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Abstract
Surge demand shows that hospital logistics automation is unexploited given that the high pressure in work, the lack of resources, and some inventory and replenishment problems still occur. Hospitals do not fully leverage the implemented automatic systems and integrate them into large-scale logistic processes. Only some tasks/ services are automated keeping nurses in charge of the logistic labour, a large stock space, and inventory and replenishment problems. Hospitals do not adopt strategies that carefully aim for a high level of logistics automation and integration, using adequate management systems/ policies to make it cost-effective. This paper presents the design of a fully automated and integrated system for asset inventory and replenishment management in hospitals. The strategy adopted in this work starts with choosing a proactive replenishment policy operating in real-time and a suitable inventory and replenishment system (RFID-enabled two-bin system). This system is then automated using adequate and recent technologies (e.g. ‘Box-Picker’, smart gravity-flow racks, and conveyors). The proposed system is modelled and simulated in a virtual hospital under a mass casualty incident. Results show a considerable reduction of assets unavailability (99.98% in the central pharmacy and 90.47% in wards) and stock space. Besides, nurses are released from logistic tasks.
Acknowledgements
The authors would like to thank the nurses from ‘Centre hospitalier de l’Université de Montréal (CHUM)’ who contribute to this study by providing the necessary medical information. Special thanks go to the ‘FlexSim’ software community for their help to resolve the troubles encountered during the modelling and simulation of the proposed automatic system on this software. The authors also thank Dr. Ioannis Konstantaras (the editor-in-chief of ‘International Journal of Systems Science: Operations and Logistics’) and the assigned reviewers for this paper revision.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Notes on contributors
Sara Jebbor
Sara Jebbor is a PhD in industrial engineering from the High National School for Computer Science and Systems Analysis at Mohammed V University in Rabat, Morocco. She received her engineering’s degree in industrial engineering from the Ecole Nationale Supérieure des Mines de Rabat (ENSMR) in 2013. Her research interests are logistics and supply chain management, supply chain 4.0, modelling and simulation, and machine learning.
Raddouane Chiheb
Raddouane Chiheb is a professor of applied mathematics at the High National School for Computer Science and Systems Analysis at Mohammed V University in Rabat, Morocco. He obtained his master degree from the National Institute of Applied Sciences of Lyon and PhD from the Jean Monnet University of Saint-Etienne. His research interests are in the area of semantic analysis, structural optimisation, education, logistics and supply chain, machine learning, and value analysis. He is president of the Moroccan Association for the Value Analysis.
Abdellatif El Afia
Abdellatif El Afia is a full professor at the High National School for Computer Science and Systems Analysis, Morocco. He received his M.Sc. degree in applied mathematics from University of Sherbrook. He obtained his Ph.D. in 1999 in operation research from University of Sherbrook, Canada. His research areas of interest are mathematical programming, Metaheuristics, recommendation systems, and machine learning.
Maryam Gallab
Maryam Gallab is an Assistant Professor at the Computer Science Department in MINES-RABAT School (ENSMR), Morocco. She obtained a PhD degree in Industrial Engineering from Engineers Mohammadia School (EMI), Morocco and from Mines ParisTech School, PSL University, France. She obtained an Engineer degree from ENSA-Agadir, Morocco. Her research interests focus on Complex Industrial Systems, Modeling & Simulation, Risk Analysis, SCM, Industry 4.0, and Systems Engineering.