Forecasting

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Forecasting
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Eight Steps to Forecasting

• Determine the use of the forecast
• What objective are we trying to obtain?
• Select the items or quantities that are to be
  forecasted.
• Determine the time horizon of the forecast.
• Short time horizon 1 to 30 days
• Medium time horizon 1 to 12 months
• Long time horizon more than 1 year
• Select the forecasting model or models
• Gather the data to make the forecast.
• Validate the forecasting model
• Make the forecast
• Implement the results

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Forecasting Models
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Model Differences

• Qualitative incorporates judgmental
  subjective factors into forecast.
• Time-Series attempts to predict the future by
  using historical data.
• Causal incorporates factors that may influence
  the quantity being forecasted into the model

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Qualitative Forecasting Models

• Delphi method
• Iterative group process allows experts to make
  forecasts
• Participants
• decision makers 5 -10 experts who make the
  forecast
• staff personnel assist by preparing,
  distributing, collecting, and summarizing a
  series of questionnaires and survey results
• respondents group with valued judgments who
  provide input to decision makers

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Qualitative Forecasting Models (cont)

• Jury of executive opinion
• Opinions of a small group of high level managers,
  often in combination with statistical models.
• Result is a group estimate.
• Sales force composite
• Each salesperson estimates sales in his region.
• Forecasts are reviewed to ensure realistic.
• Combined at higher levels to reach an overall
  forecast.
• Consumer market survey.
• Solicits input from customers and potential
  customers regarding future purchases.
• Used for forecasts and product design planning

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Forecast Error

• Bias - The arithmetic sum of the errors
• Mean Square Error - Similar to simple sample
  variance
• Variance - Sample variance (adjusted for degrees
  of freedom)
• Standard Error - Standard deviation of the
  sampling distribution
• MAD - Mean Absolute Deviation
• MAPE Mean Absolute Percentage Error

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Quantitative Forecasting Models

• Time Series Method
• Naïve
• Whatever happened recently will happen again this
  time (same time period)
• The model is simple and flexible
• Provides a baseline to measure other models
• Attempts to capture seasonal factors at the
  expense of ignoring trend

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Naïve Forecast
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Naïve Forecast Graph
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Quantitative Forecasting Models

• Time Series Method
• Moving Averages
• Assumes item forecasted will stay steady over
  time.
• Technique will smooth out short-term
  irregularities in the time series.

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Moving Averages
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Moving Averages Forecast
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Moving Averages Graph
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Quantitative Forecasting Models

• Time Series Method
• Weighted Moving Averages
• Assumes data from some periods are more important
  than data from other periods (e.g. earlier
  periods).
• Use weights to place more emphasis on some
  periods and less on others.

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Weighted Moving Average
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Weighted Moving Average
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Quantitative Forecasting Models

• Time Series Method
• Exponential Smoothing
• Moving average technique that requires little
  record keeping of past data.
• Uses a smoothing constant a with a value between
  0 and 1. (Usual range 0.1 to 0.3)

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Exponential Smoothing Data
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Exponential Smoothing
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Exponential Smoothing
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Trend Seasonality

• Trend analysis
• technique that fits a trend equation (or curve)
  to a series of historical data points.
• projects the curve into the future for medium
  and long term forecasts.
• Seasonality analysis
• adjustment to time series data due to variations
  at certain periods.
• adjust with seasonal index ratio of average
  value of the item in a season to the overall
  annual average value.
• example demand for coal fuel oil in winter
  months.

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Linear Trend Analysis Midwestern Manufacturing
Sales
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Least Squares for Linear Regression Midwestern
Manufacturing
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Least Squares Method
Where
predicted value of the dependent variable
(demand)
X value of the independent variable (time) a
Y-axis intercept b slope of the regression
line
b
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Linear Trend Data Error Analysis
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Least Squares Graph
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Seasonality Analysis
Ratio demand / average demand
Seasonal Index ratio of the average value of
the item in a season to the overall average
annual value. Example average of year 1
January ratio to year 2 January ratio. (0.851
1.064)/2 0.957
If Year 3 average monthly demand is expected to
be 100 units. Forecast demand Year 3 January
100 X 0.957 96 units Forecast demand Year 3
May 100 X 1.309 131 units
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Deseasonalized Data

• Going back to the conceptual model, solve for
  trend
• Trend Y / Season
  (96 units/ 0.957 100.31)
• This eliminates seasonal variation and isolates
  the trend
• Now use the Least Squares method to compute the
  Trend

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Forecast

• Now that we have the Seasonal Indices and Trend,
  we can reseasonalize the data and generate the
  forecast
• Y Trend x Seasonal Index