This article presents an environmentally sustainable Economic Production Quantity (EPQ) model under the influence of imperfect production and shortages. The defective items are reworked under an asynchronous rework process, where the reworking starts just after the production process stops. In this study, we have proposed two models under shortages, one with shortages satisfied by perfect production, and the other with shortages satisfied by production permitting defective item production which are reworked under asynchronous approach. During the processes of production, transportation and storage of inventory items, significant amount of carbon emissions is generated. In order to minimize these emissions and to reduce the setup costs, this paper proposes the integration of investment in green technology. The primary objective of this paper is to obtain the optimal values of lot size, backordered quantities, setup costs and green investment amounts in order to minimize the total cost of the manufacturer. To find the optimal solution, we have employed a metaheuristic method, Grey Wolf Optimization (GWO). A numerical example is also presented, accompanied with a sensitivity analysis to illustrate and validate the outcomes of the proposed inventory models.

The technological evolution that enterprises are facing is increasingly fast and dynamic, demanding more and more flexibility and agility in the application of new technologies in an increasingly globalized and competitive business environment. This work has a broad view of the industry 4.0 influence in the global supply chain companies, as it is essential for long-term business sustainability and one of the key drivers of profitability and growth. The model proposed in this research effort considers the application in the processes of a aerospace enterprises, which includes from the beginning of the demand request, also the industrial activities, to the post-sale analysis; finally, an unprecedented indicator was developed to measure the level of application of industry 4.0 concepts in organizations according to the state-of-the-art in the evaluated processes. Then, the model was applied in real cases of aerospace companies. The first findings demonstrate the ease of understanding and applicability of the model for companies to analyze the digital transformation in their processes and identify gaps to convert them into real opportunities to leverage their business.

We present a review on the applications of large language models (LLMs) in health, e.g., social media analysis, clinical conversational agents, therapy support tools, prompt engineering, multimodal learning, and ethical considerations. We integrate findings from interdisciplinary studies utilizing diverse data sources such as social media posts, electronic medical records, and multimodal inputs to enable early detection of depression, suicide risk assessment, personalized therapy support, and psychoeducational content generation. Our review highlights advancements in LLM models and annotation strategies that enhance interpretability and clinical relevance, while we also emphasize the critical role of prompt engineering for domain adaptation. We also discuss emerging multimodal fusion techniques integrating text, speech, and sensor data for improved mental health diagnosis and monitoring. Finally, we address ongoing ethical, sociotechnical, and regulatory challenges, and advocate frameworks to ensure safe, equitable, and accountable deployment of LLMs in real-world mental health care.

The integration of blockchain technology into Internet of Things (IoT) forensics transforms digital investigations and resolves challenges in the security and validation of evidence within increasingly interconnected systems. This paper explores how blockchain’s immutable, decentralized, and transparent ledger enhances the integrity, authenticity, and traceability of forensic data collected from IoT devices. By embedding blockchain within IoT ecosystems, investigators gain access to tamper-resistant records and execute credibility and admissibility in legal proceedings. Smart contracts and decentralized trust models automate security protocols, to reduce human error while enhancing efficiency. Real-world applications in smart homes, healthcare, industrial automation, and communication networks demonstrate the framework's potential to strengthen forensic processes. This paper interprets blockchain's transformative role in advancing IoT forensics, for robust, transparent, and future-ready investigative methodologies.

In recent years, management analysis has become more pragmatic. While data infrastructure is more mature and artificial intelligence (AI) tools are more powerful, the supply chain and organizational environment are more uncertain. The focus of academic research has also changed accordingly. It no longer merely pursues higher prediction accuracy but pays more attention to how to understand complex systems and how to support actionable decisions. This article selects 36 papers published in the Journal of Management Analytics in 2025 for a literature review. The results show that the annual research focuses on five themes: supply chain optimization and resilience, AI and Generative Model-driven Decision Support, Human resource analysis, optimization under Sustainability and ESG constraints, method integration and modeling innovation. This article forms an annual research map through literature review, which presents references for academic research and management practice.

Cloud computing involves collecting data and software along with seamless access over the internet rather than the computer's hard drive. Cloud storage facilitates files and programs to be downloaded and uploaded, and viewed over the internet instead of a computer's hard disc, using any digital device. This modern production age transforms industries worldwide with an increasingly digital future where the cloud provides a special processing, storage and networking capability. This technology is an essential component of Industry 4.0; it recognises emerging innovations to investigate methods to be modified to fulfil current customer requirements. Due to significant advances in the Internet of Things (IoT), robotics, cloud-based technologies and Artificial Intelligence (AI), the fourth Technological revolution is now being realised. This new revolution is triggering tremendous transformations in different areas, particularly in financial services, products, healthcare, automobiles building services. This paper details cloud computing's significant potentials and a study on different cloud computing applications for Industry 4.0. Calculation services make it possible for the platform, leading in the long term to innovative technologies, combining automation, the Internet of Things, and robotics.

