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Digital Twinning for Productivity Improvement Opportunities with Robotic Process Automation: Case of Greenfield Hospital

Weibo Liu 1, Wei Zhang 1, Bapi Dutta 1, Zhenyong Wu 1, and Mark Goh 1,2
1. The Logistics Institute – Asia Pacific, National University of Singapore, Singapore
2. NUS Business School, National University of Singapore, Singapore

Abstract—Hospitals of the future need to embrace new, disruptive, and innovative technologies, and prudently consider the cost of such technological investments to ensure that the technologies can interface well with human operators, without the high financial commitment. Such hospitals look to improve operational efficiency and productivity to be able to treat more patients without increasing cost and justify the capital investment. Put simply, the hospital staff will in future co-work and collaborate with robotic technologies in a shared workspace to deliver the same level of patient care if not better. For this to happen, there is a need to appreciate how the current human effort can be integrated seamlessly with Robotic Process Automation (RPA) activities in a hospital setting and to visually show to the operations/logistics personnel the attendant challenges/ bottlenecks. We study a Greenfield hospital in Singapore and provide a digital twin of its future operations with RPA solutions. Specifically, we design an efficient logistics system (central sterile services, materials management, food, pharmacy, linen), develop robust RPA solutions, and minimize the disruptions from automation introduction. The transportation system within the hospital, dispatching rule of the lifts and robots, are optimized through extensive simulation. 
Index Terms—digital twinning, simulation, hospitals, logistics, Singapore

Cite: Weibo Liu, Wei Zhang, Bapi Dutta, Zhenyong Wu, and Mark Goh, "Digital Twinning for Productivity Improvement Opportunities with Robotic Process Automation: Case of Greenfield Hospital" International Journal of Mechanical Engineering and Robotics Research, Vol. 9, No. 2, pp. 258-263, February 2020. DOI: 10.18178/ijmerr.9.2.258-263

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.