Chapter One — 1.1 Background to the Study

Organizations that depend on agricultural supply chain management are under increasing pressure to modernize, and Generative AI has emerged as one of the more promising avenues for doing so, given its demonstrated impact in related domains.

Despite this potential, many existing agricultural supply chain management were not originally designed with generative AI in mind, resulting in persistent gaps in system performance that limit their overall effectiveness. This study examines how Generative AI can be applied to help close that gap.

1.2 Statement of the Problem

Current agricultural supply chain management in many organizations struggle with inadequate system performance, often relying on manual processes or outdated architectures that were not designed for today's operating environment. Without a structured approach to integrating generative AI, these limitations are likely to persist, exposing organizations to inefficiency, risk, and a poor user experience. This study is motivated by the need to design and evaluate a generative AI-based approach to addressing this problem.

1.3 Objectives of the Study

1. To design and implement a generative AI-based approach to improving system performance in agricultural supply chain management.
2. To evaluate the effectiveness of Generative AI in enhancing system performance within agricultural supply chain management.
3. To identify the key requirements and constraints relevant to deploying generative AI in this context.
4. To assess user and stakeholder perception of the resulting system.

1.4 Research Questions

1. How can generative AI be applied to improve system performance in agricultural supply chain management?
2. How effective is Generative AI at enhancing system performance within agricultural supply chain management?
3. What requirements and constraints are relevant to deploying generative AI in this context?
4. How do users and stakeholders perceive the resulting system?

1.5 Significance of the Study

This study is significant to software developers and system architects seeking practical guidance on applying Generative AI within agricultural supply chain management. It is equally relevant to organizations that rely on these systems, offering a reference point for evaluating whether such an investment is justified, and it adds to the growing body of work on generative AI applications in software technology / IT.

1.6 Scope of the Study

The study is limited to the design, implementation, and evaluation of a generative AI-based approach to improving system performance within agricultural supply chain management. Reflecting its clearly defined scope, it does not extend to a full commercial rollout or long-term post-implementation review beyond the study period.