Introduction to O&SCM -- Chapter 1 • Definitions • Operations and supply chain management (OSCM) is defined as the design‚ operation‚ and improvement of the systems that create and deliver the firm’s primary products and services • Concerned with the management of the entire system that produces a product or delivers a service • Operations refers to manufacturing and service processes that are used to transform the resources employed by a firm into products desired by customers •
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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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The following data represent total personnel expenses for the Palmdale Human Service Agency for past four fiscal years: 20X1 $5‚250‚000 20X2 $5‚500‚000 20X3 $6‚000‚000 20X4 $6‚750‚000 Moving Averages 20X2-X4 $18‚250‚000 / 3 = $6‚083‚333 Weighted Moving Averages Fiscal Year Expenses Weight Weighted Score 20X2 $5‚500‚000 1 $5‚500‚000 20X3 $6‚000‚000 2 $12‚000‚000 20X4 $6‚750‚000 3 $20‚250‚000 __ ___________ 6 $37‚750‚000 20X5
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that past patterns in data can be used to forecast future data points. 1. Moving averages (simple moving average‚ weighted moving average): forecast is based on arithmetic average of a given number of past data points 2. Exponential smoothing (single exponential smoothing‚ double exponential smoothing) - a type of weighted moving average that allows inclusion of trends‚ etc. 3. Mathematical models (trend lines‚
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periods into the forecast and “smoothes” the data. Averaging models are computed by averaging data from several time periods and using the average as the forecast for the next time period. A moving average is an average that is updated or recomputed for every new time period being considered. The most recent information is utilized in each new moving average. This advantage is offset by the disadvantages that (1) it is difficult to choose
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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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stormrelated sales. Appendix 18.1 Forecasting with Minitab In this appendix we show how Minitab can be used to develop forecasts using three forecasting methods: moving averages‚ exponential smoothing‚ and trend projection. Moving Averages CD file Gasoline To show how Minitab can be used to develop forecasts using the moving averages method‚ we will develop a forecast for the gasoline sales time series in Table 18.1 and Figure 18.5. The sales data for
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Index Cover Page 1 1. Executive Summary 3 2. Background 3 3. Issue Statement 4 4. Analysis of the problem 4-9 1. Moving Average 4-6 2. Holt Winters’ Exponential Smothing 6-7 3. Simple Average 7 3. Exponential Smothing 8-9 5. Recommandations 10 6. References 11 Executive Summary In the given case study‚ Snow the revenue manager of the Hamilton hotel has to make a decision which is to accept the group of not for 22nd August. As it is a business hotel and generally it
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600 | a. Use a 2-period moving average to forecast the population of the United States in 2003. [pic] b. Use a 3-period moving average to forecast the population of the United States in 2003 c. Which averaging period provides a better historical fit based on the MAD criterion? [pic] 2. Refer to the data provided in problem 1. Use a 3-period weighted moving average to forecast the population of the United States in 2003.
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A PROJECT REPORT ON DEMAND FORECASTING OF RETAIL SUPPLY CHAIN MANAGEMENT USING STATISTICAL ANALYSIS By AVINASH KUMAR SONEE 2005B3A8582G KRISHNA MOHAN YEGAREDDY 2006B3PS704P AT HETERO MED SOLUTIONS LIMITED Madhuranagar‚ Hyderabad A Practice School–II station of [pic] BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE‚ PILANI DECEMBER‚ 2009 A PROJECT REPORT On DEMAND FORECASTING OF RETAIL SUPPLY CHAIN MANAGEMENT USING STATISTICAL ANALYSIS by AVINASH KUMAR SONEE - (M
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