World Academy of Science, Engineering and Technology

Authors: José Luis Meza de la Rosa, María del Rocío Soto Flores

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This paper introduces the conceptual foundation of a strategic governance model tailored to emerging innovation clusters in regulated high-technology industries. Using the case of Omexia — the first Artificial Intelligence–driven cluster for pharmaceutical innovation in Mexico — the study proposes a theoretical framework to understand and guide the formation, coordination, and evolution of innovation ecosystems in economies undergoing industrial transformation.The governance of innovation clusters in emerging contexts faces multiple challenges: institutional fragmentation, weak public-private articulation, limited absorptive capacity in firms, and regulatory complexity in sectors such as biopharma. In response to these conditions, this paper outlines a governance model composed of three interdependent dimensions: (1) Institutional Architecture, referring to the structural configuration and roles of key stakeholders including academia, industry, government, and civil society; (2) Strategic Coordination Mechanisms, which involve leadership models, agenda-setting, funding strategies, and collaborative capabilities; and (3) Dynamic Governance Capabilities, understood as the capacity of the cluster to adapt, learn, and respond to technological convergence, particularly between artificial intelligence and biotechnology.The Omexia case serves as an illustrative context to explore the theoretical assumptions of the model. The cluster operates in a frontier space of technological convergence, where AI and molecular design are reshaping drug discovery and development. Its emergence raises critical questions about how governance structures can foster innovation, reduce systemic barriers, and align with national industrial and health policy objectives.Rather than presenting empirical findings, this paper focuses on building a conceptual framework that can support future applied research and strategic planning in cluster development. The model proposed is intended to be a diagnostic and design tool for stakeholders in innovation ecosystems, and a contribution to the theory of innovation governance in emerging economies.

Keywords: innovation clusters, strategic governance, industrial development, emerging economies, institutional innovation

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Authors: Mohamed Laissy, Omar Mostafa Dakhil

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Project delays, cost overruns, and insufficient coordination of resources continue to challenge the construction industry. Coupled with rising environmental concerns, the sector now faces an urgent need to adopt more sustainable and data-driven approaches. This paper introduces a Sustainable Project Management Construction (SPMC) tool that uses machine learning (ML) particularly Random Forest Regressor to forecast project delays, optimize resource allocation, and incorporate clear sustainability metrics. A dataset of 33 construction projects was used, achieving an R² of 0.87 in predicting scheduling overruns, with an MSE of 11.8 days, and an R² of 0.82 for resource utilization forecasts. The tool features a Python-based user interface to support data entry and scenario analysis. Ethical data handling and privacy measures were applied throughout to maintain stakeholder trust. This study emphasizes the need for artificial intelligence in construction management to enhance efficiency and sustainability. SPMC enhances construction project management by promoting proactive decision-making, reducing resource waste, and steering it towards a more sustainable future. The paper concludes with a discussion of the system’s limitations and outlines future work to embed Internet-of-Things (IoT) data and expand sustainability indicators.

Keywords: AI, project management, prediction models, sustainable construction, SPMC

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Authors: Krittin Kulrattanaruks, Aimaschana Niruntasukrat

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In the era of Industry 4.0, reliable fault detection in rotating machinery is critical to minimizing unexpected downtime and maintenance costs. This study addresses the challenge of diagnosing compound faults, which consist of an unbalanced rotor, angular misalignment, and parallel misalignment, by introducing a baseline regression neural network for fault severity prediction. Fast Fourier Transform (FFT) was applied to the time-series vibration signal, producing amplitude features across multiple frequency ranges. A novel `smallest-severity normalization,’ where the minimum fault severity is considered as the unit severity, was introduced to enhance sensitivity to minor faults. Due to the imbalanced dataset, a stratified approach was used to split the data, ensuring that label distribution was consistent across training, validation, and test sets. Additionally, five-fold cross-validation was applied to the training data. To evaluate the accuracy of the neural network configuration, Root Mean Squared Error and Mean Error were used, while Mean Absolute Deviation from the Median was employed to assess its precision. The results reveal that limiting the input features to frequencies up to 750 Hz delivers the highest accuracy, with minimal error and slightly conservative severity predictions, which is an outcome that favors early warnings over missed detections. Conversely, including higher-frequency amplitude data increases noise and leads to underestimation of actual fault severity. These findings highlight the value of careful frequency selection and tailored normalization in multi-target fault diagnosis.

