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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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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Case study: Forecasting at Hard Rock Café 1. Hard Rock uses a 3- year weighted moving average to evaluate to evaluate managers and set bonuses and determine the café sales. A moving average is also used in which they applied 20% to sales 2 years ago. Using multiple regression‚ managers can compute the impact on demand of other menu items if the price of one item is changed. The three other areas which we think Hard Rock could use forecasting models are: • Computerized Scheduling
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influence (Multiplicative Model) 2 Smoothing Methods Smoothing methods are used to average out the irregular components of the time series in cases where the time series: is fairly stable‚ and has no significant trend‚ seasonal‚ or cyclical effects. • • Four common smoothing methods: 1) 2) 3) 4) Moving Average Weighted Moving Averages Exponential Smoothing Centered Moving Average (not for forecasting as we will see later – only a process to lead to forecasting) Measures
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Z-10 mountain bike‚ with monthly sales as show in the table. First‚ co-owner Amit wants to forecast by exponential smoothing by initially setting February’s forecast equal to January’s sales with α=1. Co-owner Barbara wants to use a three-period moving average. 1. Is there a strong lineal trend in sales over time? 2. Fill in the table with what Amit and Barbara each forecast for May and the earlier months‚ as relevant. 3. Assume that May’s actual sales figure turns out to be 405. Complete
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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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Chapter 4: Multiple Choice Questions 1. Forecasts a. become more accurate with longer time horizons b. are rarely perfect c. are more accurate for individual items than for groups of items d. all of the above e. none of the above One purpose of short-range forecasts is to determine a. production planning b. inventory budgets c. research and development plans d. facility location e. job assignments Forecasts are usually classified by time horizon into three categories a. short-range‚ medium-range
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x 950 1013 907 2005 1160 960 1025 1136 980 2006 1200 1032 1112 1158 1034 2007 1150 1087 1170 1196 1084 2008 1270 1137 1170 1155 1104 2009 1290 1186 1207 1259 1154 2010 x 1214 1236 1287 1195 B) Five-year moving average = 141.9 Three-year moving average = 78.6 Exponential smoothing (w = .9) = 45.7 Exponential smoothing (w = .3) = 110.9 C) I would use the exponential smoothing w=.9 because of the trending factor 6) A) In 2010 = 11450 units B) Sales would go from 11450
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forecasting methods In using simple exponential smoothing‚ what do we do if we do not have a forecast for the first period Which component of time series do we smoothen with exponential smoothing With moving averages As a forecasting technique‚ is exponential smoothing always better than moving averages What happens when we increase alpha EMBED Equation.DSMT4 Are we giving more or less
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E-mail: ravigor@hotmail.com Contents Introduction Some applications of forecasting Defining forecasting General steps in the forecasting process Qualitative techniques in forecasting Time series methods The Naive Methods Simple Moving Average Method Weighted Moving Average Exponential Smoothing Evaluating the forecast accuracy Trend Projections Linear Regression Analysis Least Squares Method for Linear Regression Decomposition of the time series Selecting A Suitable Forecasting Method More on Forecast
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