1,721,101 research outputs found
A Novel Personnel Planning Method to Improve Operations Management: Transferring lessons learned from manufacturing to healthcare
Get PDFThere is a solid body of knowledge on personnel planning in production and logistics, showcasing potential applications across various sectors, particularly in operations management in healthcare. This paper focuses on Medical Residency Scheduling Problems (RSP) in a cross-facility context, employing a real dataset from an Austrian hospital group to assess the applicability of production planning and control (PPC) optimization techniques. The study examines approximate, expert-driven, and exact mixed-integer programming methods, underscoring the approximate method's effectiveness and rapidity in optimizing schedules against four objectives within a constrained period. The successful application of this novel method not only marks a significant advancement in scheduling systems but also demonstrates the potential for these methods to address broader scheduling challenges, significantly improving operational efficiency and quality. This approach offers insights for time-sensitive personnel planning, suggesting a versatile applicability of production-derived methods in healthcare scheduling
Sustainable Maintenance: What are the key technology drivers for ensuring Positive Impacts of Manufacturing Industries?
No full textDespite advances in operational efficiency, industry poses a significant threat to environmental sustainability, thus preventing progress towards a net-zero economy. This research investigates the transformative potential of Industry 4.0 (I4.0) technologies in advancing sustainable maintenance practices. Defined as resource-minimizing and environmentally sustainable approaches while maintaining operational effectiveness, sustainable maintenance promises a mutually beneficial scenario for organizations and the environment. This paper employs a systematic approach including an extensive structured literature review and expert interviews with industry representatives. By analyzing the intersection of I4.0 technologies and sustainable maintenance principles, key technological solutions with the potential to significantly reduce resource consumption, minimize waste generation, and reduce emissions within industrial operations are identified. Based on literature review and expert interviews, a clear dependency between technological maturity and maintenance sustainability is identified. These findings provide decision-makers with valuable insights to navigate the complex technology landscape and implement evidence-based strategies to achieve both operational excellence and environmental responsibility
Challenges in Healthcare Supply Chain Resilience Management : A Conceptual Framework
No full textThe healthcare supply chain (HSC) is a complex and dynamic system that plays a critical role in ensuring the delivery of essential medical products and services to patients. This system faces numerous challenges that can harm the healthcare system, leading to treatment delays, patient dissatisfaction, and increased costs. Additionally, the ageing population and the rising prevalence of chronic diseases are increasing the criticality of the HSC. Three promising avenues of future research are emerging to address these challenges in the HSC: resilience, collaboration and visibility, and the use of technology. The current research is based on a literature review of HSC challenges and opportunities, highlighting the importance of a comprehensive approach to HSC management. This research presents a conceptual framework integrating resilience, collaboration, visibility, and technologies that can provide a roadmap for future research in the field. By focusing on these aspects, organisations can create a more efficient, effective, and resilient HSC better equipped to meet the needs of patients
A Conversationally Enabled Decision Support System for Supply Chain Management: A Conceptual Framework
No full textThis paper introduces a conceptual framework for integrating Conversational AI (CAI), specifically conversational agents (CAs), with Decision Support Systems (DSS) to enhance Supply Chain Management (SCM) decision-making processes. In today's complex supply chain environment, characterized by diverse processes and entities operating across different geographic locations, the effective use of AI in DSS is crucial. The proposed framework envisions a Conversationally Enabled Supply Chain (CESC) where decision-makers interact with the DSS using natural language through a CA, facilitating tasks such as data analysis, scenario analysis, and simulation. The choice of a conceptual framework as a research tool provides a systematic approach to collect and organize elements, offering a clear reference structure and a common language. This framework aims to enhance understanding, guide research and analysis, and integrate knowledge from diverse sources, contributing to a holistic understanding of the proposed CA-empowered DSS for SCM. The paper emphasizes the significance of CESC and sets the stage for future research and development in the domain, providing a foundation for ongoing work
Manufacturing Analytics System: A New IT Category Enabling Next-Level Operational Excellence
No full textISSN:2405-896
Non-stationary dynamic bayesian networks for predictive maintenance: theoretical foundation and application scenarios
No full textManual Data Collection in Assembly Lines: A Case Study on the Human Factor in Data Accuracy
No full textISSN:2405-896
Manufacturing workers fatigue:an exploratory study on predictive machine learning and cross-subject generalization with implications for work design
Get PDFManufacturing workers’ fatigue is an acknowledged concern with implications for well-being, health, safety, and operational performance. Past studies have employed physiological measurements obtained from smartwatches and wearable devices, seeking to assess and classify the fatigue state of workers. However, the extent to which models developed based on data obtained from individual workers could apply to other workers remains unclear. This paper presents the results of an exploratory study in which data from different subjects are employed to develop a range of fatigue estimation and predictive machine learning models. A cross-subject study provides evidence of sufficiently accurate performance in several cases. Further insights arise from looking into cases of lower generalization and linking these to personal characteristics
Workers Fatigue Monitoring for Well-being Improvement in Manufacturing
Get PDFIn Industry 5.0, worker well-being is paramount for organizational resilience and sustainability. Physical fatigue, work-life balance, and job competency significantly impact worker welfare and, therefore, efficiency and effectiveness. This study collects data in different industrial scenarios using non-invasive wearable devices for dynamic data and questionnaires for quasi-static data. Using Machine Learning algorithms, including Random Forest and Feedforward Neural Network models, the study predicts the physical fatigue of workers across multi-class and binary classifications. The developed Fatigue Monitoring System software integrates these models to monitor fatigue and improve worker well-being
Non-stationary dynamic bayesian networks for predictive maintenance: theoretical foundation and application scenarios
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