Designing an integrated responsive-green-cold vaccine supply chain network using Internet-of-Things: artificial intelligence-based solutions

Assessing the potential of plastic waste management in the circular economy: a longitudinal case study in an emerging economy

Plastic waste management represents a fundamental challenge in terms of environmental pollution and health in many emerging countries. Yet, some firms believe improved plastic waste management could lead to value creation and capture, especially from a circular economy perspective. This study draws on a longitudinal research approach that involved 12 organizations in assessing plastic waste management's contribution to Cameroon's circular economy. Our findings suggest that plastic waste management for value creation is still embryonic in Cameroon. Moving to the full value creation and capture stage will require overcoming various challenges identified and presented in the paper. We then discuss our findings and put forward several future research avenues.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10479-023-05386-3.

Resilient and social health service network design to reduce the effect of COVID-19 outbreak

With the severe outbreak of the novel coronavirus (COVID-19), researchers are motivated to develop efficient methods to face related issues. The present study aims to design a resilient health system to offer medical services to COVID-19 patients and prevent further disease outbreaks by social distancing, resiliency, cost, and commuting distance as decisive factors. It incorporated three novel resiliency measures (i.e., health facility criticality, patient dissatisfaction level, and dispersion of suspicious people) to promote the designed health network against potential infectious disease threats. Also, it introduced a novel hybrid uncertainty programming to resolve a mixed degree of the inherent uncertainty in the multi-objective problem, and it adopted an interactive fuzzy approach to address it. The actual data obtained from a case study in Tehran province in Iran proved the strong performance of the presented model. The findings show that the optimum use of medical centers' potential and the corresponding decisions result in a more resilient health system and cost reduction. A further outbreak of the COVID-19 pandemic is also prevented by shortening the commuting distance for patients and avoiding the increasing congestion in the medical centers. Also, the managerial insights show that establishing and evenly distributing camps and quarantine stations within the community and designing an efficient network for patients with different symptoms result in the optimum use of the potential capacity of medical centers and a decrease in the rate of bed shortage in the hospitals. Another insight drawn is that an efficient allocation of the suspect and definite cases to the nearest screening and care centers makes it possible to prevent the disease carriers from commuting within the community and increase the coronavirus transmission rate.

Responsive strategies for new normal cold supply chain using greenfield, network optimization, and simulation analysis

The global-local supply chains are affected by the forward and downward propagation of COVID-19. The pandemic disruption is a low-frequency and high-impact (black swan) event. Adapting to the "New Normal" situation requires adequate risk mitigation strategies. This study proposes a methodology to implement a risk mitigation strategy during supply chain disruptions. Random demand accumulation strategies are considered to identify the disruption-driven challenges under different pre and post-disruption scenarios. The best mitigation strategy and the optimal location of distribution centers to maximize the overall profit were determined using simulation-based optimization, greenfield analysis, and network optimization techniques. The proposed model is then evaluated and validated using appropriate sensitivity analysis. The main contribution of the study is to (i) perform cluster-based supply chain disruption analysis, (ii) propose a resilient and flexible model to illustrate the proactive and reactive measures for the ripple effect, (iii) prepare the supply chain for future pandemic-like crises, and (v) reveal the relationship between the pandemic impact and supply chain resilience. A case study of an ice cream manufacturer is used to demonstrate the proposed model.

Environment and economic analysis of reverse supply chain scenarios for remanufacturing using discrete-event simulation approach

The study covers the concepts involved in reverse supply chain modeling using the case of a manufacturing company. The purpose of this study is to build a sustainable reverse supply chain model for resource conservation through remanufacturing of stator shafts by using a discrete-event simulation approach. The simulation studies in the reverse supply chain have taken up cases of either plastic or electronic waste remanufacturing, while very limited studies deal with simulation of sustainable reverse supply chains using a manufacturing industry case study from international customers. In this study, reverse supply chain using simulation study in manufacturing sector is carried out using Arena Rockwell simulation software. The simulation model is built using discrete-event simulation for returns from customers of two developed countries, i.e., Germany and the USA to Chennai, India. The study emphasizes full container load and less than container load modes of shipment scenarios and multiple return cases. The comparative analysis suggests that the value-added and non-value-added time of the reverse supply chain is slightly greater in the less container load scenario. The wait time per entity in remanufacturing processes similar for both shipment scenarios varies significantly based on return cases. The cost and carbon emission associated with transportation, in the reverse supply chain inclusive of social carbon cost, have also been estimated. Therefore, the study proposes a possible sustainable reverse supply chain framework that could be adopted by different manufacturing industries and yield opportunities for performance improvement.

