Research Paper on Coors Beer CompanyName InstitutionThesis StatementThis paper examines the case study of Coors Brewers Limited and their efforts to increase market share through the adoption of the generated formula update from the neural network. How effective is their adoption/what are its failures? And how should failures be addressed? Questions 1-5 To achieve its stated goal of increasing market share, Coors must perfect a favorable product that overcomes social stigmas despite the location or event in which it is consumed. This value proposition was further complicated by the fact that Coors would have to design a product that suited a broad set of potential moods during which it was to be consumed. Based on the market research conducted by the brewer, analytical points and impacts were identifiable. This move was aimed at increasing market share through greater consumer selection than current market share holders across a broad range of consumer categories. The quest to secure beer a large market share has been largely supported by facts and has been successful. Neural networks have also helped predict beer flavor ratings and profitability in areas where neural networks have been successfully applied. Neural networks have provided a more general framework for linking a company's financial information to its bond rating. However, neural networks are not easily interpretable: the end user must use in-depth information in the interpretation. The ongoing process of analyzing different flavor combinations is costly in terms of cost and time. Impacts within the current process include human taste test sampling, data collection times, and costs associated with producing the actual test… middle of paper… face computationally difficult problems. However, based on my search for sensory evaluation models that could solve the given problem, I found one that works well. This model is known as Multilayer Perceptron (MLP) currently selected by Coors. However, I would also recommend a submodel called Multiple Input Multiple Output (MIMO). This submodel is a specific alternative to the back propagation design. MIMO (Multiple Input, Multiple Output) model. References Harrington, R. J. (2008). Pairing food and thread: a sensorial experience. Hoboken, NJ: WSiley and Sons Inc. NeuroDimension Inc. (2012). Neural networks consultancy. Retrieved August 10, 2013, from nd.com: http://www.nd.com/resources/partners2.htmlTurban, E., Sharda, R., & Delen, D. (2011). Decision support systems and business intelligence (9th ed.). Boston: Prentice Hall..