Moving average of order N is the arithmetic average for the most recent N observations Exponential smoothing method: It combines both demand and forecast. The Current forecast is the weighted average of the last forecast and the current demand 1.1.2 Trend based methods: Regression Analysis: fits straight line to a set of data Holt’s Method : type of double exponential smoothing. Allows for simultaneous smoothing on the series and on the trend 1.2 Qualitative methods: In forecasting for non-standardized
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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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SCM 404 Demand Fulfillment Spring 2014 1. Implied Demand Uncertainty (IDU) has important implications for the structure and performance of a supply chain. Consider the table below from class on 1/9/13. For each customer characteristic or need‚ explain the meaning of the “+” or “-“ and explain why that characteristic has that effect. (3 points) Customer Need Impact on IDU Quantity of individual order + Response time (customer desired lead time) - Variety of products + Service level
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************************* MIDTERM I ***************************** 1. Operations Management is attracting increasing attention due to the recent trends in the industrial world. Which of the following statements correctly represents this recent change? a. The percentage of the world’s output produced by the U.S. has been steadily increasing since 1960. b. Compared to the other industrial countries such as Italy and France‚ the U.S. has a higher rate of productivity growth since 1960
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charts‚ graphs‚ etc.‚ may be added to the five page narrative to enhance your discussion. See “Case Analysis and Presentation” for guidance 1. Investigate the potential to apply various time series forecasting approaches‚ including exponential smoothing. 2. Discuss the benefits and limitations of time series forecasting for this application. 3. Introduce the challenges involved in managing reusable inventories that spend most of their time in the possession of customers.
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Nicole-line breaks mean new slide important questions Forecasts are needed to predict demand all different teams within the company need the forecast different users have different time requirements and detail reqts you might have to collect more data if you don’t have enough cost depends on the scope of the project need to engage the users‚ so have to provide a feedback system The top chart appears to be a ore difficult to forecast but they just narrowed the y axiz 2nd chart down
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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 .97 0.00 1‚251.26 2‚386‚650 .03 It is obvious that the superior
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PROBLEM 4.1 A) (374 + 368 + 381) / 3 = 374.33 Pints B) Forecast: (381 x .1) = 38.1 (368 x .3) = 110.4 (374 x .6) = 224.4 38.1 + 110.4 + 224.4 = 372.9 Pints C) Week Of Pints Used Forecast with exponential smoothing applied 31st Aug 360 360.00 7th Sep 389 360.00 14th Sep 410 365.80 21st Sep 381 374.64 28th Sep 368 375.91 5th Oct 374 374.33 12th Oct 374.26 Forecast : 374.26 Pints PROBLEM 4.5 A) (3700 + 3800) / 2 = 3750 Miles
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a relation in which each element of the domain is paired with exactly one element in the range. Two types of functions are the exponential functions and the logarithmic functions. Exponential functions are the functions in the form of y = ax‚ where ’’a’’ is a positive real number‚ greater than zero and not equal to one. Logarithmic functions are the inverse of exponential functions‚ y = loga x‚ where ’’a’’ is greater to zero and not equal to one. These functions have certain differences as well as
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Exponential Smoothing Forecasting Method with Naïve start Formula: Ft = α (At-1) + (1 – α) (Ft – 1) where: Ft Forecast for time t Ft – 1 Past forecast; 1 time ahead or earlier than time t At-1 Past Actual data; 1 time ahead or earlier than time t α (read as alpha) as a smoothing constant takes the
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