Aug. 21, 2020
September 1-3, 2020
SBTI’s Advanced Belt Skills electives are unique in the industry. We offer an array of custom-designed electives for Belts that advance them in critical functions. You should attend this elective if you design or interpret control charts, or if you’re a Demand Planner or have forecasting responsibilities, or if you’re a Mentor of Black Belts and Green Belts.
SBTI’s E7: Forecasting and Time Series Analysis elective will provide you with the tools and knowledge you need to choose appropriate experimental designs for specific situations, understand the influence of autocorrelation on interpretation of control charts, identify the limitations of smoothing methods as forecasting tools, interpret forecasts from decomposition models, use ARIMA models to forecast seasonal and non-seasonal data, incorporate effects of deliberate policy changes or unexpected events into forecasts, and it will provide you with experience in developing and evaluating forecasts.
The SBTI E7: Forecasting and Time Series Analysis elective is led by Richard R. (Dick) Scott, SBTI Master Consultant and Executive Director. Prior to joining SBTI, Dick spent 33 years at Eastman Kodak Company where he held a variety of management and leadership positions. Dick has demonstrated expertise in Six Sigma for Operations, Design for Six Sigma, Marketing for Six Sigma, Project Management, TQM, and Statistical Modeling.
WHAT WILL YOU LEARN?
Review of advanced regression analysis
Basic time series concepts
Decomposition methods (level, trend, seasonal components)
Forecasting with Leading Indicators
Autocorrelation and cross correlation
ARIMA (AutoRegressive Integrated Moving Average) models
Evaluating forecast performance
Frequently Asked Questions
WHO SHOULD ATTEND?
- Individuals designing or interpreting control charts
- Demand Planners and others with forecasting responsibilities
- Mentors of Black Belts and Green Belts
- Experience with multiple regression analysis at Six Sigma Black Belt level or equivalent
- Choosing appropriate experimental designs for specific situations
- Understanding influence of autocorrelation on interpretation of control charts
- Identifying limitations of smoothing methods as forecasting tools
- Interpreting forecasts from decomposition models
- Using ARIMA models to forecast seasonal and non-seasonal data
- Incorporating effects of deliberate policy changes or unexpected events into forecasts
- Experience in developing and evaluating forecasts