Keywords: fault severity prediction, rotating machinery, compound fault dataset, neural network, predictive maintenance

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Authors: Giuliana Schulz, Abigail Borja, Fiorella Duarte, Ivan Olmedo

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This paper addresses the issue of long patient waiting times at Dajia Lee’s General Hospital during peak hours. The study identifies key contributing factors, including budget constraints, low digital literacy among elderly patients, and the hospital’s need to manage large volumes of foreign workers undergoing mandatory health checks. The project aimed to improve operational management and reduce waiting times in registration, payment, and pharmacy services without increasing staff or infrastructure costs. To achieve this, Six Sigma, discrete event simulation (FlexSim), and TRIZ were employed. A major finding was the inefficiency of the ticketing system, where many patients struggled with machine interaction, resulting in delays. Through simulation and root-cause analysis, the study pinpointed process failures and proposed targeted interventions. The most notable result was a significant reduction in average waiting times—from 18.81 minutes to under 4 minutes—with registered patients experiencing just 0.70 minutes. Queue balancing and pharmacy throughput also improved. These results confirm that integrating Six Sigma methodology with TRIZ-based process innovation and AI-driven prioritization can optimize hospital workflows effectively. The simulation-based approach enabled safe testing of improvements and helped address operational bottlenecks without disrupting services. This work presents a replicable and cost-efficient framework that hospital administrators in resource-constrained settings can use to enhance service quality and patient satisfaction. The project demonstrates the feasibility and measurable benefits of combining systematic process improvement tools and intelligent technologies in real-world healthcare environments.

Keywords: simulation, six sigma, optimization, hospital efficiency management

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Authors: Devdas Shetty

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The growing need for ever-changing customer demand pressurizes the manufacturing industry to look for a flexible and fast-changing small-volume production system. As a result, additive manufacturing (AM) is one of the fastest-growing methods of changing a 3D design model to a 3D product without any process planning method. The process is commonly called 3D printing technology and has found extensive applications in areas such as automotive, architecture, manufacturing, aerospace, thermal, flexible electronics, medicine, fashion, retail, and sports. A major aspect of 3D printing technology is its ability to produce parts that are not possible with traditional manufacturing techniques. The students at any level can be introduced to the technology and understand the theoretical aspects in coordination with practice in the laboratory. The paper examines the underlying 'rules' that help companies take full advantage of additive manufacturing technologies. The paper also examines the guidelines for the design of additive manufacturing with an in-depth discussion of design constraints. These guidelines are discussed with the view of creating lightweight parts, efficient heat exchangers, and components for the aerospace industry. The paper investigates different influencing variables, including the variation due to density and porosity. Other modeling equations that influence the additive process are examined, which include energy balance equations for melting and vaporization. Post processing of 3D additive components is also critical to the outcome of the overall process, as it impacts the resulting surface quality, total cycle time, and cost.

Keywords: additive manufacturing case studies design guidelines, 3d printing of special materials, advanced fabrication, educating engineers

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Authors: Ahmed Alghamdi, Paul Mativenga

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In this study statistical analysis was employed to identify the key process parameters to control the process of using laser technology for micromachining of multilayer coated carbide. The work includes the use of Nd:YVO₄ nanosecond pulsed laser technology for surface structuring of multiple layers of Chromium (Cr), alumina (Al₂O₃) and titanium carbonatite (TiCN) coatings on a tungsten carbide-cobalt (WC-Co) substrate. The impact of different laser settings on the structure's geometry and the multilayers of coating has been discussed in detail. The impact on the micro-channel’s depth, width, and height of the burr was disused. Taguchi's design of the experiment allowed the identification of optimum settings for each parameter to achieve pre-set goals. The conformation tests demonstrate that it results were able to control the micro-channel's depth, width and burrs height.

Keywords: micro-machining, surface structuring, cutting tools, Taguchi design of experiment

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Authors: Maji I. Abubakar

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In recent years, there have been increasing studies on the significance of human error on the efficiency and effectiveness of manufacturing systems. However, an in-depth analysis of the sources of human errors relating to manual assembly operations is still scanty, and a wider study of factors responsible for human error in manual assembly operations is lacking; therefore an effective approach to evaluating this phenomenon is needed. The Social Network Analysis approach is used in this study to systematically investigate the causality and influence of human error in manual assembly operations using data from the literature. The results of this study indicate that task complexity, training/experience, cognitive demand, and task time are the most significant factors liable for human error in manual assembly operations.

Keywords: human error, manual assemble operations, manufacturing systems, social network analysis

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Authors: Christiane Frank, Raúl Rodríguez-Rodríguez, Juan-José Alfaro-Saiz

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Lean management has widely been implemented to improving operational efficiency, minimizing waste, and optimizing organizational performance. Despite its widespread adoption, many organizations struggle with lean transformations due to facts such as poor strategic execution, cultural resistance, or lack of leadership commitment. This research identifies from the scientific literature key success factors in lean management transformations, grouping them into three main clusters (people, processes and tools) to improve their global vision. A thorough understanding of these factors and their intrinsic relationships can improve organizations’ ability to implement lean management transformations more effectively. Additionally, this study identifies future research directions.

