best alpha and beta weights in exponential smoothing. Even if a firm has 10‚000 products‚ the constants can be selected very quickly and easily without human intervention. Week 1 2 3 4 5 6 7 Actual Bicycle Sales 8 10 9 11 10 13 — Three-Week Moving Average (8 10
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Lisa Brown Hsm/ 260 Week 5 – Forecasting Checkpoint 3/8/13 Exercise 9.1 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 $37‚750‚000 /6 = $6‚291‚667 Exponential Smoothing NF = $6‚300‚000
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Develop a 3-year moving average to forecast sales. b. Then estimate demand again with a weighted moving average in which sales in the most recent year are given a weight of 3 and a weight of 2 for the second past year and sales in the other 2 years are each given a weight of 1. c. Which method do you think is best? In this case‚ the 3 year moving average is the better method as the Mean Absolute Deviation (MAD) is only 3.042 as compared to 3.347 for the weighted moving average method. What
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PERENCANAAN & PENGENDALIAN PRODUKSI TIN 4113 Pertemuan 2 • Outline: – – – – – Karakteristik Peramalan Cakupan Peramalan Klasifikasi Peramalan Metode Forecast: Time Series Simple Time Series Models: • Moving Average (Simple & Weighted) • Referensi: – Smith‚ Spencer B.‚ Computer Based Production and Inventory Control‚ Prentice-Hall‚ 1989. – Tersine‚ Richard J.‚ Principles of Inventory and Materials Management‚ Prentice-Hall‚ 1994. – Pujawan‚ Demand Forecasting Lecture Note‚ IE-ITS‚ 2011
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forecast error: Average error Mean absolute deviation (MAD) Average absolute error Mean squared error (MSE) Average of squared error Mean Absolute Percent error (MAPE) Tracking signal Ratio of cumulative error and MAD Time Series Forecasting Naïve (Just move the At value over 1 and down 1 to the Ft column) Moving Average Weighted Moving Average Exponential Smoothing Trend Adjusted Forecasting Moving Average N=3 (493+498+492)/3=494.33 Weighted Moving Average .2‚ .3‚.5 (.2*493)+(
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phone is required to be off during the test. A basic calculator is allowed. 1. Use a 3-period simple moving average to develop a forecast for year 6. Year 2 3 4 5 6 a. b. c. d. e. $415 $445 $525 $605 $625 Sales $450 $495 $518 $563 $584 Forecast 2. Data collected on the annual demand for 50-pound bags of fertilizer at Pikes Garden Supply is shown below. Use a 3-year weighted moving average to forecast sales for year 6‚ where the weights are 0.5‚ 0.3‚ and 0.2‚ respectively (where 0.5 is the weight
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quarters. Based on data analysis the best method for forecasting Highline Financial Services for the upcoming year would be the Moving Average (McNamara‚ 2012). The Moving Average offers the lowest Mean Absolute Deviation ( MAD)‚ lowest means squared error (MSE)‚ and the lowest mean absolute percent error (MAPE) of the two choices selected to forecast. The weighted moving average was not utilized due to the amount of data provide. The ability to
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1. INTRODUCTION 1.1 Company Profile Toyota Motor‚ the world’s largest automotive manufacturer (overtaking GM in 2008)‚ designs and manufactures a diverse product line-up that includes subcompacts to luxury and sports vehicles‚ as well as SUVs‚ trucks‚ minivans‚ and buses. Its vehicles are produced either with combustion or hybrid engines‚ as with the Prius. Toyota’s subsidiaries also manufacture vehicles: Daihatsu Motor produces mini-vehicles‚ while Hino Motors produces trucks and buses. Additionally
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Part 3 : Acquisition & Production Support. Ch.3 Demand Forecasting. Edited by Dr. Seung Hyun Lee (Ph.D.‚ CPL) IEMS Research Center‚ E-mail : lkangsan@iems.co.kr Demand Forecasting. [Other Resource] Definition. ․ An estimate of future demand. ․ A forecast can be determined by mathematical means using historical‚ it can be created subjectively by using estimates from informal sources‚ or it can represent a combination of both techniques. - 2 - Demand Forecasting. [Other
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PROBLEM 4.9 A) Month Price per Chip ($) 2-month moving average January 1.8 February 1.67 March 1.7 1.735 April 1.85 1.685 May 1.9 1.775 June 1.87 1.875 July 1.8 1.885 August 1.83 1.835 September 1.7 1.815 October 1.65 1.765 November 1.7 1.675 December 1.75 1.675 January 1.725 B) Month Price per Chip ($) 3-month moving average January 1.8 February 1.67 March 1.7 April 1.85 1.72 May 1.9 1.74 June
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