3. T F The 3 categories of forecasting models are time series‚ quantitative‚ and qualitative. 4. T F Time-series models attempt to predict the future by using historical data. 5. T F A moving average forecasting method is a causal forecasting method. 6. T F An exponential forecasting method is a time-series forecasting method. 7. T F The Delphi method solicits input from customers or potential customers regarding
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| Chapter 14 Questions | | 3. Many companies take customer orders via Web sites. Put yourself in the place of the person at Ford Motor Company considering this approach to taking customer orders for the Ford Explorer sport utility vehicle. | What information would you need to collect from the customer? | I would collect information on the exact specifications of the Ford Explorer such as Model Year‚ engine capacity‚ interior finishing‚ power windows‚ power steering‚ and any luxury
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method 1.moving averages 1.regression analysis 2.Opinion poll 2.exponential smoothing 2.multiple regression 3.Historical Analogy 3.econometric models 4.Field Surveys 5.Business barometers 6.Extrapolation Technique 7.Input-Out put Analysis 8.Lead Lag Analysis 9.Sales force composites 10.Consumer Market survey Simple Average Method The historical data is used for extrapolating and forecasting. Either simple averages or moving averages could be
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Sheet “Population of Arizona” in HW 2 perform the following functions in Excel and answer the following questions. a. Use a 2 period moving average to forecast the Population of Arizona for the year 2010 – do the calculations from 1929-2010. (5 Points) b. Calculate the Mean Absolute Deviation for this Data Set (5 Points) c. Use a 3 period weighted moving average (previous Year 60%‚ 2 years Previous 30% and 3 years previous 10%) to forecast the Population of Arizona for the year 2010 – do the calculations
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Forecasting Methodology Forecasting is an integral part in planning the financial future of any business and allows the company to consider probabilities of current and future trends using existing data and facts. Forecasts are vital to every business organization and for every significant management decision. Forecasting‚ according to Armstrong (2001)‚ is the basis of corporate long-run planning. Many times‚ this unique approach is used not only to provide a baseline‚ but also to offer a prediction
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|56 |55 |60 | (a) Forecast the demand for pizza for Week 4‚ 5‚ and 6 using a naïve method. (b) Forecast the demand for pizza for Week 4‚ 5‚ and 6 using the simple moving average method with n = 3. (c) Repeat the forecast for Week 4‚ 5‚ and 6 by using the weighted moving average method with n = 3 and weights of 0.50‚ 0.30‚ and 0.20 with 0.50 applying to the most recent demand. (d) Calculate the MAD and MSE for each method. Q-2. The monthly demand for units manufactured
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Actual Month Demand 1 62 2 65 3 68 4 70 5 72 6 74 a. Calculate the simple 3-month moving average forecast for periods 4-6. (5 points) b. Calculate the weighted 3-month moving average using weights of 0.50‚ 0.30‚ and 0.20 for periods 4-6. (5 points) c. Calculate the exponential smoothing forecast for periods 2-6 using an initial forecast (F1) 62‚ and an of 0.30. (10 points) d.
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Altavox Excel Data (1) Week 1 2 3 4 5 6 7 8 9 10 11 12 13 Average Atlanta 33 45 37 38 55 30 18 58 47 37 23 55 40 40 Boston 26 35 41 40 46 48 55 18 62 44 30 45 50 42 Chicago 44 34 22 55 48 72 62 28 27 95 35 45 47 47 Dallas 27 42 35 40 51 64 70 65 55 43 38 47 42 48 Los Angles 32 43 54 40 46 74 40 35 45 38 48 56 50 46 Total 162 199 189 213 246 288 245 204 236 257 174 248 229 222 Altavox Data (2) Week -5 -4 -3 -2 -1 Atlanta 45 38 30 58 37 Boston 62 18 48 40 35 Chicago 62 22 72 44 48
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Forecasting Models: Associative and Time Series Forecasting involves using past data to generate a number‚ set of numbers‚ or scenario that corresponds to a future occurrence. It is absolutely essential to short-range and long-range planning. Time Series and Associative models are both quantitative forecast techniques are more objective than qualitative techniques such as the Delphi Technique and market research. Time Series Models Based on the assumption that history will repeat itself‚
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Study Guide for the Second Exam Aggregate Production Planning (APP) 1. What are the major inputs‚ constraints‚ and outputs of the aggregate production plan (APP)? 2. Does APP have to be in terms of a real product? 3. Where does APP fit in the hierarchy of plans? 4. What is a pure strategy? What is a mixed strategy? Give examples? How do we determine (judge) whether one plan is better than the other? 5. What is relevant (incremental) cost? Does it exist in accounting
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