Intelligent automation for smart service in hospitals

– Dr. Fabio Sgarbossa 

Abstract: In this presentation, I will present a recent research project between Logistics 4.0 Lab at NTNU and Wheel.me to solve challenges in the healthcare sector through the development of a proactive smart data-driven healthcare logistics system. With the solution, that consists of our autonomous wheels implemented in several critical objects (can be retrofitted on almost any existing portable healthcare object), the project aims to overcome the AMRs’ limitations. The autonomous wheels do not need any pre-loaded facility drawings for navigation, and they are controlled in a decentralized way thanks to the adoption of ML/AI decision making. In such a way, proactive and autonomous logistics functioning will be possible. Specifically, proactive actions will be based on these ML/AI decision makings rather than user-defined instructions for operation. Moreover, the use of this innovative solution will allow the real-time control of healthcare objects. The implementation of these autonomous wheels into hospitals/nursing homes will give several advantages to the user, such as: more efficient use of resources in core healthcare activities, and less involvement in no-added value activities (such as movement, transportation, control of materials etc.); proactive healthcare logistics for a better service to the patients; maximization of infection control, avoiding unnecessary movements and contacts among departments.

The main innovation of the project is to develop a smart data-driven solution for hospital logistics based on the implementation of autonomous wheels, and through the development of the following sub-innovation: (i) Decision support system to estimate which is the most profitable level of implementation of autonomous wheels in healthcare environments to make smart autonomous objects. (ii) Data-driven models for the planning and control of smart autonomous objects. (iii) Data-driven models for the service for smart autonomous objects.

Biographical Sketch: Fabio Sgarbossa is Full Professor of Industrial Logistics at the Department of Mechanical and Industrial Engineering (MTP) at NTNU (Norway) from October 2018. He was Associate Professor at University of Padova (Italy) where he also received his PhD in Industrial Engineering in 2010. He is leader of the Production Management Research Group at NTNU and he is head of the Logistics 4.0 Lab at NTNU. He has been and he is involved in several European and National Projects. He is author and co-author of more than 100 publications in relevant international journals, about industrial logistics, material handling, materials management, supply chain. He is member of Organizing and Scientific Committees of several International Conferences, and he is member of editorial boards in relevant International Journals.

Female Advantage from Artificial Intelligence

Dr. Ming-Hui Huang

Abstract: The fact that AI increasingly outperforms humans in thinking intelligence is creating a “Feeling Economy” in which AI does more thinking and humans focus more on feeling, from which females can have an advantage, due to their better (on average) feeling intelligence. Using global AI investment data from 2010 to 2020, we demonstrate that AI investment generates female advantage in two ways: physical strength is less a constraint, due to mechanical AI, and feeling intelligence becomes a strength due to thinking AI.

Biographical Sketch: Ming-Hui Huang is Distinguished Professor of AI (artificial intelligence) and service at National Taiwan University. She is the first and only Asian-based fellow of European Marketing Academy (EMAC), International Research Fellow of the Centre for Corporate Reputation, University of Oxford, UK, and Distinguished Research Fellow of the Center for Excellence in Service, University of Maryland, USA. She specializes in interdisciplinary research, with publications encompassing both academic and managerial journals in Marketing, Information Systems and Strategy, such as the J. of Marketing, J. of the Academy of Marketing Science (JAMS), Marketing Science, Harvard Business Review, MIT Sloan Management Review, California Management Review, J. of Service Research (JSR), International Journal of Research in Marketing (IJRM), J. of Management Information Systems, Decision Sciences, J. of Consumer Psychology, J. of Retailing, and Information & Management. She is Editor-in-Chief of JSR, the 11th highest-cited business journal, Associate Editor of IJRM, Information & Management, and Communications of the Association for Information Systems, and serves on the editorial boards of J. of Marketing, JAMS, Int’l J. of E-Commerce, and J. of Strategic Information Systems.