x 950 1013 907 2005 1160 960 1025 1136 980 2006 1200 1032 1112 1158 1034 2007 1150 1087 1170 1196 1084 2008 1270 1137 1170 1155 1104 2009 1290 1186 1207 1259 1154 2010 x 1214 1236 1287 1195 B) Five-year moving average = 141.9 Three-year moving average = 78.6 Exponential smoothing (w = .9) = 45.7 Exponential smoothing (w = .3) = 110.9 C) I would use the exponential smoothing w=.9 because of the trending factor 6) A) In 2010 = 11450 units B) Sales would go from 11450
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Chapter 1 Introduction to Operations Management True/False 1. Operations managers are responsible for assessing consumer wants and needs and selling and promoting the organization’s goods or services. Answer: False Page: 4 Difficulty: Easy 2. Often‚ the collective success or failure of companies’ operations functions will impact the ability of a nation to compete with other nations. Answer: True Page: 4 Difficulty: Easy 3. Companies are either producing
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forecasting methods In using simple exponential smoothing‚ what do we do if we do not have a forecast for the first period Which component of time series do we smoothen with exponential smoothing With moving averages As a forecasting technique‚ is exponential smoothing always better than moving averages What happens when we increase alpha EMBED Equation.DSMT4 Are we giving more or less
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E-mail: ravigor@hotmail.com Contents Introduction Some applications of forecasting Defining forecasting General steps in the forecasting process Qualitative techniques in forecasting Time series methods The Naive Methods Simple Moving Average Method Weighted Moving Average Exponential Smoothing Evaluating the forecast accuracy Trend Projections Linear Regression Analysis Least Squares Method for Linear Regression Decomposition of the time series Selecting A Suitable Forecasting Method More on Forecast
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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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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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for patterns in data -pattern vs noise‚ and noise is random and has zero average ideally -in real world. Noise is random but probably not with zero mean -time series usually decomposed into different affects (seasonality‚ tend‚ noise‚ etc) d(t) =(L+ back fit your data to see if you have a good forecast what would you do 1. Plot the data in excel forecast approaches were in order with charts moving average-look at last few periods and update each new period with the new data (ie
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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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|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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