Artificial Intelligence and Machine Learning: Making Practical Use of a Growing Technology

Open Access
- Author:
- Wackerman, Benjamin
- Area of Honors:
- Supply Chain and Information Systems
- Degree:
- Bachelor of Science
- Document Type:
- Thesis
- Thesis Supervisors:
- Robert Alexander Novack, Thesis Supervisor
John C Spychalski, Thesis Honors Advisor - Keywords:
- Artificial Intelligence
Machine Learning
Supply Chain
Optimization
Forecasting
Inventory - Abstract:
- Artificial intelligence (AI) and machine learning (ML) are relevant technologies that have been a topic of conversation in the business field for several years. Many strides have been made to introduce these technologies into the consumer experience and the everyday lives of the modern American. With the current supply chain limitations causing issues for firms across the world, more companies are looking to find practical uses of AI and ML that will increase supply chain efficiency and decrease costs. This thesis looks to gain a better understanding of the history and principles of AI and ML while also highlighting specific examples of use cases within firms’ supply chains. With the prevalence of research into AI and ML, a major portion of this paper contains a literature review of documents concerning the progress of AI and ML as well as examples of companies that have relevant use cases for these technologies. After an extensive literature review, this thesis contains an analysis of several conducted interviews with supply chain professionals who have considerable experience working in AI and ML. These interviews helped gain insight into real-world experiences with AI and ML in supply chain and the risks that had to be mitigated to successfully transition a process with these systems. After an analysis of all the compiled information, the thesis makes some conclusions about the state of AI and ML and where these technologies provide the most opportunity within the supply chain. The findings of this thesis include that while AI and ML have progressed over the past several years, many supply chain firms are slowly incorporating these solutions. Nevertheless, the research shows firms that make sure to thoroughly discuss where the most value could be provided, they can successfully implement AI and ML if they start small and look to utilize them within forecasting, inventory, and supply chain optimization.