Keywords: critical success factors, lean management, business transformations, clustering

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Authors: Manasa Hegde, Anil Kumar

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Current trends in mental health reveal a growing awareness of mental health issues, with more people seeking help through therapy, counseling, and support services. In spite of the advancements, challenges remain, such as a shortage of mental health professionals and unequal access to services. Users are leaning towards the use of technology (such as Virtual Reality (VR)) that can aid in alleviating their stress levels. This research will explore how guided breathing techniques, meditation, and other mindfulness practices perform when delivered through Virtual Reality (VR) compared to conventional approaches (e.g., in-person sessions or smartphone apps). The study will examine if VR can provide greater user engagement, stress alleviation, and overall effectiveness than traditional methods in light of the increased interest in VR for mental health. A between-subject experiment will be designed to evaluate the efficacy of VR in practicing mindfulness. Both qualitative (surveys, questionnaire) and quantitative (galvanic skin resistance, heart rate readings) measures will be utilized to collect data from the human participants. The overall objective of this endeavor is to ascertain whether VR can offer a more captivating and immersive setting for mindfulness and aid in creating more compelling mental health solutions. This study aims to provide insightful comparisons between VR-based and conventional mindfulness methods for mental health and digital wellness practitioners.

Keywords: guided breathing techniques, meditation, mental well-being, mindfulness, stress reduction, virtual reality

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Authors: Abdul Basit Ishaq, Rafia Rana, Sadia Ishaq, Anber Rana

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An ‘incident’, according to the Information Technology Infrastructure Library (ITIL) framework, is a breach of service level, usually involving issues or defects observed in various IT software and hardware services. The increasing frequency of incidents within a telecom operator not only hinders employee performance but also has a direct impact on customer satisfaction and experience. Thus, it emphasizes the importance of minimizing these incidents as much as possible. This study aims to examine the underlying causes of incidents for a leading Pakistani Telecom operator and reduce the number of IT-related incidents. The DMAIC (Define, Measure, Analyze, Improve, Control) methodology, a principal component of Lean Six Sigma, is applied to improve existing processes and, as a result, enhance customer experience. Our process output variable is the weekly total incidents. Seventeen input variables were selected for analysis using data obtained from Services Manager software. Correlation and regression analysis identified key relationships, followed by Cause-and-Effect Diagrams and Why-Why Analysis to pinpoint root causes. During the improvement phase, the results highlighted the need to reform the ‘Change Management Process’ across the entire department and provide training on ‘Quality Management System’. Overall, implementing these solutions resulted in a more than 50% reduction in incidents, followed by an estimated $9,000 (PKR 1.2 million) savings for the organization.

Keywords: incident, information technology, lean six sigma, quality management system

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Authors: Jemma Widdows

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I. INTRODUCTION RSSB is the independent safety, standards and research body for Great Britain’s (GB) rail network. Our work targets six main areas: safer, healthier, harmonised, efficient, future, and sustainable rail and is underpinned by the latest rail-related research and data. II. PROJECT OUTLINE The United Kingdom’s (UK) Rail Accident Investigation Branch (RAIB) tasked the RSSB with the delivery of recommendations in response to an incident in 2021 that took place near Ramsden Bellhouse. J Widdows and T Hyatt from RSSB’s Human Factors (HF) team delivered two of the three recommendations set out by RAIB for industry. This paper will seek to explain the human factors approach taken to respond to the two recommendations from RAIB summarised below: 1) To clarify the roles and responsibilities of staff in circumstances where on-track plant is travelling, including the relevant rules and standards that apply to the control of on-track plant travelling movements. Also, the role of the machine controller during such movement, including where they should be positioned to discharge their duties and ensure personal safety. 2) Establish a mechanism for periodically reviewing the availability of obstacle detection equipment suitable for fitting to all types of on-track plant. III. PROJECT OBJECTIVES This project was a HF response to RAIB recommendations that were a result of a collision between two on-track plant (OTP) in Ramsden Bellhouse, Essex in 2021. It focused on understanding the role and responsibilities of a machine controller (MC) during travelling movements. The project also looked at error in cab design for OTP and communication to reduce the risk of striking obstacles, amongst other objectives. The overarching objective of the research project was to comprehensively respond to RAIB’s 2022 recommendation to reduce the risk of on-track plant (OTP) striking objects. HF methodologies and techniques used included observations, surveys, workshops, and an adaptation of the Systematic Human Error Reduction and Prediction Approach (SHERPA). Some of the outputs included a Human Factors Error Log (HFEL) and a user guide for UK infrastructure managers to assess new technology when purchasing or updating technology used in on track machinery (OTM). The UK rail industry is recommended to use the outputs referred to in this paper as part of an independent review of new technology to reduce the risk of OTP striking objects or other OTP on or near the line, as and when new technology becomes available and/or more cost effective for the GB rail industry, in addition to when retrofitting existing OTP. The paper identifies several human factors issues with current OTP cab designs, such as illogical braking controls and inconsistent feedback systems. IV. CONCLUSION This paper will highlight how the HF approach taken enabled the recommendations to be fully analysed in a real-world context, supported by experts and the final reports, to realise the benefits of the applied research project.

