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Implementing an Order Pull System into a Medical Device Company Using Define for Lean Six Sigma Methodology 9th

Implementing an Order Pull System into a Medical Device Comp...

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9th European Lean Educator Conference on Challenging the Future with Lean (ELEC)

作者： O'Donoghue, Daniel McDermott, Olivia Trubetskaya, Anna Rosa, Angelo Kharub, Manjeet Cormican, Kathryn Univ Limerick Dept Engn Limerick Ireland Univ Galway Coll Sci & Engn Galway Ireland CVR Coll Engn Dept Mech Engn Hyderabad India Univ LUM Jean Monnet Dept Management Studies Bari Italy

ISBN: (纸本)9783031632679;9783031632655;9783031632648

This project aims to show how implementing continuous flow pull production with just-in-time Kanban processes in a Medical Device company can improve customer supply and thus mitigate device shortages in the Medtech supply chain. A case study in a medical device manufacturer utilised Define for Lean Six Sigma methodology for the design of a pull process and identification of waste in the process by using Value Stream Mapping and Kaizen events. A Pull model utilising Kanban and Hejunka was designed and implemented to produce products based only on customer demand signals. The device aged work orders in the process and waste reduced and overall service to the customer was improved while ensuring products were produced based on customer demand signals resulting in lead time and inventory reduction of over 60%, increased inventory turns, elimination of waste due to overproduction, holding inventory, and increased collaboration and communication between stakeholders. This is one of the few studies on implementing a Lean pull model in a medical device manufacturer and can be leveraged to improve supply chain accuracy and reliability. This study can demonstrate how supply chain shortages in devices that may affect patient treatments can be mitigated by embracing Lean methods.

关键词： Leadership

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An Explorative Study of AI Applications in Composite Material Extrusion Additive Manufacturing 43rd

An Explorative Study of AI Applications in Composite Materia...

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43rd ifip WG 5.7 International Conference on advances in Production Management Systems (APMS)

作者： Harper, Austin Wuest, Thorsten West Virginia Univ IMSE Dept Morgantown WV 26506 USA Univ South Carolina Dept Mech Engn Columbia SC 29208 USA

ISBN: (纸本)9783031716355;9783031716331;9783031716324

Additive Manufacturing (AM) is on the forefront of innovative advance manufacturing techniques leveraging Artificial Intelligence (AI) and Machine Learning (ML) to improve processing capabilities. We conducted a literature review to survey the current state of the art for AI/ML applications within Material Extrusion AM (MEX-AM). Furthermore, this study explored the intersection of AI applications and use of Carbon Fiber-Reinforced Polymers (CFRP) as a MEX-AM material. We found that while discontinuous CFRPs are covered in several experimental studies, there was a noticeable lack of research on continuous CFRPs among the collected papers. We found that the most common ML Solution for quality issues in MEX-AM was the artificial neural network feed forward supervised learning back propagation (ANN-FFNN-SL-BPN) Solution.

关键词： Material Extrusion Additive Manufacturing Machine Learning Artificial Intelligence Carbon-Fiber Reinforced Polymers

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Real-Time Attack Detection in Modern Automobile Controller Area Networks 17th

Real-Time Attack Detection in Modern Automobile Controller A...

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17th Critical Infrastructure Protection

作者： Martin, Edward Shenoi, Sujeet Univ Tulsa Tulsa OK 74104 USA

ISBN: (纸本)9783031495878;9783031495854;9783031495847

Modern automobiles have numerous sensors, actuators and electronic systems interconnected via internal sub-networks that are not designed with security in mind. This chapter describes a novel real-time system that employs long short-term memory networks to monitor automobile controller area networks, detect attacks and raise alerts. A repeatable design framework is employed to construct and train multiple long short-term memory networks to recognize normal controller area network message timing patterns. The framework lays out the computational resources as well as the data collection and preprocessing and long short-term memory network model development and training steps. Also, it enables new long short-term memory network models to be trained and updated for automobiles of different makes, models and years. The attack detection system leverages a server-client configuration to monitor an automobile controller area network bus. The server is an inexpensive Raspberry Pi device connected directly to the automobile controller area network bus that captures, logs and transmits controller area network message traffic to a client via a Wi-Fi network. The client, a workstation located outside the automobile, provides the computational resources for real-time attack detection. Trained long short-term memory models executing on the client workstation analyze the received controller area network messages, identify attacks and send alerts via the Wi-Fi network. Experimental results using a 2010 Toyota Prius testbed and a fully-operational 2014 Toyota Prius automobile demonstrate the effectiveness of the real-time attack detection system.

