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.

An ensemble machine learning approach for forecasting credit risk of agricultural SMEs' investments in agriculture 4.0 through supply chain finance.

Credit risk imposes itself as a significant barrier of agriculture 4.0 investments in the supply chain finance (SCF) especially for Small and Medium-sized Enterprises. Therefore, it is important for financial service providers (FSPs) to differentiate between low- and high-quality SMEs to accurately forecast the credit risk. This study proposes a novel hybrid ensemble machine learning approach to forecast the credit risk associated with SMEs' agriculture 4.0 investments in SCF. Two core approaches were used, i.e., Rotation Forest algorithm and Logit Boosting algorithm. Key variables influencing the credit risk of agriculture 4.0 investments in SMEs were identified and evaluated using data collected from 216 agricultural SMEs, 195 Leading Enterprises and 104 FSPs operating in African agriculture sector. Besides the classical measures of credit risk assessment without involving SCF, the findings indicate that current ratio, financial leverage, profit margin on sales and growth rate of the agricultural SME are the upmost important variables that SCF actors need to focus on, in order to accurately and optimistically forecast and alleviate credit risk. The output of our study provides useful guidelines for SMEs, as it highlights the conditions under which they would be seen as creditworthy by FSPs. On the other hand, this study encourages the wide application of SCF in financing agriculture 4.0 investments. Due to the model's performance, credit risk forecasting accuracy is improved, which results in future savings and credit risk mitigation in agriculture 4.0 investments of SMEs in SCF.

Internet of things for perishable inventory management systems: an application and managerial insights for micro, small and medium enterprises.

Micro, Small, and Medium Enterprises (MSMEs) operating in the food retailing sector encounter two main concerns with respect to their perishable inventory management system, i.e., the product's shelf life and investment in warehouse monitoring systems. New technologies like the Internet of Things (IoT), automated inventory control platforms, and automatic storage and retrieval systems offer effective solutions to these issues. However, MSMEs are reluctant to adopt these technologies due to their prior perception of higher implementation costs and the expected benefits. The present study aims to optimize IoT implementation in MSMEs' inventory management systems and to provide tangible proof of its feasibility and usefulness. In so doing, we propose a mathematical model and analyze the impact of IoT through two case studies. The model provides a cost-benefit analysis of IoT investments that aim to increase products' shelf life. We adopted the fractional program method, solved by particle swarm optimization on MATLAB software. The findings demonstrate the positive correlation between adopting IoT and reduced inventory costs supporting IoT deployment for improved perishability performance in MSMEs. The study offers several insights and practical guidelines in considering IoT deployment in MSMEs.

Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation.

Supply chain resilience (SCRes) and performance have become increasingly important in the wake of the recent supply chain disruptions caused by subsequent pandemics and crisis. Besides, the context of digitalization, integration, and globalization of the supply chain has raised an increasing awareness of advanced information processing techniques such as Artificial Intelligence (AI) in building SCRes and improving supply chain performance (SCP). The present study investigates the direct and indirect effects of AI, SCRes, and SCP under a context of dynamism and uncertainty of the supply chain. In doing so, we have conceptualized the use of AI in the supply chain on the organizational information processing theory (OIPT). The developed framework was evaluated using a structural equation modeling (SEM) approach. Survey data was collected from 279 firms representing different sizes, operating in various sectors, and countries. Our findings suggest that while AI has a direct impact on SCP in the short-term, it is recommended to exploit its information processing capabilities to build SCRes for long-lasting SCP. This study is among the first to provide empirical evidence on maximizing the benefits of AI capabilities to generate sustained SCP. The study could be further extended using a longitudinal investigation to explore more facets of the phenomenon.

Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries.

There has been an increased interest among scholars to investigate supply chain resilience (SCRes) in manufacturing and service operations during emerging situations. Grounded in the SCRes theory, this study provides insights into the impact of the COVID-19 outbreak on the automobile and airline supply chain. Both the short and long-term response strategies adopted by the two supply chains are assessed, using a combination of qualitative and quantitative techniques in three distinct phases. In phase one, we use a sequential mixed-method for resilience evaluation, integrating Time-to-Recovery (TTR) and Financial Impact (FI) analysis. In phase two, we conduct an empirical survey involving 145 firms to evaluate the short-term SCRes response strategies. In the third phase, we conduct semi-structured interviews with supply chain executives both from the automobile and airline industries to understand the long-term SCRes response strategies. Our findings indicate that: (i) the automobile industry perceived that the best strategies to mitigate risks related to COVID-19, were to develop localized supply sources and use advanced industry 4.0 (I4.0) technologies. (ii) The airline industry on the other hand, perceived that the immediate need was to get ready for business continuity challenges posed by COVID-19, by defining their operations both at the airports and within the flights. (iii) Importantly, both the sectors perceived Big Data Analytics (BDA) to play a significant role by providing real-time information on various supply chain activities to overcome the challenges posed by COVID-19. (iv) Cooperation among supply chain stakeholders is perceived, as needed to overcome the challenges of the pandemic, and to accelerate the use of digital technologies.

Infectious Waste Management Strategy during COVID-19 Pandemic in Africa: an Integrated Decision-Making Framework for Selecting Sustainable Technologies.

The emerging and underdeveloped countries in Africa face numerous difficulties managing infectious waste during the SARS-CoV-2 disease, known as the COVID-19 pandemic. Hence, the main aim of this paper is to help decision-makers in African countries to select the best available waste management strategy during the COVID-19 pandemic. The present research undertakes seamless assessment and prioritization of infectious solid waste (SW) and wastewater (WW) treatment technologies based on a criteria system involving four dimensions, i.e., environment-safety, technology, economics, and sociopolitics. A combined approach that integrates the results of life-cycle assessments and life-cycle costs (LCA-LCC), analytic hierarchy process (AHP), and VIKOR method in an interval-valued fuzzy (IVF) environment is proposed. The results reveal that combined incineration and chemical disinfection approach, and combined chlorination and ultraviolet irradiation are the most sustainable technologies for managing infectious SW and WW treatment in the present context. The proposed approach, alongside the findings of the study, constitutes a reference to devise urgent planning for contagious waste management in African countries as well as developing countries worldwide.

Investigating the effects of renewable energy on international trade and environmental quality.

This research investigates how renewable energy consumption interacts with international trade and environmental quality in Nordic counties from 2001 to 2018. The present study adopted the CIPS unit root test and cross-sectional dependence (CD) test to test the stationarity and diagnose the cross-sectional dependence issues, respectively. Further, the study employed a dynamic common correlated effect (DCCE) model for robustness. The findings revealed that renewable energy strongly and positively associated with international trade in Nordic countries. Also, the results indicate that renewable energy consumption improved to environmental quality. Therefore, policies to promote renewables can provide for economic growth and environmental sustainability and ensure crucial sustainable development goals. Further, the findings provide theoretical support for the formulation of eco-friendly policies to understand the role of renewable energy in spurring international trade, which supports balancing eco-environmental sustainability.