发帖
雷达卡
krajewski运营管理 考试题库
样题：
153.____________ are produced by averaging independent forecasts based on different methods or different data, or both.
Answer: Combination forecasts
Reference: Using Multiple Techniques
Difficulty: Moderate
Keywords: combination forecast
154.____________ selects the best forecast from a group of forecasts generated by individual techniques.
Answer: Focus forecasting
Reference: Using Multiple Techniques
Difficulty: Moderate
Keywords: focus forecasting
155.A history file of past demand will often be separated into two parts; the ____________ part will reflect irregular demands.
Answer: nonbase (data)
Reference: Putting it All Together: Forecasting as a Process
Difficulty: Moderate
Keywords: forecasting process, nonbase data
156.Forecasting is a(n) ____________ that should continually be reviewed for improvements.
Answer: (nested) process
Reference: Putting it All Together: Forecasting as a Process
Difficulty: Moderate
Keywords: forecasting process
SHORT ANSWERS
157.Discuss the combination of forecasts used by Unilever. If they were forced to use only one technique, which would be the most accurate and why?
Answer: Unilever uses a CDP system developed by Manugistics to perform times-series (and possibly causal-model) forecasting. These forecasts are then modified by judgment techniques, including salesforce estimates. Answers will vary regarding a single best technique, but students may be drawn toward a causal model with predictors for a time element, advertising expenditures and form of media, and other elements.
Reference: Introduction
Difficulty: Moderate
Keywords: forecasting methods, judgment, causal, time series
158.Why are forecasts for product families typically more accurate than forecasts for the individual items within a product family?
Answer: More accurate forecasts are obtained for a group of items because the individual forecast errors for each item tend to cancel each other.
Reference: Key Decisions on Making Forecasts
Difficulty: Moderate
Keywords: aggregate forecast accuracy
159.Which forecasting technique would you consider for technological forecasts?
Answer: I would consider the Delphi method because technological change takes place at a rapid pace and often the only way to make forecasts is to get the opinion of experts who devote their attention to those issues.
Reference: Judgment Methods
Difficulty: Moderate
Keywords: technological forecasts, Delphi method
160.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?
Answer: The answer depends on whether Pho Bulous believes that last month’s and this month’s results are aberrations or the start of something new. Both causal and time series techniques assume that there has been no change in how the world works, that is, independent factors of time or other variables will permit the forecaster to make accurate predictions about the future. If Pho Bulous believes that there is a significant change in the system, for example, a new competitor in the Edmond restaurant scene, a significant change in population or in their disposable income, then they might try multiple regression to include these factors or weight more recent data more heavily in a time-series model (the scenario isn’t specific about which technique they have used thus far). Pho Bulous might also try a combination approach if they feel their situation has changed significantly. On the other hand, if Pho Bulous feels that these two months are not reflective of any major paradigm shift for the restaurant crowd in Edmond, they could continue to use the model(s) they have had success with in the past.
Reference: Multiple Sections
Difficulty: Moderate
Keywords: forecast accuracy
161.Explain how the value of alpha affects forecasts produced by exponential smoothing.
Answer: The smoothing constant alpha allows recent demand values to be emphasized or deemphasized depending on how the forecaster wishes to incorporate previous values. Larger values emphasize recent levels of demand and result in forecasts more responsive to changes in the underlying average. Smaller alpha values treat past demand more uniformly and result in more stable forecasts.
Reference: Time-Series Methods
Difficulty: Moderate
Keywords: alpha value, exponential smoothing
162.What is the difference between mean absolute deviation (MAD) and mean squared error (MSE)?
Answer: Both MAD and MSE are measurements of the amount of forecast error, and smaller values of both metrics reflect superior forecasting methods. The difference between the two is that MAD places less emphasis on an outlier while MSE is more sensitive to one. A forecast technique that seeks to minimize MSE will have overall forecast accuracy hurt by one extreme outlier more than a forecast developed using a MAD-minimizing technique.
Reference: Choosing a Time-Series Method
Difficulty: Moderate
Keywords: MAD, MSE, mean absolute deviation, mean squared error
163.How is a typical forecasting process similar to the Plan-Do-Study-Act (PDSA) cycle (See Chapter 5 for more information on PDSA)?
