Planning Autonomous Material Transportation in Hospitals

Fragapane, Giuseppe; Roy, Debjit; Sgarbossa, Fabio; Strandhagen, Jan Ola

doi:10.1007/978-3-030-85906-0_3

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Applications of simulation in healthcare vary in scope, including material flow planning in hospitals [24], COVID-19 RT-PCR testing processes [25], patient scheduling [26], patient registration processing [27], using virtual reality [28], and assessing health technology [29]. On the other hand, lean, SS, and LSS interventions (LSS) have also been implemented with different approaches and goals, e.g., redesigning a supply chain for an operating room [30], improving the discharge process [31], redesigning the scheduling process for elective surgery [32], enhancing the patient flow in emergency department (ED) [33], or improving controlled drug processes [34].…”

Section: Introductionmentioning

confidence: 99%

Lean, Six Sigma, and Simulation: Evidence from Healthcare Interventions

et al. 2022

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In the Industry 4.0 era, healthcare services have experienced more dual interventions that integrate lean and six sigma with simulation modeling. This systematic review, which focuses on evidence-based practice and complies with the PRISMA guidelines, aims to evaluate the effects of these dual interventions on healthcare services and provide insights into which paradigms and tools produce the best results. Our review identified 4018 studies, of which 39 studies met the inclusion criteria and were selected. The predominantly positive results reported in 73 outcomes were mostly related to patient flow: length of stay, waiting time, and turnaround time. In contrast, there is little reported evidence of the impact on patient health and satisfaction, staff wellbeing, resource use, and savings. Discrete event simulation stands out in 74% of the interventions as the main simulation paradigm. Meanwhile, 66% of the interventions utilized lean, followed by lean-six sigma with 28%. Our findings confirm that dual interventions focus mainly on utilization and access to healthcare services, particularly on either patient flow problems or problems concerning the allocation of resources; however, most interventions lack evidence of implementation. Therefore, this study promotes further research and encourages practical applications including the use of Industry 4.0 technologies.

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Section: Introductionmentioning

confidence: 99%

Lean, Six Sigma, and Simulation: Evidence from Healthcare Interventions

et al. 2022

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Queueing network models for the analysis and optimisation of material handling systems: a systematic literature review

Amjath,

Kerbache,

Elomri

et al. 2023

Flex Serv Manuf J

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Material handling systems (MHSs) are an integral part of logistics functions in manufacturing and service organizations. Material handling equipment (MHE) is considered the pivotal actor of any given MHS. Decisions ranging from the strategic level, such as selecting the proper MHE, capacity, and ownership (in-house or outsourcing) to operational level decisions such as resource allocation, scheduling, and routing of MHEs, are critical to the efficiency of an MHS. Industry practitioners use various methods and tools to evaluate these MHSs to find the best policies for their operations. This study identifies past works related to the performance evaluation and optimisation of MHSs using queueing network models. Moreover, this study provides a comprehensive analysis of identified research questions. The study methodology adopts a systematic literature review, bibliometric, and content analysis techniques proposed in similar research studies. This study provides material logistics scholars and practitioners with a thorough understanding of queueing networks as a modelling tool for analysing MHS applications in various domains.

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Planning Autonomous Material Transportation in Hospitals

Cited by 2 publications

References 21 publications

Lean, Six Sigma, and Simulation: Evidence from Healthcare Interventions

Lean, Six Sigma, and Simulation: Evidence from Healthcare Interventions

Queueing network models for the analysis and optimisation of material handling systems: a systematic literature review

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