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8th Changeable, Agile, Reconfigurable and Virtual Production Conference, CARV 2021 and 10th World Mass Customization and Personalization Conference, MCPC 2021

作者： Dürr, Simon Silbernagel, Rainer Bartsch, Hannah Steier, Gwen Louis Huber, Marco F. Lanza, Gisela Dr. Ing. h.c. F. Porsche AG Porscheplatz 1 Stuttgart70435 Germany University of Stuttgart Institute of Industrial Manufacturing and Management IFF Allmandring 35 Stuttgart70569 Germany Institute of Production Science wbk Kaiserstraße 12 Karlsruhe76131 Germany Fraunhofer Institute for Manufacturing Engineering and Automation IPA Nobelstraße 12 Stuttgart70569 Germany

ISBN: (纸本)9783030906993

Customer satisfaction is a key factor to ensure long-term business success. Therefore, automotive manufacturers offer various options to individualize a car. Furthermore, customers and dealers are allowed to change their configuration until the vehicles are scheduled for production. This point is called order freeze. While vehicle specifications remain nearly unchanged in a build-to-order fulfillment strategy, this is not the case in build-to-stock. Mass-customized products with billions of possible car configurations, changing customer demands, or a dynamic environment are some of the challenges that confront the manufacturers in the planning and ordering processes. In this paper, the concept of multiple option-specific order freeze points is developed, which allows customers and dealers to change the configuration specifications at an even later point. For this purpose, the planning process, customer preferences, feasibility rules as well as technical and sales-operated option dependencies are evaluated. Furthermore, independent option-specific order freeze points are detected based on data-driven methods to handle the requirements for agile production systems by using current analytical technologies. The concept of multiple option-specific order freeze points has a high potential to be applied in a practical usage and is validated by a real-world use case of the Dr. Ing. h.c. F. Porsche AG. © 2022, Springer Nature Switzerland AG.

关键词： Customer satisfaction data-driven analytics Demand uncertainty Mass customization Order freeze point

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data-driven ESG assessment for blockchain services: A comparative study in textiles and apparel industry

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RESOURCES CONSERVATION AND RECYCLING 2023年 190卷

作者： Liu, Xinlai Yang, Yu Jiang, Yishuo Fu, Yelin Zhong, Ray Y. Li, Ming Huang, George Q. Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Peoples R China Xi An Jiao Tong Univ Sch Management Xian 710049 Peoples R China Shenzhen Univ Coll Econ Shenzhen Peoples R China Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China

This paper introduces a data-driven ESG assessment approach using blockchain technology and stochastic multicriteria acceptability analysis (SMAA-2) to address the data opaqueness and assessment subjectivity problems. On the one hand, blockchain provides a transparent and trackable ledger to store and share ESG data among the listed companies, investors, and stakeholders alike. On the other hand, SMAA-2 provides the ad-vantages of robustness and no subjective weight preferences to assess the ESG data. Based on real ESG data, this research quantitatively compares the ESG performances of 71 textiles and apparel listed companies in Hong Kong. Sensitivity analyses are conducted to verify the stability of the proposed approach with scaling weight preferences of environmental criteria (e.g., water consumption, energy consumption, and greenhouse gas emission). Results show that the proposed data-driven ESG assessment approach can analyze the companies' sustainability performances and benchmark the sustainability level of the company in the peer industry.

关键词： ESG assessment data-driven analytics Blockchain SMAA-2

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INVESTIGATION ON HEART DISEASE USING MACHINE LEARNING ALGORITHMS

INVESTIGATION ON HEART DISEASE USING MACHINE LEARNING ALGORI...

