Context Of Industry Research Articles

Analysis of the disruptions of the digital revolution and its impacts on the evolution of continuous improvement methodologies and lean six-sigma

This research paper delves into the importance of continuous and lean improvements within the context of Industry 4.0. As businesses navigate the digital revolution, enhancing efficiency, effectiveness, and adaptability becomes paramount for maintaining competitiveness. The paper examines the principles of continuous and lean improvements, assessing their relevance in the Industry 4.0 landscape. Utilizing a grounded theory approach, it scrutinizes Industry 4.0 characteristics, market dynamics, disruptive forces, change management tools, and the attainment of lean operations. The findings emphasize the crucial role of continuous and lean improvements in enabling firms to navigate the dynamic Industry 4.0 landscape, effectively respond to market disruptions, and deliver enhanced value to customers. By providing actionable insights, the research paper equips companies to thrive in the digital revolution and leverage Industry 4.0 for sustainable growth. Data from 156 firms, gathered through a five-points-Likert scale questionnaire, informs the research paper. The Statistical Package for the Social Sciences (SPSS) and Analysis of Moment Structures (AMOS) software are used for statistical analyses that look at multicollinearity, content validity, and consistency, construct validity, Analysis of Variance (ANOVA), and the links between technological disruptions, the rise of agile companies, and overall performance. Results indicate that industry transformations directly and indirectly influence the evolution of Industry 4.0 and agility, empowering companies to develop effective strategies to overcome challenges posed by the digital revolution. To achieve this, companies must overhaul production systems, adopt innovative relationship management approaches, recognize and utilize existing organizational knowledge, and integrate it into product development, production operations, and marketing strategies.

The Implementation of Value Co-Creation in the Food Industry

In this study, we analyse, and classify the literature that discusses the implementation of value co-creation in the food industry. For this purpose, we conducted a systematic literature review identified from Scopus database search results. Then, it was further screened and filtered to obtain specific literature that could answer the proposed research question. Our analysis focuses on how those studies put value co-creation into real business and industrial contexts. We are looking at the value co-creation phases, mechanisms, and how those could create particular value. We found that value co-creation can be implemented in various ways tailored to the context that the actors wish to achieve. Another classification is made to find out the focus of the present research. This classification is based on the different functions they were focusing on across the food industry. Finally, we organise and summarise the suggestion for future research directions

Developing a strategic roadmap towards integration in Industry 4.0: A dynamic capabilities theory perspective

In recent years, the topic of the fourth industrial revolution has been discussed by industries and organizations. Currently, this topic has evolved beyond conceptualization, focusing more on challenges, capabilities, and benefits. This research aims to identify dynamic capabilities within the context of Industry 4.0. Employing literature review and content analysis, we utilized grounded theory to identify capabilities. Validation was achieved through feedback from 11 scholars using questionnaires and the Delphi method. The analysis confirmed the presence of 26 dynamic capabilities essential for Industry 4.0. Subsequently, the Dynamic Capabilities theory was applied to categorize these capabilities into ‘sensing,’ ‘seizing,’ and ‘reconfiguration.’ As a result, this study establishes a roadmap model that prioritizes capabilities for Industry 4.0 integration across these categories. It organizes capabilities based on priority for implementation and within each category, highlighting the most crucial ones determined by scholars' scores. This model provides a comprehensive understanding of the path towards integration in Industry 4.0.

Remaining Useful Life prediction based on physics-informed data augmentation

Current approaches for monitoring machine health (SOH) and effective prognostics depend on the extensive use of complete degradation data trajectories, implying the reliance on data generation techniques that involve functional degradation of the real system until the failure state is reached. These commonly adopted approaches that depend on labeled target data remain operationally and economically nonviable for most industries and safety critical systems. This paper presents novel approaches that alleviate the existing dependence of most prognostics procedures on Remaining Useful Life (RUL) labeled data for training. To this end, firstly, a hybrid data augmentation procedure is proposed that enables the integration of system knowledge available a priori as well as physics of failure, within the training data. Secondly, an unsupervised Health Index (HI) extraction approach is developed, followed by a long-term prediction of this same HI, that leads to an efficient prediction of RUL without labeled data. Finally, a reliability-based assessment is performed to validate the proposed approach. This comprehensive approach (i.e. integrating all the various stages involved in achieving a RUL prediction based on unlabeled data) is tested on a real industrial aircraft system demonstrating the effectiveness of the proposed approach in real industrial context.

