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thomas cesena

on 13 September 2012

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Transcript of forecasting

MKTG 188 - Cesena, Thomas - Row 4 Forecasting Methods Forecasting: A blend of art and science Use observed results from previous period as forecast for the upcoming period 8 - naive approach 6 steps of forecasting process :
Determine the purpose
Establish a time horizon
Select technique
Gather and analyze data
Prepare the forecast
Monitor Elements of a good forecast:
Measured in meaningful units
In writing
Easy to understand Based on Judgements and Opinions
Originated in 1948 with Rand Corporation 9 - Delphi method Methodology: Series of surveys given to managers and staff, each formulated from the previous.

*Each member must have ability to contribute meaningfully*

Goal: Achieve a consensus forecast http://trendsoutheast.org/2011/wp-content/uploads/2011/12/2513262775_672082cb0c.jpg
http://www.ops.fhwa.dot.gov/publications/fhwahop12009/sec5.htm http://highered.mcgraw-hill.com/sites/dl/free/0072443901/21708/ste43901_ch03qxd.pdf Averages actual recent values and updates as new values are available 7 - Simple Moving Average Advantages:
Little to no cost
Easily understood
Best in short run Disadvantages:
Not highly accurate
Has lag of one period
Doesn't smooth data
Not as reliable in long run Advantages:
Easy to compute and understand
Reflects recent change
Safe because forecasts stay within historical data Disadvantage:
All values carry the same weight
Does not emphasize most recent events Similar to simple moving average except more recent values in a data series are given more weight(importance) 6 - Weighted moving average Can incorporate as many data points as desired
More data points = less responsive
Less data points = more responsive Advantages:
More reflective of recent occurrences than a simple moving average
More likely to pick up trends Weighted averaging method based on previous forecast plus a percentage of the forecast error 5 - Exponential smoothing Provides a look at the linear relationship between an independent variable and dependent variable. 2 - Simple linear regression Advantages:
Requires limited amount of data
Relatively simple
Expandable to trend and seasonal models Disadvantages:
Lags behind actual data
Incapable of including causal factors Advantages:
Allows anonymity
Less biased
Flexible and applicable variety of issues Disadvantages:
May not be representative
Can be time consuming Formula: Y = a + bX + e
Y = Independent variable
a = Intercept of line
b = Slope of the line
X = Dependent variable
e = Residual or error Advantages:
Can prove significant correlation between variables
Easy to use with computer software Disadvantages:
Possibility of outliers can throw off data Technique used to study the relationship of one dependent variable and multiple independent variables 1 - Multiple regression Advantages:
Allows explicit control
Can run multiple tests with computer software
Flexible (independent variables may be categorical or numerical) http://www.ehow.com/info_12070171_advantages-disadvantages-multiple-regression-model.html
http://arthritis-research.com/content/figures/ar1164-1-l.jpg Disadvantages:
Possibility of outliers
Multicollinearity http://labs.fme.aegean.gr/decision/images/stories/docs/healthcare_forecast.pdf http://highered.mcgraw-hill.com/sites/dl/free/0072443901/21708/ste43901_ch03qxd.pdf Example: Can be used in medical field to find correlations between different variables that may cause disease. Disadvantages:
Weights are chosen arbitrarily
Disregards historical data that could be relevant
May take some trial and error
Can produce false signals of trends http://labs.fme.aegean.gr/decision/images/stories/docs/healthcare_forecast.pdf Weights can range from 0 to 1
*The larger the weight, the more important (subjectively)
* Weights must add up to 1 http://highered.mcgraw-hill.com/sites/dl/free/0072443901/21708/ste43901_ch03qxd.pdf
http://highered.mcgraw-hill.com/sites/dl/free/0070951640/354829/lind51640_ch16.pdf Short term regular variations ranging occurring throughout the day, week, month, or year 3 - Seasonal Variation Examples:
Back to school supplies
Sporting goods for correlating seasons
Toy sales rise during Christmas season Advantages:
Easily identifiable with sufficient data
Firms can focus on marketing during these times
Firms can plan production levels accordingly Disadvantages:
Takes time to collect sufficient data
May disregard other factors Can be used for:
Stable series
Seasonal variations
Trend Simple moving average equation Alpha is closer to 0 - less responsive/smoother
Alpha is closer to 1 - more responsive/less smooth Exponential smoothing equation http://highered.mcgraw-hill.com/sites/dl/free/0070951640/354829/lind51640_ch16.pdf Occurs when there is a steady long term increase or decrease in the data 4 - Linear Trend Examples:
Trend in women's participation in the labor force Advantages:
Can be used to predict further than one period into the future
Acts as a "best fit" model
Simple to use Disadvantages:
Cannot always be used for long term predictions
Doesn't account for seasonal or cyclical trends Great method when there is no historical data to rely on 10 - Forecasts based on judgement and opinion Executive Opinions
Useful in long term planning and product development
Potential for great collaboration
*Group can fall victim to groupthink* Consumer Surveys
Allow a sample of consumer opinions
Firms receive feedback
*Costly and time consuming* Salesforce Opinions
Have direct contact with customers
Get an inside look at consumer's future plans
*May have a conflict of interest* http://highered.mcgraw-hill.com/sites/dl/free/0072443901/21708/ste43901_ch03qxd.pdf
http://www.ssc.wisc.edu/~bhansen/390/390Lecture6.pdf Proposed Forecasting Method: Seasonality White flesh nectarine case 5 step procedure:
Determine demand for white flesh nectarines on east coast last season
Compare demand from last season to potential harvest this season
Determine if there will be a surplus or shortage
Contact east coast buyers with news
Allocate the nectarines as necessary
Example: Forecasting number of doctor visits for the following year Goal: produce forecast with lowest mean square error(MSE) "Best Fit" trend line Actual Data Weighted moving average Vs. Simple moving average
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