to see which was the most accurate. The winner would be used for next period's forecast (but he still made forecasts all possible ways so he could use the system again for the following period). The more formal name for this technique is: A) combination forecasting. B) post-hoc forecasting. C) focus forecasting. D) shotgun forecasting.

Q: A forecasting system that brings the manufacturer and its customers together to provide input for forecasting is a(n): A) nested system. B) harmonically balanced supply chain. C) iterative Delphi method system for the supply chain. D) collaborative planning, forecasting, and replenishment system.

Q: Traditional data processing applications are capable of handling big data.

Q: Better forecasting processes yield better forecasts.

Q: Focus forecasting selects the best forecast from a group of forecasts generated by individual techniques.

Q: Combination forecasting is most effective when the techniques being combined contribute different kinds of information to the forecasting process.

Q: Combination forecasting is a method of forecasting that selects the best from a group of forecasts generated by simple techniques.

Q: Three weeks of data are available from a restaurant. Develop a forecast and explain why your approach is reasonable.

Q: The demand for an item over the last year is plotted below. Develop a forecast and explain why your approach is reasonable.

Q: A local moving company has collected data on the number of moves they have been asked to perform over the past three years. Moving is highly seasonal, so the owner/operator, who is both burly and highly educated, decides to apply the multiplicative seasonal method (based on a linear regression for total demand) to forecast the number of customers for the coming year. What is his forecast for each quarter?

Q: Explain how the value of alpha affects forecasts produced by exponential smoothing.

Q: ________ is a time-series method used to estimate the average of a demand time series by averaging the demand for the n most recent time periods.

Q: A(n) ________ is a portion of data from more recent time periods that is used to test different models developed from earlier time period data.

Q: A(n) ________ forecast is a time-series method whereby the forecast for the next period equals the demand for the current period.

Q: In an exponential smoothing model a ________ value for alpha results in greater emphasis being placed on more recent periods.

Q: Table 8.9 Consider the following results from the last ten periods of student enrollment forecast by the Operations Management department chairman. Period Forecast Actual 1 25 26 2 32 31 3 42 45 4 53 50 5 64 70 6 70 72 7 81 78 8 88 90 9 95 93 10 102 105 Use Table 8.9 to determine the cumulative sum of forecast errors as of period 6 for the department chairman's forecast. A) -10 B) -6 C) -8 D) -4

Q: Use Table 8.9 to determine the MAD f Table 8.9 Consider the following results from the last ten periods of student enrollment forecast by the Operations Management department chairman. Period Forecast Actual 1 25 26 2 32 31 3 42 45 4 53 50 5 64 70 6 70 72 7 81 78 8 88 90 9 95 93 10 102 105 or period 5 for the department chairman's forecast. A) 2.0 B) 2.8 C) 2.67 D) 2.42

Q: If forecast errors are normally distributed with a mean of 0, the relationship between σ and MAD is: A) 1.25MAD ≈ σ B) MAD ≈ 1.25σ C) MAD ≈ 0.5σ D) 0.8MAD ≈ σ

Q: Graph 8.1 Data plotted in the graph appear in the table below. Obs # Day Demand Obs # Day Demand 1 Mon 33 12 Fri 54 2 Tue 34 13 Sat 95 3 Wed 37 14 Sun 92 4 Thu 42 15 Mon 58 5 Fri 44 16 Tue 63 6 Sat 79 17 Wed 67 7 Sun 86 18 Thu 70 8 Mon 51 19 Fri 74 9 Tue 50 20 Sat 114 10 Wed 51 21 Sun 119 11 Thu 52 Refer to Graph 8.1. Use a trend projection to forecast the next week's demand. Then apply seasonal indices to determine the demand on Saturday of the fourth week. What is the demand projected to be? A) 141.4 B) 146.2 C) 151.3 D) 158.9

Q: A forecaster that uses a holdout set approach as a final test for forecast accuracy typically uses: A) the entire data set available to develop the forecast. B) the older observations in the data set to develop the forecast and more recent to check accuracy. C) the newer observations in the data set to develop the forecast and older observations to check accuracy. D) every other observation to develop the forecast and the remaining observations to check the accuracy.

Q: Which statement about forecast accuracy is true? A) A manager must be careful not to "overfit" past data. B) The ultimate test of forecasting power is how well a model fits past data. C) The ultimate test of forecasting power is how a model fits holdout samples. D) The best technique in explaining past data is the best technique to predict the future.