Keywords: human factors and ergonomics in organisational design and management, human factors and ergonomics, integration of design, processes of human factors, technology-driven change, human-machine interface, control interface survey, on track plant, traveling movements, machine controllers, UK infrastructure, GB railway

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Authors: Ismail Mahmoud

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The recent health crisis has highlighted the structural vulnerabilities of the hospital system, exacerbating challenges related to resource management and the quality of care. Despite the exemplary commitment of healthcare professionals, these shortcomings require an organizational overhaul to ensure the optimal functioning of healthcare facilities under all circumstances. In this context, Lean Management and Operational Excellence emerge as strategic levers to enhance hospital efficiency, reduce waste, and improve patient care. This article presents a study conducted in the emergency department of a provincial hospital in Morocco, aimed at identifying key improvement areas and applying Lean tools to optimize hospital workflows. The field analysis led to the implementation of concrete solutions, including: (i) proactive patient arrival management to mitigate overcrowding, (ii) optimized triage through telemedicine and enhanced training for ambulance personnel, (iii) reduced waiting times through an efficient prioritization system, (iv) improved staff efficiency by minimizing administrative tasks, and (v) better patient orientation toward appropriate healthcare structures to optimize resource allocation. The results demonstrate that integrating Lean Management principles into a hospital setting significantly enhances organizational performance and the quality of care. However, its adoption remains limited, requiring increased efforts in training, awareness-raising, and change management. These findings underscore the importance of rethinking hospital management strategies to strengthen the resilience of healthcare institutions while ensuring a better experience for both patients and healthcare professionals.

Keywords: lean management, operational excellence, hospital management, workflow optimization, healthcare improvement

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Authors: Suryapeta Akshitha

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Artificial Intelligence (AI) is increasingly shaping service operations, particularly through advanced AI models that go beyond mechanical intelligence. This study examines the impact of these AI capabilities on customer satisfaction within the hospitality industry, where AI-driven interactions are becoming more prevalent. Using customer feedback data from various online review sources, this study analyzes how intuitive and empathetic AI influences service perceptions. A mixed-methods approach is employed, combining sentiment analysis with qualitative content analysis to identify patterns in customer experiences. The study also investigates key moderating factors, such as customer expectations, AI transparency, and service context, to understand how different interactions shape overall satisfaction levels. Findings indicate that AI systems with intuitive capabilities, such as adaptive decision-making and real-time personalization, enhance perceived service quality by aligning with customer preferences. Meanwhile, empathetic AI, designed to recognize and respond to emotions, fosters trust and engagement. However, the effectiveness of these AI models depends on customer familiarity with AI technologies, the complexity of service requests, and the level of human-AI collaboration. Additionally, concerns related to AI decision transparency and emotional authenticity emerge as critical factors influencing customer perceptions. This research contributes to service operations literature by offering a deeper understanding of AI-driven customer experiences beyond traditional automation. It highlights the potential of AI to not only streamline service efficiency but also build meaningful customer relationships. The study provides practical insights for businesses on integrating AI in service environments while ensuring transparency and customer confidence. Future research should explore long-term customer-AI relationships and the ethical implications of AI-driven interactions in hospitality and beyond.

