Sustainable Assessment in Supply Chain and Infrastructure Management

Assessing sustainability in supply chain and infrastructure management is important for any organization in the competitive business environment or public domain. Public buildings such as higher education institutions are responsible for a substantial portion of energy consumption and anthropogenic greenhouse gas (GHG) emissions. Roukouni et al. (contribution twelve) developed truck platooning and multi-sided digital platforms games for barge transportation, both improving the sustainability of hinterland transportation. Besides these studies, Özdemir et al. (contribution thirteen) assessed the efficiency of the operations strategy matrix in the healthcare system amid COVID-19.

Developing a Decision-Making Framework to Improve Healthcare Service Quality during a Pandemic

The COVID-19 pandemic has significantly impacted almost every sector. This impact has been especially felt in the healthcare sector, as the pandemic has affected its stability, which has highlighted the need for improvements in service. As such, we propose a collaborative decision-making framework that is capable of accounting for the goals of multiple stakeholders, which consequently enables an optimal, consensus decision to be identified. The proposed framework utilizes the best–worst method (BWM) and the Multi-Actor Multi-Criteria Analysis (MAMCA) methodology to capture and rank each stakeholder’s preferences, followed by the application of a Multi-Objective Linear Programming (MOLP) model to identify the consensus solution. To demonstrate the applicability of the framework, two hypothetical scenarios involving improving patient care in an intensive care unit (ICU) are considered. Scenario 1 reflects all selected criteria under each stakeholder, whereas in Scenario 2, every stakeholder identifies their preferred set of criteria based on their experience and work background. The results for both scenarios indicate that hiring part-time physicians and medical staff can be the effective solution for improving service quality in the ICU. The developed integrated framework will help the decision makers to identify optimal courses of action in real-time and to select sustainable and effective strategies for improving service quality in the healthcare sector.

Satisfaction of E-Learners with Electronic Learning Service Quality Using the SERVQUAL Model

Electronic learning (E-learning) is an innovative learning tool that provides an opportunity for technology-driven distance teaching. E-learning not only enhances learning quality, but it has also become the only medium of learning during the COVID-19 pandemic, when people have been forced to stay at home. Due to the pandemic, most countries have initiated online learning to continue the learning process, thus lessening the study gap. Against this backdrop, it is imperative to explore the perception and satisfaction level of e-learners regarding e-learning tools. In this study, a quantitative approach was conducted on the students of two leading public universities (graduates and postgraduates) to identify the impact of the determinants of the SERVQUAL model (reliability, responsiveness, assurance, empathy, and website design), as well as ‘learning content’, on overall perceived quality and satisfaction. In total, 895 respondents participated in the study, and data were analyzed with Amos 23 to confirm the hypotheses, utilizing structural equation modeling. The findings reveal that all the variables of the measurement model had a significant effect on perceived service quality and thus user satisfaction except for responsibility. The results make a significant contribution to those decision-makers and university authorities attempting to ensure e-learners’ satisfaction.

The use of a recurrent neural network model with separated time-series and lagged daily inputs for waste disposal rates modeling during COVID-19.

A new modeling framework is proposed to estimate mixed waste disposal rates in a Canadian capital city during the pandemic. Different Recurrent Neural Network models were developed using climatic, socioeconomic, and COVID-19 related daily variables with different input lag times and study periods. It is hypothesized that the use of distinct time series and lagged inputs may improve modeling accuracy. Considering the entire 7.5-year period from Jan 2013 to Sept 2020, multi-variate weekday models were sensitive with lag times in the testing stage. It appears that the selection of input variables is more important than waste model complexity. Models applying COVID-19 related inputs generally had better performance, with average MAPE of 10.1%. The optimized lag times are however similar between the periods, with slightly longer average lag for the COVID-19 at 5.3 days. Simpler models with least input variables appear to better simulate waste disposal rates, and both 'Temp-Hum' (Temperature-Humidity) and 'Temp-New Test' (Temperature-COVID new test case) models capture the general disposal trend well, with MAPE of 10.3% and 9.4%, respectively. The benefits of the use of separated time series inputs are more apparent during the COVID-19 period, with noticeable decrease in modeling error.

Challenges to COVID-19 vaccine supply chain: Implications for sustainable development goals.

The COVID-19 outbreak has demonstrated the diverse challenges that supply chains face to significant disruptions. Vaccine supply chains are no exception. Therefore, it is elemental that challenges to the COVID-19 vaccine supply chain (VSC) are identified and prioritized to pave the way out of this pandemic. This study combines the decision-making trial and evaluation laboratory (DEMATEL) method with intuitionistic fuzzy sets (IFS) to explore the key challenges of the COVID-19 VSC. The IFS theory tackles the uncertainty of key challenges while DEMATEL addresses the interlaced causal relationships among crucial challenges to the COVID-19 VSC. This work identifies 15 challenges and reveals that 'Limited number of vaccine manufacturing companies', 'Inappropriate coordination with local organizations', 'Lack of vaccine monitoring bodies', 'Difficulties in monitoring and controlling vaccine temperature', and 'Vaccination cost and lack of financial support for vaccine purchase' are the most critical challenges. The causal interactions along with mutual relationships among these challenges are also scrutinized, and implications for sustainable development goals (SDGs) are drawn. The results offer practical guidelines for stakeholders and government policy makers around the world to develop an improved VSC for the COVID-19 virus.

Modeling of municipal waste disposal rates during COVID-19 using separated waste fraction models.

