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Industry is changing. Businesses are plagued with problems of inefficiency, ineffectiveness, and waste. Many of these issues arise from some common mistakes within established management structures; these issues include lack of expertise in leadership positions, lack of unity across the organization, and imbalance within the business. Using Information Measurement Theory,

Industry is changing. Businesses are plagued with problems of inefficiency, ineffectiveness, and waste. Many of these issues arise from some common mistakes within established management structures; these issues include lack of expertise in leadership positions, lack of unity across the organization, and imbalance within the business. Using Information Measurement Theory, the Kashiwagi Solution Model, and leadership theories, this thesis presents a simple approach to creating a business structure through explaining the basic tenets of a successful modern business. It was determined that the first and most important task of a business is to set realistic long-term goals for the organization. This thesis proposes that the basic needs of a successful business also includes having the right individuals, team formation, positive leadership, and the proper alignment of resources. It was found that it is best to hire individuals that exhibit some Type A characteristics because those individuals are likely to effectively carry out the goals of the business. Forming these individuals into small teams increases their processing speeds and encourages a balance of accountability, innovative solutions, and a network of learning. Furthermore, consistent, positive leadership that lives the company culture is a key element to successfully maintaining the business vision and maximizing associate effectiveness. It was also determined that aligning the organization to work towards the business vision can be performed through implementing a flat structure, placing individuals in roles that maximize effectiveness, and establishing the right business goals so that there is a consistent business vision at all levels of the organization. This thesis also provides guidance on how to implement these tenets in a simple, dominant way. Ultimately, the four proposed tenets working in unison towards business goals can lead to a successful and adaptable modern business.
ContributorsMeade, Payton Drew (Author) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Description
In the words of W. Edwards Deming, "the central problem in management and in leadership is failure to understand the information in variation." While many quality management programs propose the institution of technical training in advanced statistical methods, this paper proposes that by understanding the fundamental information behind statistical theory,

In the words of W. Edwards Deming, "the central problem in management and in leadership is failure to understand the information in variation." While many quality management programs propose the institution of technical training in advanced statistical methods, this paper proposes that by understanding the fundamental information behind statistical theory, and by minimizing bias and variance while fully utilizing the available information about the system at hand, one can make valuable, accurate predictions about the future. Combining this knowledge with the work of quality gurus W. E. Deming, Eliyahu Goldratt, and Dean Kashiwagi, a framework for making valuable predictions for continuous improvement is made. After this information is synthesized, it is concluded that the best way to make accurate, informative predictions about the future is to "balance the present and future," seeing the future through the lens of the present and thus minimizing bias, variance, and risk.
ContributorsSynodis, Nicholas Dahn (Author) / Kashiwagi, Dean (Thesis director, Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05