Mind the gap: A scoping review of skills gaps for graduates in the creative industries

This scoping review paper explores skills gaps for graduates in creative industries as identified and discussed in global industrial and education contexts. Adopting the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) system, our review presents findings from 63 texts published from 2007 to 2023 that were located via five databases. These findings suggest that the top-ranked hard skills gaps for graduates are digital or related skills, such as those related to marketing and data analysis. The top-ranked soft skills are communication and collaboration, both of which are cited more frequently than any hard skill gap and appear key for graduate employability. Factors identified as contributing to these skills gaps include higher education offerings, industry needs and cultural policy. These contributing factors are correlated to the rise of creative industries, shifting values in cultural sectors, change in governments in western influenced cultures and digital transformation.

Achieving Sustainable Customer Loyalty in the Petrochemical Industry: The Effect of Service Innovation, Product Quality, and Corporate Image with Customer Satisfaction as a Mediator

Amidst the intense competition in the petrochemical industry, the determinants of sustainable customer loyalty remain underexplored. This study investigates the factors influencing corporate sustainable customer loyalty, focusing on service innovation, product quality, and corporate image, with customer satisfaction as a mediating variable. In the context of intense competition, understanding these factors is crucial for sustaining customer relationships and ensuring long-term success. The primary aim is to explore how these elements interact to shape sustainable customer loyalty. Using a cross-sectional survey method, data were collected from 120 corporate clients through structured questionnaires. The study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the relationships between the variables. The findings reveal that product quality significantly impacts both customer satisfaction and loyalty, highlighting its pivotal role in meeting customer expectations and fostering long-term relationships. While service innovation does not directly affect immediate satisfaction, it significantly enhances sustainable customer loyalty, indicating its importance for long-term engagement. Corporate image also plays a crucial role in strengthening sustainable customer loyalty, underscoring the value of maintaining a positive reputation. The study concludes that customer satisfaction mediates the relationship between product quality and loyalty, emphasizing the cyclical nature of these constructs. This research fills a notable gap in the literature by providing a comprehensive model that integrates these variables within the petrochemical industry context, offering new insights into effective sustainable customer loyalty strategies.

Deep learning-based fault diagnosis of servo motor bearing using the attention-guided feature aggregation network

This paper introduces a novel approach to fault detection in the servo motor bearings of industrial robots within the context of Industry 4.0 prognostics and health management. The proposed solution leverages the innovative feature aggregation network for robotic fault detection in the application of smart factory. Overcoming challenges associated with traditional techniques that include handcrafted features, transfer learning, and deep learning models, the proposed approach offers a hierarchical information aggregation mechanism. The model is customized through hyperparameter tuning, resulting in a streamlined architecture with significantly fewer parameters. This parameter efficiency is notably distinct when compared to off-the-shelf transfer learning models that commonly feature extensive parameter counts in the range of hundreds of thousands or millions. The proposed model subjected to rigorous validation across diverse experimental scenarios that affirm its adaptability and robust performance. The model showcases accuracy in fault detection under both simple and welding motion scenarios, while its generalization capabilities are demonstrated as it successfully predicts health states in welding motion, showcasing versatility and reliability across various operational scenarios.

The investigation of mechanism isoniazid adsorption onto cassia fistula-based activated carbon

The utilization of activated carbon as an efficient adsorbent is well-established, driven by its porous structure and expansive surface area. This study investigates the potential of Cassia fistula (Golden shower) as a precursor for activated carbon synthesis using K2CO3 activation, leveraging its organic properties known for high porosity and adsorption capacity. This research aims to investigate the feasibility of utilizing Cassia fistula-derived activated carbon (GSAC) for isoniazid removal from water. The study encompasses a two-step activation process—chemical and physical—with varying parameters to optimize surface area and porosity. The carbonization process involves hydrothermal and pyrolysis techniques with controlled conditions. The temperature used in this study is based on the TGA analysis to examine its thermal stability. Batch experiments examine the adsorption equilibrium and kinetics of isoniazid onto GSAC samples, revealing high adsorption capacity and rapid equilibrium attainment by GSAC 1:1 (700°C). The study culminates in the identification of a strong chemical bond between GSAC and isoniazid, implying efficient adsorption potential as confirmed by FTIR and SEM analysis before and after adsorption. The adsorption characteristic is examined with an isotherm and kinetic model. The highest predicted GSAC capacity reaches 219,807 mg/g, emphasizing its promising adsorption capabilities. This work underscores Cassia fistula-based activated carbon as a viable, cost-effective, and eco-friendly adsorbent for isoniazid removal, with implications for diverse applications. The synthesis process parameters, activation methods, and insights into the adsorption mechanism contribute to the understanding of effective adsorbent production and enhance the potential of activated carbon for various industrial contexts.

