Industrial Case Research Articles

Investigation of the effect of mineral composition and size on particle separation in Wet low and high intensities magnetic separators: An industrial case

Magnetic separation is used in ore processing to separate the minerals according to their magnetic susceptibility. Particle size also plays a role in the separation although few industrial results are presented in the literature to discuss this matter. Results from sampling industrial Wet Low Intensity Magnetic Separators (WLIMS) and Wet High Intensity Magnetic Separators (WHIMS) for processing an iron ore show that except for the strongly paramagnetic magnetite (Fe3O4), the recovery of minerals decreases with increasing particle size for both WLIMS and WHIMS. The examination of polished sections of the coarse size fractions from the reject streams of the WHIMS does not provide ground to suspect liberation to be responsible for the reduced recovery of coarse particles. Results also show that it is difficult to establish clear relationships between the mineral recoveries and their reported magnetic susceptibilities for WLIMS while for WHIMS a weak trend is observable.

Critical factors to adopt sustainable agrifood supply chain management in developing countries: The case of Ethiopian coffee industry

AbstractGlobal agrifood supply chains are under increasing pressure to address sustainability issues due to growing concerns. However, numerous organizations within the agrifood industry are struggling to incorporate sustainable supply chain management practices and address the concerns. Therefore, this research is designed to identify the key critical factors, such as drivers, enablers, and barriers to adopting sustainable practices. This study considers the Ethiopian coffee industry, which is an important sector not only for the Ethiopian economy but also for the global agrifood supply chain. To accomplish the objectives of this study, we applied a systematic literature review and empirical survey. A systematic literature review was conducted to identify critical factors for adopting sustainability in agrifood supply chains. An empirical survey was then undertaken in the Ethiopian coffee industry to rank the key critical factors. Hence, the study has revealed that economic and productivity improvement, cost effectiveness and improvement in the overall performance, and difficulty in mindset and cultural changes as the key critical factors that determine the adoption of sustainability initiatives from the perspectives of the Ethiopian coffee supply chain. The findings can be used as input by government regulatory bodies and policymakers to craft strategies and policies to adopt sustainability initiatives and ensure sustainable development. Furthermore, the research is expected to contribute to the existing literature by bringing in the perspective of suppliers in developing countries.

A local-level agricultural transformation in the making? The case of the cassava industry in the Ashanti and Volta regions of Ghana

ABSTRACT In sub-Saharan Africa, Ghana is recognized as one of the countries undergoing rapid economic transformation. Drawing on an agricultural transformation framework, this study sheds light on the current farm and nonfarm developments in the cassava subsector in five communities across four districts in Ghana. Using a qualitative exploratory research design based on field-work surveys, the study identifies several factors that together showcase manifestations of a transformation process in the studied areas. These include a combination of growing farm sizes, use of new technologies, higher productivity, expanding market opportunities, increased profitability, improved lives and livelihoods, and changing labour structures. These micro-level findings allow the study to offer a richer understanding of dynamics of change underway that could complement macro-oriented analyses of agricultural transformation in Africa.

Pre-connected and trainable adjacency matrix-based GCN and neighbor feature approximation for industrial fault diagnosis

Industrial fault diagnosis methods based on graph convolution network (GCN) becomes a hot topic for its great feature extraction ability to multivariate time-series data. However, GCNs ignore inter-sample temporality when constructing the adjacency matrix (AM), leading to low prediction accuracy. A novel fault diagnosis method based on pre-connected and trainable AM-based GCN and neighbor feature approximation (PTGCN-FA) is proposed at the node-level task. Firstly, PTGCN-FA introduces the temporal nearest neighbors into spatial nearest neighbors to pre-connect and construct the AM. Then, the AM is trained only where the samples are connected, which makes the best weights obtained and reduces the time complexity of the model. Finally, after the GCN layers, the trained AM is introduced into the approximation of features, which are neighbors in the original sample space. Two process industry cases are carried out, and the simulation results including diagnosis accuracy, confusion matrix, study to the ratio of labeled data and an ablation experiment verify PTGCN-FA has more efficient and accurate diagnostic performance than related methods. Additionally, the analysis of the temporal neighborhood weight parameter shows that the performance of fault diagnosis can be improved by considering both temporal and spatial information between samples.