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11th International Conference of Computer Communication and Informatics (ICCCI)

作者： Sivabalaselvamani, D. Selvakarthi, D. Rahunathan, L. Eswari, S. Nandhini Pavithraa, M. Sridhar, M. Kongu Engn Coll Dept Comp Applicat Erode 638060 Tamil Nadu India Kongu Engn Coll Dept Elect & Instrumentat Engn Erode 638060 Tamil Nadu India

ISBN: (纸本)9781728158754

Coronary delivery path coronary disease is caused in coronary corridors by atherosclerosis which results in heart failure and respiratory failure. Angiography, an exorbitantly tedious and deeply specialized intrusive technique, is used to conclude CAD (Coronary Artery Disease). Thus, experts are triggered by elective approaches, for example, AI calculations that will use non-intrusive scientific knowledge to assess and measure the severity of the cardiovascular condition. In this report, we present another way to deal with CAD examination combination, including recognizable peril factor realities utilizing the atom, swam upgrade mission strategy, and K-Means bundling computation decision of the relationship-based segment subset. The WEKA instrument is an open-source apparatus utilized for information pre-handling, grouping, and arranging. Overseen learning recreations, for example, multi-layer perceptron, multinomial strategic relapse, fluffy unordered principle acceptance calculation, and C4.5, are then used to exhibit CAD situations.

关键词： smart healthcare Big data analytics data-driven medicine Digital health Health intelligence data-driven analytics Expert systems

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Solar Photovoltaic Modules' Performance Reliability and Degradation Analysis-A Review

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ENERGIES 2022年第16期15卷 5964-5964页

作者： Alimi, Oyeniyi A. Meyer, Edson L. Olayiwola, Olufemi, I Univ Ft Hare Ft Hare Inst Technol ZA-5700 Alice South Africa

The current geometric increase in the global deployment of solar photovoltaic (PV) modules, both at utility-scale and residential roof-top systems, is majorly attributed to its affordability, scalability, long-term warranty and, most importantly, the continuous reduction in the levelized cost of electricity (LCOE) of solar PV in numerous countries. In addition, PV deployment is expected to continue this growth trend as energy portfolio globally shifts towards cleaner energy technologies. However, irrespective of the PV module type/material and component technology, the modules are exposed to a wide range of environmental conditions during outdoor deployment. Oftentimes, these environmental conditions are extreme for the modules and subject them to harsh chemical, photo-chemical and thermo-mechanical stress. Asides from manufacturing defects, these conditions contribute immensely to PV module's aging rate, defects and degradation. Therefore, in recent times, there has been various investigations into PV reliability and degradation mechanisms. These studies do not only provide insight on how PV module's performance degrades over time, but more importantly, they serve as meaningful input information for future developments in PV technologies, as well as performance prediction for better financial modelling. In view of this, prompt and efficient detection and classification of degradation modes and mechanisms due to manufacturing imperfections and field conditions are of great importance towards minimizing potential failure and associated risks. In the literature, several methods, ranging from visual inspection, electrical parameter measurements (EPM), imaging methods, and most recently data-driven techniques have been proposed and utilized to measure or characterize PV module degradation signatures and mechanisms/pathways. In this paper, we present a critical review of recent studies whereby solar PV systems performance reliability and degradation were analyzed. The ai

关键词： photovoltaics degradation characterization data-driven analytics machine learning

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Benchmarking AutoML-Supported Lead Time Prediction

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Procedia Computer Science 2022年 200卷 482-494页

作者： Janek Bender Martin Trat Jivka Ovtcharova FZI Research Center for Information Technology Haid-und-Neu-Strasse 10-14 76135 Karlsruhe Germany

In manufacturing companies, especially in small and medium enterprises (SME) in the make-to-order domain, predicting accurate lead times for highly customisable products poses a challenge. One avenue to tackle this challenge is using machine learning to create data-driven models. With the advent of Automated Machine Learning (AutoML), there is a potential also for non-data-scientists in the SMEs to utilise machine learning in a self-sufficient manner. In this paper, we benchmarked three AutoML solutions on data from two SMEs and compared their models with manual predictions currently in productive use as well as simple mean models. We furthermore investigated how these solutions support the model development process. While we found that the models generated by the AutoML solutions outperformed manual predictions, labour-intensive parts of the model development process such as data understanding and pre-processing as well as feature engineering were not supported sufficiently by the benchmarked solutions.

关键词： AutoML production data-driven analytics machine learning lead time prediction

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Prescriptive Maintenance of Railway Infrastructure: From data analytics to Decision Support 6

Prescriptive Maintenance of Railway Infrastructure: From Dat...