Keywords: artificial intelligence, customer satisfaction, hospitality industry, service operations

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Authors: Zainab Haieder, Nijem Saad

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In advanced manufacturing applications, it is essential to design a production process that is environmentally friendly. The process of 3D printing is a forward-moving, distinctive, original, and cutting-edge additive manufacturing method. Due to their ability to create free-form shapes without limitations and quickly implement new designs, progressive Additive Manufacturing (AM) processes are becoming more significant than traditional subtractive methods in Industry. The present paper studies the effect of the infill density on the flexural and impact behavior of PLA, other mechanical and fracture behavior, the percentage infill (25%, 50%, 75%, 100%) with a hexagonal infill pattern, In additive manufacturing (AM), in this study the print orientation in (0⁰, 45⁰, 90⁰). The ASTM D790 standard was followed in the design of the flexural specimen and ASTM D 256 for the impact specimen. The results show flexural strength and flexural modulus increasing with increasing infill density and flex rule modulus max value in 0⁰ and min value in 45⁰. impact energy and impact energy increase with the increase in fill density and max value in 0⁰ direction.

Keywords: 3D printing, FDM, infill density, PLA, hexagonal

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Authors: Mohsen Alamooti, Moones Alamooti

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The mineral processing industry faces significant environmental challenges in tailings management, necessitating innovative approaches to waste reduction and resource efficiency. This study examines the systematic application of Lean Project Management (LPM) methodologies in mineral processing operations to address these challenges. The research demonstrates the effectiveness of LPM principles in reducing waste and minimizing environmental impacts while maintaining operational efficiency. The methodology incorporates value stream mapping, 5S implementation, and continuous improvement processes to analyze mineral processing workflows. Through systematic evaluation of operational processes, the study identifies critical waste sources and inefficiencies in tailings management. The research employs quantitative metrics to assess environmental impact reduction and resource recovery improvements. The implementation framework includes comprehensive stakeholder engagement strategies and regulatory compliance measures. The results demonstrate significant improvements in tailings management efficiency through LPM implementation. Key findings include enhanced resource recovery rates, reduced environmental contamination risks, and improved operational cost-effectiveness. The study reveals that systematic application of LPM principles leads to measurable reductions in both material waste and process inefficiencies. Furthermore, the research establishes a correlation between improved tailings management practices and enhanced community safety outcomes. The study contributes to the development of industry best practices by providing a structured approach to integrating LPM principles into mineral processing operations. The findings indicate that the LPM methodology effectively balances environmental protection with operational efficiency while generating additional economic value through improved resource utilization. This research establishes a framework for sustainable mining practices that address both environmental concerns and operational requirements, offering valuable insights for industry practitioners seeking to enhance their environmental performance while maintaining economic viability.

Keywords: environmental management, lean project management, mineral processing, waste management

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Authors: Awadh Alshanfari

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Effective operation of a Liquefied Petroleum Gas (LPG) plant relies heavily on the competence and efficiency of panel operators during their shifts. However, traditional methods of operation often lack real-time visibility and comprehensive data analysis, leading to inefficiencies and potential safety hazards. This paper proposes the integration of visualization tools into LPG plant operations to enhance the performance of panel operators. By providing intuitive and interactive interfaces, these tools enable operators to monitor plant processes, analyze data trends, and make informed decisions promptly.[3]. Through a combination of case studies and practical insights, this article explores the benefits and challenges of implementing visualization tools in LPG plants. Moreover, it discusses strategies for optimizing operator training and workflow integration to maximize the effectiveness of these tools in improving operational performance, safety, and overall plant productivity.

Keywords: LPG plants, visualization tools, operator performance, process optimization

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Authors: Touil Issam, Bouraghda Skander

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This paper elaborates on the comprehensive design, development, and evaluation of an Arduino-powered robot car operated through an Android-based application. The system is built upon an Arduino UNO microcontroller, leveraging Bluetooth technology to facilitate seamless communication between the robot and the Android control interface. The primary objective of the project is to provide users with an intuitive and interactive means to control autonomous systems while ensuring simplicity, cost-efficiency, and reliability. The architecture of the system encompasses hardware and software integration, where the microcontroller acts as the central processing unit, handling signals received via Bluetooth and translating them into precise motor commands. The Android application serves as a user-friendly interface, enabling real-time control of the robot's movement and functionality. This paper delves into the technical aspects of system architecture, including the hardware components, wiring schematics, and Bluetooth module integration. Additionally, it highlights the software development process, emphasizing the programming logic, algorithm design, and debugging techniques employed. Testing and validation phases are thoroughly documented, showcasing the system's performance under various conditions, including speed, maneuverability, and Bluetooth signal range. The results confirm the project's success in achieving its goals, offering a robust and accessible solution for educational and practical applications in robotics.