关键词： Automobiles Controller Area Networks Real-Time Attack Detection Long Short-Term Memory Networks

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Explanations for Core Decomposition 20th

Explanations for Core Decomposition

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20th International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Konstantopoulos, Christos Tsichlas, Konstantinos Univ Patras Dept Comp Engn & Informat Patras Greece

ISBN: (纸本)9783031632211;9783031632198;9783031632181

The problem of core decomposition in networks is fundamental in understanding their structural organization. Core decomposition methods have been applied to different problems and various scientific fields ranging from Social Network Analysis (community detection) to Epidemiology and Disease Spread (identification of core groups where transmission rates are high). This study delves into the causal explanations underlying core decomposition, with ultimate aim to unravel the intricate mechanisms driving the emergence and evolution of cores within *** causal explanations are based on the interventional notion of causality as introduced by Halpern and Pearl. Leveraging techniques developed previously by some authors, we pinpoint the causal relationships between network connectivity and core decomposition. To this end, at first we define what a causal explanation is, according to the core decomposition problem. Then, apart from the algorithmic contribution in identifying the causal explanations, we also provide different related experimental results that demonstrate its use.

关键词： Causal Explanations Networks Core Decomposition

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Multi-depot Electric Vehicle Routing Problem with Half-Open Routes and Rotations: A Mathematical Formulation 43rd

Multi-depot Electric Vehicle Routing Problem with Half-Open ...

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43rd ifip WG 5.7 International Conference on advances in Production Management Systems (APMS)

作者： Erdes, Hakan Kesen, Saadettin Erhan Konya Tech Univ Fac Engn & Nat Sci Dept Ind Engn Selcuklu Konya Turkiye

ISBN: (纸本)9783031716478;9783031716454;9783031716447

The electrification spreads all around the world with the goal of global net zero emission, especially in transportation sector. As a natural consequence of this phenomenon, many countries and organizations announce their targets relating to the Electric Vehicles (EVs). Although shifting from fossil-fuel towards EVs is a major step forward, there are other precious actions to be taken, one of which is to integrate EVs into scientific studies along with the routing improvements. To this end, we introduce the Multi-Depot Electric Vehicle Routing Problem with Half-Open Routes and Rotations (MDEVRP-HORR) as an extension of the MDEVRP where the rotation refers to set of all routes assigned to anEV. EVs can be recharged at the depots or at the recharging stations along the route with full recharging strategy. The problem is formulated as a 0-1 mixed integer linear program (MILP) and existing procedures are applied to improve the model. The proposed model relaxes the restriction that the departure depot of the first route and arrival depot of the last route of an EV must be the same. It also avoids from creating dummy nodes for recharging stations and replenishment depots. We conducted experimental studies by using small-sized instances from the recent literature due to problem intractability. Out of 36 instances, results reveal that inclusion of half-open routes leads to a cost reduction for 20 instances. Besides, inclusion of both half-open rotations and half-open routes results in cost reduction for 28 instances.

关键词： Electric Vehicle Multi-Depot Electric Vehicle Routing Problem Half-Open Route and Rotation

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From Tweets to Reddit: Leveraging Semi-supervised Domain Adaptation for Improving Data Filtering 20th

From Tweets to Reddit: Leveraging Semi-supervised Domain Ada...

