754 research outputs found
A Novel Personnel Planning Method to Improve Operations Management: Transferring lessons learned from manufacturing to healthcare
No full textThere 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
Performance Differences in the Ageing Workforce Era: An Experimental Study with Industry 4.0 Assistive Technologies
No full textAdopting I4.0 technologies in current industrial scenarios ensures better performance and efficiency of the systems. Nevertheless, less is known about the human-centric impact of assistive technologies, and particularly their effect on differently aged workers. Due to the ageing workforce phenomenon, it is essential to understand how the performance of aged workers is affected by I4.0 smart devices. The present study explores the performance of young (aged 22-25) and old (aged 45+) participants engaged in assembly and order-picking tasks with varying levels of technological assistance. The study categorizes assistive technologies into "semi-assistive" and "fully assistive" levels and evaluates their impact on user performance, measured through Task Completion Time (TCT). Results indicate that the higher familiarity of young participants with technology ensures higher performance than the old ones, despite having less task-related experience. The paper underscores the need for tailored training programs and the redesign of workplaces to accommodate the ageing workforce and minimize performance differences between user categories. Findings highlight that more empirical works are needed to deepen the ageing theme, stressing the importance of improving technology acceptance and usability.publishedVersio
Recensione a: Ansari di Herat, Le cento pianure dello Spirito, a cura di C. Saccone, EMP, Padova 2012
No full textRecensione di volume di Ansari di Herat, un autore mistico persiano dell' XI sec.review of the book: Ansari of Herat, a mystical Persian author of XI centur
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 textDesign and development of automatic recommendation generation module of prescriptive maintenance model (AutoPriMa)
No full textMit Industry 4.0 wurde eine neue Ära in der Instandhaltung eingeleitet. Mit dem Aufkommen von Cyber-Physical Production Systems (CPPS) und der ständigen Verfügbarkeit von Sensordaten änderte sich die Wartung von der vorausschauenden zur präskriptiven Instandhaltung. Internet of Things (IoT), Data Science und Artifical Intelligence (AI) spielen daher eine wichtige Rolle bei der Entwicklung von Fertigungstechnologien. Es wird erwartet, dass die vorschreibende und präskriptive Instandhaltung bis 2022 um etwa 39% auf jährlich 10,96B by 2022. Smart Manufacturing Leadership Coalition (SMLC) has also predicted that the following targets can be achieved by data driven analytics in smart manufacturing (1), 30% reduction in capital intensity, (2) up to 40% reduction in product cycle times, and (3) overarching positive impact across energy and productivity. Lueth K. et al. (2016) stated in their report that 79% of all decision makers of Original Equipment Manufacturers will see predictive and prescriptive maintenance as one of the most important applications in the next 1-3 years. In the area of prescriptive analytics the goal is to find the best course of action for a given problem, by using techniques like recommendation engines and neural networks for solving a problem. Those techniques can then be converted for use in maintenance. A rising demand for prescriptive maintenance, which offers decision support can be anticipated, while currently predictive maintenance mostly consists of inappropriate maintenance strategies and conditions. According to Cheng et al. (2018) and R. Ranjan (2014) state of the art decision-making processes combine different data sources with data science methods to either improve the system intelligence or establish an automated big data pipeline Cheng et al. (2018) and R. Ranjan (2014). The concept Knowledge Based Maintenance (KBM)9101112 is a key enabler for digital transformation to prescriptive maintenance. As stated by Ansari, Glawar, et al. (2019), the PriMa model and its four-step methodology have been introduced and an applied as part of a proof-of-concept study, however while the paper specifies the methodology and approach in detail, it does not go into detail on how to achieve problem 1 (P1) the data input into the data warehouse, problem 2 (P2) how to build aggregator functions and most importantly, how to handle the feedback loop between the Knowledge-Base and the Decision Support Dashboard problem 3 (P3). This works aims to design an automated PriMa model, specifically focusing on the knowledge pipeline from the textual data from maintenance reports to the recommendation of a solution for the problem identified in the report. These questions have been answered by looking into the requirements given by ODonovan et al. (2015) for the data ingest process and proposing an own requirement list for a data warehouse solution (P1). In the next step three machine learning (ML) algorithms, namely Hamilton Monte-Carlo (HMC), Random Forest (RF) and Neural Networks (NN) reasoning have been generated and minimum working examples provided. Their outputs later on have been aggregated by a weighted hybrid function (P2). For the knowledge pipeline a Natural Language Processing (NLP) algorithm was applied which uses maintenance reports and extracts nouns and verbs. Those than can be matched against an ontology by using case-based reasoning (CBR) with the help of SPARQL. To sum up, the present thesis contributes on design and technical realization of the knowledge pipeline in the context of maintenance by analyzing technical requirements and developing a proof of concept demonstrator
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