Business Knowledge Series course

Presented by Charles Chase, Jr., Advisory Industry Consultant, SAS Retail and Consumer Packaged Goods Global Practice

This course explores analytics-driven forecasting with a focus on people, processes, analytics, and technology that drive improved demand forecasting and planning within retail/manufacturing companies. After completing this two-day course, you will be able to implement a multi-step, analytics-driven demand forecasting process and champion the critical shift in leadership to change the corporate culture that is necessary to drive adoption and accountability while never losing focus on continually improving demand forecasting and planning excellence. Commercial and demand forecasting professionals, manufacturers of consumer products, electronics, sportswear, appliances, automotive, pharmaceuticals, and other related companies, as well as retailers, can master the key principles of analytics-driven planning, gain new analytics skill sets, and implement an analytics-driven planning process that your company can implement to transition to the next generation demand management.

This course is a combination of lecture, demonstrations, and hands-on exercises that use the latest SAS Demand-Driven Planning solutions. During demonstrations, students will be able to follow along with the instructor and use the actual technology.

Note: This course was previously titled Next Generation Demand Management: People, Process, Analytics, and Technology.

Learn how to

• develop the analytics-driven demand management process
• use demand sensing and pattern recognition to support demand forecasting and planning
• define demand sensing, demand shaping, and demand shifting
• define data requirements, skills, and technology capabilities
• apply statistical modeling techniques, starting with moving averaging, exponential smoothing, dynamic regression, ARIMA, and ARIMAX methods
• interpret and apply causal models
• measure the effects of promotions (lifts)
• evaluate the adequacy of models
• build holistic models that account for key business drivers (demand signals) that influence demand (trend, seasonality, price, in-store merchandising, sales promotions, advertising, and more)
• apply consumption-based models using a MTCA (multi-tiered causal analysis) process to measure the push/pull effects of a company's business.

Who should attend
Directors of demand forecasting and planning; directors of consumer insights and analytics; managers of forecasting and planning, marketing analytics, demand planning, forecast analytics, marketing planners, and sales planners

Formats available	Standard duration
Classroom:	2.0 days

Before attending this course, you should have some knowledge of basic statistics and demand forecasting.

This course addresses SAS Forecast Server software.

• why companies must shift toward analytics-driven forecasting
• analytics-driven forecasting requirements
• benefits of an analytics-driven forecasting process

• introduction?underlying methods
• segmenting your products

• understanding the model-fitting process
• quantitative time series methods

• introduction to regression
• simple regression
• multiple regression
• multicollinearity
• Durbin-Watson statistic
• intervention variables (or dummy variables)
• key activities in building a multiple regression model
• cautions about regression models

• ARIMA step-by-step approach
• seasonal ARIMA models
• explanatory variables
• transfer functions
• ARIMA model results

• averaging combined forecasting methods
• weighted combined forecasting methods

• using the MTCA (multi-tiered causal analysis) process