In response to significant disruption, supply chain optimization became sensitive to increasing consumer expectations, unexpected demand fluctuation, and inventory costs. Proactive movement, understanding, and empowerment have fostered the beneficial results of supply chain optimization, cooperation, and operational resilience. These pioneering activities are critical to achieving a paradigm shift in the supply chain, even agility in response to changing demand. However, sophisticated analytics such as artificial intelligence (AI) and blockchain are supposed to overcome these challenges to make smarter decisions on a daily basis. Due to these facts, this study aimed to model AI’s and blockchain’s role in supply chain optimization by conducting a systematic literature review based on the idealized framework of Rejeb et al. (2022) and the SALSA mechanism. In addition, this paradigm-shifting approach will provide fairer views and options for managing forecasting, planning, monitoring, and reporting across the entire supply chain. The emphasis remains on real-time accuracy, easy access, and optimization of operational indicators such as sales, visibility, and end-to-end supply chain operations at all times and from any location. It will be an eye-opening experience to enable stakeholders and partners to communicate information collaboratively, consistently, and efficiently.

Artificial intelligence (AI) and smart automation are revolutionizing the global supply chain ecosystem at an accelerated pace, providing tremendous potential for resilience, innovation, efficacy, and profitability. This paper examines how AI, machine learning (ML), and robotic process automation (RPA) influence supply chain operations to adjust to the risks and vulnerabilities. It focuses on how AI and other relevant technologies will enhance forecasting to predict actual demand, expedite logistics, increase warehouse efficiency, and promote instantaneously making decisions. This study utilizes thematic analysis to find AI-driven supply chain applications, including logistics optimization, forecasting demand, and risk mitigation, among 383 peer-reviewed articles (2017–2024). It provides a strategic framework for dealing with vulnerabilities, operational excellence, and resilient solutions. Additionally, the research investigates how AI contributes to supply chain resilience by predicting disruptions and automating risk mitigation strategies. This paper identifies critical success factors and challenges in adopting intelligent automation by analyzing real-world industry implementations. The findings will propose a strategic framework for organizations aiming to leverage AI to achieve operational excellence, agility, and real-time information flow for effective decision-making.

(1) Introduction: Transportation logistics play a pivotal role in facilitating both individual mobility and supply chain operations on a global scale. However, conventional transportation systems have contributed significantly to urban congestion and environmental degradation. In response to these challenges, there is growing momentum to investigate the potential of renewable energy to power electric vehicles (EVs) and flying automobiles, aiming to reduce fossil fuel dependence and carbon emissions. (2) Objectives: By analyzing key drivers and opportunities for integrating flying vehicles into existing infrastructure through dynamic modeling, this study seeks to accelerate the transition to sustainable transportation. (3) Methodology: A mixed-method approach, combining literature review and causal loop diagram analysis, is employed to understand the factors influencing EV and flying car adoption. (4) Results: Findings highlight the significant potential of renewable energy in reducing emissions and reliance on fossil fuels through widespread EV and flying vehicle adoption. The major drivers and challenges in infrastructure, safety, and airspace management are also identified. In addition, this research emphasizes the importance of sustainable transportation in addressing urban congestion, pollution, and energy security. (5) Conclusions: By leveraging renewable energy and embracing technological advancements, a low-carbon future for transportation can be achieved, benefiting both the environment and society.

The dairy industry plays a significant role in the global food system, providing essential nutrients for human consumption and creating rural employment. A small-scale dairy can assist a family in maintaining their livelihoods in Bangladesh. However, it is also associated with various environmental and social impacts, making it crucial for achieving sustainability. The triple bottom line of sustainability intends to achieve sustainability through improving productivity, implementing sustainable practices, and incorporating waste management. The dairy industry can continue to provide nutritious diets, ensuring sustainability practices. This research is a follow-up paper of Nasir et al. to find better sustainable results. It considers the triple bottom line of sustainability theory to improve the farm environment by reducing waste, managing resources efficiently, and promoting environmentally friendly practices. This paper is a case study on a dairy farm of 400 cattle in Bangladesh. The system dynamics method and simulation modeling were employed to draw dairy supply chain networks and examine the existing dataset to find better utilization of the dairy waste produced on the farm. Consequently, the simulation model incorporates waste management and value addition concepts to find better resource exploitation for gaining sustainable outcomes. Finally, this paper summarizes the simulation outcomes and articulates possible extensions for achieving further economic, social, and environmental benefits for the industry and surrounding community.

Purpose: This paper reviews the role of two significant smart technologies, artificial intelligence (AI) and blockchain technology (BT) in achieving sustainable tourism success. Such changes will maintain consistent tourism growth in the Middle East and North Africa (MENA) region by improving environmental, social and economic sustainability.Design/methodology/approach: This study uses a qualitative approach focusing on AI and blockchain-based adaptations towards sustainability in technology-driven platforms, particularly in the tourism sector. However, this study is conceptualized with Day's (1989) IMRAD (introduction, methods, results and discussion) framework and Hall's (2012) suggestions. This reinforced the structure of this article by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) concept. In addition, the entire study proceeds by way of accentuating the research question.Findings: The potential of these technologies (AI and blockchain) can internalize a win-win situation for economic prosperity and sustainable conservation of environmental resources. Even though, apathy among potential tourists (of traditional mass tourism) can be revived under sustainable tourism in the region through smart technology. Replacing traditional practices and policies with innovative technologies can reduce the adverse effect on environmental and cultural resources.Originality/value: This research justifies the potential for AI and blockchain to balance economy-oriented tourism and sustainability-prone technologically advanced tourism as a caustic issue for the MENA region. Indeed, this study has revitalized and transformed the less eventful margins of leading cultural-heritage-based tourism into sustainable profitability with concerns over diversity, socio-economic conditions, economic recession and burgeoning geopolitical instability.

Transportation 5.0 is an advanced and sophisticated system combining technologies with a focus on human-centered design and inclusivity. Its various components integrate intelligent infrastructure, autonomous vehicles, shared mobility services, green energy solutions, and data-driven systems to create an efficient and sustainable transportation network to tackle modern urban challenges. However, this evolution of transportation is also intended to improve accessibility by creating environmentally benign substitutes for traditional fuel-based mobility solutions, even when addressing traffic management and control issues. Consequently, to promote synergy for sustainability, the diversified nature of the Transportation 5.0 components ought to be efficiently and effectively managed. Thus, this study aims to reveal the involvement of Transportation 5.0 core component prediction in the sustainable transportation system through a systematic literature review. This study also contemplates the causal model under system dynamics modeling in order to address sustainable solutions and the movement toward sustainability in the context of Transportation 5.0. From this review, in addition to the developed causal model, it is identified that every core component management method in the sustainable Transportation 5.0 system reduces environmental impact while increasing passenger convenience and the overall efficiency and accessibility of the transport network, with greater improvements for developing nations. As the variety of transportation options, including electric vehicles, is successfully integrated, this evolution will eventually enable shared mobility, green infrastructure, and multimodal transit options.