Robotics are becoming prevalent in our everyday lives. Agriculture is the world's most significant industry, with a tremendous technological demand. It is presently appropriate to adopt robotics applications in farming since the global food chain is under strain from factors such as population expansion, climate change, population drift from rural to urban areas, and ageing populations. Robotics are seen as a means of escaping the unsettling reality. The robots are very sophisticated; they know how much water a specific plant needs. The same situation holds with fertiliser. Every plant will get just the proper quantity of fertiliser to keep it healthy. They may move into fields fast and resemble corn plants. This paper is about the need for robotics in agriculture. Several available cooperative robots, their tasks, and the associated challenges and prospects for the agriculture domain are discussed. Finally, it identified and discussed significant applications of Robotics for Agriculture. Robots can be used to monitor every plant in a field, whether big or small. This can assist in spotting any faults or concerns and provide their report immediately to the farmers. Farmers may quickly determine what types of problems exist in their fields in this manner without having to inspect them physically. These robots are remarkable for their accuracy and fineness. Agricultural robots are specialised technological devices that may help farmers with various tasks. They may be designed to develop and adapt to meet the requirements of different activities, and they can assess, consider, and perform multiple tasks. There are some limitations to implementing this technology in agriculture, such as safety, maintenance, environmental factors, high initial cost, training requirements, and increased unemployment. In the future, robots will determine the optimum planting locations, the ideal harvesting times, and the best paths for crisscrossing the farms.

Implementing Lean Startup (LS) and Lean Product Development (LPD) can significantly impact innovation when developing new products and, consequently, the manufacturing stage. This study empirically conducts ideation and value prospection phases to favor the manufacturing stage in a low-complexity system of meliponiculture (stingless beekeeping) through the lens of socio-technical system theory. A case study was conducted through three main phases (ideation, value prospection and learning) by employing seven lean practices and involving more than 160 participants in a survey to conduct hypothesis testing. The findings from the application of the questionnaire were obtained through Cronbach’s Alpha to assess the reliability and Chi-square to test the hypothesis. The study’s findings state the importance of socio-technical theory in a low-complexity system, integrating LS and LPD to develop sustainable, customer-centered, viable bee boxes and informative booklets. It addresses a solution to promote income generation for vulnerable coastal communities, considering the interaction between social and technical aspects essential to support the manufacturing stage ahead. The study provides a holistic perspective through the interplay of people, technologies and the environment in the initial stages of product development to anticipate promising scenarios for effective and efficient manufacturing processes.

Purpose: The Tripper Notifier Application (TNA) is a mobile application developed for tourists to receive notifications about location-based attractions, hotels and restaurants in close proximity while traveling. The government funded this application as part of a stimulus scheme project aimed at enhancing the tourism sector. This research aims to examine a factor that affects the intention and adoption behavior of tourists, focusing on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2). Design/methodology/approach: Data were collected from 417 tourists in Thailand by utilizing a self-administrated questionnaire. Through the analysis using Structural Equation Modeling (SEM) of tourist respondents, it was discovered that some of the structural paths are not consistent with prior literature. This study concludes that this application’s determinant factors are effort expectancy, social influence, habits and facilitating conditions. These factors become significant predictors of behavioral intention toward TNA usage by explaining about 81.7% of the total variation. Findings: The findings of this study show that trust and perceived personal experience do not have a positive impact on behavioral intention to use TNA by travelers, while effort expectancy, social influence, habits and facilitating conditions are the key drivers toward the TNA usage. Originality/value: The novel contribution of this study is to provide characteristics of local tourists’ perceptions for mobile app adoption created by the government by combining UTAUT-2 for future business strategies in local businesses.

Work-related injuries claim the lives of a large number of individuals annually. Consequently, organizations are intensifying their focus on safety measures, whereas Industrial Safety encompasses the implementation of protocols and guidelines to ensure the well-being of both workers and plant facilities, shielding them from potential hazards. Industry 4.0 characterizes the surge in digitalization, product interconnectivity, customization, and the adoption of cutting-edge technologies across various sectors globally. This research endeavors to elucidate how these technological advancements of Industry 4.0, particularly the Internet of Things (IoT), can play a pivotal role in bolstering the safety of industrial workers while concurrently streamlining operational processes. Through a scoping literature review, this study examines the diverse applications of IoT, highlighting its significant contribution to improving worker safety. Research provides a fundamental difference between IoT and IIoT. The paper outlines how the integration of IoT with key technologies enables predictive maintenance, real-time hazard monitoring, and automation, thereby promoting a safer work environment. The study provides insight into how IoT transforms industrial safety, spotlighting its applications in enhancing worker safety and the pivotal role of Artificial Intelligence (AI) and Machine Learning (ML) technologies in achieving this goal. The research provides a review of the transformative role of IoT, specifically in enhancing the safety and operational efficiency of industrial workers. Moreover, this research outlines implications for industry practices and suggests future research avenues to fortify industrial safety measures.