In this paper, we will provide an extensive analysis of how Generative Artificial Intelligence (GenAI) could be applied when handling Supply Chain Management (SCM). The paper focuses on how GenAI is more relevant in industries, and for instance, SCM where it is employed in tasks such as predicting when machines are due for a check-up, man-robot collaboration, and responsiveness. The study aims to answer two main questions: (1) What prospects can be identified when the tools of GenAI are applied in SCM? Secondly, it aims to examine the following question: (2) what difficulties may be encountered when implementing GenAI in SCM? This paper assesses studies published in academic databases and applies a structured analytical framework to explore GenAI technology in SCM. It looks at how GenAI is deployed within SCM and the challenges that have been encountered, in addition to the ethics. Moreover, this paper also discusses the problems that AI can pose once used in SCM, for instance, the quality of data used, and the ethical concerns that come with, the use of AI in SCM. A grasp of the specifics of how GenAI operates as well as how to implement it successfully in the supply chain is essential in assessing the performance of this relatively new technology as well as prognosticating the future of generation AI in supply chain planning.

In today’s fast-moving, disrupted business environment, supply chain risk management is crucial. More critically, Industry 4.0 has conferred competitive advantages on supply chains through the integration of digital technologies into manufacturing and logistics, but it also implies several challenges and opportunities regarding the management of these risks. This paper looks at some ways emerging technologies, especially Artificial Intelligence (AI), help address pressing concerns about the management of risk and sustainability in logistics and supply chains. The study, using a systemic literature review (SLR) backed by a mapping study based on the Scopus database, reveals the main themes and gaps of prior studies. The findings indicate that AI can substantially enhance resilience through early risk identification, optimizing operations, enriching decision-making, and ensuring transparency throughout the value chain. The key message from the study is to bring out what technology contributes to rendering supply chains resilient against today’s uncertainties.

Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer demand efficiently. This paper aims to Investigate on the application of generative AI models in demand forecasting. Two models were used: Long Short-Term Memory (LSTM) networks and Variational Autoencoder (VAE), and results were compared to select the optimal model in terms of performance and forecasting accuracy. The difference of actual and predicted demand values also ascertain LSTM’s ability to identify latent features and basic trends in the data. Further, some of the research works were focused on computational efficiency and scalability of the proposed methods for providing the guidelines to the companies for the implementation of the complicated techniques in demand forecasting. Based on these results, LSTM networks have a promising application in enhancing the demand forecasting and consequently helpful for the decision-making process regarding inventory control and other resource allocation.

The study explores improving opportunities of forecasting accuracy from the traditional method through advanced forecasting techniques. This enables companies to optimize inventory management, production planning, and reducing the travelling time thorough vehicle route optimization. The article introduced a holistic framework by deploying advanced demand forecasting techniques i.e., AutoRegressive Integrated Moving Average (ARIMA) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) models, and the Vehicle Routing Problem with Time Windows (VRPTW) approach. The actual milk demand data came from the company and two forecasting models, ARIMA and RNN-LSTM, have been deployed using Python Jupyter notebook and compared them in terms of various precision measures. VRPTW established not only the optimal routes for a fleet of six vehicles but also tactical scheduling which contributes to a streamlined and agile raw milk collection process, ensuring a harmonious and resource-efficient operation. The proposed approach succeeded on dropping about 16% of total travel time and capable of making predictions with approximately 2% increased accuracy than before.

The perspectives of economic students in Can Tho City, Vietnam were investigated in order to have a deeper understanding of the relationship between green supply chain management (GSCM) and social performance. A comprehensive survey was conducted on a sample size of 526 undergraduate students enrolled in business administration and international business courses. This study effort examined the impact of several subcomponents of GSCM on social performance. The inclusion of green production, green distribution, green supply chain management, and environmental education was seen. The coefficients of 0.24 and 0.115 suggest a favorable relationship between green procurement and internal environmental management and social performance. The existing scholarly literature presents several instances in which the implementation of Green Supply Chain Management (GSCM) has resulted in enhanced societal performance. The objective of this study is to contribute to the existing literature by investigating the many factors that influence the performance of Green Supply Chain Management (GSCM) in improving financial outcomes. The investigation also encompasses the examination of Green Supply Chain Management (GSCM) and its influence on societal performance. The authors propose that the extent to which graduates were exposed to GSCM education throughout their college years will have a substantial impact on their contributions to their respective fields and to society as a whole. Individuals who proactively pursue higher education by enrolling in college and focusing their studies on attaining a business degree are more likely to increase their chances of achieving success as entrepreneurs. Hence, these affluent proprietors of companies possess the potential to expand their operations and provide significant economic benefits at a macro level. In order to ensure the enduring viability of businesses, local communities, and the natural environment, educational institutions should provide curricula including corporate social responsibility, volunteerism, and ecologically conscious manufacturing methods. The integration of environmental stewardship with ethical business practices is crucial.