Enhancing industrial decision-making through Multi-Criteria Decision-Making approaches and ML-Integrated Frameworks

Decision-making in current industrial contexts has shifted from intuition to a data-driven approach, requiring prompt processing of huge datasets. However, conventional Multi-Criteria Decision Making (MCDM) methodologies fall short of navigating the intricacy of large datasets. This paper introduces an innovative decision-support system integrating multi-criteria methods with machine learning techniques such as artificial neural networks. The proposed six-step framework aims to optimize operational decisions by analyzing real-time performance data. The research contributes to the advancement of decision-making methodologies in the industrial field, offering dynamic responsiveness and improved recommendations compared to traditional MCDM methods. While results are promising, future work should focus on robustness testing particularly in terms of its dependence on real-time data, to ensure sustained efficacy and mitigate potential biases in recommendations over time.

Human-Robot Collaboration in Mixed-Flow Assembly Line Balancing under Uncertainty: An Efficient Discrete Bees Algorithm

In the evolving landscape of manufacturing and remanufacturing, assembly lines play a crucial role. Within the context of Industry 5.0, human workers are seen as a valuable and irreplaceable resource. Human-robot collaboration is a promising production model that combines the strengths of human workers and robots, thereby enhancing production efficiency while reducing occupational risks related to ergonomics. Despite these advancements, inherent uncertainties within assembly processes, the integration of human-robot partnerships, and the dynamic nature of market demands pose significant challenges to traditional assembly methods. To address these challenges, this research introduces a novel modelling approach through a mixed-flow assembly line balancing problem designed for uncertain environments, fostering collaboration between humans and robots. The primary goal is to facilitate efficient collaboration within a type-I assembly line balancing problem framework, where predefined assembly beats guide the workflow. In this research, the use of interval type-2 fuzzy sets capabilities was investigated to address uncertainties in the assembly process. Furthermore, the potential of pairing human operators of different abilities with robots of different models for collaborative tasks at workstations was explored, enhancing flexibility and adaptability in the assembly line. In response to the complexity of the problem, this research proposes an efficient multiobjective discrete bees algorithm that incorporates innovative operators and search strategies. Rigorously tested across diverse case studies, this algorithm consistently outperforms other comparator algorithms. This research not only offers novel perspectives on addressing assembly line balancing challenges but also provides valuable insights for the effective implementation of human-robot collaborative assembly in uncertain environments.

Exploring Microbial Dynamics: The Interaction between Yeasts and Acetic Acid Bacteria in Port Wine Vinegar and Its Implications on Chemical Composition and Sensory Acceptance

Port wine vinegar, a product of the esteemed Port wine, is renowned for its intricate blend of flavors and aromas, a result of complex microbial interactions. This study delves into the fascinating world of yeast and acetic acid bacteria (AAB) interactions during fermentation, which significantly influence the vinegar’s chemical composition and sensory properties. We specifically investigate the role of yeasts in fermenting sugars into ethanol, a process that AAB then converts into acetic acid. The impact of these interactions on the production of secondary metabolites, such as gluconic acid, ketones, aldehydes, and esters, which contribute to the vinegar’s unique sensory profile, is thoroughly examined. Advanced analytical techniques, including GC-MS and e-nose technology, alongside sensory evaluation, are employed to assess these effects. The research underscores the significance of ethanol tolerance in AAB and other production challenges in determining vinegar quality and underscores the importance of optimizing fermentation conditions and sustainable practices. The findings of this study underscore the importance of strain interactions and production techniques, which can significantly enhance the quality and market appeal of Port wine vinegar, providing valuable insights for the industry. This review also identifies exciting and critical areas for future research, inspiring further exploration and proposing strategies for advancing production and application in culinary, health, and industrial contexts.

Deep learning model for optimizing control and planning in stochastic manufacturing environments

Within the context of Industry 4.0, manufacturing plants implement smart technologies, which adopt machine learning and deep learning, to identify manufacturing problems and provide sensible solutions to the experts. In literature, such applications have demonstrated high-performance in terms of feature extraction and big data analysis compared to typical machine learning methods. However, in the context of control and planning, the research efforts are still focused on integrating ad-hoc solutions, e.g., heuristic algorithms and deep reinforcement learning, thus making it difficult to generate general scheduling rules for manufacturing processes that could be easily applied to a variety of stochastic manufacturing systems. To this end, this paper considers a deep learning application for addressing the scheduling optimization problem in multi-stage stochastic manufacturing environments. The aim is the formulation of a predictive model that could derive suitable authorizations for activities under uncertainty. Findings demonstrate that the proposed predictive model generates policies that improve the performance of the integrated operations within manufacturing systems, constituting to efficient and sustainable manufacturing with effective material management and near-to-zero waste.

