Data driven flexible supplier network of selfcare essentials during disruptions in supply chain.

During disruptive events, supply chains struggle to meet the demand due to limitations posed by logistics, transportation and supply side failures. In the present study, a flexible supplier network of personal protective equipment (PPEs), such as face masks, hand sanitizers, gloves, and face shields, has been modelled using an extensive risk enabled data driven decision making for addressing disruptions in the supply chain. This paper studies various risks which exists in PPE supply chain and evaluates the total supplier risk based on them. Furthermore, the paper proposes a Multi-objective Mixed Integer Linear Program (MOMILP) to optimally select suppliers and the sustainable allocation of orders under various risks, namely disruption, delay, receivables, inventory, and capacity. The proposed MOMILP model is also extended to promptly revise the orders to other suppliers under a disruption scenario enabling an effective response resulting in minimization of stockouts. The criteria-risk matrix is developed with the help of supply chain experts from industry and academia. Conclusively, the numerical case study and its computational analysis is conducted on the PPE data received from distributors to demonstrate the applicability of the proposed model. The findings suggest that the proposed flexible MOMILP can optimally revise allocations during disruptions to drastically reduce the stockouts and minimize overall cost of procurement in the PPE supply network.

Emergency order allocation of e-medical supplies due to the disruptive events of the healthcare crisis.

The availability of electronic (e-medical) homecare essentials, such as thermometers, oximeters, and oxygen concentrators during the peaks of the pandemic coronavirus disease (COVID-19), has been witnessed as critical in saving the lives of people across the world. This paper presents a supply order allocation strategy of e-medical homecare essentials (HCEs) in a multi-supplier environment by a distributor while ensuring sufficient and timely availability for emergency consumption during pandemic peaks. The results, based on the actual demand data of HCEs obtained from a regional HCE distributor during the pandemic peak of the second wave in India, i.e. April-May 2021, suggest that a minimum (maximum) average of 94% (98%) availability of e-medical HCEs respectively at pharmacies could be achieved during the peak demand period using the proposed emergency order allocation algorithm in this study. Conclusively, the analysis of this study could generate insightful implications for emergency operations decisions in the HCEs supply-distribution channel.

Implications for sustainable healthcare operations in embracing telemedicine services during a pandemic.

Technological interventions in the healthcare sector, namely, telemedicine services, have helped the government and people in these extraordinarily challenging times of ongoing COVID-19 pandemic. We identify and group key success factors relevant to telemedicine services under 7 contextual criteria. Furthermore, we explore the causal relations among them using the decision-making trial and evaluation laboratory (DEMATEL) method. Then, by applying the Bayesian best-worst method (BWM), we compute the relative importance of these criteria. Thereafter, we rank six hospitals that have provided telemedicine services through a comparative evaluation using the VIsekriterijumsko KOmpromisno Rangiranjie (VIKOR) method. The threefold findings of our study reveal that (i) the technological criteria provide the highest causal impact, while the environmental criteria provide the least causal impact. (ii) The hierarchical model of criteria, achieved through the Bayesian BWM score, shows that the criteria weights for both technological and organizational criteria are maximum (0.205) and minimum (0.087), respectively. (iii) The evaluation of six hospitals with VIKOR based on seven criteria ranks the Himalayan hospital as first, showing that it is best in providing telemedicine services to patients. Public health policymakers could use the results of our study to devise an effective plan for patient care in crisis, like COVID-19.

Predictors of COVID-19 Vaccine Confidence: Findings from Slums of Four Major Metro Cities of India.

There are limited studies on COVID vaccine confidence at the household level in urban slums, which are at high risk of COVID-19 transmission due to overcrowding and poor living conditions. The objective was to understand the reasons influencing COVID-19 vaccine confidence, in terms of barriers and enablers faced by communities in urban slums and informal settlements in four major metro cities in India. A mixed method approach was adopted, where in field studies were conducted during April-May 2021. First, a survey of at least 50 subjects was conducted among residents of informal urban settlements who had not taken any dose of the COVID-19 vaccine in Mumbai, Bengaluru, Kolkata and Delhi; second, a short interview with five subjects who had taken at least one dose of the vaccine in each of the four cities to understand the factors that contributed to positive behaviour and, finally, an in-depth interview of at least 3 key informants in each city to ascertain the vaccination pattern in the communities. The reasons were grouped under contextual, individual/group and vaccine/vaccination specific issues. The most frequent reason (27.7%) was the uncertainty of getting the vaccine. The findings show the need for increasing effectiveness of awareness campaigns, accessibility and the convenience of vaccination, especially among vulnerable groups, to increase the uptake.

Selection of healthcare waste disposal firms using a multi-method approach.

The aim of this study is to propose a hybrid multi criteria decision making model with a linear programming (LP) model to tackle the issue of safe disposal of hazardous and infectious healthcare waste. For this, ten criteria in this study have been identified from literature and field surveys which are modelled using Decision making trial and evaluation (DEMATEL) and Analytic network process (ANP) methods to select the best disposal firm i.e. single sourcing for a hospital. We found that Experience of the firm, Technology for disposal, and Waste collection infrastructure acts as the most vital criteria in selecting a healthcare waste disposal firm for single sourcing. Furthermore, to optimize the total value of disposal and mitigating the risk involved in disposing waste through single sourcing; the LP model considering constraints such as waste lose constraint and waste processing constraint etc. Is solved for multiple sourcing using Lingo 18.0. The solution to LP results into allocation of 500, 500, and 1000 (kg/day) disposables to healthcare waste disposal firms D1, D2 and D3, respectively. The multi-method approach proposed in this study helps the hospital management in selecting economically, socially, and environmentally sustainable healthcare waste disposal firm.

The interplay of circular economy with industry 4.0 enabled smart city drivers of healthcare waste disposal.

Generation of healthcare waste from different patient care activities in hospitals, pathology labs and research centres has been a matter of great concern for environmental and social bodies across the world. This concern comes from its infectious and hazardous nature which brings life taking disease such as human immunodeficiency virus and Hepatitis-B. Moreover, with the outbreak of corona virus disease 2019 (COVID-19) pandemic across the world, healthcare waste has become even more infectious like never before and showing its potential for claiming lives if not disposed properly. Additionally, the COVID-19 has put up another challenge in terms of exponentially increasing demand for personal protective equipments for healthcare workers such as doctors, nurses, ward boys, and sanitation workers. In this paper, seven criteria related to smart healthcare waste disposal system infused by circular economy aspects to recover value from disposables are identified and analysed using a decision making trial and evaluation laboratory (DEMATEL) method. The criteria have been prioritized by its importance and net cause and effect relationship through a causal diagram. Two criteria, (i) digitally connected healthcare centres, waste disposal firms and pollution control board, and (ii) providing a pollution control board's feedback app to public and other stakeholders, feature as strong reasons for a smart healthcare waste disposal system. Conclusively, this study provides a causal relationship model among the intertwined drivers of industry 4.0 and circular economy for developing a smart healthcare waste disposal system enriched with the benefits of circular economy.