Keywords: Arduino Robot, car, microcontroller, Bluetooth communication

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Authors: Slawomir Lasota, Tomasz Kajdanowicz

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This research examines the application of predictive modelling and optimization algorithms to improve production efficiency in smart factories. Utilizing gradient boosting and neural networks, the study builds robust KPI estimators to predict production outcomes based on real-time data. Optimization methods, including Bayesian optimization and gradient-based algorithms, identify optimal process configurations that maximize availability, efficiency, and quality KPIs. The paper highlights the modular architecture of a recommender system that integrates predictive models, data visualization, and adaptive automation. Comparative analysis across multiple production processes reveals significant improvements in operational performance, laying the foundation for scalable, self-regulating manufacturing systems.

Keywords: predictive modeling, optimization, smart factory, efficiency

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Authors: Christoph Mans, Marné de Vries

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With the continual advancement in software development, the decision-making process for selecting an appropriate software solution becomes increasingly difficult. The selection of incorrect software solutions can have adverse and negative consequences on the industry of interest. The aim of this paper is to identify a method for software selection that will conform to the requirements for event guest management of large events. A literature review is used to identify existing methods or methodologies on software selection for event guest management. The literature search is expanded to identify general software selection methodologies, not only related to event guest management, to compensate for the lack of literature available within the area of interest. Literature on multiple industries and component software selection is analyzed to identify decision-making techniques for selecting software that can be implemented for the management of large events.

Keywords: software selection, event guest management, multi-criteria decision-making, MCDM

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Authors: Ahmadreza Rezaei, Qiong Liu

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Supply chain network made based on suppliers and product architecture design. these networks are complex and vulnerable that may be expose disruption risks. any supply chain network configuration has its own related complexity and robustness that can have direct effect on its efficiency. So it's necessary to evaluate any configuration with considering complexity and robustness aspects together. However, there is a lack of research about this subject to managers can evaluate their supply chain configurations and choose configuration with balanced complexity and robustness together. In this study, developed indicators improve robustness of supply chain with using framework to evaluate relationships between complexity and robustness of supply chain network under different network configurations . this framework includes Investigation and analysis of quantitative indicators based on network characteristics. Moreover, overall metrics of Shannon entropy is presented to evaluate network topological complexity. So we will analyze two factor of complexity and robustness of networks based on supply chain configurations As result, Complexity and Robustness are two integral components of network that show network resistances under disruption. It's necessary to attain a balanced level of complexity and robustness in network configurations. the proposed framework could be used in supply chain network to improve efficiency.

Keywords: supply chain design, structural complexity, robustness, supply chain configuration, Shannon entropy

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Authors: Danica Terese Valda, Segundo Villa III, Michiko Yasuda, Jomel Tagaro

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This study conducts a thorough literature review of logistics optimization techniques that aimed at improving the efficiency of supply chain operations. Logistics optimization encompasses key areas such as transportation management, inventory control, and distribution network design, each of which plays a critical role in streamlining supply chain performance. The review identifies mixed-integer linear programming (MILP) as a dominant method, widely used for its flexibility in handling complex logistics problems. Other methods like heuristic algorithms and combinatorial optimization also prove effective in solving large-scale logistics challenges. Furthermore, real-time data integration and advancements in simulation techniques are transforming the decision-making processes within supply chains, leading to more dynamic and responsive operations. The inclusion of sustainability goals, particularly in minimizing carbon emissions, has emerged as a growing trend in logistics optimization. This research highlights the need for integrated, holistic approaches that consider the interconnectedness of logistical components. The findings provide valuable insights to guide future research and practical applications, fostering more resilient and efficient supply chains.

Keywords: logistics, techniques, supply chain, land transportation

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Authors: Tolulope Aremu

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This paper is based on the idea of using deep learning methodology for optimizing production yield by tuning a few key process parameters in a manufacturing environment. The study was explicitly on how to maximize production yield and minimize operational costs by utilizing advanced neural network models, specifically Long Short-Term Memory and Convolutional Neural Networks. These models were implemented using Python-based frameworks—TensorFlow and Keras. The targets of the research are the precision molding processes in which temperature ranges between 150°C and 220°C, the pressure ranges between 5 and 15 bar, and the material flow rate ranges between 10 and 50 kg/h, which are critical parameters that have a great effect on yield. A dataset of 1 million production cycles has been considered for five continuous years, where detailed logs are present showing the exact setting of parameters and yield output. The LSTM model would model time-dependent trends in production data, while CNN analyzed the spatial correlations between parameters. Models are designed in a supervised learning manner. For the model's loss, an MSE loss function is used, optimized through the Adam optimizer. After running a total of 100 training epochs, 95% accuracy was achieved by the models recommending optimal parameter configurations. Results indicated that with the use of RSM and DOE traditional methods, there was an increase in production yield of 12%. Besides, the error margin was reduced by 8%, hence consistent quality products from the deep learning models. The monetary value was annually around $2.5 million, the cost saved from material waste, energy consumption, and equipment wear resulting from the implementation of optimized process parameters. This system was deployed in an industrial production environment with the help of a hybrid cloud system: Microsoft Azure, for data storage, and the training and deployment of their models were performed on Google Cloud AI. The functionality of real-time monitoring of the process and automatic tuning of parameters depends on cloud infrastructure. To put it into perspective, deep learning models, especially those employing LSTM and CNN, optimize the production yield by fine-tuning process parameters. Future research will consider reinforcement learning with a view to achieving further enhancement of system autonomy and scalability across various manufacturing sectors.

