Wal-Mart specifically is pioneering the development of Radio Frequency Identification (RFID) projects with up to two thousand of its key suppliers today (Kumar, 2007). The goal of this pilot is to enable food and beverage suppliers to coordinate more effectively with Wal-Mart on mixed pallet mode shipping of products (Kumar, 2007). This is critical for Wal-Mart from the standpoint of supporting their expansion strategies into smaller, more diverse stores in terms of selection. The focus of Wal-Mart going forward will be on smaller stores in urban and suburban locations in the U.S., and smaller stores throughout their most successful global locations as well. The use of RFID as a technology to enable greater logistics and supply chain performance is already showing signs of supporting much greater levels of shipping accuracy and cost control. RFID tags can be read on pallets traveling at 40 miles per hour through warehouses and from a distance of 100 yards or more depending on the electronics in the tag itself. These technologies translate into a trend that will eventually re-order the food and beverage industry from an expansion, reporting, product planning and development standpoint. RFID is also already showing signs of being able to deliver exceptionally higher levels of inventory turns and cost reductions with product categories that had once been relegated to being commodities. RFID and analytics together show the potential to lengthen product lifecycles as well, making it possible for food and beverage companies to better manage the long-term profitability of their product lines as well (Kumar, 2007). Finally RFID also generates a massive amount of data per transaction, leading to a greater use of data mining as well (Liao, 2009). The third major trend affecting the food and beverage industry is the demand from manufacturers for greater visibility and transparency of their suppliers on quality management and safety metrics (Zubko, 2008). The growth of analytics and more efficient supply chain processes have led to the development of more effective quality management and compliance systems and platforms throughout the industry on a global scale. This has also been in response to greater levels...

On conjunction with this development has been the growth of integrated trading networks and initiatives to unify all members of a supply chain globally, enforcing a consistently high level of product quality over time (Kumar, 2007).
Conclusion

Analytics, logistics and supply chain synchronization driven by RFID technologies, and greater transparency are all major trends affecting the food and beverage industry today. The focus on how to mitigate risk while also attaining higher levels of efficiency continues to be a major focus within many of the food and beverage manufacturers today with analytics being used for measuring supply chain performance, planning and implementing new products, and also managing pricing as effectively as possible. There are also the uses of analytics for better managing logistics and supply chain operating sin conjunction with RFID technologies which make it possible to manage entire warehouses nearly 20% more effectively than previous manually-based approaches. The use of analytics and RFID technologies are also making it possible for businesses to have greater visibility into quality management and compliance of their suppliers as well. Being able to manage supply chains to quality management and compliance standards is crucial for the long-term growth of the food and beverage industry. All technologies mentioned in this analysis are used for attaining that goal.

References

R Cigolini, & T. Rossi. (2006). A note on supply risk and inventory outsourcing. Production Planning & Control, 17(4), 424.

Joanna Cosgrove. (2003, February). Riding the retail wave. Beverage Industry, 94(2), 35-46.

Sameer Kumar. (2007). Connective technology as a strategic tool for building effective supply chain. International Journal of Manufacturing Technology and Management, 10(1), 41.

Liao, C.. (2009). Consumers' behavior in the food and beverage industry through data mining. Journal of Information & Optimization Sciences, 30(4), 855.

Singh, A.. (2008).…