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BIS Application Chapter two

BIS Application Chapter two. Forecasting. Forecasting. Forecasting is the process of extrapolating the past into the future

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BIS Application Chapter two

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  1. BIS Application Chapter two Forecasting

  2. Forecasting Forecasting is the process of extrapolating the past into the future Forecasting is something that organization have to do if they are to plan for future. Many forecasts attempt to use past date in order to identify short, medium or long term trends, and to use these patterns to project the current position into the future. Backcasting: method of evaluating forecasting techniques by applying them to historical data and comparing the forecast to the actual data

  3. Forecasting Why Forecasting? Characteristics of Forecasts Forecasts are usually wrong or seldom correct Aggregate forecasts are usually more accurate Less accurate further into the future Assumptions of Forecasting Models Information (data) about the past is available The pattern of the past will continue into the future.

  4. The forecasting approach to forecasting • Starts with gathering and recording information about the situation • Entering the data into the worksheet, • Creating graphs • The data and graphs are examined visually to get some understanding of the situation (judgmental phase) • Developing hypotheses and models • Trying alternative forecasting approaches and doing what if analysis to check if the resulting forecast fits the data

  5. Forecasting Approaches 1- Qualitative Forecasting Forecasting based on experience, judgment, and knowledge 2- Quantitative Forecasting Forecasting based on data and models

  6. Quantitative models Market survey Expert opinion Decision conferencing Data cleaning Data adjustment Environmental factors Forecasting Approaches Judgmental/Qualitative Time Series Causal Moving average Regression Curve fitting Exponential smoothing Econometric Trend projection Seasonal indexes

  7. Quantitative Forecasting Time Series Models: Casual Models: Sales1999 Sales1998 Sales1997 …… Year 2000 Sales Time Series Model Price Population Advertising …… Causal Model Year 2000 Sales

  8. Time series model • Is based on the hypothesis that the future can be predicted by analyzing historical data samples. The time series model have the following type , which can be classifies as shown below: • Forecasting directly from the data value (non seasonal) • Moving average • Exponential smoothing • Forecasting by identifying patterns in the past data (seasonal) (Chapter 3) • Trend projections • Seasonal influences • Cyclical influences

  9. Time series model The Time series model can be also classified as Non-seasonal Model • Trend • Moving average • Exponential smoothing Seasonal Model • Seasonal Decomposition • Cyclical influence

  10. Causal Models (Chapter 3) Causal forecasting seeks to identify specific cause-effect relationships that will influence the pattern of future data. Causes appear as independent variables, and effects as dependent , response variables in forecasting models. Independent variable Dependent, response variable Price demand Decrease in population decrease in demand Number of teenager demand for jeans The issue is to determine the approximate functional relationships, the model, and the parameter of the model that relate the input(independent) and output(dependent) variables.

  11. Causal Models (Chapter 3) Regression analysis Curve Fitting: Simple Linear Regression One Independent Variable (X) is used to predict one Dependent Variable (Y): Y = a + bX Find the regression line with Excel • Use Function: a = INTERCEPT(Y range; X range) b = SLOPE(Y range; X range) • Use Solver • Use Excel’s Tools | Data Analysis | Regression

  12. Causal Models (Chapter 3) Curve Fitting: Multiple Regression Two or more independent variables are used to predict the dependent variable: Y = b0 + b1X1 + b2X2 + … + bpXp Use Excel’s Tools | Data Analysis | Regression

  13. Evaluation of Forecasting Model To judge how well a forecasting model, or indeed any forecast, fit the past observation , both precision and bias must be considered. a- Measuring the precision of a forecasting model: There are four possible measures used to evaluate precision of forecasting systems, each based on the error or deviation between the forecasted and actual values: Average of the deviation, MAD, MAS, MAPE b - Measuring the bias of a forecasting model: The bias of a forecasting model is examined on the basis of the spread of a set of data which can be measured by its variance, which depends on the sum of squares of the differences between the values and their mean. The more of the spread that is accounted for by the fitted model , the more precise the fit of the model to the data. R2 – used only for curve fitting model such as regression

  14. Evaluation of Forecasting Model The arithmetic mean of the errors (the average deviation ) n is the number of forecast errors Excel: =AVERAGE (error range)

  15. Evaluation of Forecasting Model Mean Absolute Deviation - MAD No direct Excel function to calculate MAD

  16. Evaluation of Forecasting Model Mean Square Error - MSE Excel: =SUMSQ(error range)/COUNT(error range)

  17. Evaluation of Forecasting Model Mean Absolute Percentage Error - MAPE

  18. Which of the measure of forecast accuracy should be used? • Straight average is not used because positive and negative deviations cancel out. • The most popular measures are MAD and MSE. • The problem with the MAD is that it varies according to how big the number are. • MSE is preferred because it is supported by theory, and because of its computational efficiency.