Rethinking assessment strategies to improve authentic representations of learning: using blogs as a creative assessment alternative to develop professional skills

This research explores using blogs as an innovative assessment tool to enhance authentic learning and professional skill development in students. Unlike traditional methods, blogs foster active knowledge building and peer interaction, making learning more engaging and aligned with industry contexts. The study involved third-year planning students, in the course Governance and Planning, and utilised questionnaires, focus groups, and blog post analyses. Results indicated that blogs promoted reflective practice, facilitated peer review, and improved learning efficiencies. Despite some initial resistance and concerns about academic rigor, students found that blogging enhanced their understanding of course content and professional practices. Teacher support and peer feedback played a crucial role in this process. The literature supports blogs’ effectiveness in motivating students and aligning learning activities with real-world applications. However, assumptions about students’ familiarity with blogging were challenged, highlighting the need for thorough induction and support. Overall, blog-based assessments proved beneficial in creating authentic learning experiences and preparing students for their future careers. Future research should consider long-term studies on graduate outcomes and further explore peer review mechanisms.

Forever I am yours: the interrelationship between service brand love, psychological contracts and customer switching barriers in the banking industry

PurposeThis study aims to examine the influence of dimensions of the psychological contract on strengthening customers' switching barriers through the mediating role of service brand love.Design/methodology/approachThe study used a cross-sectional survey research design to collect data from 406 respondents from commercial banks in the banking industry. A quantitative approach using structural equation modeling was used to analyze the data collected through structured questionnaires.FindingsThe findings revealed that dimensions of the psychological contract, namely, ideological, transactional and relational psychological contract, significantly influence the strengthening of customers' switching barriers when mediated by service brand love.Practical implicationsManagers should consider adopting high-intensity relationship approaches that go beyond mere customer satisfaction to ensure customer retention.Originality/valueWhile customer retention remains the primary avenue for establishing competitive advantages, there remain unresolved issues regarding what determines customers’ intentions to stay or switch. This study represents one of the initial endeavors to explore the psychological contract within the context of the service industry. It contributes to the existing knowledge by enhancing the understanding of the mechanisms that can impact customers' switching barriers and complements the literature on customer retention in the service domain.

Accounting for modern slavery risk transparency in Nigerian businesses: Institutional context, disclosure and the way forward

This paper examines the state of engagement of Nigerian companies with modern slavery risk and how current accounting and reporting might be better enabled to contribute towards ending modern slavery practices. Crane's (2013) theoretical framework is used to guide understanding of the contexts behind modern slavery risk disclosure in Nigeria, including industry, socioeconomic, regulatory, cultural and geographic factors. Cross-sectional content and thematic analysis are undertaken, as published in the annual reports, sustainability reports and websites of top companies listed on the Nigerian Stock Exchange. In line with Crane (2013), supply chain codes of conduct and collaboration are identified as a possible first-stage intervention moderator between industry context and ending modern slavery in Nigeria. However, a second moderator, affordable micro-credit, is notably absent from disclosures, and a third moderator, private or civil regulators, as a substitute for government, is considered ineffective. Management capability mediators as circuit breakers, reducing accounting opacity, require further research. This is the first paper to present evidence about modern slavery supply chain risk disclosures in Nigeria and to develop possible contextual, moderating and mediating variables aimed at policies and actions to end modern slavery practices in Nigeria.

