Rain or shine, in good or bad times, redundancy is a sensitive management decision in the legal aspect of human resources management. It evokes a lot of tension and requires operational excellence at each stage of the process. Redundancy management is multi-dimensional and encompasses key variables such as organisational justice, legal aspects of human resources management and situational leadership. Using a comparative HR practices social science research approach, the study examined a redundancy process for a global organisation that affected 55 staff in 14 countries across all the 4 global geographic regions of Asia, Europe, Africa and the Americas. It concludes that there are key redundancy execution standards to ensure a fair process and outcome. The study posits that an organisation’s redundancy process is inherently a complex process that requires an organisational design approach anchored on principles, an organisational model that clarifies roles and responsibilities, data and insights-driven legal aspects of human resources management and is characterised by interests-based bargaining, which underpins the engagement between senior leadership and works councils/staff representatives.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu/0147/a.php" alt="Hit counter" /></p>

Purpose This study addresses the inconsistent evidence on whether environmental, social, and governance (ESG) hinders or enhances performance. Drawing on the practice-based view (PBV) and the competitive progression theory (CPT), we argue that this inconsistency stems from how firms approach sustainability, prioritizing ESG reporting over the operational foundation for ESG integration. We conceptualize this foundation as Operational Excellence (OE), a firm-wide commitment to excellence through the persistent adoption of multiple practices and theorize that OE results in ESG adoption and effectiveness. We also hypothesize about the mediating effect of OE-driven ESG adoption on performance. Design/methodology/approach Using a longitudinal panel of 3,394 U.S. manufacturing firm-year observations (2010–2023), we apply System Generalized Method of Moments (GMM) to test the role of OE in ESG adoption and effectiveness. To capture the key elements of our OE conceptualization, we consider the breadth (number of practices) and persistence (the consistency of adoption over time). Several robustness tests are conducted to ascertain the findings. Findings OE facilitates sustainability engagement and the adoption of a broad range of ESG practices. It also enhances ESG effectiveness, as performance gains occur when ESG practices are grounded in prior OE. Results reveal a sequential pattern: firms first adopt OE practices, then ESG practices, leading to improved performance. This reflects temporal mediation, where OE influences performance through ESG practices. Originality/value We conceptualize OE as a firm-wide commitment to excellence that underpins ESG effectiveness—enabling ESG to improve performance by being fully integrated into core operations and aligned with other practices.

Purpose This study aims to examine the evolution of Operational Excellence (OpEx) by integrating academic and practitioner perspectives, and to propose a conceptual framework that links past foundations with future themes shaped by digitalisation and sustainability. Design/methodology/approach The study is based on a qualitative thematic analysis of insights from 31 global experts (15 academics and 16 practitioners) who shared their perspectives on the definition, past highlights and future themes of OpEx. These empirical findings complement relevant literature to provide context and ensure theoretical grounding. Findings he study consolidates a working definition of OpEx in the digital era and identifies five dominant past themes (continuous improvement, quality culture, top management commitment, productivity and process enhancement and either leadership or tools and techniques). It further highlights five emerging future themes, with consensus on digital transformation, sustainability and human–machine integration, as well as divergence around agility/resilience versus holistic approaches and evolving leadership. A conceptual framework is proposed that synthesises these findings, showing the progression of OpEx from its foundations to its future priorities. Practical implications The framework offers managers a roadmap to align OpEx initiatives with the twin imperatives of digitalisation and sustainability. It provides sector-specific guidance: in manufacturing, leveraging data-driven Lean Six Sigma with Industry 4.0 tools; in services, strengthening culture and leadership to sustain improvement; and in the public sector, integrating agility and stakeholder engagement to enhance transparency and citizen value. Originality/value This paper contributes originality by bridging academic and practitioner viewpoints, consolidating a definition of OpEx for the digital age, and proposing a novel framework that connects historical foundations with future strategic themes. Unlike prior reviews, it explicitly positions OpEx as a socio-technical system integrating cultural, technological and sustainability dimensions, offering both conceptual clarity and actionable guidance for organisations.

