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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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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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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previous forecast value of 99‚ and an alpha of .4‚ the exponential smoothing forecast for the next period would be FCT 2 A forecasting method has produced the following over the past five months. What is the mean absolute deviation (MAD) ActualForecastErrorError1011-11810-22108 2266 0098 11 FCT 3 What is the forecast for May based on a weighted moving average applied to the following past demand data and using the weights .5‚ .3‚ .2 (largest weight is for most recent data) Nov.Dec.Jan.Feb.Mar
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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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that indicates how much on average each of the values in the distribution deviates from the mean (or center) of the distribution. Bollinger Bands‚ created by John Bollinger in the 1960s‚ is an indicator that uses this statistical measure to determine support and resistance levels. This indicator consists of three lines and is very simple to derive; the middle line is a simple moving average of the underlying price data and the two outside bands are equal to the moving average plus or minus one standard
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