Exponential smoothing

used. Demand forecasting uses • Data from current activities within a company • Historic data in order to access future capacity requirements One vital method applied is exponential smoothing, which uses weights to the values observed. Due to its high-end spectrum of time convenience and its quick application, exponential smoothing creates a huge advantage of the reliability of forecasts for multitudes of industries. The most important utilization of this methodology is when the mean square error is

1. (24 points) If needed, additional workspace is provided on the next sheet. Doug Moodie is the president of Garden Products Limited. Over the last 5 years, his vice president of marketing has been providing the sales forecast using his special “focus” forecasting technique. The actual sales for the past ten years and the forecasts from the vice president of marketing are given below. |Year |Sales |VP/Marketing

established itself over the years can afford to adopt Moving Average Technique to arrive at a quick forecast, as the input data would not vary much over a considerable period. However, for more important strategic decisions the suggested method is Exponential Smoothing. | Laptop | Dell | Comparison of the performance of a similar successful product and research on why a particular brand fared poor will give valuable inputs to arrive at a demand forecast. Historical Analogy is the suggested method for this

a sales forecast for 2004. Greaves provided five years and two months of annual sales data. Using Stat Tools, the following analysis were run: Moving Average, Exponential Smoothing Simple, Exponential Smoothing Holt’s, and Exponential Smoothing Winter’s. Following a comparison on the average on all models, the Exponential Smoothing Winter’s was found to be the most suitable model for the case. A graph analysis is captured below. StatTools Report | | | | | Analysis: | Forecast

showed an increasing trend both for the GSP of Kansas and the Deposit. a. Naive Approach: Answer: Year Deposit GSP 2003 108.9 million 5.3 billion b. Moving Average: For Ma =3 For Ma=5 c. Exponential smoothing: A=.3 A=.5 A=.7 d. Linear Trend analysis e. f. e. Linear regression: DEPOSIT = -17.64 + 13.596 (gsp) IV. Alternative courses of action to be taken: Naive

following is the simple moving average forecast for year 2014? (Points : 10) 100.5 122.5 133.3 135.6 139.3 Question 4.4. (TCO 5) If a firm produced a standard item with relatively stable demand, the smoothing constant alpha (reaction rate to differences) used in an exponential smoothing forecasting model would tend to be in which of the following ranges? (Points : 10) 5% to 10% 20% to 50% 20% to 80% 60% to 120% 90% to 100% Question 5.5. (TCO 2) Various financial data for SunPath

shown to have trend and seasonality we will evaluate the data using four different time series models and compare the results of each to see which model is the most accurate. The models we are going to use are the Modified Naïve model, Winters Exponential Smoothing model, Time Series Decomposition, and Autoregressive Integrated Moving Average (ARIMA). We will also test a multiple regression model to attempt to forecast future NHS, while taking

Revenue management (RM) is the art and science of selling the right product to the right customer at the right time for the right price, through a combination of inventory controls and pricing (Cross 1997). The practice has first immerged from the airline industry and rapidly grew to its status today as major business practice widely used in different industries, such as Walt Disney, Hotel Chain, Car Rental and supporting consulting companies. RM relies on optimization models that take the demand

Altavox Excel Data (1) Week 1 2 3 4 5 6 7 8 9 10 11 12 13 Average Atlanta 33 45 37 38 55 30 18 58 47 37 23 55 40 40 Boston 26 35 41 40 46 48 55 18 62 44 30 45 50 42 Chicago 44 34 22 55 48 72 62 28 27 95 35 45 47 47 Dallas 27 42 35 40 51 64 70 65 55 43 38 47 42 48 Los Angles 32 43 54 40 46 74 40 35 45 38 48 56 50 46 Total 162 199 189 213 246 288 245 204 236 257 174 248 229 222 Altavox Data (2) Week -5 -4 -3 -2 -1 Atlanta 45 38 30 58 37 Boston 62 18 48 40 35 Chicago 62 22 72 44 48

2. Basic idea of smoothing If is believed to be smooth, then the observation at , near should contain information about the value of at . Thus it should be possible to use something like local average of data near to construct an estimator of . Smoothing of a data set , involves the approximation of the mean response curve in the regression relationship. The function of interest could be the regression curve itself, certain derivatives of it or functions of derivatives such as extrema or