The e-commerce sector in India has experienced unprecedented growth over the past decade, emerging as a key driver of the country’s digital economy. The rapid adoption of internet services, widespread smartphone penetration, and an increase in digital literacy have collectively transformed the way Indian consumers shop and interact with brands. The sector has evolved from being dominated by a few major players to a highly competitive landscape encompassing a wide range of companies, including e-retailers, marketplace platforms, and specialized niche providers. This research paper examines the present scenario and future prospects of e-commerce companies in India, highlighting market dynamics, technological innovations, consumer trends, opportunities, and challenges. Currently, India stands as one of the fastest-growing e-commerce markets globally, with billions of dollars in annual transactions and a rapidly expanding customer base. Major players such as Amazon, Flipkart, Reliance Digital, and emerging startups have leveraged technological advancements, robust logistics networks, and innovative business models to capture diverse market segments. The ongoing COVID-19 pandemic further accelerated digital adoption, as consumers increasingly relied on online shopping for essentials, groceries, electronics, and fashion products. This shift has underscored the importance of seamless digital experiences, secure payment systems, and reliable delivery mechanisms, which have become critical factors for success in the competitive e-commerce landscape. The paper also explores the drivers of growth and emerging opportunities for e-commerce companies in India. Factors such as the rise of mobile commerce, increasing internet penetration in semi-urban and rural areas, digital payment adoption, and government initiatives under Digital India are enabling e-commerce platforms to expand their reach. Additionally, advancements in artificial intelligence, big data analytics, and supply chain automation are facilitating personalized consumer experiences, predictive marketing, and operational efficiency. The growing preference for regionallanguage content and hyper-local delivery models is opening new avenues for reaching untapped markets. Looking ahead, the future outlook for e-commerce companies in India remains highly promising. Emerging trends such as social commerce, voice commerce, augmented reality shopping, and subscription-based models are likely to redefine the consumer experience. The integration of advanced technologies like AI-driven chatbots, machine learning-based recommendation engines, and blockchain-enabled supply chains will enhance customer engagement, transparency, and trust. However, the sector also faces challenges, including regulatory compliance, cybersecurity threats, rising competition, and logistical complexities, which require strategic planning and innovation to overcome. In conclusion, e-commerce in India represents a dynamic and rapidly evolving ecosystem with immense potential for growth. Companies that adapt to technological advancements, consumer preferences, and ethical business practices are expected to thrive in the competitive market. This research underscores the strategic importance of digital innovation, operational excellence, and customer-centric approaches to sustain and accelerate the growth of e-commerce companies in India’s digital economy.

Guest editorial: Operational excellence and quality improvement in the African continent

Purpose: This study aims to examine the application of omnichannel selling and customer ‎engagement in achieving operational excellence within the US pharmaceutical industry, with a ‎focus on the adoption of a project-based lean approach.‎ Approach: A quantitative research design was employed, using a standardized questionnaire ‎completed by 383 professionals from the US pharmaceutical industry. Data analysis was ‎conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the ‎relationships between omnichannel selling, customer engagement, and operational excellence.‎ Results: The results indicate that customer engagement has a significant positive impact on ‎operational excellence (β = 0.425, p-value = 0.000). Additionally, the implementation of ‎omnichannel marketing positively influences both customer engagement (β = 0.448, p-value = ‎‎0.000) and operational excellence (β = 0.334, p-value = 0.000). The study found that the impact ‎of implementing a project-driven lean framework on operational excellence was moderate.‎ Conclusions: The findings underscore the importance of customer engagement in enhancing ‎operational efficiency and compliance within the pharmaceutical sector. Further research is ‎recommended to explore how integrating digital transformation mechanisms can automate ‎marketing functions and improve operational performance‎.

