LSS Black Belt Training Forecasting

Forecasting Models Forecasting Techniques Qualitative Models Delphi Method Jury of Executive Opinion Sales Force Composite Consumer Market Survey Time Series Methods Naive Moving Average Weighted Moving Average Exponential Smoothing Trend Analysis Seasonality Analysis Multiplicative Decomposition Causal Methods Simple Regression Analysis Multiple Regression Analysis

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

Qualitative Forecasting Models Delphi method Iterative group process allows experts to make forecasts Participants: decision makers: 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

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

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

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

Naïve Forecast

Naïve Forecast Graph

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.

Moving Averages

Moving Averages Forecast

Moving Averages Graph

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.

Weighted Moving Average

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

Exponential Smoothing Data

Exponential Smoothing

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.

Linear Trend Analysis Midwestern Manufacturing Sales

Least Squares for Linear Regression Midwestern Manufacturing

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 =

Linear Trend Data & Error Analysis

Least Squares Graph

Seasonality Analysis 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. ( )/2 = Ratio = demand / average demand If Year 3 average monthly demand is expected to be 100 units. Forecast demand Year 3 January: 100 X = 96 units Forecast demand Year 3 May: 100 X = 131 units

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

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