BIS Application Chapter two
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Transcript of BIS Application Chapter two
BIS Application Chapter two
Forecasting
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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
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Forecasting Why Forecasting? Characteristics of Forecasts
Forecasts are usually wrong or seldom correctAggregate forecasts are usually more accurateLess accurate further into the future
Assumptions of Forecasting ModelsInformation (data) about the past is availableThe pattern of the past will continue into the
future.
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The forecasting approach The forecasting approach to forecasting 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
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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan
Devi
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Demand Forecast A Deviation AJan 11.8 7.1 4.7Feb 6.3 11.8 5.5Mar 9.5 6.3 3.2Apr 5.3 9.5 4.2May 10.1 5.3 4.8Jun 7 10.1 3.1Jul 11.3 7 4.3Aug 7.3 11.3 4Sep 9.5 7.3 2.2Oct 5 9.5 4.5Nov 10.7 5 5.7Dec 6 10.7 4.7Jan 6 6
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Forecasting Approaches1- Qualitative
Forecasting Forecasting based on experience,
judgment, and knowledge
2- Quantitative Forecasting
Forecasting based on data and models
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Judgmental/Qualitative Quantitative models
Time Series Causal
Moving average
Exponential smoothing
Trend projection
Seasonal indexes
RegressionCurve fitting
Econometric
Market survey
Expert opinion
Decision conferencing
Data cleaning
Data adjustment
Environmental factors
Forecasting Approaches
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Quantitative ForecastingTime Series Models:
Time Series Model
Year 2000 Sales
Sales1999 Sales1998
Sales1997……
Casual Models:
Causal Model
Year 2000 Sales
Price PopulationAdvertising……
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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
Time series modelTime series model
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The Time series model can be also classified as
Non-seasonal Model Trend Moving average Exponential smoothing
Seasonal Model Seasonal Decomposition Cyclical influence
Time series modelTime series model
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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.
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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
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Curve Fitting: Multiple RegressionCurve Fitting: Multiple RegressionTwo or more independent variables are used to
predict the dependent variable: Y = b0 + b1X1 + b2X2 + … + bpXp
Use Excel’s Tools | Data Analysis | Regression
Causal Models (Chapter 3)
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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
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Period Demad Forecast Deviation1 33 36 3 C2-B22 37 29 -8 C3-B43 32 41 9 C4-B44 35 30 -5 C5-B4
-0.25 AVERAGE(E2:E5)
The arithmetic mean of the errors (the average deviation )
n is the number of forecast errors
Excel: =AVERAGE (error range)
nError
nForecast) - (Actual
BIAS
Evaluation of Forecasting Model
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Mean Absolute Deviation - MAD
No direct Excel function to calculate MAD
nError
nForecast) - (Actual
BIAS
Period Demad Forecast Absollute Deviation
1 33 36 3 ABS(C2-B2)2 37 29 8 ABS(C3-B4)3 32 41 9 ABS(C4-B4)4 35 30 5 ABS(C5-B4)
6.25 AVERAGE(E2:E5)
Evaluation of Forecasting Model
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Mean Square Error - MSE
Excel: =SUMSQ(error range)/COUNT(error range)
n(Error)
nForecast) - (Actual
MSE22
Evaluation of Forecasting Model
Period Demad Forecast Squared Deviation
1 33 36 9 (C2-B2) 2̂2 37 29 64 (C3-B3) 3̂3 32 41 81 (C4-B4) 4̂4 35 30 25 (C5-B5) 5̂
6.68954 SQRT(AVERAGE(E2:E5))
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Mean Absolute Percentage Error - MAPE
nActual
|Forecast - Actual|
MAPE
%100*
Evaluation of Forecasting Model
Period Demad Forecast Squared Deviation
1 33 36 9.09% ABS((C2-B2)/B2)2 37 29 21.62% ABS((C3-B3)/B3)3 32 41 28.13% ABS((C4-B4)/B4)4 35 30 14.29% ABS((C5-B5)/B5)
18.28% AVERAGE(E2:E5)
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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.
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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
Which of the measure of forecast accuracy should be used?
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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.
Good Fit – Bad Forecast
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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.
a- Dirty Data
0102030405060708090
100
J an Feb Mar Apr May J un J ul Aug Sep Oct Nov Dec J an Feb
PDemand data with an outlier
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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
b- Causal data adjustment
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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.
c- Illusory (misleading) patterns
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To prepare a valid forecast, the following factors To prepare a valid forecast, the following factors that influence the forecasting model must be that influence the forecasting model must be examine:examine:
- Company actions
- Competitors actions
- Industry demand
- Market share
- Company sales
- Company costs
- Environmental factors
Time series forecasting model
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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
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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
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Components of A Time Series
Time
Trend
Randommovement
Time
Cycle
Time
Seasonalpattern
Dem
and
Time
Trend with seasonal pattern
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-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
First : Forecasting directly from First : Forecasting directly from the data value : the data value : Moving averageMoving average
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-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
First : Forecasting directly First : Forecasting directly from the data value : from the data value : Moving Moving average average
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Mounth Demand Moving Avarage Forecast
Oct 6Nov 5Dec 5Jan 1.63 5.33 AVARAGE(B3:B5)
Feb 1.95 3.88 AVARAGE(B4:B6)
Mar 7.5 2.86 AVARAGE(B5:B7)
Apr 2.49 3.69 AVARAGE(B6:B8)
May 6.18 3.98 AVARAGE(B7:B9)
Jun 9.18 5.39 =
Jul 5.24 5.95 =
Aug 8.3 6.87 =
Sep 2.72 7.57 =
Oct 7.43 5.42 =
Nov 7.49 6.15 =
Dec 9.58 5.88 =
Jan 8.02 8.17 =
Feb 4.13 8.36 =
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Longer-period moving averages (larger n) react to actual changes more slowly
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-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 = F2 = *D1 +(1- *D1 +(1- )*F1)*F1
First : Forecasting directly First : Forecasting directly from the data value : from the data value : Exponentional smoothing Exponentional smoothing
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•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.
First : Forecasting directly First : Forecasting directly from the data value : from the data value : Exponentional smoothing Exponentional smoothing
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First : Forecasting directly First : Forecasting directly from the data value : from the data value : Exponential Exponential smoothing 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.
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Trend-Adjusted Exponential Trend-Adjusted Exponential SmoothingSmoothing
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+ F2 = FiT1+ *(D1-FiT1)*(D1-FiT1)
The formula to calculate the trend component is
T2 = T1 + T2 = T1 + * * *(D1-FiT1) *(D1-FiT1)
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Optimizing Trend-Adjust Exponential Optimizing Trend-Adjust Exponential SmoothingSmoothing
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.