Pharmaceutical and MedTech manufacturing environments are becoming more and more complicated, which requires smart digital solutions to be integrated into their regimes to improve their overall operational excellence. This investigation suggests an adaptive intelligent call bot framework that will have the ability to interact with digital twins (DTs) and Unified Namespace (UNS) structures, providing real-time communication, data access and referencing, and contextual decision-making assistance. A systematic literature review and architectural analysis of the research help determine the main enabling technologies such as MQTT, Sparkplug B, and large language models (LLMs) aiding in the application of scalable, compliant, and interoperable AI systems in regulated areas. The proposed solution would work well to align the call bot with the Industrial Internet of Things (IIoT) standards, the industry 4.0 principle, and the objectives of Pharma 4.0, which makes it evident in terms of efficiency in operations, human-machine interaction, and data-driven responsiveness in smart manufacturing environments. These results establish a basis on which conversational AI agents will be developed and deployed in the regulated industrial automation environment in future

ABSTRACT Navigating the complexities of achieving resilience and operational excellence in healthcare supply chains requires urgent and strategic digital transformation (DT). This study, grounded in Resource-Based View (RBV) and Organisational Information Processing Theory (OIPT), investigates how transformational leadership and information sharing influence DT adoption in hospitals. Based on survey data from 200 healthcare managers, the findings reveal that information sharing significantly drives DT adoption, whereas transformational leadership shows no direct effect, challenging common assumptions. DT adoption positively impacts transparency, collaboration, and resilience, which are essential for operational excellence. However, while collaboration and resilience enhance performance, transparency alone shows a negative impact, suggesting that visibility without support structures may expose inefficiencies. These results highlight the non-linear and context-dependent nature of DT outcomes in healthcare, offering both theoretical and practical contributions. The study provides actionable insights for managers and policymakers to craft effective, context-aware digital strategies that strengthen supply chain capabilities in dynamic healthcare environments.

Higher education institutions (HEIs) often struggle to implement improvement initiatives that promise significant benefits but fail to produce lasting change. This paper integrates the Shingo Model with Lean, Six Sigma, and Hoshin Kanri to cultivate a culture of continuous improvement while minimizing complexity. Through a systematic two-phase literature synthesis, we analyze core principles, perform a comparative evaluation highlighting that the Shingo Model excels in cultural focus, leadership, and change management, while Lean provides rapid implementation. Our findings also map critical failure factors (CFFs) to Shingo principles, revealing how principles like "Lead with Humility" and "Create Constancy of Purpose" address management commitment and training inadequacies. We develop the Principles-Tools-Align (PTA) framework, emphasizing the Shingo Model’s foundational principles, Lean's operational tools, and Hoshin Kanri's strategic alignment. This research illustrates how the PTA framework offers distinct strategies to enhance infrastructure development, align curricula with labor market needs, and maximize resource utilization. By providing HEIs with a robust roadmap that resolves the tools-only paradox, the PTA framework facilitates sustained academic excellence through the synergy of culture, execution, and alignment, ultimately optimizing operational effectiveness and enhancing stakeholder satisfaction.

The purpose of this study is to examine the effectiveness of Management Information Systems (MIS) followed in manufacturing industries by synthesizing existing scholarly literature through a systematic bibliometric analysis. The study aims to identify dominant research themes, theoretical foundations, and emerging directions that explain how MIS contributes to operational efficiency, decision-making effectiveness, and strategic competitiveness in manufacturing organizations. The study adopts a bibliometric research design based on peer-reviewed journal articles indexed in the Scopus database. A PRISMA-guided literature selection process was employed to ensure transparency and rigor. Keyword co-occurrence and network visualization techniques were applied using VOSviewer to identify thematic clusters and intellectual linkages within MIS and manufacturing research. The bibliometric analysis yielding five testable propositions for empirical validation via PLS-SEM. The findings indicate that MIS effectiveness in manufacturing industries is multi- dimensional, encompassing decision-support intelligence, operational integration, engineering–management coordination, performance evaluation, and sustainability orientation. The literature shows a clear transition from traditional reporting-based MIS to analytics-driven and strategically aligned information systems, particularly in the context of Industry 4.0. The study relies on secondary bibliometric data and does not empirically test causal relationships. While the findings provide a comprehensive conceptual synthesis, future research may validate the identified dimensions using empirical and longitudinal research designs. The study offers actionable insights for manufacturing managers to align MIS investments with decision intelligence, operational excellence, and long-term sustainability goals. It also highlights the importance of integrating shop-floor data with managerial decision systems. This study contributes to the MIS and manufacturing literature by structuring fragmented research into coherent thematic dimensions and by positioning MIS as a strategic organizational capability rather than a purely operational support system.

