Online Workshop: Business Forecasting Techniques, Best Practices & Application Using Forecast Pro

Introduction to Forecasting
A broad overview of business forecasting and its various uses within the organization. Topics include approaches to forecasting, features of data, the role of judgment, selection of appropriate forecasting methods for varied data sets and resources for forecasters.

Components of Data
An in-depth look at the different components found in time series data including trends, seasonal patterns, business cycles, trading-day variations, interventions (events) and noise. Discussion includes the forms the components can take, spotting local vs. global components, interpretation of business cycle indicators and the use of decomposition routines.

Exponential Smoothing
A survey of exponential smoothing techniques with particular emphasis on the Holt-Winters family of models and Croston’s intermittent demand model. Topics include the pros and cons of using these models, when they are best used, how they work, identifying model components, parameter optimization and model diagnosis.

Box-Jenkins (ARIMA) Models:
An exploration into the use of ARIMA models for business forecasting. Topics include the advantages/disadvantages of using these models, how and when they should be applied, automatic identification procedures and model diagnostics.

Forecast Accuracy and Evaluation
A detailed look at evaluating the accuracy of forecasting methods. Topics include the distinction between within-sample and out-of-sample errors, a survey of error measurement statistics, a summary of findings from forecasting competitions, and an explanation of how to use both real-time tracking reports and simulations as predictors of model performance.

Identifying Problems in Your Forecasting Process
Approaches for focusing on critical items when forecasting large volumes of data. Topics include evaluating and forecasting SKU data, filtering and ABC (Pareto) classification, outlier detection and correction, exception reporting and measuring accuracy across multiple time series.

Event-Index Models
Event-index models extend the functionality of exponential smoothing models by providing adjustments for promotions, strikes and other non-calendar-based events. This session addresses how these models work, how and when they should be used, and how to customize their design to best suit your needs.

Multiple-Level Forecasting
This session explores hierarchical forecasting techniques. Topics include the need for forecasting at various levels, product vs. geographical hierarchies, reconciliation strategies, top-down vs. bottom-up approaches, the use of proportional allocation and adjustment for seasonality.

New Product Forecasting
This session explores various approaches for forecasting new products. Topics include the pros and cons of different methods based on a product’s classification and a review of popular methods including item supersession, forecast by analogy and the Bass diffusion model.

Dynamic Regression
A detailed look into the ins and outs of regression fore­casting. Topics include when regression models are best applied, how to build the models, ordinary least squares, leading indicators, lagged variables, Cochrane-Orcutt models, hypothesis testing and the use of “dummy” vari­ables.

Virtual Workshop
This session provides the opportunity for attendees to discuss their forecasting processes and challenges with the instructors and other attendees. When attendees are willing to share their Forecast Pro projects with the group (there are usually a few who do) we walk through their current approaches and make recommendations for improvements.