Keywords: production yield optimization, deep learning, tuning of process parameters, LSTM, CNN, precision manufacturing, TensorFlow, Keras, cloud infrastructure, cost saving

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Authors: Muhammad Yasir Latif

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Industrial Engineering and Management (IEM) is a continuous, adaptable, and dynamic branch of engineering. The purpose of this study is to use a knowledge-based course classification method to investigate four IEM educational programs in Europe. Furthermore, the relative weight of each sector was determined using the credit value of the courses. IEM-specific locations and pooled areas were the two related kinds of areas that were used. The results show that, among the four program curricula, Production Management is the specific area with the largest weight, while the specialism field of IEM has a similar weight. This method has proved to be useful for curriculum analysis. The results show that one characteristic of IEM curriculum programs is diversity in the knowledge domains related to IEM specialism. The research also highlights the importance of an organized structure for defining IEM applications, allowing benchmarking efforts, and promoting communication between academics and the IEM community.

Keywords: industrial engineering and management, knowledge areas, curriculum analysis, community

Procedia PDF Downloads 50

Authors: Abdulrahman R. Alenezi

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This research investigates the effects of facility disruptions on-demand allocation within the context of the Reliable Facility Location Problem (RFLP). We explore two distinct scenarios: one where primary and backup facilities can fail simultaneously and another where such simultaneous failures are not possible. The RFLP model is tailored to reflect these scenarios, incorporating different approaches to transportation cost calculations. Utilizing a Lagrange relaxation method, the model achieves high efficiency, yielding an average optimality gap of 0.1% within 12.2 seconds of CPU time. Findings indicate that primary facilities are typically sited closer to demand points than backup facilities. In cases where simultaneous failures are prohibited, demand points are predominantly assigned to the nearest available facility. Conversely, in scenarios permitting simultaneous failures, demand allocation may prioritize factors beyond mere proximity, such as failure rates. This study highlights the critical influence of facility reliability on strategic location decisions, providing insights for enhancing resilience in supply chain networks.

Keywords: reliable supply chain network, facility location problem, reliable facility location model, LaGrange relaxation

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Authors: Bahare Askarian, Suchithra Rajendran

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Maintaining the freshness and quality of perishable goods during distribution is a critical challenge for logistics companies. This study presents a comprehensive framework aimed at optimizing the distribution of perishable goods through a mathematical model of the Transportation Inventory Location Routing Problem (TILRP). The model incorporates the impact of product age on customer demand, addressing the complexities associated with inventory management and routing. To tackle this problem, we develop both simple and hybrid metaheuristic algorithms designed for small- and medium-scale scenarios. The hybrid algorithm combines Biogeographical Based Optimization (BBO) algorithms with local search techniques to enhance performance in small- and medium-scale scenarios, extending our approach to larger-scale challenges. Through extensive numerical simulations and sensitivity analyses across various scenarios, the performance of the proposed algorithms is evaluated, assessing their effectiveness in achieving optimal solutions. The results demonstrate that our algorithms significantly enhance distribution efficiency, offering valuable insights for logistics companies striving to improve their perishable goods supply chains.

Keywords: perishable goods, meta-heuristic algorithm, vehicle problem, inventory models

Procedia PDF Downloads 54

Authors: Wadea Ameen

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Rapid prototyping (RP) technology, such as fused deposition modeling (FDM), is gaining popularity because it can produce functioning components with intricate geometric patterns in a reasonable amount of time. A multitude of process variables influences the quality of manufactured parts. In this study, four important process parameters such as layer thickness, model interior fill style, support fill style and orientation are considered. Their influence on three responses, such as build time, model material, and support material, is studied. Experiments are conducted based on factorial design, and the results are presented.

