Chapter One — 1.1 Background to the Study

The rapid evolution of Artificial Intelligence has transformed the way organizations design, deploy, and manage agricultural supply chain management. As institutions seek to modernize legacy processes, Artificial Intelligence offers new opportunities to improve service delivery, reduce manual overhead, and respond more effectively to user needs.

In practice, however, adoption of artificial intelligence within agricultural supply chain management has been uneven, and its actual impact on decision support is not yet well understood in a rigorous, evaluable way — a gap this study is positioned to address.

1.2 Statement of the Problem

Current agricultural supply chain management in many organizations struggle with inadequate decision support, often relying on manual processes or outdated architectures that were not designed for today's operating environment. Without a structured approach to integrating artificial intelligence, 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 artificial intelligence-based approach to addressing this problem.

1.3 Objectives of the Study

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

1.4 Research Questions

1. How can artificial intelligence be applied to improve decision support in agricultural supply chain management?
2. How effective is Artificial Intelligence at enhancing decision support within agricultural supply chain management?
3. What requirements and constraints are relevant to deploying artificial intelligence in this context?
4. How do users and stakeholders perceive the resulting system?

1.5 Significance of the Study

Beyond its immediate technical contribution, this study offers value to organizations evaluating whether to invest in artificial intelligence for their own agricultural supply chain management, and contributes to the broader literature on applied software technology / IT by documenting a concrete implementation and evaluation case.

1.6 Scope of the Study

The study is limited to the design, implementation, and evaluation of a artificial intelligence-based approach to improving decision support within agricultural supply chain management. Reflecting its BSc-level scope, it does not extend to a full commercial rollout or long-term post-implementation review beyond the study period.