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20th International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Gupta, Shelly Alshehri, Jumanah Hai, Ameen Abdel Otudi, Hussain Obradovic, Zoran Temple Univ Ctr Data Analyt & Biomed Informat Philadelphia PA 19122 USA Imam Abdulrahman Bin Faisal Univ Dammam Saudi Arabia

ISBN: (纸本)9783031632259;9783031632235;9783031632228

Reddit has emerged as a leading platform for microblogging data collection, providing valuable insights into patterns and knowledge discovery. However, the process of gathering and preparing this data presents significant challenges, particularly when it comes to ensuring its accuracy. Existing methodologies often yield an abundance of irrelevant posts, and datasets for relevance prediction on Reddit are rare. To overcome these obstacles, we propose a new semi-supervised framework that filters Reddit posts based on their topic relevance. Our approach combines annotated data from Twitter with weak labels generated from Wikipedia pages associated with relevant subreddits to automatically label Reddit posts. To enhance the model's generalization performance, we utilize a domain adversarial adaptation network to bridge the distribution gap between Twitter and Reddit data. Our novel framework achieves an accuracy of 73% and an F1 score of 0.77, which is a significant improvement of 20% compared to baseline models. Additionally, we address important research questions regarding the effectiveness of automatic labeling, the use of weakly labeled data, the contextual requirements for training domain adaptation models, and the optimal weak labeling method.

关键词： Domain Adaptation Reddit Twitter Semi-supervised Relevance learning

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Towards a Service Marketplace to Empower Circular Economy Transition: An Example Application in the Supply Chain of Textile Industry 43rd

Towards a Service Marketplace to Empower Circular Economy Tr...

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43rd ifip WG 5.7 International Conference on advances in Production Management Systems (APMS)

作者： Franke, Marco Deng, Quan Hribernik, Karl A. Thoben, Klaus-Dieter Ciaccio, Gessica Bremer Inst Prod & Logist GmbH D-28359 Bremen Germany Univ Bremen Fac Prod Engn D-28359 Bremen Germany Italian Natl Agcy New Technol Energy & Sustainabl I-00196 Rome Italy

ISBN: (纸本)9783031716249;9783031716225;9783031716218

The environmental impact of the textile industry is driving a shift towards circular economy (CE) practices. The EU's 2022 strategy mandates the Digital Product Passport (DPP) for textiles, which requires environmental and circularity data along the value chain. To comply with DPP, it is necessary to use digital services for CE in supply chains to assess the environmental impact of textile products. CE services require addressing interoperability issues and facilitating data exchange between these CE services used by different companies for their individual products. Although various CE services are available as commercial off-the-shelf products, they are often provided with a generic data model for a specific product and significant effort is required by SMEs to address customisation. Existing service marketplaces are either not domain specific or do not facilitate easy (re-)use and adoption of these CE services. This paper proposes a marketplace to address this challenge. It connects solution providers offering CE services with SMEs seeking solutions. The marketplace implements Software as a Service (SaaS) and provides public APIs and domain & product specific data connectors to facilitate data exchange and interoperability between individual services from companies and CE services in the marketplace. This will minimise the effort required to discover, deploy and use CE services within SMEs, and facilitate the (re)use of CE services across different products. With this, the marketplace aims to promote sustainability and traceability in the circular economy of the textile sector, which is shown for the Preferential Certificate of Origin.

关键词： Circular Economy Digital Product Passport B2B marketplace for CE Sustainability of digital services for DPP

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Enriching Scene-Graph Generation with Prior Knowledge from Work Instruction 43rd

Enriching Scene-Graph Generation with Prior Knowledge from W...

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43rd ifip WG 5.7 International Conference on advances in Production Management Systems (APMS)

作者： Jesko, Zoltan Tuan-Anh Halaszl, Gergely Abonyi, Janos Ruppert, Minas Univ Pannonia Dept Syst Engn Egyet Str 10 Veszprem Hungary Univ Pannonia HUN REN PE Complex Syst Monitoring Res Grp Egyet Str 10 Veszprem Hungary Univ Pannonia Dept Proc Engn Egyet Str 10 Veszprem Hungary

