page 106 (Chapter 3) of Text Book Heizer‚Render & Rajashekhar (a) Using exponential smoothing‚ with α = .6‚ then trend analysis‚ and finally linear regression discuss which forecasting model fits best for Salinas’s strategic plan. Justify the selection of one model over another. Answer: We have done forcasting using exponential smoothing and linear regression methods. Below are the forcast values: Method Exponential smoothing MAD 3.5 Linear Regression 10.6 Year 1 1 2 3 4
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emphasis (weight) Weighted Moving Average Uses an average of a specified number of the most recent observations‚ with each observation receiving a different emphasis (weight) Exponential Smoothing A weighted average procedure with weights declining exponentially as data become older Trend Adjusted Exponential Smoothing An exponential smoothing model with a mechanism for making
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maintaining and repairing a car? (Ans: 374) 4. The time to failure of a component in an electronic device has an exponential distribution with a median of four hours. Calculate the probability that the component will work without failing for at least five hours. (Ans: 0.42) 5. A company has two electric generators. The time until failure for each generator follows an exponential distribution with mean 10. The company will begin using the second generator immediately after the first one fails
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JGT Task 3 MEMORANDUM To: Alistair Wu‚ Plant Operating Director Cynthia Crowninshield VP From: Holly Lindsay Date: 10/26/2014 A. In this task‚ we were asked to decide which method Shuzworld should consider for the manufacturing of its sneakers at all possible volumes of output. The possible methods to be considered are: Reconditioning the plant equipment Purchasing new equipment Outsourcing to other manufacturing operations The figures for fixed and variable costs for each section were derived
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A short introduction to the Arena simulation software Version 1.0 dr. Kees Jan Roodbergen dr. Iris F.A. Vis © 2007 ©2007‚ K.J. Roodbergen and I.F.A. Vis All rights reserved. No part of this publication or the related models may be reproduced in any form or by any means without prior permission of the authors. 1 Contents 1 2 Introduction ................................................................................................................ 6 Terminology .................
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Time Series Models for Forecasting New One-Family Houses Sold in the United States Introduction The economic recession felt in the United States since the collapse of the housing market in 2007 can be seen by various trends in the housing market. This collapse claimed some of the largest financial institutions in the U.S. such as Bear Sterns and Lehman Brothers‚ as they held over-leveraged positions in the mortgage backed securities market. Credit became widely available to unqualified borrowers
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levels for a product: Time Period (t) Actual Demand (A1) Forecast Demand (F1) 1 50 50 2 42 50 3 56 48 4 46 50 5 49 The first forecast F1 was derived by observing A1 and setting F1 equal to A1. Subsequent forecasts were derived by exponential smoothing. Using the exponential smoothing method‚ find the forecast time for period 5. (Hint: You need to first find the smoothing constant‚ α.) To find α: 50= 50 + α(42-50) -8α = -2 α = 0.25 F5 = 50 + 0.25(46 – 50) F5 = 49
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MANAGEMENT RESEARCH PROJECT INTERIM REPORT ON Analyze Big Bazaar’s customer queues at cash counter and reducing customer waiting time by proposing the optimum number of cash counter. Submitted By: Ravi Kumar Mishra Enroll no: 07BS3347 Batch: (2007-09) ICFAI BUSINESS SCHOOL‚ LUCKNOW
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“American contribution to the growth of the global economy from 1945 to 1973 has been over-rated.” How far do you agree? The global economy from 1945 to 1973 grew at an astounding rate even though many of the countries had been badly affected after World War II (WII) in 1945. Many factors have resulted in the explosive growth‚ and the role of the Americans is one of them. The Bretton Woods System and multi-national companies (MNCs) have also contributed to the growth. However‚ I do not agree that
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(b) [i] Naive The coming January = December = 23 [ii] 3-month moving (20 + 21 + 23)/3 = 21.33 [iii] 6-month weighted [(0.1 17) + (.1 18) + (0.1 20) + (0.2 20) + (0.2 21) + (0.3 23)]/1.0 = 20.6 [iv] Exponential smoothing with alpha = 0.3 [v] Trend Forecast = 15.73 + .38(13) = 20.67‚ where next January is the 13th month. (c) Only trend provides an equation that can extend beyond one month 4.23 Students must determine the naive forecast for the four months
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