Keywords: fused deposition modeling, factorial design, optimization, 3D printing

Procedia PDF Downloads 52

Authors: R. Ananthanarayanan, S. B. Nakula, D. R. Seenivasagam, J. Naua, B. Sharma

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Product packaging is a critical component of production, trade, and marketing, playing numerous vital roles that often go unnoticed by consumers. Packaging is essential for maintaining the shelf life, quality assurance, and safety of both manufactured and agricultural products. For example, harvested produce or processed foods can quickly lose quality and freshness, making secure packaging crucial for preservation and safety throughout the food supply chain. In Fiji, agricultural packaging has primarily been managed by local companies for international trade, with gradual advancements in these practices. To further enhance the industry’s performance, this study examines the challenges and constraints hindering the optimization of agricultural packaging practices in Fiji. The study utilizes Multi-Criteria Decision Making (MCDM) tools, specifically Interpretive Structural Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). ISM analyzes the hierarchical structure of barriers, categorizing them from the least to the most influential, while MICMAC classifies barriers based on their driving and dependence power. This approach helps identify the interrelationships between barriers, providing valuable insights for policymakers and decision-makers to propose innovative solutions for sustainable development in the agricultural packaging sector, ultimately shaping the future of packaging practices in Fiji.

Keywords: agricultural packaging, barriers, ISM, MICMAC

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Authors: M. Hisham, S. Cabemaiwai, S. Prasad, T. Dauvakatini, R. Ananthanarayanan

Abstract:

A solenoid valve is an important part of many fluid systems. Its purpose is to regulate fluid flow in a machine. Due to the crucial role of the solenoid valve and its design intricacy, it is quite expensive to obtain in Fiji and is not manufactured locally. A concern raised by the local health industry is that the housing of the solenoid valve gets damaged when machines are continuously being used and this part of the valve is very costly to replace due to the lack of availability in Fiji and many other South Pacific region countries. This study explores the agile manufacturing of a solenoid coil housing using the Fused Deposition Modeling (FDM) process. An emergy study was carried out to analyze the feasibility and sustainability of producing the part locally after estimating a Unit Emergy Value (or emergy transformity) of 1.27E+05 sej/j for the electricity in Fiji. The total emergy of the process was calculated to be 3.05E+12 sej, of which a majority was sourced from imported services and materials. Renewable emergy sources contributed to just 16.04% of the total emergy. Therefore, the part is suitable to be manufactured in Fiji with a reasonable quality and a cost of $FJ 2.85. However, the loading on the local environment is found to be significant and therefore, alternative raw materials for the filament like recycled PET should be explored or alternative manufacturing processes may be analyzed before committing to fabricating the part using FDM in its analyzed state.

Keywords: emergy analysis, fused deposition modeling, solenoid valve housing, sustainable production

Procedia PDF Downloads 66

Authors: Damous Mohamed, Zeroudi Nasredine

Abstract:

High-speed milling of thin-walled parts with complex curvilinear profiles often encounters machining instability, commonly referred to as chatter. This phenomenon arises due to the dynamic interaction between the cutting tool and the part, exacerbated by the part's low rigidity and varying dynamic characteristics along the tool path. This research presents a dynamic model specifically developed to predict machining stability for such curved thin-walled components. The model employs the semi-discretization method, segmenting the tool trajectory into small, straight elements to locally approximate the behavior of an inclined plane. Dynamic characteristics for each segment are extracted through experimental modal analysis and incorporated into the simulation model to generate global stability lobe diagrams. Validation of the model is conducted through cutting tests where acoustic intensity is measured to detect instabilities. The experimental data align closely with the predicted stability limits, confirming the model's accuracy and effectiveness. This work provides a comprehensive approach to enhancing machining stability predictions, thereby improving the efficiency and quality of high-speed milling operations for thin-walled parts.

Keywords: chatter, curved thin-walled part, semi-discretization method, stability lobe diagrams

Procedia PDF Downloads 57

Authors: Haruna Daddau

Abstract:

This study investigates the effect of sustainable supply chain management (SSCM) on the performance of agricultural firms in Nigeria. Green packaging, product design, waste reduction and supply chain design were examined. The ecological modernization theory, which suggests the economic benefit of the environment, was used to underpin the study. The research is quantitative in nature, and a survey research method was adopted where information was obtained using questionnaires distributed directly to the top managers of 6 agricultural firms in Nigeria. STATA and SPSS were used for the data analysis, and regression analysis was used to examine the effects. Findings showed that SSCM positively improves the performance of the firms. Also, detailed information about the study’s selected variables' effect on performance was provided. Additionally, the significant role of SSCM in accelerating the firms’ performance was highlighted. It is recommended that SSCM should be given serious attention by integrating it into the overall firm's business strategy.

Keywords: sustainable supply chain management, green packaging, product design, waste reduction, supply chain design and performance

Procedia PDF Downloads 76