Dr. Raja Jayaraman
Dr. raja jayaraman Associate Professor Associate Chair of Undergraduate studies Management Science And Engineering

Contact Information
raja.jayaraman@ku.ac.ae +97123124177


Raja Jayaraman holds the position of Associate Professor and Associate Chair of Undergraduate Studies in the Department of Management Science & Engineering at Khalifa University, located in Abu Dhabi, United Arab Emirates. He completed his Ph.D. in Industrial Engineering at Texas Tech University and holds a Master of Science degree in Industrial Engineering from New Mexico State University, as well as Master's and Bachelor’s degrees in Mathematics from India. During his postdoctoral research, he specialized in the adoption of technology and implementation of innovative practices for healthcare supply chain, logistics, and service delivery. Dr. Jayaraman has successfully led numerous research projects and pilot implementations of supply chain data standards within the US healthcare system.

Dr. Jayaraman’s current research interests focus on a multidisciplinary approach to engineering problems applying systems engineering, process optimization, operational excellence, and digital transformation tools to characterize, model, and study complex systems. His research targets applications in supply chains, interconnected logistics, maintenance planning, and healthcare delivery. Dr. Jayaraman has over 15 years of experience in higher education institutions spanning India, the USA, and the UAE. He teaches graduate and undergraduate courses in supply chain and logistics, optimization, stochastic models, systems engineering, and quality management. 

Dr. Jayaraman has over 120 journal publications in the domains of engineering, technology and business and his research contributions have appeared in several top-tier journals including Annals of Operations Research, IISE Transactions, Computers & Industrial Engineering, IEEE Transactions on Engineering Management, IEEE Engineering Management Review, Production Planning & Control, Energy Policy, Technology in Society, Knowledge-Based Systems, Future Generation Computer Systems, Journal of Cleaner Production, Technology Forecasting and Social Change, Engineering Management Journal, and others. 

Dr. Jayaraman has been recognized in 2022 Stanford University World’s Top 2% scientists and most influential researcher in a study by Elsevier and Stanford University, under subject categories: Operations Research, Artificial Intelligence, and Information and Communication Technologies. Dr. Jayaraman is a senior member of the Institute of Industrial & Systems Engineering (IISE) and a member of the Institute for Operations Research and Management Science (Informs). He currently serves as Associate Editor for the International Journal of Quality and Reliability and an editorial board member of several journals.

  • Ph.D. Industrial Engineering, Texas Tech University, USA
  • MSc. Industrial Engineering, New Mexico State University, USA
  • MSc. Mathematics, Anna University, Chennai, India
  • BSc. Mathematics, University of Madras, India

  • Advanced Stochastic Processes (ISYE432)
  • Introduction to Industrial & Systems Engineering (ECCE432)
  • Operations Research 1 (ISYE251)
  • Quality Control & Reliability (ISYE311)
  • Supply ChainLogistics and Transportation Networks (ISYE430)
  • Systems Engineering (ESMA633)
  • Systems Optimization (ENGR605)

Affiliated Research Institutes/Centers
  • Research Center for Digital Supply Chain and Operations Management

Research Interests
  • Digital Manufacturing and Supply Chain; Industry 4.0 tools and applications
  • Industry 4.0 tools and applications
  • Quality 4.0, Operational Excellence
  • Multicriteria Optimization
  • Healthcare Systems Engineering

Research Projects

Predictive Analytics for Supply Chains 

Supply chain and logistics management primarily deals with managing efficient flow of information and products from several sources to their respective destinations. Globalization and rapid development across several industries has made supply chains more complex, and susceptible to higher risks that requires sophisticated tools and analytical solutions to manage and predict demand owing to longer lead times. Making decisions merely based on past data and trends can no longer provide competitive advantage to companies. In turn, supply chain analytic tools exploit the dynamics and interconnectedness in forecasting, pricing, procurement, storage, distribution and other areas. Globalized supply chains have offered new business opportunities and markets with a renewed focus on quality and cost.  Supply Chain Analytics aims at researching new methods and tools to integrate and improve-performance of complex and extended enterprise supply chains using predictive analytics and self-optimizing logistics. As supply chains get bigger and more complex, the amount of data generated by the network grows correspondingly. Data is also being collected from several channels such as linked instrumentation and sensors. To fully understand and act on the "state" of the supply network, it is becoming increasingly important to use a data-driven approach to process and analyze this data in support of supply chain planning and management requirements. This project employs predictive analytics techniques to analyze variables such as inventory levels, customer demand, capacity of transportation channels and even equipment maintenance requirements.