Smart Lean-Green Framework for Sustainable Energy Optimization and Operational Excellence in Earthmoving Equipment Manufacturing under Industry 4.0

Effective complaint management systems are increasingly recognised as critical enablers of operational excellence in higher education institutions. However, empirical evaluations of such systems within university settings remain limited. This study evaluates the performance and governance effectiveness of UiTM’s e-Aduan system as the university’s centralised digital platform for recording, monitoring, and resolving complaints. The study aims to assess how the system contributes to service responsiveness, resolution efficiency, and institutional accountability. Using a three-year dataset (2023–2025), the analysis examines complaint volumes, closure rates, and response timeliness, supplemented by workflow documentation, dashboard analytics, governance records of 242 moderators across 87 departments, and both internal and external audit reports. The findings indicate a steady improvement in service performance, with complaint resolution rates rising from 81% in 2023 to over 90% in 2025, alongside consistent outcomes across departments. These results suggest that a well-structured and systematically governed complaint management system strengthens transparency, enhances organisational performance, and supports the pursuit of operational excellence in university administration. This study contributes to the limited literature on digital complaint management in higher education and offers practical insights for institutions seeking to improve service quality and stakeholder trust through data-driven governance mechanisms.

The manufacturing sector is undergoing a structural transformation driven by digitalization, advanced automation, sustainability requirements, and global competition. As manufacturing systems grow more complex and capital intensive, the traditional role of engineers—historically centered on design optimization and problem-solving—is expanding toward strategic leadership. This paper examines the evolution of engineers into technical strategists and analyzes how technically informed leadership influences manufacturing performance, economic outcomes, and long-term business competitiveness. Drawing on peer-reviewed manufacturing and management literature, the paper explores Industry 4.0, high-automation and precision manufacturing, and the linkage between operational metrics and enterprise-level financial performance. The analysis demonstrates that organizations which elevate engineers into strategic roles achieve superior alignment between technology investment, operational excellence, and business results.

In the rapidly evolving digital marketplace landscape, understanding the drivers of brand loyalty has become crucial for e-commerce platforms, particularly those specializing in luxury fashion brands. This study examines the influence of brand satisfaction and luxury brand attachment on brand loyalty among Zalora marketplace consumers in Medan City, Indonesia. The research employed a quantitative methodology using purposive sampling technique to collect data from 100 respondents who had made purchases on Zalora marketplace at least twice. Multiple linear regression analysis was conducted using SPSS 25.0 to examine the relationships between variables. The findings reveal that brand satisfaction significantly and positively influences brand loyalty (t = 4.114, p < 0.001), with an unstandardized coefficient of 0.336 indicating a strong predictive relationship. Luxury brand attachment also demonstrates a significant positive effect on brand loyalty (t = 2.625, p = 0.010), with an unstandardized coefficient of 0.188. The combined influence of both variables on brand loyalty is statistically significant (F = 85.187, p < 0.001), with an adjusted R² of 0.630, indicating that 63% of the variance in brand loyalty is explained by brand satisfaction and luxury brand attachment collectively. The standardized coefficients reveal that brand satisfaction (β = 0.504) has a stronger influence compared to luxury brand attachment (β = 0.321). Both brand satisfaction and luxury brand attachment serve as significant predictors of brand loyalty in the luxury e-commerce marketplace context, providing valuable insights for marketplace managers and luxury brand strategists seeking to enhance customer retention and loyalty through integrated operational excellence and emotional engagement strategies.