Municipal waste disposal behaviors in Regina, the capital city of Saskatchewan, Canada have significantly changed during the COVID-19 pandemic. About 7.5 year of waste disposal data at the Regina landfill was collected, verified, and consolidated. Four modeling approaches were examined to predict total waste disposal at the Regina landfill during the COVID-19 period, including (i) continuous total (Baseline), (ii) continuous fraction, (iii) truncated total, and (iv) truncated fraction. A single feature input recurrent neural network model was adopted for each approach. It is hypothesized that waste quantity modeling using different waste fractions and separate time series can better capture disposal behaviors of residents during the lockdown. Compared to the baseline approach, the use of waste fractions in modeling improves both result accuracy and precision. In general, the use of continuous time series over-predicted total waste disposal, especially when actual disposal rates were less than 50 t/day. Compared to the baseline approach, mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE) were reduced. The R value increased from 0.63 to 0.79. Comparing to the baseline, the truncated total and the truncated fraction approaches better captured the total waste disposal behaviors during the COVID-19 period, probably due to the periodicity of the weeklong data set. For both approaches, MAE and MAPE were lower than 70 and 22%, respectively. The model performance of the truncated fraction appears the best, with an MAPE of 19.8% and R value of 0.92. Results suggest the uses of waste fractions and separated time series are beneficial, especially if the input set is heavily skewed.

Identification of behaviour patterns in waste collection and disposal during the first wave of COVID-19 in Regina, Saskatchewan, Canada.

The novel coronavirus (2019-nCov) has had significant impacts on almost every aspect of daily life. From 'stay-at-home' orders to the progressive lifting of restrictions, the COVID-19 pandemic has had unprecedented effects on consumer behaviours and waste disposal habits. The purpose of this short communication is to examine time series waste collection and disposal data in a mid-sized Canadian city to understand how behavioural changes have affected municipal waste management. The results suggest that private waste disposal increased during the pandemic. This may be due to people doing home renovations in order to accommodate working from home. Furthermore, it appears that changes in consumer habits destabilized the consistency of waste disposal tonnage when compared to the same time period in 2019. When considering curbside residential waste collection, there was also an increase in tonnage. This may be the result of more waste being generated at home due to changes in eating and cooking habits, and cleaning routine. Finally, the ratio of residential waste collection to total disposal is examined. More residential waste is being generated, which may have environmental and operational effects, especially related to collection and transportation. The results from this study are important from an operational perspective, and will help planners and policy makers to better prepare for changes in the waste stream due to pandemics or other emergencies.

Waste disposal characteristics and data variability in a mid-sized Canadian city during COVID-19.

COVID-19, declared a global pandemic by the World Health Organization, has caused governments to react swiftly with a variety of measures to quell the spread of the virus. This study investigates changes in waste disposal characteristics and the relationship between the mass of biomedical waste disposed and new COVID-19 tests performed in Regina, Canada. Results suggest that between May and September 2020, significant differences in the median amount of waste disposed exist. The amount of monthly waste disposed was slightly lower to about 450-550 tonnes/month. Monthly waste data variability, however, was significantly lower. Seasonal effects on total waste disposal is observed, but is less obvious than pre-COVID time. Furthermore, the distribution of different waste fractions varies, probably due to operational and industrial characteristics. A non-linear relationship exists between the number of COVID-19 tests performed and the mass of biomedical waste disposed, perhaps due to a lagged relationship between biomedical waste generation and disposal.

Strategies to Manage the Impacts of the COVID-19 Pandemic in the Supply Chain: Implications for Improving Economic and Social Sustainability

This paper aims to identify the negative impacts of the COVID-19 outbreak on supply chains and propose strategies to deal with the impacts in the context of the readymade garment (RMG) industry supply chain of an emerging economy: Bangladesh. To achieve the aims, a methodological framework is proposed through a literature review, expert inputs, and a decision-aid tool, namely the grey-based digraph-matrix method. A total of 10 types of negative impacts and 22 strategic measures to tackle the impacts were identified based on the literature review and expert inputs. Then, the grey-based digraph-matrix was applied for modeling the strategic measures based on their influence to deal with the impacts. Findings reveal that the strategies “manufacturing flexibility”, “diversify the source of supply”, and “develop backup suppliers”have significant positive consequences for managing the impacts of the COVID-19 pandemic in the RMG supply chain. The findings help industrial managers recover from supply chain disruptions by identifying and classifying the impacts and strategies required to manage the major supply chain disturbances caused by the COVID-19 pandemic. As a theoretical contribution, this study is one of few initial attempts to evaluate the impacts of the COVID-19 outbreak and the strategies to deal with the impacts in the supply chain context.

Improving supply chain sustainability in the context of COVID-19 pandemic in an emerging economy: Exploring drivers using an integrated model.

Motivated by the COVID-19 pandemic and the challenges it poses to supply chain sustainability (SCS), this research aims to investigate the drivers of sustainable supply chain (SSC) to tackle supply chain disruptions in such a pandemic in the context of a particular emerging economy: Bangladesh. To achieve this aim, a methodology is proposed based on the Pareto analysis, fuzzy theory, total interpretive structural modelling (TISM), and Matriced Impacts Cruoses Multiplication Applique a un Classement techniques (MICMAC). The proposed methodology is tested using experienced supply chain practitioners as well as academic experts' inputs from the emerging economy. This study reveals the influential relationships and indispensable links between the drivers using fuzzy TISM to improve the SCS in the context of COVID-19. Findings also reveal that financial support from the government as well as from the supply chain partners is required to tackle the immediate shock on SCS due to COVID-19. Also, policy development considering health protocols and automation is essential for long-term sustainability in supply chains (SCs). Additionally, MICMAC analysis has clustered the associated drivers to capture the insights on the SCS. These findings are expected to aid industrial managers, supply chain partners, and government policymakers to take initiatives on SSC issues in the context of the COVID-19 pandemic.