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Browsing by Author "Bhawna, Bhawna"

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    AI and VR: shaping the next generation of adaptive learning and development programmes
    (2024) Enstroem, Rickard; Bhawna, Bhawna; Kumar, Dinesh; Suthar, Nidhi; Taherdoost, Hamed; Madanchian, Mitra
    This chapter explores the transformative potential of integrating Artificial Intelligence (AI) with Virtual Reality (VR) in developing adaptive learning and development (L&D) programs. Traditional L&D methodologies are increasingly inadequate in the face of rapidly changing business environments. AI and VR technologies offer unprecedented opportunities to personalize learning experiences, enhance engagement, and improve outcomes. This chapter provides a comprehensive overview of current trends, applications, challenges, and future directions of AI and VR in L&D. Key findings emphasize the role of these technologies in fostering continuous learning cultures, addressing individual learner needs, and enhancing organizational effectiveness. Practical insights and case studies are included to guide HR professionals in leveraging AI and VR for innovative and effective L&D programs.
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    E-learning
    (2026) Enstroem, Rickard; Bhawna, Bhawna
    E-learning leverages digital technologies and learning platforms to deliver educational content that is flexible, scalable, accessible, and interactive. In higher education, it supports several delivery modalities, including online, hybrid, and face-to-face formats. This chapter examines the theoretical principles underpinning e-learning, its foundational design components, and its role in supporting learner engagement and personalization. It highlights persistent challenges like digital equity and academic integrity and explores emerging trends, including artificial intelligence and immersive technologies. The chapter addresses these themes and provides insights into how e-learning reshapes higher education to meet evolving pedagogical needs.
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    Enhancing supply chain resilience through supervised machine learning: supplier performance analysis and risk profiling for a multi-class classification problem
    (2025) Kang, Parminder Singh; Bhawna, Bhawna
    Purpose This paper explores the application of supervised machine learning (ML) classification models to address supplier performance analysis and risk profiling as a multi-class classification problem. The research highlights that current applications of machine learning in supplier selection primarily focus on binary classification problems, underscoring a significant gap in the literature. Design/methodology/approach This research paper opts for a structured approach to solve supplier selection and risk profiling using supervised machine learning multi-class classification models and prediction probabilities. The study involved a synthetic data set of 1,600 historical data points, creating a supplier selection framework that simulates current supply chain (SC) performance. The “Supplier Analysis and Selection ML Module” guided supplier selection recommendations based on ML analysis. Real-world variability is introduced through random seeds, impacting actual delivery dates, quantity delivered and quality performance. Supervised ML models, with hyperparameter tuning, enable multi-class classification of suppliers, considering past delivery performance and risk calculations. Findings The study demonstrates the effectiveness of the supervised ML-based approach in ensuring consistent supplier selection across multi-class classification problems. Beyond evaluating past delivery performance, it introduces a new dimension by predicting and assessing supplier risks through ML-generated prediction probabilities. This can enhance overall SC visibility and help organizations optimize strategies associated with risk mitigation, inventory management and customer service. Research limitations/implications The findings highlight the adaptability of ML-based methodologies in dynamic SC environments, providing a proactive means to identify and manage risks. These insights are vital for organizations aiming to bolster SC resilience, particularly amid uncertainties. Practical implications The practical implications of this study are significant for both commercial and humanitarian supply chain management (SCM). For commercial applications, the ML-based methodology allows businesses to make more informed supplier selection decisions, reducing risks and improving operational efficiency. In disaster and humanitarian SC contexts, the use of ML can improve preparedness and resource allocation, ensuring that critical supplies reach affected areas promptly. Social implications The study’s implications extend to disaster and humanitarian SCM, where timely and efficient delivery is critical for saving lives and alleviating suffering. ML tools can improve preparedness, resource allocation and coordination in these contexts, enhancing the resilience and responsiveness of humanitarian supply chains. Originality/value Unlike conventional methods focused on quality, cost and delivery performance aspects, the current study introduces supervised ML to identify and assess supplier risks through prediction probabilities for multi-class classification problems (delivery performance as late, on-time and ahead), offering a refined understanding of supplier selection in dynamic SC environments.
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    The harmonized information-technology and organizational performance model (HI-TOP)
    (2024) Enstroem, Rickard; Kang, Parminder Singh ; Bhawna, Bhawna
    This study introduces the Harmonized Information-Technology and Organizational Performance Model (HI-TOP), which addresses the need for a holistic framework that integrates technology and human dynamics within organizational settings. This approach aims to enhance organizational productivity and employee well-being by aligning technological advancements with human factors in the context of digital transformation. Employing a two-phased methodology, the HI-TOP model is developed through a literature review and text mining of industry reports. This approach identifies and integrates critical themes related to ICT integration challenges and opportunities within organizations.
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    A text mining study of competencies in modern supply chain management with skillset mapping
    (2025) Kang, Parminder Singh; Enstroem, Rickard; Bhawna, Bhawna; Bennett, Owen
    This study explores the skills and competencies required by modern supply chain management professionals, focusing on the shift toward advanced technological capabilities. We analyze job advertisements from a prominent Canadian employment platform using web scraping, natural language processing, and machine learning techniques, including Latent Dirichlet Allocation and Term Frequency-Inverse Document Frequency. The findings reveal that job postings primarily emphasize traditional operational skills such as logistics, inventory control, and customer relationship management. However, there is a noticeable underrepresentation of advanced technological competencies, such as machine learning, data analytics, and automation, which are increasingly critical in today's supply chain environment. This gap highlights the need for greater alignment between job market demands and supply chain management's evolving digital transformation landscape. The study identifies key themes, including technical, managerial, and soft skills integration, emphasizing adaptability, data literacy, and strategic decision-making. The results suggest a misalignment between the competencies highlighted in job advertisements and the skills necessary for managing the complexities of a digitalized supply chain. This research offers practical recommendations for industry leaders to refine hiring strategies, academic institutions to modernize curricula, and job platforms to better showcase emerging skill requirements. Addressing this gap is essential to equip supply chain professionals with the tools and expertise to meet the challenges of a technology-driven future.