Q: Table 8.7 A sales manager wants to forecast monthly sales of the machines the company makes using the following monthly sales data. Month Balance 1 $3,803 2 $2,558 3 $3,469 4 $3,442 5 $2,682 6 $3,469 7 $4,442 8 $3,728 Use the information in Table 8.7. What is the forecast for period 9 using a naive forecast? A) $3,728 B) $3,803 C) $4,442 D) $4,085

Q: Table 8.6 Month Demand January 480 February 520 March 535 April 550 May 590 June 630 Use the information in Table 8.6. Use the exponential smoothing method with = 0.5 and a February forecast of 500 to forecast the sales for May. A) fewer than or equal to 530 B) greater than 530 but fewer than or equal to 540 C) greater than 540 but fewer than or equal to 550 D) greater than 550

Q: Table 8.6 Month Demand January 480 February 520 March 535 April 550 May 590 June 630 Use the information in Table 8.6. Use an exponential smoothing model with a smoothing parameter of 0.30 and an April forecast of 525 to determine what the forecast sales would have been for June. A) fewer than or equal to 535 B) greater than 535 but fewer than or equal to 545 C) greater than 545 but fewer than or equal to 555 D) greater than 555

Q: Table 8.5 Use the information in Table 8.5. Using the exponential smoothing method, with alpha equal to 0.2, what is the forecasted demand for November? Use an initial value for the forecast equal to 277 units. A) fewer than or equal to 260 units B) greater than 260 but fewer than or equal to 275 units C) greater than 275 but fewer than or equal to 285 units D) more than 285 units

Q: Table 8.5 Use the information in Table 8.5. Using the 4-month weighted moving-average technique and the following weights, what is the forecasted demand for November? Time Period Weight Most recent month 50% One month ago 20% Two months ago 20% Three months ago 10% A) fewer than or equal to 250 units B) greater than 250 but fewer than or equal to 265 units C) greater than 265 but fewer than or equal to 280 units D) more than 280 units

Q: Table 8.5 Use the information in Table 8.5. Using the simple moving-average technique for the most recent three months, what will be the forecasted demand for November? A) fewer than or equal to 260 units B) greater than 260, but fewer than or equal to 275 units C) greater than 275, but fewer than or equal to 290 units D) more than 290 units

Q: It is now near the end of May and you must prepare a forecast for June for a certain product. The forecast for May was 900 units. The actual demand for May was 1,000 units. You are using the exponential smoothing method with α = 0.20. The forecast for June is: A) fewer than 925 units. B) greater than or equal to 925 units but fewer than 950 units. C) greater than or equal to 950 units but fewer than 1,000 units. D) greater than or equal to 1,000 units.

Q: Demands for a newly developed salad bar at the Great Professional restaurant for the first six months of this year are shown in the following table. What is the forecast for July if the 3-month weighted moving-average method is used? (Use weights of 0.5 for the most recent demand, 0.3, and 0.2 for the oldest demand.) A) fewer than or equal to 432 units B) greater than 432 units but fewer than or equal to 442 units C) greater than 442 units but fewer than or equal to 452 units D) greater than 452

Q: Demand for a new five-inch color TV during the last six periods has been as follows: What is the forecast for period 7 if the company uses the simple moving-average method with n = 4? A) fewer than or equal to 115 B) greater than 115 but fewer than or equal to 120 C) greater than 120 but fewer than or equal to 125 D) greater than 125

Q: Which one of the following statements about forecasting is false? A) The method for incorporating a trend into an exponentially smoothed forecast requires the estimation of three smoothing constants: one for the mean, one for the trend, and one for the error. B) The cumulative sum of forecast errors (CFE) is useful in measuring the bias in a forecast. C) The standard deviation and the mean absolute deviation measure the dispersion of forecast errors. D) A tracking signal is a measure that indicates whether a method of forecasting has any built-in biases over a period of time.

Q: With the multiplicative seasonal method of forecasting: A) the times series cannot exhibit a trend. B) seasonal factors are multiplied by an estimate of average demand to arrive at a seasonal forecast. C) the seasonal amplitude is a constant, regardless of the magnitude of average demand. D) there can be only four seasons in the time-series data.

Q: When the underlying mean of a time series is very stable and there are no trend, cyclical, or seasonal influences: A) a simple moving-average forecast with n = 20 should outperform a simple moving-average forecast with n = 3. B) a simple moving-average forecast with n = 3 should outperform a simple moving-average forecast with n = 15. C) a simple moving-average forecast with n = 20 should perform about the same as a simple moving-average forecast with n = 3. D) an exponential smoothing forecast with a = 0.30 should outperform a simple moving-average forecast with α = 0.01.

Q: Which one of the following statements about forecasting is false? A) You should use the simple moving-average method to estimate the mean demand of a time series that has a pronounced trend and seasonal influences. B) The weighted moving-average method allows forecasters to emphasize recent demand over earlier demand. The forecast will be more responsive to change in the underlying average of the demand series. C) The most frequently used time-series forecasting method is exponential smoothing because of its simplicity and the small amount of data needed to support it. D) In exponential smoothing, higher values of alpha place greater weight on recent demands in computing the average.

Q: The trend projection with regression model can forecast demand well into the future.

Q: An exponential smoothing model with an alpha equal to 1.00 is the same as a naive forecasting model.

Q: A simple moving average of one period will yield identical results to a naive forecast.

Q: A naive forecast is a time-series method whereby the forecast for the next period equals the demand for the current period.

Q: The naive forecast may be adapted to take into account a demand trend.

Q: Time-series analysis is a statistical approach that relies heavily on historical demand data to project the future size of demand.