Development of russian-mongolian foreign trade relations in food industry

In the context of shifting trade emphasis to the East, Russia attaches great importance to the development of cooperation with the Asia-Pacific countries, where Mongolia is a key partner. The purpose of the article is to reveal global trade trends and identify key areas for the development of Russian-Mongolian foreign trade relations in the food industry. Having conducted a comprehensive analysis of the economic and political factors shaping bilateral trade, the article provides insights into the challenges and opportunities for expanding cooperation in the food sector. Global trend in the food market is that China a leader. China accounts for the largest share (35%) in Russia turnover also. Mongolian trade ties with Russia are becoming closer year by year. This was demonstrated by the official visit of Russian President Vladimir Putin to Mongolia in September 2024. We can observe that Mongolia closely cooperates with such a region of Russia as Buryatia. Through a detailed examination of trade patterns, regulatory frameworks, and market trends, the study uncovers areas of untapped potential and identifies barriers to optimal trade flow. Drawing on industry best practices and case studies from other global food trade partnerships, the paper offers a series of practical recommendations for stakeholders. These recommendations cover infrastructure development, promotion of cultural exchange, harmonization of legal and regulatory frameworks, absorption and use of technology to create a more robust and sustainable trade ecosystem.

Revolutionizing Retail: An Empirical Study on the Impact of Generative AI in Omnichannel Strategies

This article examines the transformative impact of Generative AI (Gen AI) on omnichannel retail, exploring its role in enhancing customer experiences and streamlining operations across digital and physical platforms. Through a comprehensive analysis of current literature, industry case studies, and empirical data, the study investigates six key applications of Gen AI in retail: personalization and dynamic content creation, virtual shopping assistants, inventory and fulfillment optimization, marketing content generation, customer insights and sentiment analysis, and immersive AR/VR experiences. The findings reveal that Gen AI significantly improves personalization, operational efficiency, and cross-channel integration in retail. However, the research also identifies challenges in implementation, including data privacy concerns and the need for seamless technology integration. This article contributes to the growing body of knowledge on AI in retail and provides practical insights for retailers seeking to leverage Gen AI in their omnichannel strategies. The paper concludes by discussing future research directions and the potential long-term implications of Gen AI on consumer behavior and global retail practices.

Project managers’ competencies in the era of digitalization: the case of the construction industry

PurposeWith emerging technologies rapidly changing work processes, it is important to understand the skills and characteristics project managers (PMs) need to effectively manage projects in the digital era. This study determines the underlying competencies needed for digitalization among PMs in the construction industry. The study also identified the most significant competencies needed by PMs in the era of digitalization.Design/methodology/approachThe methodology adopted for the research study was quantitative. It was founded on a thorough review of pertinent literature, which went through a pilot survey study from six project management experts in the construction industry. Based on the comment and feedback, a questionnaire survey was developed and distributed to participants through a convenience sampling technique. The data retrieved were from 100 professional PMs out of 130 questionnaires distributed in the Ghanaian construction industry. Data collected were analyzed using fuzzy synthetic evaluation (FSE).FindingsBased on FSE, the three competency parameters (knowledge, skills, personal characteristics) generated significant indices indicating that all three competencies are significant among construction PMs in the digitalization era. Under the knowledge competency parameter, six sub-competencies comprising a total of 12 variables were identified. For skills, seven sub-competencies consisting of 23 variables were identified. Regarding personal attributes, six sub-competencies with 17 variables were highlighted. Under knowledge, technical knowledge was found to be the most important with an index of 4.212. For skills, leadership skills were rated highest with an index of 4.240. Regarding personal attributes, social or interpersonal skills were deemed most critical with an index of 4.199.Practical implicationsThe results provide guidance to both industry and academic stakeholders. For PMs and their employers, the study highlights priority areas for competency development and training related to the era of digitalization. It also informs educational institutions on how to structure project management curricula to best prepare students for jobs of the future. This study gives more insight into the competencies that need more attention for PMs in the digitalization era. As a result, firms that adopt the identified competency will benefit from implementing digitalization in project delivery.Originality/valueThis study makes an original contribution as one of the first to empirically investigate the competencies required of construction PMs in the construction industry in the digitalization era. By focusing on the developing country context of Ghana, the study extends knowledge to an under-researched region and market. It provides a foundation for future comparative research across diverse global contexts.

