Choose one of the forecasting methods and explain the rationale behind using it in real life. I would choose to use the exponential smoothing forecast method. Exponential smoothing method is an average method that reacts more strongly to recent changes in demand than to more distant past data. Using this data will show how the forecast will react more strongly to immediate changes in the data. This is good to examine when dealing with seasonal patterns and trends that may be taking place. I would
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data. 6. Make the forecast. 7. Validate and implement results. Forecasting Methods 1. Qualitative Method- Used when a situation is vague and little data exist. Used for new products and new technology. Involves intuition‚ experience. E.G.‚ forecasting sales on Internet. a. Jury of executive opinion: Pool opinions of high-level experts‚ sometimes augmented by statistical models. b. Delphi Method: Panel of experts‚ queried iteratively (questions you keep doing) c. Sales force composite:
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ISQS 3344 Test 1 review Chapter 1 What employers want The ability to think cross functionally Working in teams and collaborative learning Increase productivity and knowledge by 50% Operations management- the science and art of ensuring that goods and services are created and delivered successfully to customers. Planning Directing Controlling Organizing Government regulations- California 2006 Increase mpg standard for all vehicles or pay fine Lots of hybrids sold but companies
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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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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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output‚ t = -.412 and p = .689. A stationary model seems appropriate since the linear term‚ Period‚ is not significant. 7.1 c. Forecast for January -- 19‚ for upcoming year – 12*19 = 228 7.1 d. Forecast for January -- 20.4 e. 4 month moving average. MAD is 1.72 7.2 See files Ch7.2a.xls and Ch7.2b.xls a. Forecast for January -- 18.86 7.2 b. See file Ch7.2b.xls Forecast for January -- 20.28 c. = .6 gives the lower MSE 7.3 See file Ch7.3.xls a. b. Coefficients
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Step 4 Gather and analyze data Step 3 Select a forecasting technique Step 2 Establish a time horizon Step 1 Determine purpose of forecast A PPROACHES TO FORECASTING Q ualitative methods: c onsist mainly of s ubjective i nputs‚ which often d efy p recise numerical description . Quantitative methods: I nvolve either the projection of h istorical data o r the d evelopment of a ssociative models t hat attempt to u tilize c asual (explanatory ) variables to make a forecast. Quantitative
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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
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000 20X3 $6‚000‚000 20X4 $6‚750‚000 For moving averages and weighted moving averages‚ use only the data for the past three fiscal years. For weighted moving averages‚ assign a value of 1 to the data for 20X2‚ a value of 2 to the data for 20X3‚ and a value of 3 to the data for 20X4. Forecast personnel expenses for fiscal year 20X5 using moving averages‚ weighted moving averages‚ exponential smoothing‚ and time series regression. Moving Averages Fiscal Year Expenses 20X2 $5
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years: 20 X 1 = $5‚250‚000 20 X 2 = $5‚500‚000 20 X 3 = $6‚000‚000 20 X 4 = $6‚750‚000 Forecast personnel expenses for fiscal year 20X5 using moving averages weighted moving averages‚ exponential smoothing‚ and time series regression. For moving averages and weighted moving averages‚ use only the data for the past three fiscal years. For weighted moving averages‚ assign a value of 1 to the data for 20 X 2‚ a value of 2 to the data for 20 X 3‚ and a value of 3 to the data for 20 X 4. For exponential
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