Making sense of data may benefit from high volume data acquisition and analysis using GARCH and VAR-MGARCH (Datta et al 2007) techniques in addition to and in combination with other tools for forecasting and risk analysis. In this work, we explored the possibility of using advanced forecasting methods in a context of supply chains. It remains unexplored if concomitant business process transformation may be necessary to obtain better results. The proposed advanced forecasting models, by its very construction requires high volume data. Availability of high volume data may not be the limiting factor in view of the renewed interest in automatic identification technologies (AIT) that may facilitate acquisition of real-time data from products or objects with RFID tags or sensors. Although speculative, it stands to reason that use of advanced forecasting methods may enhance profitability from IT investments required to acquire real-time data. However, understanding the “meaning” of the information from data is an area still steeped in quagmire but may soon begin to experience some clarity if the operational processes take advantage of the increasing diffusion of the semantic web and organic growth of ontological frameworks to support ambient intelligence in decision systems coupled to intelligent agent networks (Datta 2006). To move ahead, we propose to bolster the GARCH proof of concepts through pilot implementations of analytical engines in diverse verticals and explore advanced forecasting models through integration with real-world business data, processes and systems.