Evolution to entrepreneurial hospital led by open innovation: A case of Korean hospital industry

The role of hospitals as leaders of innovation in the bio health industry is emerging in the bio health industry. Hospitals are evolving from medical treatment-centered traditional hospitals to innovation-centered entrepreneurial hospitals that focus on research and development (R&D), and technology commercialization. A series of commercialized medical technologies by clinicians in hospital turns into business models in collaboration with firms. However, few studies have attempted a practical approach to entrepreneurial hospitals. Therefore, this study aims to explore the transformation of traditional hospitals to entrepreneurial hospitals through Korean hospital cases. This study studied the Korean government's initiatives related to supporting entrepreneurial hospitals. In addition, this study conducted interviews with key stakeholders involved in hospital-based innovation networks. Korea has been promoting entrepreneurial hospitals based on open innovation since 2004, and various government-supported projects have been carried out in parallel, making it suitable for examining the development trajectory and challenges from a research perspective. The findings of this study provide a theoretical framework for research on hospital-based open innovation.

Occurrence Type Classification for Establishing Prevention Plans Based on Industrial Accident Cases Using the KoBERT Model

Occurrence Type Classification for Establishing Prevention Plans Based on Industrial Accident Cases Using the KoBERT Model

The Impact of digital payments on small coffee business in India

India's coffee industry has experienced notable growth, fuelled by rising demand for specialty coffee and a thriving start-up culture. However, small coffee businesses continue to face challenges such as fierce competition, limited resources, and reliance on a cash-driven economy. This study examines the impact of digital payments on these small enterprises, focusing on key aspects like convenience, security, speed, data-driven insights, and reduced costs associated with cash handling. Utilizing a mixed-methods approach, the research integrates both quantitative and qualitative analyses. Primary data is gathered through structured surveys from 200-300 small coffee business owners and customers, complemented by in-depth interviews with 10-15 coffee shop owners. Additionally, secondary data from industry reports and case studies enriches the analysis. Statistical tools, including descriptive and inferential statistics, correlation analysis, and factor analysis, are employed for quantitative data, while thematic analysis interprets qualitative insights. The study reveals that digital payments significantly improve operational efficiency, enhance customer satisfaction, and boost revenue for small coffee businesses. Challenges such as digital literacy gaps, cyber security concerns, and infrastructural limitations remain. The findings recommend that small coffee businesses can optimize the benefits of digital payments by investing in digital literacy, addressing security issues, and leveraging data insights for informed decision-making.

Attitude and behavior towards chatbots: case of the beauty care industry

Purpose This paper aims to provide a model that expands the technology acceptance model (TAM) by identifying aspects that influence consumers’ attitude and behavioral intention toward chatbots in the beauty care industry. Design/methodology/approach This study is based on an online questionnaire responded by 211 Generation Z individuals from Morocco. Data was collected based on a convenience nonprobability sampling technique, and a partial least squares-structural equation modeling was used to validate the model and test the hypotheses. Findings Perceived ease of use, perceived usefulness and subjective norms are significant influencers of the attitude toward chatbot. Perceived risk was not found to be influencing behavioral intention to use chatbot, and personality does not moderate the relationship between perceived ease of use and attitude. Research limitations/implications This paper provides insights into the motives behind customers’ attitude and behavior toward chatbots in the beauty care industry and can be relied on as a guideline for implementations technology to boost sales and build strong relationship with customers. Practical implications This research provides new insight for retailers on the implementation of chatbots at the point of sale. Marketers and business owners should be aware of the usefulness and ease of use of technology for Generation Z and be able to interact with them on this basis in order to generate increased economic performance and better brand image. Originality/value The model extends the original TAM and introduces two new constructs, subjective norms and perceived risk, in addition to including personality as a moderating factor. The data was gathered from the under-studied region of North Africa.