Recent transformations of healthcare analytics with artificial intelligence, machine learning, and modern big data have helped in guiding clinical decision-making, allocating resources, and improving clinical outcomes. Healthcare organizations are challenged with managing the rapid inflow of electronic health records, medical imaging, genomic sequencing, wearable technologies, and real-time patient monitoring devices, which require analytics infrastructures beyond what traditional systems can handle. Cloud-native architectures, distributed computing models, and scalable data stores enable the new generation of predictive analytics for anticipatory care models, which leverage cutting-edge artificial intelligence algorithms such as deep learning, natural language processing, and time-series analysis to extract insights from multi-dimensional and heterogeneous healthcare data and generate predictions of clinical deterioration, readmissions, and operational bottlenecks. Health systems show real-world implementations can reduce mortality, enhance intensive care unit capability and flow from the emergency department, and increase operating room capacity. Organizations with more advanced analytics capabilities and experience can achieve greater clinical impact, operational efficiencies, and cost reductions while remaining regulatory compliant and acting ethically. The ultimate vision for AI-enabled transformation in healthcare is a learning health system, in which clinical data continuously collected from the real world feed into predictive models to inform clinical decision-making across the individual patient population. Achieving this vision requires active cultural, cross-domain (clinical/technical/regulatory/ethical), and technological advancement.

Elective surgery delays are a major challenge in operating room management, affecting service quality, resource efficiency, and patient satisfaction. At Restu Ibu Hospital, delay rates ranged from 25–30% monthly prior to the intervention. This study evaluated the effectiveness of comprehensive OR management interventions: block scheduling, EMR-integrated digital reminders, standardized preoperative SOPs, and STARKES monitoring integration in reducing elective surgery delays. A quasi-experimental interrupted time series design with segmented regression analysis was employed. Data were collected from EMR and STARKES dashboards covering six months pre-intervention (July–December 2024) and six months post-intervention (February–July 2025). Qualitative interviews with key stakeholders illuminated the dynamics of implementation and contextual factors. Pre-intervention baseline delay rate averaged 26.8%, with a non-significant upward trend of 0.4% per month. Post-intervention, significant improvements emerged: an immediate level reduction of 13.5% (p = 0.021) and a sustained monthly decline of 1.8% (p = 0.008), reducing monthly delays to 12–15%, a 50% improvement. Secondary outcomes showed reduced first-case start delays (>20%), shorter turnover times (15–20%), and lower overtime rates (<15%). Qualitative findings identified key causes of delay, including patient tardiness, incomplete assessments, delayed OR readiness, and suboptimal coordination. The comprehensive intervention package effectively reduced elective surgery delays through integrated process standardization, scheduling reform, digital enablement, and performance monitoring. Findings demonstrate that operational excellence in resource-constrained settings derives from effective management rather than extensive infrastructure investment.

ABSTRACT Blockchain integrated with Machine Learning (ML) has recently attracted the attention of academia and industries in the health care system because it improves security, predictive features, and operational work. However, the potential of having all these technologies combined is most often rather underestimated. This article offers a systematic review and an up-to-date literature review to provide a meta-analysis of the current literature on the integration of blockchain and machine learning toward the purpose of identifying their impact on addressing significant problems in the field of health care including confidentiality for patients, integration, and elaborate diagnosing. A total of 35 studies were considered in articles with applications covered by staking sharing, prediction and analysis, and pharmacogenomic individualized therapy. Combined blockchain and ML applications in health care demonstrated substantial advantages through the published meta-analysis. The pooled effect size was 17.85 (95% CI: 10). As a measure of improvement, the meta-analysis found the combination of blockchain with ML raises health care system data protection alongside diagnostic reliability and operational excellence (95% CI: 80 to 24.89). The many variations found across studies generated excessive heterogeneity as demonstrated bymjI2 equaling 100% and Tau2 equaling 451.94. These results confirm the strong transformational potentials of these technological solutions. Future research projects should establish the feasibility of adding blockchain along with machine learning systems because the appointed answers show their potential to improve health care functionality.

