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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
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ISO Certification: 9001:2015
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 10

Published on: October 2026

AI-DRIVEN SMART BLOOD BANK OPTIMIZATION, COMPONENT MATCHING & EMERGENCY COLD-CHAIN LOGISTICS SYSTEM

Prathamesh Jagtap Harshwardhan Bhagat Yashwant Bothe Shivmani Shinde Prof. Sudarshana Dhongale

Dr. Sushama V. Telrandhe

Department of Computer Science and Engineering, Gurunanak Institute of Engineering and Technology, Nagpur, India
DOI:https://doi.org/10.55041/ijcope.v2i10.024

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Abstract

Transfusion medicine and critical emergency care networks worldwide are severely hampered by systemic logistical fragmentation, substantial cross-matching turnaround latencies, and critical bio-wastage rates of perishable blood components such as Platelets and Packed Red Blood Cells (PRBC) [1, 2]. Conventional blood banking architectures rely on localized, reactive ledger registries that lack component-level tracking, real-time cross-facility visibility, automated multi-tier compatibility verification, and continuous cold-chain compliance auditing [5, 8]. To resolve these challenges, this paper presents the complete architectural formulation, mathematical modeling, and production-grade implementation of the AI-Driven Smart Blood Bank Optimization, Component Matching & Emergency Cold-Chain Logistics System. Built upon a decoupled MERN (MongoDB, Express.js, React.js, Node.js) architecture with React Context API and Axios interceptor middleware, the system incorporates an intelligent rule-based multi-component compatibility engine (evaluating ABO/Rh and sub-component compatibility), MongoDB 2dsphere-indexed geospatial proximity dispatch, simulated dashboard cold-chain thermal anomaly auditing with auto-lock interlocks, predictive 7-day rolling horizon stockout and expiry forecasting models, autonomous air-corridor drone transit terminal routing, and direct patient emergency request verification via dedicated tracking portals. Rigorous empirical validation against legacy regional workflows demonstrates a 97.3% reduction in emergency allocation latency (cutting turnaround from ~75 minutes to 3.2 seconds), 100% elimination of clerical compatibility mismatches, a reduction of perishable component spoilage from 16.4% down to under 2.2%, and seamless immutable audit tracking for regulatory healthcare governance [4, 12].

Keywords: Healthcare Logistics, Smart Blood Bank Optimization, Blood Component Fractionation, Cold-Chain Telemetry, Geospatial Dispatch, ABO/Rh Compatibility Engine, Predictive Analytics, Autonomous Drone Transit, Direct Patient Request Portal, MERN Stack

How to Cite this Paper

Jagtap, P., Bhagat, H., Bothe, Y., Shinde, S. & Dhongale, S. (2026). AI-Driven Smart Blood Bank Optimization, Component Matching & Emergency Cold-Chain Logistics System. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(10), 1-9. https://doi.org/10.55041/ijcope.v2i10.024

Jagtap, Prathamesh, et al.. "AI-Driven Smart Blood Bank Optimization, Component Matching & Emergency Cold-Chain Logistics System." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 10, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i10.024.

Jagtap, Prathamesh,Harshwardhan Bhagat,Yashwant Bothe,Shivmani Shinde, and Sudarshana Dhongale. "AI-Driven Smart Blood Bank Optimization, Component Matching & Emergency Cold-Chain Logistics System." International Journal of Creative and Open Research in Engineering and Management 02, no. 10 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i10.024.

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References


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  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Oct 06 2026
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