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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suitable technique to generate the forecast of unemployment rate using data from the series of Labour Force Surveys. The models understudied are based on Univariate Modelling Techniques i.e. Naïve with Trend Model‚ Average Change Model‚ Double Exponential Smoothing and Holt’s Method Model. These models are normally used to determine the short-term forecasts (one quarter ahead) by analyzing the pattern such as quarterly unemployment rates. The performances of the models are validated by retaining
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estimate in part (2) to make a case for or against excess 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
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Waiting Line System Queuing Systems Queuing System Input Characteristics Queuing System Operating Characteristics Analytical Formulas Single-Channel Waiting Line Model with Poisson Arrivals and Exponential Service Times Multiple-Channel Waiting Line Model with Poisson Arrivals and Exponential Service Times Economic Analysis of Waiting Lines Slide 1 Structure of a Waiting Line System Queuing theory is the study of waiting lines. Four characteristics of a queuing system are:
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the naïve method‚ a two-period movingaverage‚ and exponential smoothing with an α = 0.2. (Hint: Use naïve to start the exponentialsmoothing process.)MonthSales Naïveb) Compare the forecasts using MAD and decide which is best.Exponential = MAD = Σ | actual – forecast | = 13.73 8+8+4+7/4= 6.75nNaïve = MAD = Σ | actual – forecast | = 8.25 10+10+5+8/4= 8.25n2 period = MAD = Σ | actual – forecast | = 8.25 2.5+15+10+8/4= 8.25nAnswer: Exponential is bestc) Using your method of choice‚ make a forecast
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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 to 1100‚ which is about 3.9% reduction/loss C) Sales would decrease from 11450 to 9650
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producció. Carles Ramírez Estrada Marcel Subirana Florats Curs 2011-2012 G23 22/03/2012 4.47 City Cycles has just started selling the new 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
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DEMAND FORECASTING Demand forecasting is the process of predicting future average sales on the basis of historical data samples and market intelligence. The volatility of demand from an average level is supplied from the safety inventory. Any forecast is likely to be wrong‚ so the focus should be on understanding the range of potential forecast errors and the level of safety inventory that will cater for peak demand. An important additional calculation is forecast bias. This is the cumulative
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operations (addition‚ subtraction‚ multiplication‚ division and taking roots). All rational functions are algebraic. Transcendental functions are non-algebraic functions. The following are examples of such functions: i. iii. v. Trigonometric functions Exponential functions Hyperbolic functions ii. iv. vi. Logarithmic functions Inverse trigonometric functions Inverse hyperbolic functions In this chapter we shall study the properties‚ the graphs‚ derivatives and integrals of each of the transcendental function
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FORECASTING FUNDAMENTALS Forecast: A prediction‚ projection‚ or estimate of some future activity‚ event‚ or occurrence. Types of Forecasts * Economic forecasts * Predict a variety of economic indicators‚ like money supply‚ inflation rates‚ interest rates‚ etc. * Technological forecasts * Predict rates of technological progress and innovation. * Demand forecasts *
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