Browsing Centre for Supply Chain Improvement by Authors
A lean-TOC approach for improving emergency medical services (EMS) transport and logistics operations.Garza-Reyes, Jose Arturo; Villarreal, Bernardo; Kumar, Vikas; Diaz-Ramirez, Jenny; University of Derby; Universidad de Monterrey; University of the West of England; Centre for Supply Chain Improvement, The University of Derby, Derby, UK; Engineering Department, Universidad de Monterrey, San Pedro Garza Garcia, Mexico; Bristol Business School, University of the West of England, Bristol, UK; et al. (Taylor and Francis, 2018-08-21)The improvement of transport and logistics performance of Emergency Medical Services (EMSs) systems has been mainly addressed through mathematical modelling, operations research, and simulation methods. This paper proposes an alternative and/or complementary improvement approach based on the adaptation and simultaneous deployment of lean thinking and Theory of Constraint (TOC) methods and tools. The paper briefly reviews key aspects of the application of lean in the logistics and healthcare industries and conceptually develops the proposed lean-TOC approach. The approach is then tested, through an individual detail case study, in the EMS transport and logistic system of the Red Cross operating in the metropolitan area of Monterrey, Mexico. The results obtained from the case study suggest that the proposed systematic lean-TOC approach may be an effective alternative and/or complement to mathematical modelling, operations research, and simulation methods to improve EMS transport and logistics operations.
A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systemsSalazar-Reyna, Roberto; Gonzalez-Aleu, Fernando; Granda-Gutierrez, Edgar M.A.; Diaz-Ramirez, Jenny; Garza-Reyes, Jose Arturo; Kumar, Anil; Universidad de Monterrey, San Pedro Garza Garcia, Mexico; University of Derby; London Metropolitan University (Emerald, 2020-12-07)The objective of this paper is to assess and synthesize the published literature related to the application of data analytics, big data, data mining, and machine learning to healthcare engineering systems. A systematic literature review (SLR) was conducted to obtain the most relevant papers related to the research study from three different platforms: EBSCOhost, ProQuest, and Scopus. The literature was assessed and synthesized, conducting analysis associated with the publications, authors, and content. From the SLR, 576 publications were identified and analyzed. The research area seems to show the characteristics of a growing field with new research areas evolving and applications being explored. In addition, the main authors and collaboration groups publishing in this research area were identified throughout a social network analysis. This could lead new and current authors to identify researchers with common interests on the field. The use of the SLR methodology does not guarantee that all relevant publications related to the research are covered and analyzed. However, the authors’ previous knowledge and the nature of the publications were used to select different platforms. To the best of the authors’ knowledge, this paper represents the most comprehensive literature-based study on the fields of data analytics, big data, data mining, and machine learning applied to healthcare engineering systems.