In response to significant disruption, supply chain optimization became sensitive to increasing consumer expectations, unexpected demand fluctuation, and inventory costs. Proactive movement, understanding, and empowerment have fostered the beneficial results of supply chain optimization, cooperation, and operational resilience. These pioneering activities are critical to achieving a paradigm shift in the supply chain, even agility in response to changing demand. However, sophisticated analytics such as artificial intelligence (AI) and blockchain are supposed to overcome these challenges to make smarter decisions on a daily basis. Due to these facts, this study aimed to model AI’s and blockchain’s role in supply chain optimization by conducting a systematic literature review based on the idealized framework of Rejeb et al. (2022) and the SALSA mechanism. In addition, this paradigm-shifting approach will provide fairer views and options for managing forecasting, planning, monitoring, and reporting across the entire supply chain. The emphasis remains on real-time accuracy, easy access, and optimization of operational indicators such as sales, visibility, and end-to-end supply chain operations at all times and from any location. It will be an eye-opening experience to enable stakeholders and partners to communicate information collaboratively, consistently, and efficiently.

Internet of Things (IoT), artificial intelligence (AI), and digital connectivity offer potential opportunities to improve dairy production and resource management, but their application in developing countries remains constrained by infrastructure, cost, and technical limitations. This study develops an integrated System Dynamics model to examine the potential effects of digital technology adoption on dairy production, livestock development, resource recovery, and economic activities in Bangladesh. Drawing on a systematic literature review and secondary evidence, the model represents technology-related improvements through scenario-dependent efficiency coefficients rather than physically implemented IoT systems or trained AI algorithms. The study examined baseline and technology adoption scenarios over 60 months, supported by parameter sensitivity analysis. The simulations show higher milk production, livestock development, and resource recovery outputs under the technology adoption assumptions. Sensitivity analysis, configured for 200 simulations across three technology-related parameters, indicates increasing variation in selected outputs over time. The findings demonstrate how digital technology assumptions can influence interconnected production and resource flows, while highlighting the importance of connectivity, affordability, and institutional support. The framework supports exploratory scenario assessment rather than empirical prediction of individual dairy farms.