Influence of material in sheet-bulk metal forming from coil

AbstractSheet-bulk metal forming (SBMF) represents an innovative approach for the efficient manufacturing of functional integrated parts from sheet metal by applying bulk forming processes to sheet metal. It combines the advantages of part weight optimization and process chain shortening. In view of the increasing demand for functionally integrated components, the forming from coil offers an economical possibility to produce high output rates. Nevertheless, an underfilling of functional elements as well as an asymmetric part forming are current challenges. In order to apply SBMF from coil in industrial contexts, it is necessary to understand the causes of these process limits and to develop measures for improving the forming of functional elements and thereby push existing forming limits. In this paper, both a lateral and a backward extrusion process from coil for the forming of internal and external geared SBMF parts are investigated to develop a fundamental process understanding. In a combined numerical-experimental approach, the influence of the flow behavior of different materials as well as the impact of the main material flow direction of the process on the dimensional part accuracy is analyzed. This enables the evaluation of fundamental material-related dependencies and influences regarding the geometrical part accuracy, the mechanical component properties and the tool load for forming of the conventional deep-drawing steel DC04 (t0 = 2 mm), a high-strength steel material (HC260LA) and an aluminum alloy (AA6082 T6). Based on these findings, measures for material flow control are identified and investigated to counteract the challenges of SBMF from coil. These findings permit the identification of potential transferability of the identified cause-effect mechanisms to different processes, component geometries and materials. Thereby, in particular, an increase in coil width as well as lower a strain hardening behavior of the material offers the possibility to improve the geometrical accuracy of the components.

Computational analysis of nanoparticles and waste discharge concentration past a rotating sphere with Lorentz forces

Abstract As industries rely more and more on magnetohydrodynamic (MHD) systems for different uses in power, production, and management of the environment, it becomes essential to optimize these operations. The study seeks to improve the effectiveness and productivity of cooling structures, chemical reaction reactors, and contaminant control methods by investigating these intricate interconnections. Because of this, the work scrutinizes the endothermic/exothermic (EN/EX) chemical processes, convective boundary conditions, and pollutant concentration impacts on MHD nanofluid circulation around a rotating sphere. The governing equations based on the above assumptions are reduced into a system of ordinary differential equations and solved numerically with Runge–Kutta Fehlberg’s fourth- and fifth- order schemes. The obtained numerical outcomes from the numerical scheme are presented with the aid of graphs, and the results show that the rate of mass transfer decreases with an increase in the external pollutant local source and solid volume percentage. For changes in the values of the activation energy parameter and solid fraction, the rate of thermal dispersion drops for the EN case and upsurges for the EX case. The concentration profile shows increment with the addition of the external pollutant source variation parameter and local pollutant external source parameter. The outcomes of the present work can be helpful in cooling equipment, developing advanced methods for controlling pollution, environmental management, MHD generators, and various industrial contexts.

Cinnamon Particle‐Stabilized Gas Marbles: A Novel Approach for Enhanced Stability and Versatile Applications

AbstractSuspension of solid particles at interfaces is a common phenomenon, pivotal in natural, and industrial contexts. This study focuses on gas marbles which are single soap‐like air bubbles in air environment stabilized by colloidal particles. A unique stabilization method employing edible cinnamon particles is introduced. Unlike previous studies using spherical particles, the irregularly‐shaped micrometer‐sized cinnamon particles demonstrate remarkable long‐term stability through jamming and interlocking at the air‐liquid interface. Comprehensive characterization via optical and scanning electron microscopy, and contact angle experiments, elucidates the stabilization mechanism. These gas marbles exhibit exceptional resilience against drying, heating, freezing, and mechanical stress. They can be fabricated with various edible liquids offering extensive customization possibilities. This breakthrough introduces a promising method for creating stable gas marbles with edible particles, offering versatility for various fields and applications. The application scope includes surface catalysis and shock/vibration sensors, and also molecular gastronomy.

A framework for analysis of stakeholder dynamics and value creation in industrial maintenance projects: the stakeholder ipot

This paper proposes a methodological approach that can be applied in practice for evaluating stakeholder dynamics and assessing projects against appropriate value propositions within an industrial maintenance project context. A conceptual framework is proposed and is demonstrated through a case analysis. It is expected that the proposed methodology, the Stakeholder Interdependent Performance Opportunities and Threats, (Stakeholder iPOT), can advance project management practice by offering a mechanism for analysing stakeholder expectations and responses to the opportunities and threats that different project events present. This study highlights the need for continued investigation not only within the context of industrial maintenance projects but also in other sectors to improve our understanding and ability to effectively manage stakeholder dynamics.

Accuracy and Uncertainty of Position Detection of Moving Objects by Resonator Arrays

Arrays of magnetically coupled resonators have recently been proposed as position sensors for both static and dynamic applications. Despite their promising characteristics, an accurate evaluation of their functioning in non-ideal conditions is fundamental and necessary to adapt them to industrial contexts. This article analyses the accuracy of the position measurement in resonator arrays under system parameter variations. Appropriate parameters for the evaluation of the sensor performance have been discussed and analysed considering non-ideal system components by exploiting the Monte Carlo method.