Introduction: The honey supply chain has an important economic, nutritional, environmental, and food security role. Honey and other bee-related products are used in food, pharmaceutical, cosmetic, and health-related industries. Honeybees also contribute to agricultural production and ecosystem sustainability through pollination. Iran has potential for honey production because of its climatic diversity and biological resources. However, the honey supply chain in Iran faces challenges such as weak standardization, fraud, colony diseases, dependence on climatic conditions, ineffective marketing, limited technological capacity, weak traceability, and low consumer trust. Artificial Intelligence (AI) can potentially support monitoring, disease prediction, authentication, quality control, and market analysis. Nevertheless, AI adoption requires a realistic assessment of the supply chain’s current strategic position. Therefore, this study aimed to analyze the readiness of Iran’s honey supply chain for AI adoption using the SWOT model.
Materials and Methods: This could be considered as an applied study in terms of purpose as well as a descriptive-analytical and exploratory-survey one in terms of methodology. The required data were collected in 2025 through a literature review, expert interviews, and a researcher-made questionnaire. The statistical population included experts and practitioners in beekeeping, honey production, processing, marketing, and honey supply chain management. Due to the specialized and exploratory nature of the topic, purposive sampling was used, and 20 complete questionnaires were analyzed. The inclusion criteria included specialized experience in beekeeping or one of the stages of the honey supply chain, familiarity with production, processing, market, or supply chain issues, and the ability to evaluate strategic factors affecting smart transformation. The SWOT model was used as the main analytical framework. Internal and external factors were identified through the literature and expert interviews, revised based on expert opinions, and classified into Strengths, Weaknesses, Opportunities, And Threats (SWOT). The Internal Factor Evaluation (IFE) and External Factor Evaluation (EFE) matrices were used to determine the strategic position of the supply chain. The importance of each factor was assessed using a five-point Likert scale. The normalized weight of each factor was calculated from the mean importance score in the relevant matrix. The strategic rating was determined on a scale from 1 to 4 based on the current status of the honey supply chain. Cronbach’s alpha assessed reliability, Kendall’s coefficient examined expert agreement, and sensitivity analysis tested the stability of the strategic position.
Results and Discussion: The study findings identified 17 strengths, 23 weaknesses, 23 opportunities, and 19 threats affecting the readiness of Iran’s honey supply chain for the AI adoption. The main strengths included the role of bees in pollination and agricultural production, environmental compatibility, production at different scales, diversity of bee-related by-products, local branding potential, and entering digital markets. These strengths could provide a basis for smart systems in production, quality control, marketing, and product traceability. The chain also faced internal weaknesses, including dependence on climatic conditions and floral resources, colony diseases, insufficient technical knowledge, lack of specialized bee health centers, weak standardization and quality control, limited financial resources, poor access to modern equipment, ineffective marketing, weak cooperation among chain actors, low productivity, lack of trust, and fraud. Many of these weaknesses were informational, managerial, technological, and institutional. Therefore, AI might help address some of them in the long run, but only after basic infrastructures were developed. The total IFE score was 2.22, which was below the midpoint of 2.5 and indicated the relative dominance of internal weaknesses over strengths. The total EFE score was 2.15 (also below 2.5), showing that the honey supply chain did not currently respond effectively to environmental opportunities and threats. Important opportunities included growing demand for natural and organic honey, export market development, use of applications and sensors, online sales, supplement markets, local processing and packaging centers, international quality certificates, and Iran’s biodiversity. In contrast, climate change, excessive pesticide use, reduced plant diversity, fraudulent honey, weak market supervision, disease-related losses, export restrictions, weak policy attention, and difficulty in accessing modern tools limited the effective use of these opportunities. The combination of IFE and EFE results placed Iran’s honey supply chain in the defensive WT quadrant, reflecting the dominance of internal weaknesses and external threats. Accordingly, AI should not be adopted as an immediate, large-scale, and costly strategy; instead, AI adoption should follow a gradual, problem-oriented, and infrastructure-based pathway. Sensitivity analysis also showed that despite changes in the weights of the most important factors, IFE and EFE scores remained below 2.5; therefore, the WT position was relatively stable.
Conclusion and Suggestions: The results indicated that successful AI adoption in Iran’s honey supply chain would require prior development of data, institutional, educational, financial, and policy infrastructures. The initial stage should focus on digital registration of apiary information, databases on colony diseases and losses, product traceability systems, quality control mechanisms, digital training for beekeepers, and pilot smart beekeeping projects. Strengthening beekeeper cooperatives can reduce digital transformation costs through shared access to equipment, laboratories, packaging facilities, online platforms, and data-based services. Due to the importance of fraud prevention and consumer trust, developing a traceability system from apiary to consumer is recommended. AI-based tools may support climate risk warning, disease prediction, colony monitoring, market analysis, honey authentication, and data-driven decision-making; however, these should be interpreted as potential capacities rather than empirically proven effects. Future studies should validate the findings using larger samples, broader geographical coverage, and complementary methods such as Delphi, AHP, QSPM, cost-benefit analysis, and technology readiness assessment.