ISBN: (纸本)9783031658969;9783031658945;9783031658938

With the current focus on human resources in Industry 5.0, analysing the work movements of industrial operators is the important first step in optimising labour performance. Thanks to the popularity of camera sensors, vision-based Human Activity Recognition models have become useful engines for real-time monitoring tools, in which scene-graphs play an important role. Traditional scene-graph generation methods rely primarily on visual data for perception, neglecting a valuable source of process-oriented prior knowledge: the work instruction. Therefore, an extension of the scene-graph paradigm by integrating ground truth elaborated on elements from the work instruction is elaborated to complement and enhance the understanding of human activities in industrial environments, and improve the tracking capability with micro and repetitive movements. This conceptual paper discusses the basic design of this approach with potential applications in industrial environments, which is validated by a simulated use case of an electronic assembly process. Based on the proposed extension, the Human Activity Recognition model can be lightweight and robust. Further integration of multi-modal sensory inputs beyond visual cues, such as environmental and human-centric data, can enrich scene interpretation and provide a more comprehensive understanding of work behaviour, paving the way for more effective labour utilisation and improved productivity.

关键词： Scene-graph Human-centered Activity recognition Industry 5.0 Operator 4.0

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Digital Tools Supporting Lean Program in a Multinational Enterprise 9th

Digital Tools Supporting Lean Program in a Multinational Ent...

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9th European Lean Educator Conference on Challenging the Future with Lean (ELEC)

作者： Frecassetti, Stefano Ferrazzi, Matteo Staudacher, Alberto Portioli Politecn Milan Dept Management Econ & Ind Engn Piazza Leonardo da Vinci 32 I-20133 Milan Italy

ISBN: (纸本)9783031632679;9783031632655;9783031632648

Researchers have increasingly explored the intricate interplay between Lean methodologies and digital technologies in recent years, shedding light on synergistic effects and potential integration strategies. However, scarce attention has been given to the practical application of digital tools in supporting Lean programs and associated challenges. This paper presents a case study of a multinational company's utilization of digital tools to bolster its corporate Lean program. The findings showed a positive mutual relationship wherein digital tools augment the efficacy of Lean practices. The positive outcomes underscore the potential of digital technologies to enhance and streamline Lean initiatives. Nevertheless, the research also underscores significant challenges encountered during and after digitalization. In particular, the paramount importance of individuals in the digitalization journey has emerged. Their empowerment and active involvement emerge as fundamental factors throughout the entire process. The findings underscore that being Lean before digitalization is crucial, as it creates an environment and a change-oriented culture that is more receptive to incorporating technologies.

关键词： Lean Production Industry 4.0 Digitalization

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Enhancing Monkeypox Detection: A Machine Learning Approach to Symptom Analysis and Disease Prediction 1

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20th International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Magsino, Dea Louisa B. Mercado, Russel Lenard O. Rivera, Francesca Nicole F. Magboo, Ma Sheila A. Magboo, Vincent Peter C. Univ Philippines Manila Dept Phys Sci & Math Manila Philippines

ISBN: (数字)9783031632112

ISBN: (纸本)9783031632136;9783031632112;9783031632105

Monkeypox disease, caused by the monkeypox virus, is a highly communicable disease and has prompted the World Health Organization to declare it as a Public Health Emergency. Early recognition of the disease is imperative to stop the community transmission of monkeypox. The objective of the study is to detect monkeypox based solely on its clinical symptoms consisting of boolean and categorical features through the use of various machine learning models: decision tree, logistic regression, Naive Bayes, support vector machine, random forest, and Adaptive Boosting. The best performing modelwas obtained by the support vector machine with the highest area under the precision recall curve of 79.67%, 88.35% recall, 72.86% precision and 84.53% F2-score under the baseline model configuration (without feature selection, without class imbalance correction). The superior recall of the best model highlights its ability to correctly diagnose monkeypox resulting in a much lower false negatives or fewer missed cases of monkeypox crucial in halting its community spread. The integration of these models in the clinical setting can serve as a decision support tool for prompt monkeypox recognition. As such, swift medical intervention to those afflicted can be administered as well as prompt institution of quarantine measures to prevent the community transmission of monkeypox.

关键词： Monkeypox disease feature selection SMOTE undersampling

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