ABSTRACT
Coronavirus Disease 2019 (COVID-19) affected global economics and society, unprecedentedly. Supply chains, linking customers, manufacturers, and suppliers, are more susceptible to disruption risks when facing pandemics, like COVID-19. As matter of fact, there is an emerging literature on supply chain management (SCM) and COVID-19. This chapter explores how supply chain managers may evaluate supply chain risks due to pandemics. Managers may analyze alternatives to mitigate the situation. The purpose of this chapter is to present a mathematical model for assessing disruption risks in supply chains affected by pandemics. A multi-criteria decision analysis (MCDA) model is developed from the consolidated literature of SCM. Analytic Hierarchy Process (AHP) and Technique of Order Preference by Similarity to Ideal Solution (TOPSIS), two leading MCDA methods were combined in the development of the assessment model. The model is tested with the case study of a multinational automotive company that operates in both efficient and responsive supply chains. For efficient supply chains, the model resulted in a focus on capacity management, demand planning, and sales forecasting, to avoid risks disruptions. For responsive supply chains, the focus shall move to operations management. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Emergency Care Networks (ECNs) are integrated healthcare systems comprised of emergency departments (EDs). ECNs are called to be the primary response of healthcare authorities to deal with the expected uptick in the future demands for emergency care during the current Covid-19 pandemic. Lean Six Sigma (LSS) has been proposed to address this challenge since it allows managers to detect factors contributing to the extended waiting times (WT) throughout the patient journey. The suggested framework follows the DMAIC cycle that was initiated with the project charter definition;in the meantime, a SIPOC diagram was drawn to analyze the emergency care process and pinpoint critical process variables. Following this, a nested Gage R&R study was undertaken to study the measurement system performance;subsequently, a normal-based capability analysis was carried out to determine how well the ECN process satisfies the specifications. The next step was to identify the potential causes separating the ECN nodes from the desired target. Afterwards, improvement strategies were devised to lessen the average WT. After suitable data collection, a before-and-after analysis was performed to verify the effectiveness of the implemented strategies. Ultimately, a control plan containing an I-MR control chart was designed to maintain the improvements achieved with the LSS implementation. The results revealed that the average WT of the showcased node passed from 190.02 min to 103.1 min whereas the long-term sigma level increased from −0.06 to 0.11. The proposed framework was validated through a case study including the involvement of a medium-sized hospital from the public sector. © 2021, Springer Nature Switzerland AG.