Industrial Engineering

The role played by social factors in implementing advanced technologies on energy as well as food sectors is examined in this paper. The exploration is done by studying various examples and analyzing aspects such as acceptance by communities, involvement of stakeholders, and support from regulations. The study compares the adoption of technologies such as renewable energy, smart grids, and carbon capture in the power sector with that in the food sector where precision farming and alternative proteins are applied. Hornsdale Power Reserve serves as an illustration of successful projects that show how much community support through open communication can go while on the other hand, Kemper Project represents failure due to a lack of sound regulation and public skepticism. This paper argues that in order to improve technology, interdisciplinary approaches should be used, adaptive management techniques should be promoted, and robust monitoring systems should be established. Recommendations made include fostering stakeholders' trust; investing in building local capacities; and incorporating social considerations into technological development.

The main focus of this study is the implementation of industrial management ideas to increase productivity and sustainability in the global food processing sector. The food processing industry faces several challenges, such as pollution and resource depletion, in its attempt to make agricultural goods edible. To address these issues, this research examines how Industry 4.0 technology, Lean Manufacturing tools and techniques, Six Sigma methodologies, and Total Quality Management concepts can all be applied. This has made the employment of advanced equipment for process optimization and real-time monitoring feasible. The study's approach is based on a thorough evaluation of the literature, mostly on peer-reviewed works published between 2012 and the present that highlight important discoveries and difficulties faced along the road. Also, integrating these principles will enhance operational efficiency, sustainability, and product quality. However, high implementation costs and extensive employee training should also be considered. Finally, recommendations are provided in order to promote their use and implementation.

Social acceptance is essential for the successful implementation of renewable energy technologies and can be divided into three main categories: socio-political approval, community acceptance, and market acceptance. Socio-political acceptance refers to broad public support for policies and technologies, although local resistance often persists due to decisions about siting. Community acceptance involves the support of local stakeholders and is influenced by the Not in My Back Yard (NIMBY) phenomenon, though research indicates varied reactions to renewable projects. Market acceptance concerns the adoption of innovations by consumers and investors, affected by the complexity of energy infrastructures. Policy frameworks are crucial for fostering community engagement, inclusivity, and transparency. Incentives aligned with community motivations enhance participation. The integration of AI, VR, and blockchain can further improve engagement platforms. Sustainable community development requires active participation, capacity building, diversified financing, flexibility, social equity, and continuous impact evaluation. Sharing knowledge and replicating successful models across communities is vital for broader impact.

This study investigates cutting-edge industrial management methods and technical interventions for minimizing food wastage. Amid rising global concerns, both internal and external, particularly regarding pollution caused by food waste, this study reviews twenty selected journals that examine contributions of key technologies like AI-enabled solutions, smart refrigerators, waste-to-energy conversion systems, and tracking mechanisms ways of minimizing wastage in diverse sectors. The paper demonstrates an increasing focus on combining management approaches with technology, particularly in Europe and Asia, from which most articles originate. The main findings show that although automated waste quantification and data-driven waste management systems are promising technologies, obstacles including high implementation costs, public opposition, and challenges in integrating new and old technologies continue to prevent their widespread adoption. To effectively reduce food wastage, a holistic approach is required, collaboration among stakeholders, the creation of favorable regulatory frameworks, and continuous improvements in waste management practices. The study concludes by suggesting that public awareness campaigns should be enhanced, research and development investments should be increased, and industries encouraged to collaborate to overcome existing barriers. It also recommends that waste management solution should be driven by specific technological innovations and supported by policies aimed at promoting sustainability. Ultimately, this research demonstrates that merging technological innovation with efficient management techniques is crucial for tackling the problem of food wastage and ensuring a sustainable future.

Sociocultural factors have a big influence on how sustainable practices are integrated in the energy and agriculture sectors. This article examines the effects of media, social impact, cultural norms, and social values on people's and communities' perceptions of sustainability in a variety of circumstances. Social norms and values have a big impact on sustainable behavior. Adopting sustainable methods can be strongly influenced by peer pressure and social impact. It is essential to comprehend these sociocultural components to develop solutions that enhances sustainability in the energy and agricultural industries. By employing these findings, practitioners and policymakers may work together to create focused campaigns, encourage settings, and increase the adoption of sustainable practices.

Renewable green hydrogen is a crucial energy source for achieving worldwide carbon neutrality targets. Nevertheless, due to its relative novelty, the high costs associated with its production impede broader acceptance. This study investigates the application of lean manufacturing ideas, notably Value Stream Mapping (VSM), Kaizen, and Total Productive Maintenance (TPM), to improve efficiency and reduce costs in green hydrogen generation. This study demonstrates how lean tools and strategies can enhance operational performance, increase equipment reliability, and minimize waste by evaluating current processes and using lean tools. Case studies like Thompson et al., 2023 and Zhang & Williams, 2023 reveal that lean methods implemented in green hydrogen facilities can lead to cost savings between 20-35% and energy savings ranging from 18-28%. The research also delves into the integration of Industry 4.0 technologies, such as predictive maintenance and process automation, to further enhance the effectiveness of lean strategies in reducing the costs of green hydrogen production.

This review investigates the role of Continuous Improvement (CI) methodologies, including Lean, Six Sigma, and Kaizen, in enhancing sustainability in hydrogen production-a sector crucial to achieving global carbon neutrality. CI practices are evaluated for their capacity to optimize hydrogen production processes, minimize resource waste, and reduce carbon footprints. Through a structured analysis of CI applications, the study identifies efficiency gains in green hydrogen production, including reductions in energy consumption and emissions. The findings demonstrate CI as a transformative approach, offering operational and environmental benefits that support cost-effective, low-emission hydrogen solutions. Recommendations emphasize integrating CI principles across the hydrogen sector to drive decarbonization, calling for policy support and industry commitment to leverage CI as a tool for sustainable energy innovation.

