The thesis explores artificial intelligence (AI) in agricultural (Ag) supply chains (SCs) and presents a new typology to understand artificial intelligence-based solutions in agricultural SCs. The thesis was performed utilizing a research-based review to investigate the current uses of artificial intelligence-based solutions in agricultural SCs. The AI-based solutions were found in case studies that reviewed AI operations in different areas internationally.
The typology was formed on the foundation of two dynamics, the location of AI applications in Ag SCs and the driving values to integrate the AI applications. In order to develop the typology, the AI applications were studied in a series of different analyses. The analyses helped to critique and scrutinize the AI applications to gain new perspectives. The series of analyses consists of exploring the AI applications’ location within the supply chain, the value additions to the supply chain from integrating the AI applications, and the resulting depth of the effect of AI application has on the supply chain. Each additional evaluation of the AI applications examining another parameter further exposed more insight and started to build a structured ideology of AI.
The proposed typology aims to create a tool of measurement to infer AI technology’s relation in the SCs and create a new viewpoint that will lead investigation and provide insight for predictions of AI’s future in agricultural SCs. In addition, the new typology should aid agriculture firms in understanding and capturing the potential synergies stemming from the driving values of innovation.
The study found that AI applications with a strong relationship in the supply chain provide the greatest beneficiary relationship between technology value creation and supply chain logistics. Furthermore, AI applications will have the strongest relationship and implementation when operating in collaboration with other supply chain locations and AI integrated firms. Concluding the thesis, relevant policy and business practice recommendations are proposed.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-3932 |
| Date | 01 August 2021 |
| Creators | Ault, Samantha Jane |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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