Barrett

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.

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Robotics Based Durability Testing of Dendritic Identifiers for a Secure Produce Supply Chain

Description

A description of the robotics principles, actuators, materials, and programming used to test the durability of dendritic identifiers to be used in the produce supply chain. This includes the application of linear and rotational servo motors, PWM control of a DC motor, and hall effect sensors to create an encoder.

ContributorsRobertson, Stephen (Author) / Kozicki, Michael (Thesis director) / Manfredo, Mark (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Collaborative Thesis - Disrupting the Supply Chain: Application of A.I. and Machine Learning in Spares Inventory Management

Description
This thesis discusses the case for Company X to improve its vast supply chain by implementing an artificial intelligence solution in the management of its spare parts inventory for manufacturing-related machinery. Currently, the company utilizes an inventory management system, based on previously set minimum and maximum thresholds, that doesn’t use…

This thesis discusses the case for Company X to improve its vast supply chain by implementing an artificial intelligence solution in the management of its spare parts inventory for manufacturing-related machinery. Currently, the company utilizes an inventory management system, based on previously set minimum and maximum thresholds, that doesn’t use predictive analytics to stock required spares inventory. This results in unnecessary costs and redundancies within the supply chain resulting in the stockout of spare parts required to repair machinery. Our research aimed to quantify the cost of these stockouts, and ultimately propose a solution to mitigate them. Through discussion with Company X, our findings led us to recommend the use of Artificial Intelligence (A.I.) within the inventory management system to better predict when stockouts would occur. As a result of data availability, our analysis began on a smaller scale, considering only a single manufacturing site at Company X. Later, our findings were extrapolated across all manufacturing sites. The analysis includes the cost of stockouts, the capital that would be saved with A.I. implementation, costs to implement this new A.I. software, and the final net present value (NPV) that Company X could expect in 10 years and 25 years. The NPV calculations explored two scenarios, an external partnership and the purchase of a small private company, that lead to our final recommendations regarding the implementation of an A.I. software solution in Company X’s spares inventory management system. Following the analysis, a qualitative discussion of the potential risks and market opportunities associated with the explored implementation scenarios further guided the determination of our final recommendations.
ContributorsHolohan, Joseph Michael Houston (Co-author) / Shahriari, Rosie (Co-author) / Aun, Jose (Co-author) / Heineke, Christopher (Co-author) / Gurrola, Macario (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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Supply Chain Logistics Network Optimization: Risk Mitigation and Cost Reduction Within the Aerospace Industry

Description

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…

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.
ContributorsAnanieva, Lorena (Author) / Keane, Katy (Thesis director) / Manfredo, Mark (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / Department of Economics (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Morrison School of Agribusiness (Contributor)
Created2022-05