Adaptative scheme for diffuse field response of multilayered structures

The computation of the diffuse field response of multilayered homogeneous structures leads to integrals which should be computed efficiently. This is all the more true as the frequency increases, since the integrand has localized peaks. The objective of this communication is to propose an adaptive integration scheme that aims to limit the number of resolutions of the physical problem and to maximize the reuse of previous evaluations in the course of the method. After discussing the usual integration methods, the proposed method will be presented. A first approximation of the integral is performed with two nested integration schemes with a coarse mesh of the initial integration domain. A local error indicator is derived and leads to a first partition. On each set of the latter, and depending on the local error estimated previously, an adequate numerical scheme is chosen. The error is controlled during all the process and it is iterated until convergence. The method is tested first on academic cases and then on industrial cases. The performance of the method is discussed, taking into account not only the number of evaluations of the physical function, but also the additional numerical cost generated by the adaptation process.

Introducing uncertainties in acoustic engineering studies

PIBE (Prévoir l'Impact du Bruit des Éoliennes - Predicting the impact of wind-turbine noise) is a research project funded by ANR (French National Research Agency) which main objectives are to better understand the sound level created by wind-turbines and assess noise-reduction solutions. One topic of this project focuses on the propagation of uncertainties. Code_TYMPAN(tm) is an open-source software for calculating industrial noise in the environment. Its open architecture, implemented by a Python API, allows advanced users to build and solve models programmatically. This open architecture made possible the implementation of a parametrical computational tool. This tool generates input data, launches the calculations with Code_TYMPAN(tm), generates and post-processes output data. Thus, sensitivity analysis and propagation of uncertainties were performed on an industrial test case. This development demonstrates the feasibility of introducing uncertainties in acoustic engineering studies.

Examining a Conservative Phase-Field Lattice Boltzmann Model for Two-Phase Flows

In this study, a conservative phase-field lattice Boltzmann model is examined for simulating immiscible two-phase flows with large density and viscosity ratios. This model is built upon the Allen–Cahn equation, incorporating a filtered collision operator and high-order corrections in the equilibrium distribution functions. The numerical model is also integrated with the volumetric boundary scheme and the immersed boundary method to achieve various stationary and moving boundary conditions on arbitrary geometries for different application scenarios. A comprehensive evaluation of this phase-field solver is conducted through a range of academic and industrial cases. First, droplet splashing cases at different Reynolds numbers are studied. The phase-field LB model effectively captures the surface wave motion and splashing of the droplet, although some smearing of small structures is observed due to the diffused interface property of the numerical model. Second, a tuned liquid damper is simulated, accurately capturing sloshing forces on solid walls. In the simulation of a dam-breaking case, predicted gauge pressure and free-surface elevation time histories compare well with experimental data. Lastly, a gearbox case under dip lubrication is simulated, with both the gearbox churning loss and oil distribution being well predicted across a range of rotating speeds. The validation studies demonstrate the effectiveness of the present phase-field lattice Boltzmann model in simulating immiscible two-phase flows with large and realistic density and viscosity ratios. The strengths, limitations, and weaknesses of the conservative phase-field LB model are discussed, and suggestions for the development and application of this numerical model are provided.

Challenges of blockchain adoption for manufacturing supply chain to achieve sustainability: A case of rubber industry