One of the most important ways to cut carbon emissions and fight climate change is to switch to green hydrogen, a clean energy source made by electrolysis using renewable resources. A significant obstacle to this shift, nevertheless, is the lack of a trained labor force that can handle the complexity of hydrogen technologies. In addition to addressing technical skills, this study highlights the importance of a people-centric approach to workforce training that promotes flexibility, creativity, and lifelong learning. Organizations can bridge the skills gap and promote sustainable growth in the green hydrogen economy by giving individual learning needs and career development priority. This study examines the important connections between training accessibility, skills acquisition, institutional support, and workforce preparation using both theoretical and empirical data. The findings reveal that fostering familiarity with green hydrogen concepts significantly enhances skill acquisition and readiness to transition into this evolving sector. Furthermore, the study identifies major barriers to training-namely cost, inaccessibility, and time constraints-and suggests that hybrid and online training models, supported by strong institutional partnerships, offer the most effective solutions. This paper concludes by proposing a people-centric training framework that can empower the workforce to meet the demands of the green hydrogen economy, ultimately contributing to a just and efficient energy transition.

Across all industries, especially in the recent century, the 4Ms framework-Man, Machine, Material, and Method-is a fundamental component of Continuous Improvement (CI), providing an articulated means of improving operational effectiveness. Although the "Machine" and "Method" components have been transformed by Industry 4.0 technology, this study argues that the "Man" component is not just crucial but pivotal, even if it is often prone to underestimation. Through an analysis of essential human-centered elements such as organizational culture, skill development, leadership, and adaptability, this study emphasizes the workforce's indispensable contribution to maintaining CI initiatives in the face of automation. This article demonstrates how people-centric strategies are crucial to developing CI initiatives by providing a thorough examination of human-technology synergy. This allows organizations to scale new heights in creativity, resilience, and sustainable success in a rapidly changing digital ecosystem. The review demonstrates why organizations should prioritize human capital and human capital development, even as they invest in technology, positioning people at the center of and as active drivers of CI in Industry 4.0 and beyond.

Contemporary physical science studies rely on the effective analyses of geographically dispersed spatial data and simulations of physical phenomena. Single computers and generic high-end computing are not sufficient to process the data for complex physical science analysis and simulations, which can be successfully supported only through distributed computing, best optimized through the application of spatial principles. Spatial computing, the computing aspect of a spatial cyberinfrastructure, refers to a computing paradigm that utilizes spatial principles to optimize distributed computers to catalyze advancements in the physical sciences. Spatial principles govern the interactions between scientific parameters across space and time by providing the spatial connections and constraints to drive the progression of the phenomena. Therefore, spatial computing studies could better position us to leverage spatial principles in simulating physical phenomena and, by extension, advance the physical sciences. Using geospatial science as an example, this paper illustrates through three research examples how spatial computing could (i) enable data intensive science with efficient data/services search, access, and utilization, (ii) facilitate physical science studies with enabling high-performance computing capabilities, and (iii) empower scientists with multidimensional visualization tools to understand observations and simulations. The research examples demonstrate that spatial computing is of critical importance to design computing methods to catalyze physical science studies with better data access, phenomena simulation, and analytical visualization. We envision that spatial computing will become a core technology that drives fundamental physical science advancements in the 21st century. Globalization over the past decades has raised human awareness of global challenges, such as global warming, severe weather conditions and rapid diffusion of contagious diseases (1, 2). These challenges require advancement in the physical sciences to better understand the world around us. Spatial data, the information related to space and time, are critical to scientific advancement by providing multidimensional (3D space, 1D time, and important parameters) records of the Earth. Large-scale data are typically collected, generated, and stored in geographically dispersed locations and must therefore be supported by distributed computing facilities. Seamless sharing and access to these resources calls for a spatial cyberinfrastructure (CI) to enable the use of spatial data for the advancement of the physical sciences . Global initiatives, such as the Global Earth Observation System of Systems (GEOSS, an initiative to integrate Earth observation data to address regional to global problems such as climate change), Digital Earth (an initiative to integrate Earth referenced data to provide intuitive and better end-user tools), and the Spatial Data Infrastructure (SDI, an initiative to build an infrastructure to seamlessly share spatial data) (5-8), all call for the integration of many spatial resources to solve complex problems, such as climate change prediction and rapid emergency response. These problems must be addressed in a multidimensional context and conceptualized as spatiotemporal or geospatial dynamics in order to be effectively resolved (9, 10). improvement is 20% when 6 CPU cores are used (see ). Although it may not be important for long-term dust-storm forecasting, finer resolution predictions may take a longer time and can be very significant for real-time dust-storm simulations when results are desired in less than 1 h (42). It was observed that the network speedup ratio increases when the CPU core number increases with more CPU cores involved and more communication required, but speedup ratio decreases after 6 CPU cores. This is because two or more CPU cores of each computing node contributed to the dust simulation. It is part of the communication occurring among different cores within one computing node. Communication between these cores will not go through the Internet, therefore reducing the communication overhead (spatial principles 1.a.i and 1.a.ii). Thus, the network speedup ratio decreased after 6 CPU cores with 3 cores per computing node were involved as more communication took place within one computing node. And, spatial principle 1.a.i can be utilized to reduce the communication overhead because faster communication can be achieved by selecting closer computing nodes.