Barrett, The Honors College Thesis/Creative Project Collection
Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.
Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.
Filtering by
- All Subjects: Supply Chain
- Creators: Department of Economics
The goal of this project is to develop a deeper understanding of how machine learning pertains to the business world and how business professionals can capitalize on its capabilities. It explores the end-to-end process of integrating a machine and the tradeoffs and obstacles to consider. This topic is extremely pertinent today as the advent of big data increases and the use of machine learning and artificial intelligence is expanding across industries and functional roles. The approach I took was to expand on a project I championed as a Microsoft intern where I facilitated the integration of a forecasting machine learning model firsthand into the business. I supplement my findings from the experience with research on machine learning as a disruptive technology. This paper will not delve into the technical aspects of coding a machine model, but rather provide a holistic overview of developing the model from a business perspective. My findings show that, while the advantages of machine learning are large and widespread, a lack of visibility and transparency into the algorithms behind machine learning, the necessity for large amounts of data, and the overall complexity of creating accurate models are all tradeoffs to consider when deciding whether or not machine learning is suitable for a certain objective. The results of this paper are important in order to increase the understanding of any business professional on the capabilities and obstacles of integrating machine learning into their business operations.
Purpose: This paper serves to illustrate the risks that affect multinational organizations during this new era of global production and increased supply chain complexity. This paper also strives to showcase the benefits of conducting a Network Optimization analysis on a firm’s logistics system including but not limited to reducing the impact of supply chain market and operational risk, improving efficiency, and increasing cost savings across the organization. Approach: This paper will have two main sections beginning with an in depth look into the theory supporting supply chain logistics network optimizations. Through this literature review, the best practices in the industry will be compared to risk mitigation methodology to determine an analytical process that can be applied to companies considering conducting a network optimization. The second stage of this paper takes a clinical look at the aerospace industry and the implementation process of a Logistics Network Optimization at an industry leader to ultimately recommend additional considerations they should implement into their process. Recommendation: To ensure the effective adoption of a network optimization in the aerospace industry, and other manufacturing industries, the maintenance of logistics data and creation of long term 3PL partnerships are needed for success. It is also important to frame a network optimization not as an operational project, but rather a critical business process aimed to mitigate risk within the supply chain though a four-stage risk identification process.