The growing complexity of clinical product logistics enterprises necessitates advanced analytical frameworks to evaluate and forecast buyer engagement worth. In healthcare-oriented logistics systems, buyer engagement extends beyond transactional frequency and encompasses behavioral interactions, service dependency, and long-term collaboration. This study develops a comprehensive framework for forecasting buyer engagement worth using behavioral metrics, integrating supply chain optimization, sustainability principles, and innovation management perspectives.

The research adopts a conceptual analytical approach by synthesizing insights from supply chain design, reverse logistics, and lifecycle assessment literature. Behavioral metrics, including engagement recency, transaction frequency, and economic contribution, are operationalized within a predictive framework tailored to clinical logistics enterprises. The model also incorporates sustainability considerations, network optimization strategies, and innovation lifecycle processes to enhance forecasting accuracy.

Findings suggest that behavioral metrics provide a robust foundation for forecasting buyer engagement worth when combined with advanced supply chain optimization techniques. The integration of closed-loop supply chain principles and reverse logistics enhances the ability to capture long-term value, particularly in environments characterized by regulatory complexity and resource constraints. Additionally, innovation management frameworks contribute to improved alignment between customer engagement strategies and organizational capabilities.

The study contributes to the literature by bridging behavioral analytics with clinical logistics and sustainable supply chain management. It highlights the importance of integrating analytical models with operational and environmental considerations to achieve strategic decision-making. Limitations include the conceptual nature of the model and the absence of empirical validation, indicating opportunities for future research involving data-driven implementations and advanced predictive algorithms.

Overall, the research provides a structured and interdisciplinary framework for forecasting buyer engagement worth, offering valuable insights for healthcare logistics enterprises aiming to optimize performance and achieve sustainable competitive advantage.

Buyer Engagement, Behavioral Metrics, Clinical Logistics, Supply Chain Optimization, Reverse Logistics, Sustainable Supply Chains, Predictive Analytics, Healthcare Logistics, Lifecycle Assessment

Badran, M. F., El-Haggar, S. M. ( 2006 ). Optimization of municipal solid waste management in Port Said - Egypt. Waste Management, 26 ( 5 ), 534–545.

Chaabane, A., Ramudhin, A., Paquet, M. ( 2012 ). Design of sustainable supply chains under the emission trading scheme. International Journal of Production Economics, 135 ( 1 ), 37–49.

Chatzouridis, C., Komilis, D. ( 2012 ). A methodology to optimally site and design municipal solid waste transfer stations using binary programming. Resources, Conservation and Recycling, 60, 89–98.

Cooper, R.G., Winning at New Products - Accelerating the Process from Idea to Launch, Perseus Books Group (Cambridge (US), 2001).

Cooper, R.G., "Perspective: The Stage-Gate® Idea-to-Launch Process-Update, Whats New, and NexGen Systems," Journal of Product Innovation Management, Vol. 25, No. 3 (2008), pp. 213-232.

Dehghanian, F., Mansour, S. ( 2009 ). Designing sustainable recovery network of end-of-life products using genetic algorithm. Resources, Conservation and Recycling, 53 ( 10 ), 559–570.

Govindan, K., Soleimani, H. ( 2017 ). A review of reverse logistics and closed-loop supply chains: a Journal of Cleaner Production focus. Journal of Cleaner Production, 142, 371–384.

Graikos, A., Voudrias, E., Papazachariou, A., Iosifidis, N., Kalpakidou, M. ( 2010 ). Composition and production rate of medical waste from a small producer in Greece. Waste Management, 30 ( 8–9 ), 1683–1689.

Hu, T. L., Sheu, J. B., Huang, K. H. ( 2002 ). A reverse logistics cost minimization model for the treatment of hazardous wastes. Transportation Research Part E: Logistics and Transportation Review, 38 ( 6 ), 457–473.

Kemp, P. "Perspective on the challenges of conducting clinical trials in Regenerative Medicine," Regenerative Medicine: A New Frontier for Therapeutic Intervention, Cambridge, UK, 2010, The Society for Medicines Research.

Manfredi, S., Tonini, D., Christensen, T. H. ( 2011 ). Environmental assessment of different management options for individual waste fractions by means of life-cycle assessment modelling. Resources, Conservation and Recycling, 55 ( 11 ), 995–1004.

Martin, P., R. Hawksley, and A. Turner, "The Commercial Development of Cell Therapy - Lessons for the Future?," REMEDI-EPSRC. (2009).

Mason, C., et al., "Regenerative Medicine - Glossary PAS 84:2008," Regenerative Medicine, Vol. 4, No. 4 (Suppl.1) (2009), pp. 1-88.

NIH, "Regenerative Medicine. (2006), National Institutes of Health, US.

Olapiriyakul, S., Pannakkong, W., Kachapanya, W., Starita, S. ( 2019 ). Multiobjective Optimization Model for Sustainable Waste Management Network Design. Journal of Advanced Transportation, 2019.

Pishvaee, M. S., Razmi, J., Torabi, S. A. ( 2012 ). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy Sets and Systems, 206 1–20.

Williams, D., R. Archer, and A. Dent, "Building a viable regenerative medicine industry - a guide for stakeholders," Remedi. (2010), Loughborough University, Loughborough, UK.