Business Analytics for Business Analysts in Manufacturing

Business Analytics for Business Analysts in Manufacturing

Coleen Wilder (Valparaiso University, USA) and Ceyhun Ozgur (Valparaiso University, USA)
DOI: 10.4018/978-1-4666-7272-7.ch007
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Many of the skills that define analytics are not new. Nonetheless, it has become a new source of competitive advantage for many corporations. Today's workforce, therefore, must be cognizant of its power and value to effectively perform their jobs. In this chapter, the authors differentiate the role of a business analyst by defining the appropriate skill level and breadth of knowledge required for them to be successful. Business analysts fill the gap between the experts (data scientists) and the day-to-day users. Finally, the section on Manufacturing Analytics provides real-world applications of Analytics for companies in a production setting. The ideas presented herein argue in favor of a dedicated program for business analysts.
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Business Analytics is something most people have heard about but fewer know or can agree on the definition. Some argue it is nothing new, simply another word for Business Intelligence. Others argue Business Intelligence and Business Analytics are two different disciplines, each with their own set of skills and software (Gnatovich, 2006). The purpose of this paper is not to debate these issues; the two terms will therefore be used interchangeably. The focus herein is to position Business Analytics, by any name, in the undergraduate curriculum in a manner that best serves students. This task will begin with a discussion on the value of analytics to today’s businesses and will follow with suggestions on how to incorporate it into the curriculum. The expected challenges to implementing these ideas will be summarized in a separate section ending with ideas for future research.

Key Terms in this Chapter

Data Scientist: The skill set must include “a solid foundation in math, statistics, probability, and computer science” with the ability to write code at the forefront.

Business Analytics: The use of statistics and other operations research techniques, such as simulation, decision trees and other operations research techniques.

Data Driven Companies: Companies that have real world problems involving big data and business analytics.

Business Intelligence: The process of gathering and transforming raw data into actionable insights yielding better decisions.

Big Data: The current phrase used to describe the changes in the accumulation of data over the past decade; the distinguishing factors of which are volume (2.5 Exabyte’s per day), velocity (speed at which data is created), and variety (images, texts, videos, etc.).

Business Analysts: A person needs enough conceptual knowledge and quantitative skills to be able to frame and interpret analyses of a business problem involving big data in an effective way.

Analytics in Manufacturing: The use of statistical techniques to solve real world manufacturing problems.

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