Digital Operations Management 

Due to globalization, the modern supply chains often span several countries and continents. Traceability of the products in such supply chains becomes hard or almost impossible due to different registration systems involved in the process. At the same time, visibility, traceability and transparency for the end customers are the major efficiency and sustainability pillars for any Supply Chain System. All of these three pillars require enhanced digitalization for enabling paperless, borderless and highly trusted access to all relevant information for all parties using the supply chains. Blockchain is a newly emerging technology that is capable to provide necessary security, visibility, traceability and transparency of the digital transactions in any supply chain (and other businesses). Blockchains are decentralized, distributed ledgers of transactions that run on multiple computers around the world—making a storage system that is robust against attacks and hacking. Blockchains can be public or private network . Also, Ethereum blockchain allows for programmability to include algorithms, rules, conditions, and flow logic to be executed by all participating blockchain nodes. The execution outcomes of all of these nodes are shared and eventually agreed by all blockchain nodes—and therefore, achieving high trust and credibility throughout the blockchain ledgers which is globally accessed by participating stakeholders. Smart contract code will be developed and written to provide undisputed, credible, and authenticated control functionalities related to cargo and shipment identity, access, generated data (e.g., normal readings, push and pull notifications, alerts, etc.), ownership, origination and history, state, movements, etc.  Ethereum Blockchain can be used to record, track, govern access, and even perform automating billing using Ether cryptocurrency of a container or a cargo as it is being shipped, moved, handled, inspected, and approved among multiple stakeholders.

IoT-Driven Blockchain Solution for Superior Management of Healthcare Supply Chain and Medical Data

Cold supply chain management in healthcare is the generic term for management of sensitive medical items throughout the manufacturing, shipment, transport, distribution and storage processes until its ultimate use on patients.  During the supply chain phases, medical items and products such as pharmaceuticals, vaccines, blood samples, human tissue, and organs should be stored, shipped, and transported with stringent conditions that have to be met all the time. These conditions include temperature, humidity, light, oxygen, vibration, etc. Static measurements at points of transfer are commonly recorded throughout healthcare supply chains, but capabilities in current systems do not permit continuous monitoring, recording and tracking the temperature as products are transported locally within a country and or globally across international borders. In addition, it is pivotal to verify the quality and integrity to ensure that the shipment has not been tampered with and all vital conditions are within a specified range. Due to the presence of multiple uncontrolled variables in the manufacturing-distribution process, developing an appropriate monitoring system is essential. IoT and blockchain can provide the perfect solution for this critical problem.  

Metaverse-based Solutions in Healthcare with Secure Data Sharing, Access, and Management

In this project, we aim to develop Metaverse-based healthcare solutions that will specifically: (i) provide superior healthcare services, treatment, delivery, diagnosis, and training; (ii) offer safe, secure, personalized, user-friendly, and immersive simulation environments to perform clinical procedures without risking patients’ lives; (iii) manage data of Metaverse-enabled healthcare systems in a way that is decentralized, globally accessible, transparent, traceable, auditable, secure, trustworthy, and fully compliant with GDPR and HIPAA regulations; (iv) create digital twins/replicas of medical equipment and mint them with NFTs for use in the Metaverse-enabled healthcare supply chain, as well as evaluate and test the tools used to create digital twins; and (v) devise novel and efficient schemes for self-sovereign and global decentralized digital identity for Metaverse actors, with reputation, accountability, and anonymity based on zero-knowledge proof, as well as ways to securely link, connect, authenticate, authorize, and trace these actors across different Metaverse environments and platforms.

Research Staff and Graduate Students:

Ilhaam A Omar Research Associate
Haya Hasan Research Associate
Walaa Alkhader Post Doctoral Fellow
Ahmad Musamih Post Doctoral Fellow
Madine Mohammed PhD Student
Mariam Bader PhD Student
Alaa Jamal Alqaryuti PhD Student