Designing humanitarian logistics network for managing epidemic outbreaks in disasters using Internet-of-Things. A case study: An earthquake in Salas-e-Babajani city

Along with the destructive effects of catastrophes throughout the world, the COVID-19 outbreak has intensified the severity of disasters. Although the global aid organizations and philanthropists aim to alleviate the adverse impacts, many employed actions are not impactful in dealing with the epidemic outbreak in disasters. However, there is a gap in controlling the epidemic outbreak in the aftermath of disasters. Therefore, this paper proposes a novel humanitarian location-allocation-inventory model by focusing on preventing COVID-19 outbreaks with IoT-based technology in the response phase of disasters. In this study, IoT-based systems enable aid and health-related organizations to monitor people remotely, suspect detection, surveillance, disinfection, and transportation of relief items. The presented model consists of two stages; the first is defining infected cases, transferring patients to temporary hospitals promptly, and accommodating people in evacuation centers. Next, distribution centers are located in the second stage, and relief items are transferred to temporary hospitals and evacuation centers equally regarding shortage minimization. The model is solved by the LP-metric method and applied in a real case study in Salas-e-Babajani city, Kermanshah province. Then, sensitivity analysis on significant model parameters pertaining to the virus, relief items, and capacity has been conducted. Using an IoT-based system in affected areas and evacuation centers reduces the number of infected cases and relief item's shortages. Finally, several managerial insights are obtained from sensitivity analyses provided for healthcare managers.

A heuristic-based hybrid algorithm to configure a sustainable supply chain network for medical devices considering information-sharing systems

In today's hyper-competitive marketplace, the crucial role of the sustainability concept has been highlighted more. Hence, managers' attention has been attracted to the concept of sustainable supply chains. On the other hand, after the COVID-19 outbreak, the importance of medical devices and their demand has drastically enhanced, which has led to shifting the attention of researchers toward this industry. In this regard, based on the importance of the mentioned points, the current study configures a sustainable supply chain network for the medical devices industry. In this way, given the crucial role of the oxygen concentrator during the COVID-19 outbreak, the present study investigates the supply chain of the mentioned goods as a case study. Also, this research develops an efficient hybrid solution method based on goal programming, a heuristic algorithm, and the simulated annealing algorithm to solve the suggested model. Eventually, sensitivity analysis is conducted to examine the influence of the crucial parameters of the model on the outputs, and managerial insights are provided. According to the achieved results, the suggested model and the developed hybrid method demonstrate a good performance which shows their efficiency.

The Impact of COVID-19 on Pharmaceutical Shortages and Supply Disruptions for Non-Communicable Diseases Among Public Hospitals of South West, Oromia, Ethiopia

Background

COVID-19 is a pandemic disease that has led to inequitable supply and shortages of essential medicines worldwide due to decreased production capacity, export bans, and national stockpiling which are affecting the global pharmaceutical supply chain. Access to essential pharmaceuticals is dependent on well-functioning supply chain systems that move medicines from the manufacturer to end users at service delivery point.

Objective

To assess impact of Covid-19 pandemic disease on pharmaceutical shortages and supply disruptions for non-communicable diseases among public hospitals of South West, Oromia, Ethiopia.

Methods and Materials

A multi-institutional cross sectional study design was employed. Quantitative and qualitative methods were utilized concurrently to gather data from four public hospital warehouses, dispensaries, patients, and local health authorities from March 1–30, 2021 in Ilu-Ababor and Buno-Bedelle Zones, Oromia, Ethiopia. Data were analyzed by using SPSS (version 23.0). A semi-structured interview guide was used to gather qualitative information from key informants from DTC members, patients, and local health authorities and finally analyzed through thematic approach.

Results

From six public hospitals in the two zones; cost of medicine increased more in Dedesa hospital compared to the other public hospital found in that zone and the effect of Covid-19 on essential medicines used for treatment of non-communicable diseases was less in Darimu Hospital. Stock status of PFSA and transportation were the major challenges during the procurement process due to Covid-19 pandemic disease. Among the EMs assessed at public hospitals, drugs used for cardiovascular diseases were out of stock for more than 90 days while anti-asthma drugs were out of stock for less than 40 days.

Conclusion

The availability of essential medicine was low and there was also poor inventory management practice in some of the public hospitals during Covid-19 pandemic in the study period.

Social Sustainability and Resilience in Supply Chains of Latin America on COVID-19 Times: Classification Using Evolutionary Fuzzy Knowledge

The number of research papers interested in studying the social dimension of supply chain sustainability and resilience is increasing in the literature. However, the social dimension is complex, with several uncertainty variables that cannot be expressed with a traditional Boolean logic of totally true or false. To cope with uncertainty, Fuzzy Logic allows the development of models to obtain crisp values from the concept of fuzzy linguistic variables. Using the Structural Equation Model by Partial Least Squares (SEM-PLS) and Evolutionary Fuzzy Knowledge, this research aims to analyze the predictive power of social sustainability characteristics on supply chain resilience performance in the context of the COVID-19 pandemic with representative cases from Mexico and Chile. We validate our approach using the Chile database for training our model and the Mexico database for testing. The fuzzy knowledge database has a predictive power of more than 80%, using social sustainability features as inputs regarding supply chain resilience in the context of the COVID-19 pandemic disruption. To our knowledge, no works in the literature use fuzzy evolutionary knowledge to study social sustainability in correlation with resilience. Moreover, our proposed approach is the only one that does not require a priori expert knowledge or a systematic mathematical setup.