  19. Which of the measure of forecast accuracy should be used? • The ratio of MAD or MSE to the average demand which describes the relative percentage of error, may be used • MAPE is not often used. • In general, the lower the error measure (BIAS, MAD, MSE) or the higher the R2, the better the forecasting model

  20. Good Fit – Bad Forecast As its discussed that neither MAD nor MSE gives an accurate indication of validity of forecast. Thus, judgment must be used. Raw data sample should always be subjected to managerial judgment, and analyzed and adjusted before formal quantitative techniques can be applied.

  21. a- Dirty Data Outlier: may result from simple data entry errors, or they may be correct but atypical observed values (ex can occur in time periods when the product was just introduced or about to be phased out). So experienced analyst are well aware that raw data sample may not be clear. Demand data with an outlier P

  22. b- Causal data adjustment • Before quantitative analysis is performed, the historical data sample needs to be examined from the point of view of cause-and-effect relationships. • A multitude of causes may affect the patterns in data sample : • The data sample before a particular year may not be applicable because: • - Economic conditions have changed • - The product line was changed • Data for a particular year may not be applicable because: • - There was an extraordinary marketing effort • - A natural disaster prevented demand from occurring

  23. c- Illusory (misleading) patterns The meaning of a :good fit” is subject to interpretation, so before a forecast is accepted for action, quantitative techniques must be augmented by such judgmental approaches as decision conferencing and expert consultations.

  24. To prepare a valid forecast, the following factors that influence the forecasting model must be examine: • Company actions • Competitors actions • Industry demand • Market share • Company sales • Company costs • Environmental factors

  25. Time series forecasting model

  26. Time Series Model Building • Historical data collection • Data plotting (time series plot) • Forecasting model building • Evaluation and selection of model • Forecasting with the final selected model

  27. Components of A Time Series • Trend: long term overall up or down movement • Seasonality: periodic pattern repeating every year • Cycles: up & down movement repeating over long time frame • Random Variations: random movements follow no pattern

  28. Components of A Time Series Cycle Trend Random movement Time Time Seasonal pattern Trend with seasonal pattern Demand Time Time

  29. First : Forecasting directly from the data value : Moving average • the forecast is the mean of the last n observation. The choice of n is up to the manager making the forecast • If n is too large then the forecast is slow to respond to change • If n is too small then the forecast will be over-influenced by chance variations

  30. First : Forecasting directly from the data value : Moving average • This approach is considered as a “quick and dirty” approach for forecasting • This approach can be used where a large number of forecasting needed to be made quickly, for example in a stock control system where next week’s demand for every item needs to be forecast

  31. Demand Forecast

  32. Longer-period moving averages (larger n) react to actual changes more slowly

  33. First : Forecasting directly from the data value : Exponentional smoothing • it gives weight to all past observations, in such a way that the most resent observation has the most influence on the forecast, and older observation always has less influence than the more recent one. • It is only necessary to store two values (the last actual observation and the last forecast, plus the value of the smoothing constant) in order to make the next period’s forecast. • Smoothing constant () the proportion of the different between the actual value and the forecast. • F2 = *D1 +(1- )*F1

  34. First : Forecasting directly from the data value : Exponentional smoothing • Alpha (smoothing constant) must set between 0 and 1. Normally the value of the smoothing constant is chosen to lie in the range 0.1 to 0.3. • Typically, a value closer to 0 is used for demand that is changing slowly, and a value closer to 1 for demand that is changing more rapidly. • There is no way to calculate F1 because each forecast is based on the previous forecasts.

  35. First : Forecasting directly from the data value : Exponential smoothing • How to select smoothing constant  • Sensitivity analysis is an analysis used to test how sensitive the forecast is to the change in alpha or smoothing constant. • A general rule for selecting alpha is to perform scenario analysis and pick the value that produces a reasonable value for the MAD and a forecast that is reasonably close to the actual demand.

  36. Trend-Adjusted Exponential Smoothing With trend-adjusted exponential smoothing, the trend is calculated and included in the forecast. This allows the forecast to be smoothed without losing the trend. Trend-adjusted exponential smoothing requires two parameters: the alpha value used by exponential smoothing and beta value used to control how the trend component enters the model. Both values must be between 0 and 1. The formula to calculate the forecast component is : F2 = FiT1+ *(D1-FiT1) The formula to calculate the trend component is T2 = T1 +  *  *(D1-FiT1)

  37. Optimizing Trend-Adjust Exponential Smoothing Optimizing alpha and beta with trend-adjusted exponential smoothing has a marginal impact. To find the optimum value for alpha and beta: First the original value of alpha and beta will be used in the forecasting model. Once the spreadsheet is ready, Solver is used to vary alpha and beta in order to minimize the MAD.

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