Coors Coors Case Study

817 Words2 Pages

Research paper on Coors Beer Company
Name
Institution

Thesis statement
This paper looks at the case study of Coors Brewers Limited and their effort for increased market share through the adoption of neural network generated formula update. How effective is their adoption/ what are its failures? And how should the failures be addressed?
Questions 1-5
In order to achieve its affirmed goal of increased market share, Coors has to perfect favorable product that goes beyond social stigmas in spite of the venue or event that it is consumed in. This value proposition was further complicated by the fact that Coors was expected to design a product that compliments a ranging potential mood set 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 geared towards increasing market share through increased consumer selection over current market shareholders across a wide range of consumption categories. The research to ensure the beer gained a great market share was well back up with facts and it was successful. Neural networks also helped in predicting rating of the beer flavor and profitability in areas where neural networks have been successfully applied. The neural networks provided a more general framework for connecting financial information of a firm to the respective bond rating. However, neural networks are not readily interpretable-the end user must employ insight in interpretation.
The ongoing process for analyzing different flavors combination is cost and time expensive. Impacts within the current process include human taste test sampling, data collection time, and costs associated with manufacture of the actual test pro...

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... they address computationally difficult issues. However, based on my research of sensory evaluation models that are likely to solve the given problem, I found one that works well. This model is known as the Multilayer Perceptron (MLP) currently selected by Coors. However, I would also recommend a sub-model called the Multiple Input Multiple Output (MIMO). This sub-model is a specific alternate of the Back-propagation design.

Multiple Input, Multiple Output (MIMO) model

References
Harrington, R. J. (2008). Food and Wire Pairing: A Sensory Experience. Hoboken, NJ: WSiley and Sons Inc.
NeuroDimension Inc. (2012). Neural Network consulting. Retrieved August 10, 2013, from nd.com: http://www.nd.com/resources/partners2.html
Turban, E., Sharda, R., & Delen, D. (2011). Decision support and business intelligence systems (9th ed.). Boston: Prentice Hall.
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