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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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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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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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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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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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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Forecasting Models: Associative and Time Series Forecasting involves using past data to generate a number‚ set of numbers‚ or scenario that corresponds to a future occurrence. It is absolutely essential to short-range and long-range planning. Time Series and Associative models are both quantitative forecast techniques are more objective than qualitative techniques such as the Delphi Technique and market research. Time Series Models Based on the assumption that history will repeat itself‚
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A: uniform distribution Random numbers generated by a _ process instead of a _process are psudorandom numbers. A: mathematical/physical 200 imulations runs were completed using the probability of a machine breakdown from the table below . the average number of breakdowns from the simulation trials was 1.93 with a standard deviation of .20. A: .71 Which of the following possible values
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Forecasting Methodology Forecasting is an integral part in planning the financial future of any business and allows the company to consider probabilities of current and future trends using existing data and facts. Forecasts are vital to every business organization and for every significant management decision. Forecasting‚ according to Armstrong (2001)‚ is the basis of corporate long-run planning. Many times‚ this unique approach is used not only to provide a baseline‚ but also to offer a prediction
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