The COVID-19 pandemic has caused considerable damage to various industries worldwide. Availability and supply of a wide range of raw materials, intermediate goods, and finished products have been seriously disrupted. Global Supply Chains (GSCs), which had shown a high level of efficiency and robustness against several disruptions in recent decades, are genuinely compromised. Using a critical reading and a causal analysis of figures and facts, this paper aims to investigate the impacts of the COVID-19 outbreak on the efficient and responsive operations of GSCs and to propose reconfigurations for GSCs in the futureas a remedy to enhance resilience

In the last decade, e-commerce has been growing consistently. Fostered by the covid pandemic, online retail has grown exponentially, particularly in industries including food, clothing, groceries, and many others. This growth in online retailing activities has raised critical logistic challenges, especially in the last leg of the distribution, commonly referred to as the Last Mile. For instance, traditional truck-based home delivery has reached its limit within metropolitan areas and can no longer be an effective delivery method. Driven by technological progress, several other logistic solutions have been deployed as innovative alternatives to deliver parcels. This includes delivery by drones, smart parcel stations, robots, and crowdsourcing, among others. In this setting, this paper aims to provide a comprehensive review and analysis of the latest trends in last-mile delivery solutions from both industry and academic perspectives (see Figure 1 for overview). We use a content analysis literature review to analyse over 80 relevant publications, derive the necessary features of the latest innovation in the last mile delivery, and point out their different maturity levels and the related theoretical and operational challenges.

Background: Machine learning (ML) and deep learning (DL) methods have recently garnered a great deal of attention in the field of cancer research by making a noticeable contribution to the growth of predictive medicine and modern oncological practices. Considerable focus has been particularly directed toward hematologic malignancies because of the complexity in detecting early symptoms. Many patients with blood cancer do not get properly diagnosed until their cancer has reached an advanced stage with limited treatment prospects. Hence, the state-of-the-art revolves around the latest artificial intelligence (AI) applications in hematology management.Objective: This comprehensive review provides an in-depth analysis of the current AI practices in the field of hematology. Our objective is to explore the ML and DL applications in blood cancer research, with a special focus on the type of hematologic malignancies and the patient’s cancer stage to determine future research directions in blood cancer.Methods: We searched a set of recognized databases (Scopus, Springer, and Web of Science) using a selected number of keywords. We included studies written in English and published between 2015 and 2021. For each study, we identified the ML and DL techniques used and highlighted the performance of each model.Results: Using the aforementioned inclusion criteria, the search resulted in 567 papers, of which 144 were selected for review.Conclusions: The current literature suggests that the application of AI in the field of hematology has generated impressive results in the screening, diagnosis, and treatment stages. Nevertheless, optimizing the patient’s pathway to treatment requires a prior prediction of the malignancy based on the patient’s symptoms or blood records, which is an area that has still not been properly investigated.

The COVID-19 pandemic impact on the global supply systems is continuously on the rise creating an increasing ripple effect across the automotive supply chain network. In this disruptive setting, Additive manufacturing (AM) is perceived by many supply chain professionals as well as by many global automakers amongst best options to handle the disruptions and boost the automotive supply chain resilience. This research is dedicated to analysis up to which extent Additive manufacturing (AM) is a miraculous remedy to automotive supply chain disruptions. To this end, we use the Indian automotive industry as case-model to identify major barriers towards AM wide deployment and derive recommendations on how AM can be successfully used to cope with automotive supply chain disruptions.

The COVID-19 pandemic and locust swarm outbreaks pose a significant threat to global food systems, causing severe disruptions in both local and international food supplies from farm to fork. The main objective of this study is to understand and identify the disruptions during the crises and create a map of how resilience can be established to recover and sustain the food supply chain (FSC) functions as well as food security. The detrimental impacts of the compound crises on the FSC are explored and the effects of the affected areas are estimated under optimistic and pessimistic scenarios. As a response to the disruption caused by the crisis in FSCs, reactive and proactive solutions are proposed to develop resilience at the food sector level. In the short term, the reactive solutions, consisting of smoothing the food demand, supply and delivery, and food production and processing, can be borrowed. In the long term, the proactive solutions can be conducted by developing multi-level short intertwined FSCs. Our comprehensive investigation of the resilience elements in diverse operations and potential strategies should contribute to the improvement of FSC resilience in the face of ongoing and growing threats.