Global market competitiveness and standards are pushing the manufacturing supply chain to adopt sustainable approaches to enhance quality, reduce waste, improve environmental practices, and optimize product costs. Blockchain technology has emerged as a promising tool for improving supply chain transparency and manufacturing sustainability. However, various challenges to the mainstream use of blockchain must be identified and addressed. In this regard, the study aims to identify and comprehend the challenges of adopting blockchain technology in the manufacturing sector to improve sustainable standards. To bridge this gap, the study used a three-step procedure. First, through a comprehensive literature review, 19 potential challenges were identified. Then, with the experts' discussion, a total of 18 challenges were finalized. In the second phase, data was collected from 15 professionals with industrial backgrounds. Lastly, the collected data was analyzed using Combined Compromise Solution methodologies and the Fuzzy-Decision-Making Trial and Evaluation Laboratory (fuzzy DEMATEL). This hybrid methodology prioritizes the challenges in blockchain adoption and categorizes them into causal groups. This improves the decision-making process by evaluating how the mutual impact of challenges in blockchain adoption affects achieving sustainability in the manufacturing supply chain. The outcomes of the presented work are validated through a sensitivity analysis of the outranked dimensions of blockchain adoption challenges. Results show that 'High Initial Investment Cost' and 'Lack of Digital Skills’ are the top challenges in its adoption. The findings of this work provide a roadmap to make the manufacturing supply chain sustainable and accomplish sustainability.

Rooms versus F&B: How changes in operations contribute to hotel productivity

This study investigates how different operational responses impact hotel productivity change, specifically investigating the importance of F&B operations in comparison to room operations and applying the case of the Taiwanese hotel industry, conducting 93 international tourist hotels from April 2012 to March 2021. Using regression discontinuity design analysis, first, the study finds a significant causal impact of COVID-19 travel restrictions on hotel operations. Second, using the control function methods, findings show that F&B operation is a more efficient strategy compared to rooms to recover hotel productivity from crises. This research provides insights into how hotels can reallocate their operational resources in response to crises and offers an alternative measure of productivity for the hotel industry.

Leveraging AI and Machine Learning to Optimize SAP-Driven Financial Operations: A New Paradigm in Risk Management and Fraud Detection

As the global business landscape continues to evolve, organizations are increasingly relying on advanced technologies to enhance their financial operations. The integration of Artificial Intelligence (AI) and Machine Learning (ML) with SAP-driven financial systems represents a transformative shift in how companies manage risk and detect fraud. This article explores how AI and ML can optimize SAP systems for proactive risk management and real-time fraud detection. It highlights the benefits, challenges, and best practices for implementing these technologies within SAP environments, drawing on industry case studies and expert insights

NAVIGATING BUSINESS INTELLIGENCE TOOLS: STRATEGIES TO DRIVE BUSINESS GROWTH

This study explores the evolution and current state of business intelligence (BI) tools and their strategic role in driving business growth. The research utilizes a combination of market analysis, industry case studies, and theoretical frameworks, including the DIKW hierarchy and Resource-Based View, to examine BI adoption trends. The results highlight the importance of data quality, cross-functional collaboration, and user adoption in maximizing BI effectiveness. Key findings indicate that cloud-based and self-service BI tools significantly improve data-driven decision-making, while challenges remain in data governance and integration. To address these challenges, organizations must implement robust data policies and empower users through training and self-service capabilities. The study concludes that integrating BI tools into digital transformation initiatives provides a competitive edge, enabling strategic planning, operational efficiency, and innovation. This research offers new insights into how organizations can leverage BI tools for sustained growth and enhanced decision-makingl.

Multi-Station Multi-Robot Welding System Planning and Scheduling Based on STNSGA-D: An Industrial Case Study

Multi-Station Multi-Robot Welding System Planning and Scheduling Based on STNSGA-D: An Industrial Case Study

Using Generative AI for Strategic HR Planning: How AI Can Elevate Your People Strategy

This article explores how generative artificial intelligence (AI) technologies such as machine learning and deep learning can be applied to enhance strategic human resource (HR) planning. Drawing from academic literature and industry case studies, the article outlines key techniques of gen AI and principles for responsible implementation. It then discusses specific opportunities for gen AI to elevate HR processes like recruiting, training, performance management, compensation planning, and cultural transformation from descriptive to prescriptive levels through data-driven decision making and insights. Aligning AI goals with organizational values around diversity, ethics and worker well-being is emphasized. The essay aims to translate technical concepts into practical recommendations while arguing that when guided properly, gen AI shows strong potential to empower HR leaders and elevate their people strategy through personalization, prediction, continuous improvement and robust measurement of ROI.