Food and Beverage Industry Analyzing

Food and Beverage Industry

Analyzing Three Trends that have Affected

The food and beverage industry is going through a fundamental transformation as the economies globally are redefining the cost and performance structure of supply chains this industry relies on. The continued economic uncertainty has made all participants in this industry concentrate on operational efficiencies that range from analytics to the streamlining of logistics and supply chain operations, to en masse adoption of quality management standards and greater transparency. The intent of this analysis is to identify how these three trends are affecting the food and beverage industry and its many operations.

Analysis of Trends Affecting the Food and Beverage Industry

The greater the level of economic uncertainty, the higher the level of investment in analytics and business intelligence that taken together can reduce uncertainty and risk (Liao, 2009). Given the inflation levels throughout many of the supply chains that food and beverage manufacturers are reliant on, predictive analytics especially have broad, growing appeal (Cosgrove, 2003). Analytics is today being used in the food and beverage industry for managing suppliers to quality standards, ensuring the health and trust of customers is maintained (Zubko, 2008). Analytics is pervasively used throughout pricing and distribution decision frameworks food and beverage manufacturers rely on as well, ensuring the highest possible gross margins are attained given current pricing elasticity by product and market (Cosgrove, 2003). Analytics are also extensively used throughout the product development, product launch and marketing strategies of food and beverage companies as well. Using the techniques of data mining and analys8is, food and beverage companies are working to create personas, or statistical representations of their top customers which are used for guiding their product, marketing and services strategies (Liao, 2009). The food and beverage companies who are pioneering these efforts include Proctor & Gamble and Unilever who have agreements in place with Wal-Mart to share pricing and sales-out data to benefit these companies in designing future generation products and pricing (Liao, 2009).

Analytics and data mining is also extensively being used by food and beverage distributors, retailers and members of the distribution channels globally to better manage inventory turns, create more accuracy in pricing, and create more effective promotions (Liao, 2009). Of these, Wal-Mart's data mining and analytics expertise is dominating the industry based on its accuracy, speed and precision . Wal-Mart has satellite links on each of its stores that upload sales-out data every evening for automated processing and analysis in their Bentonville, Arkansas headquarters (Singh, 2008). Wal-Mart's expertise in this area is so advanced the analysts can define and predict the pricing elasticity and demand curve for each product in their stores over time (Singh, 2008). Wal-Mart uses this analytics capability as a means to compete in the food and beverage industry relative to regional and national competitors, often using analytics to determine the optimal mix of products for bundling and promotions. Wal-Mart also uses these techniques for deciding which products go on sale when based on seasonality that the analytics applications they are using indicate is the best possible week of the year to sell given food or beverage (Singh, 2008). Wal-Mart also uses these analytics to coordinate with its primary tier of suppliers, nearly 3,500 companies globally, to determine the optimal level of inventory turns and order points based on each regio9n of the world they are operating in (Singh, 2008). Wal-Mart credits analytics as a means to trim back extraneous spending through the use of insight and risk reduction through more effective use of the massive amount of data they collect in transactions daily on a global level. Combining the analytics from Proctor & Gamble and Wal-Mart, both companies have been able to increase inventory turns by 20%, saving millions of dollars in expenses while making sure only the products customers want are on the shelves of the retailer (Liao, 2009).

Another trend affecting the food and beverage industry is the revolutionary advancements in logistics and supply chain operations (Cosgrove, 2003). As is the case with analytics, the retailers in the food and beverage industry are driving this area of change as well. 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 of legislation surrounding product quality and stability of supply chain sources to ensure health of consumers (Zubko, 2008). 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).