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
Forecasting Models

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.
Forecasting Models

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
Forecasting Models

5. Forecasting Models

6. Forecasting Approaches
1- Qualitative Forecasting
Forecasting based on experience, judgment, and knowledge
2- Quantitative Forecasting
Forecasting based on data and models
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7. Forecasting Approaches
Judgmental/Qualitative
Quantitative models
Market survey
Expert opinion
Decision conferencing
Data cleaning
Data adjustment
Environmental factors
Time Series
Causal
Moving average
Regression
Curve fitting
Exponential smoothing
Econometric
Trend projection
Seasonal indexes
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8. Quantitative Forecasting
Time Series Models:
Sales1999
Sales1998
Sales1997 ……
Year 2000
Sales
Time Series
Model
Casual Models:
Price
Population
Advertising ……
Causal
Model
Year 2000
Sales
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9. 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
Forecasting Models

10. 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
Forecasting Models

11. 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.
Forecasting Models

12. 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 + b X
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
Forecasting Models

13. 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
Forecasting Models

14. 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
Forecasting Models

15. Evaluation of Forecasting Model
The arithmetic mean of the errors (the average deviation )
n is the number of forecast errors
Excel: =AVERAGE (error range)
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16. Evaluation of Forecasting Model
Mean Absolute Deviation - MAD
No direct Excel function to calculate MAD
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17. Evaluation of Forecasting Model
Mean Square Error - MSE
Excel: =SUMSQ(error range)/COUNT(error range)
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18. Evaluation of Forecasting Model
Mean Absolute Percentage Error - MAPE
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19. 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.
Forecasting Models

20. 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
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21. 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.
Forecasting Models

22. 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
Forecasting Models

23. 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
Forecasting Models

24. 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.
Forecasting Models

25. 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
Forecasting Models

26. Time series forecasting model

27. 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
Forecasting Models

28. 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
Forecasting Models

29. Components of A Time Series
Cycle
Trend
Random
movement
Time
Time
Seasonal
pattern
Trend with
seasonal pattern
Demand
Time
Time
Forecasting Models

30. 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
Forecasting Models

31. 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
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32. Demand
Forecast
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33. Longer-period moving averages (larger n) react to actual changes more slowly
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34. 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
Forecasting Models

35. 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.
Forecasting Models

36. 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.
Forecasting Models

37. 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)
Forecasting Models

38. 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.
Forecasting Models