The implementation of a preventive maintenance planning system in the rolling mill company, supported by the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, has demonstrated significant operational and strategic benefits. By applying DEMATEL, complex interrelationships among maintenance factors were systematically analyzed using evaluation parameters such as Virtually Impossible (VI), Very Unlikely (VU), Unlikely (U), Fairly Unlikely (FU), Fair Chance (FC), and Even Chance (EC). This structured decision-making framework enabled the organization to identify critical cause–effect relationships among equipment reliability, maintenance scheduling, resource allocation, and operational risks. The findings reveal that proactive maintenance practices substantially improve equipment reliability, reduce unplanned downtime, enhance workplace safety, and optimize maintenance costs. Regular inspections, lubrication routines, condition monitoring, and timely replacement of worn components effectively prevented major breakdowns and extended equipment life cycles. Furthermore, data-driven maintenance planning strengthened asset management strategies by improving spare parts planning and long-term investment decisions. The integration of DEMATEL with preventive maintenance planning not only improved technical performance but also enhanced workforce confidence, operational stability, and productivity. Overall, this case study confirms that a structured, analytical, and proactive maintenance approach provides sustainable operational excellence, long-term cost efficiency, and improved reliability, establishing a strong foundation for bestpractice preventive maintenance frameworks in rolling mill industries.

This conceptual paper addresses the critical gap between the global demand for operational efficiency and the increasing need for value-based, ethically guided business practices, particularly within the context of the Indonesian Halal industry. The study aims to formulate a integrating Lean Management principles with the goals of Maqashid Syariah. Employing a theoretical synthesis method based on an analytical review of Indonesian scholarly literature published between 2021 and 2025, the research establishes a robust conceptual synergy. The findings demonstrate that Lean methodologies serve as the precise operational mechanism for achieving the ethical objectives of Syariah. Specifically, the elimination of waste (Muda) directly supports Hifz al-Mal (protection of wealth) by preventing Tabzir (extravagance), while the Lean philosophy of Respect for People aligns with Hifz al-Nafs (protection of life/welfare). Furthermore, the core principle of continuous improvement (Kaizen) finds its spiritual anchor in the Islamic ethos of Ihsan and Itqan (professional excellence). This framework posits that Maqashid Syariah must function as the comprehensive governance pillar guiding all operational decisions, ensuring that efficiency efforts lead to widespread Maslahah (public welfare). The primary contribution is a sustainable, ethical model of Operational Excellence that necessitates a new system of measurement, the Maslahah Index, to track both economic productivity and moral accountability.Keywords : Lean Management, Maqashid Syariah, Operational Excellence, Maslahah, Kaizen, Hifz al-Mal, Conceptual Framework

Offshore commissioning projects demand precise alignment between equipment selection strategies and operational performance outcomes to ensure safety, efficiency, and cost optimization in complex marine environments. This review synthesizes current literature and industrial practices to develop a predictive decision model that links equipment selection parameters—such as reliability indices, environmental adaptability, and lifecycle cost—with measurable commissioning outcomes. By integrating predictive analytics, multi-criteria decision-making (MCDM), and risk-based optimization frameworks, the study explores how equipment characteristics influence downtime, schedule adherence, and performance reliability. Emphasis is placed on the role of digital tools like simulation-based design, AI-driven reliability modeling, and digital twin systems in forecasting commissioning risks and optimizing asset readiness. The review further examines decision-support methodologies incorporating Bayesian networks, fuzzy logic, and data-driven sensitivity analysis for enhanced equipment selection under uncertain offshore conditions. Findings highlight that a unified predictive decision model enables cross-functional collaboration among engineers, project managers, and procurement specialists, thereby improving project execution and return on investment. Ultimately, the paper provides a conceptual foundation for developing quantitative models that bridge the gap between equipment strategy formulation and operational excellence in offshore commissioning performance. Keywords: Offshore Commissioning, Equipment Selection Strategies, Predictive Decision Model, Reliability Optimization, Multi-Criteria Decision Analysis (MCDA), Digital Twin Integration.