Answer: The authors indicate that forecasting is a process that should be continually reviewed for improvements; the PDSA cycle provides one vehicle for continuous improvement. The authors present a six step cycle for forecasting: 1) adjust the history file, 2) prepare initial forecasts, 3) consensus meetings and collaboration, 4) revise forecasts, 5) review by the operating committee, and 6) finalize and communicate the forecasts. The history file adjustment in step 1 provides a check of forecast accuracy; if results have been less than stellar, then planners and forecasters will explore different techniques and/or independent variables to prepare future forecasts. This approach closely parallels the PDSA cycle of methodically trying a new approach and checking results before acting system-wide.
Reference: Putting It All Together: Forecasting as a Process
Difficulty: Moderate
Keywords: forecasting process, Plan-Do-Study-Act, PDSA
164. What are some of the principles organizations can observe to improve their forecasting process?
Answer: (See Table 13.2 in the text.) Some principles organizations can observe to improve their forecasting process include:
1. Better processes yield better forecasts
2. Demand forecasting is being done in virtually every company, either formally or informally. The challenge is to do it well—better than the competition.
3. Better forecasts result in better customer service and lower costs, as well as better relationships with suppliers and customers.
4. The forecast can and must make sense based on the big picture, economic outlook, market share, and so on.
5. The best way to improve forecast accuracy is to focus on reducing forecast error.
6. Bias is the worst kind of forecast error—strive for zero bias.
7. Whenever possible, forecast at more aggregate levels. Forecast in detail only where necessary.
8. Far more can be gained by people collaborating and communicating well than by using the most advanced forecasting technique or model.
Reference: Putting It All Together: Forecasting as a Process
Difficulty: Moderate
Keywords: forecasting process, principles
PROBLEMS
165.Calculate three forecasts using the following data. First, for periods 4 through 10, develop the exponentially smoothed forecasts using a forecast for period 3 (F3) of 45.0 and an alpha of 0.4. Second, calculate the three-period moving-average forecast for periods 4 through 10. Third, calculate the weighted moving average for periods 4 through 10, using weights of .70, .20, and .10, with 0.70 applied to the most recent data. Calculate the mean absolute deviation (MAD) and the cumulative sum of forecast error (CFE) for each forecasting procedure. Which forecasting procedure would you select? Why?
Month Demand
1 45
2 48
3 43
4 48
5 49
6 54
7 47
8 50
9 46
10 47
Answer:
Month Demand Exponential Error Absolute Deviation Simple Moving Average Error Absolute Deviation Weighted Moving Average Error Absolute Deviation
Smoothing
1 45
2 48
3 42 45.00
4 48 43.80 4.20 4.20 45.00 3.00 3.00 43.5 4.50 4.50
5 49 45.48 3.52 3.52 46.00 3.00 3.00 46.8 2.20 2.20
6 54 46.89 7.11 7.11 46.33 7.67 7.67 48.1 5.90 5.90
7 47 49.73 -2.73 2.73 50.33 -3.33 3.33 52.4 -5.40 5.40
8 50 48.64 1.36 1.36 50.00 0.00 0.00 48.6 1.40 1.40
9 46 49.18 -3.18 3.18 50.33 -4.33 4.33 49.8 -3.80 3.80
10 47 47.91 -0.91 0.91 47.67 -0.67 0.67 46.9 0.10 0.10
Sum 9.37 23.02 5.33 22.00 4.90 22.20
Mean 1.34 3.29 0.76 3.14 0.70 3.33
Exponential Simple Moving Average Weighted Moving Average
Smoothing
CFE 9.37 5.33 4.90
MAD 3.29 3.14 3.33
Using MAD, the simple moving average is best. However, the weighted moving average does better on CFE.
Reference: Multiple sections
Difficulty: Moderate
Keywords: time-series forecast, exponential smoothing forecast, simple moving average forecast, weighted moving average forecast, MAD, mean absolute deviation, CFE, cumulative forecast error
提升卡
置顶卡
沉默卡
变色卡
抢沙发
千斤顶
显身卡
京公网安备 11010802022788号
