The paper reports some initiatives to freshen up the typical undergraduate business forecasting course. These include (1) students doing research and presentations on contemporary tools and industry practices such as neural networks and collaborative forecasting (2) insertion of Logistic Regression in the curriculum (3) productive use of applets available on the Internet to convey abstract concepts underlying ARIMA models and (4) showcasing forecasting tools in timely or familiar applications. These initiatives align with the best practices framed across the "Making Statistics More Effective in Schools of Business" (MSMESB) conferences. Course experiences and student feedback are also discussed. (Contains 2 figures.)