SOLVING PROBLEMS BASED ON WINQSB FORECASTING TECHNIQUES

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1 SOLVING PROBLEMS BASED ON WINQSB FORECASTING TECHNIQUES Mihaela - Lavinia CIOBANICA, Camelia BOARCAS Spiru Haret University, Unirii Street, Constanta, Romania mihaelavinia@yahoo.com, lady.camelia.yahoo.com Abstract: It is a well known fact that in a competitive economy, those who have the ability to adapt to changes in the economic environment of inventiveness and creativity, are always favored. The technical outlook acts to support this process of managerial work; the objectives of the organization shall be defined through it, and so shall the modalities of action to achieve them and to allocate the necessary resources. WINQSB is one of the management tools that can facilitate the use of different methods for forecasting and information product. Keywords: forecasting, extrapolation, adjustment, regression and performance. I. GENERAL FORECAST FOR SOLVING PROBLEMS BY BROWN EXPONENTIAL SMOOTHING MODEL Brown's exponential smoothing model around the average is used for stationary data series for which there is no trend and cyclical or seasonal variations. The method is based on the assumption that the forecast for the next period P t+1 must contain 2 components: the real value of Y t past period and the amount forecasted for the last P t (trend) and those taken with the coefficient and (1 - ). The relationship underlying Brown's exponential smoothing method is: P t+1 = P t + * e t = P t + * (Y t P t ) = Y t + (1 - )* P t, where: P t+1 = projected sales for a future period; P t = expected value of sales in a previous period; = smoothing constant which expresses the probability of forecasting error; [0, 1]; e t = adjustment error determined as follows: e t = Y t - P t ; Y t = real value of sales in a previous period. Differently formulated, exponential smoothing is based on the equation: The new forecast = the old forecast + (the most recent comment - - the old forecast) The coefficients and (1 - ) called adjusting constants signify attitudes toward the present and the past. Regarding this aspect, there are two cases: 1. If = 0 then P t+1 = P t, situation in which emphasis to achieve this forecast is placed only on the past. 2. If = 1 then P t+1 = Y t, situation in which the emphasis to achieve the forecast is placed only on the present achievements, ignoring the past trend of the phenomenon. Note that the choice of affects forecast accuracy, as well, so that: if time series is strongly oscillating and contains a substantial random variable an coefficient as small as possible must be used in order to make an estimate as close to reality as possible; if time series is stable, with a small random variable it is preferable to use constant of high values because they have the advantage that, in case of occurrence of significant

2 forecast errors, they can adjust the forecast immediately, giving it a capacity for rapid response to conditions changes. II. FORECASTING AND LINEAR REGRESSION MODULE DESCRIPTION OF THE COMPUTER PRODUCT WINQSB One of the 19 modules of the computer product WINQSB is the module Fc: Forecasting and Linear Regression, which seeks forecast and linear regression. 2.1 Options and Features Options: 1. Time Series Forecasting forecast based on time series; 2. Linear Regression - linear regression. Features: The algorithms used by the forecast on time series are: a) Simple Average; b) Moving Average; c) Weighted Moving Average; d) Moving Average with Linear Trend; e) Single Exponential Smoothing (SES); f) Single Exponential Smoothing with Linear Trend; g) Double Exponential Smoothing with Linear Trend; h) Linear regression; i) Holt Winters Additive Algorithm; j) Holt - Winters multiplicative Algorithm. For forecast results calculate mean absolute deviation, mean square error, cumulative forecast error, mean of the reports between absolute deviations and actual data, trend signal; Linear regression module determines the function of multidimensional regression, correlation coefficients, makes dispersion analysis and residue analysis, calculate confidence intervals for forecasts. 2.2 Forecasting and Linear Regression module menu Figure 1. Forecasting and Linear Regression module menu 2.3 Practical application for determination of sales forecast using exponential smoothing TABLE 1 presents the information necessary for the short term forecast of monthly sales of tires at an auto parts store by the method of exponential smoothing. TABLE 1. Development of monthly sales of tires Period Sales Volume (Y t ) August 50 September 100

3 October 150 November 200 December 220 January 235 Knowing that the forecast for August was 100 pieces, and the exponential smoothing constant = 0.3 you are to determine the forecast for February using first rate exponential smoothing. Solving with WINQSB information product involves the following steps: a) launch the running of WINQSB application of the Forecasting module, and from the menu File of the menu bar select option New. The effect of this action is the appearance on screen of the dialog box Forecasting Problem Specification in Figure 2. Figure 2. Forecasting Problem Specification b) inside this window, select forecast type option Time Series Forecasting and subsequently complete the fields on the title of the problem (Problem Title), work per unit time (Time Unit) and the number of historical data (Number of Time Units). To confirm these values press the OK button. c) in the table contained in the Solving Problem in Forecasting Techniques based on WINQSB dialog box, introduce the historical values of the recorded sales, as shown in Figure 3. Figure 3. Solving Problem in Forecasting Techniques based on WINQSB d) the Forecasting Setup dialog box in the upper left contains a list of forecasting methods; choose the Single Exponential Smoothing method (SES), as shown in Figure 4. e) the right side of the same window refers to how it works; select Search the Best estimation method, which allows searching for optimal values (Figure 4). f) the comparison criterion by selecting the radio button corresponding to the following options is stated next: MAD (mean absolute deviation);

4 CFE (cumulative forecast error); MSE (mean square error); MAPE (mean absolute percentage error). g) Initial data are defined, namely: Number of Periods to Forecast - 6; Smoothing constant alpha - the constant value = 0.3; Initial Value F (0) if known - the initial value corresponding to the first forecast, if known. This parameter is 100. The three parameters are found in the window in Figure 4. Figure 4. Forecasting Setup h) After completing these steps, select the OK button and the effect is the appearance of TABLE 2. TABLE 2. Forecast Result for Solving problems in forecasting techniques based on WINQSB

5 References [1] Albu, C., Albu, N., 2004, Performance Management Tools, Economic Publishing Houase, Bucharest, page 135. [2] Andrew, Ana, Oprea, Ghe, Marin, D., Mitrea, D., Roman, M., 2001, Dynamic models of optimal management of business company s activities, ASE Publishing House, Bucharesst, page 256. [3] Luban, F., 2005, Simulation in Business, ASE Publishing House, Bucharest, page 198. [4] Oprea, Ghe, Marin, D., Mitrea, D., Andrew, A., 2003, Cyber modeling of control mechanisms of economic systems, ASE Publishing House, page 364. [5] Ratiu-Suciu, C., 2009, Economic modeling and simulation. Roundup, Economic Publishing House, Bucharest, page 173. [6] Ratiu-Suciu, C., 2005, Modeling and simulation of economic processes. Theory and Practice, Fourth Edition - a, Economic Publishing House, Bucharest, page 231. [7] Stancioiu, I., 2004, Operational research to optimize economic decisions, Economic Publishing House, Bucharest, page 270.