predictions of automobile sales in the US for the month of March 2012. The prediction is to take into account the historic data (provided) and current marketing environment. At first‚ two approaches of the analytical (quantitative) method were used – moving average and exponential smoothing. The objective of doing so was to get an idea of the prediction based on historic data only. Once that was done‚ the marketing environment was taken into consideration - to see how it would effect the predictions made
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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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EXAM REVIEW WEEK ONE Chapters 1‚ 2‚ and 6 1. Describe the main elements of an “Operations Systems” model. a. The main elements of an Operations Systems model are the inputs‚ that go through the transformation process‚ then they become outputs. There is also the planning and control subsystem which is the feedback mechanism. 2. What are the primary differences between manufacturing and service operations? b. There are 5
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DEMAND FORECASTING Demand forecasting is the process of predicting future average sales on the basis of historical data samples and market intelligence. The volatility of demand from an average level is supplied from the safety inventory. Any forecast is likely to be wrong‚ so the focus should be on understanding the range of potential forecast errors and the level of safety inventory that will cater for peak demand. An important additional calculation is forecast bias. This is the cumulative
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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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Littlefield Technologies Game Strategy- Group 28 I. PROJECT MANAGEMENT: We can apply multiple project management concepts to planning the project‚ scheduling the project‚ and controlling the project. First‚ the project was planned and scheduled by setting a goal of completion. Considering the group’s total allotted time‚ our goal was to have the description of the game strategy completed 48 hours before the deadline‚ and to work collaboratively on the statistical spreadsheet 24 hours before the
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Forecasting Forecast can help managers by reducing some of the uncertainty‚ thereby enabling them to develop more meaningful plans than they might otherwise. A forecast is a statement about the future. Features common to all forecasts 1. The same underlying causal system that existed in the past will continue to exist in the future. 2. Forecasts are rarely perfect; actual results usually differ from predicted values. 3. Forecasts for groups of items tend to be more accurate than forecasts
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1.From your knowledge of restaurant‚ from the video‚ from the Global Company Profile that opens this chapter‚ and from the case itself. Identify how each of the 10 decisions of operations management is applied at Hard Rock Cafe? 1) Design of goods and service Hard Rock Cafe us famous for foods from classic American -burgers and chicken wings- . So they try to be good in service to customers and always modifying the menu. The experience concept is to provide not only a custom meal from the menu
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correlation coefficient‚ bias‚ mean absolute deviation (MAD)‚ mean squared error (MSE)‚ and mean absolute percent error (MAPE) are shown. Correlation Bias MAD MSE MAPE Naïve -- 541.38 6865.52 69‚856‚200 .19 Moving Average (3 periods) -- 491.36 6‚138.27 59‚540‚560 .17 Weighted Moving Average (3 period; .6‚ .3‚ .1) -- 424.81 6‚501.58 61‚107‚180 .18 Exponential smoothing (alpha = 0.5) -- 794.28 5‚880.56 50‚755‚960 .16 Trend Analysis .54 0.00 4‚355.70 31‚285‚700 .12 Seasonal Additive Decomposition
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1. (24 points) If needed‚ additional workspace is provided on the next sheet. Doug Moodie is the president of Garden Products Limited. Over the last 5 years‚ his vice president of marketing has been providing the sales forecast using his special “focus” forecasting technique. The actual sales for the past ten years and the forecasts from the vice president of marketing are given below. |Year |Sales |VP/Marketing
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