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6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

作者： Consilvio, Alice Sanetti, Paolo Anguita, Davide Crovetto, Carlo Dambra, Carlo Oneto, Luca Papa, Federico Sacco, Nicola Univ Genoa DIME Genoa Italy Univ Genoa DIBRIS Genoa Italy Hitachi Rail STS Genoa Italy

ISBN: (纸本)9781538694848

One of the main benefits of the railways digital transformation is the possibility of increasing the efficiency of the Asset Management process through the combination of data-driven models and decision support systems, paving the road towards an Intelligent Asset Management System (IAMS). The paper describes the whole IAMS decisional process based on a real railway signaling use case: from field data acquisition to decision support. The process includes data collection, preparation and analytics to extract knowledge on current and future assets' status. Then, the extracted knowledge is used within the decision support system to prioritize asset management interventions in a fully-automated way, by applying optimization logics and operational constraints. The target is to optimize the scheduling of maintenance activities, to maximize the service reliability and optimize both usage of resources and possession times, avoiding (or minimizing) contractual penalties and delays. In this context, a real use case related to signaling system and, in particular, to track circuits, is presented, applying the proposed methodology to an Italian urban rail network and showing the usefulness of the approach and its possible further developments.

关键词： railway prescriptive maintenance data-driven analytics decision support decision automation

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A data-driven multicriteria decision model for healthcare workforce retention strategies

Healthcare Analytics

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Healthcare analytics 2025年 8卷

作者： Debora Di Caprio Sofia Sironi Fan-Yun Lan Ramin Rostamkhani Department of Economics and Management University of Trento Trento Italy Institute of Health and Welfare Policy National Yang Ming Chiao Tung University Taipei Taiwan Department of Environmental Health Harvard T.H. Chan School of Public Health Boston MA USA School of Management Universiti Sains Malaysia Penang Malaysia

The retention of nurses and physicians in Hospitals is a global problem affecting the healthcare system worldwide. This study focuses on the healthcare workforce retention problem considering the current situation in Taiwan. Healthcare staff in Taiwan are undergoing a critical phase, with an increasing number of experienced workers leaving their job to go to work for private organizations or as freelancers. We develop a data-driven four-phase methodology based on the design of a satisfaction index that allows to rank different groups of employees against a given set of criteria. First, criteria are identified and clustered to describe different job dimensions (phase 1). Hence, subjective evaluations of the criteria are collected from healthcare workers while experts provide pairwise comparisons among them (phase 2). An adjusted analytic hierarchy process (AHP) is used to weight the job dimensions and the criteria within each job dimension (phase 3). Finally, the satisfaction index is formalized and computed for different groups of employees (phase 4). The methodology has been implemented with data collected from healthcare workers employed in three healthcare institutions in Northern Taiwan. The proposed index represents a novel decision support tool for managers and policy makers in designing intervention strategies able to address different needs of different groups of employees. Besides, it allows for innovative applications to quality management (QM) by extending the standard QM approach to hospitals and healthcare centers far beyond the common focus on patients' satisfaction. Finally, the mathematical formulation of the index is very flexible and allows for applications to any employment sector through a variety of analyses based on different categorizations of the workers.

关键词： data-driven analytics Healthcare workforce retention Multicriteria modeling Satisfaction index Workforce planning

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Drinking Modulates Age-Appropriate Cortical Thinning in Adolescence: A data driven Approach

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BIOLOGICAL PSYCHIATRY 2021年第9期89卷 S274-S274页

作者： Sun, Delin Adduru, Viraj Phillips, Rachel Sotiras, Aristeidis Michael, Andrew DeBellis, Michael Morey, Rajendra A. Duke Univ Duke UNC Brain Imaging & Anal Ctr Durham NC USA Rochester Inst Technol Chester F Carlson Ctr Imaging Sci Rochester NY 14623 USA Durham VA Med Ctr VA Midatlantic MIRECC Durham VA USA Washington Univ Sch Med St Louis MO 14263 USA Duke Univ Durham NC USA Duke Univ Ctr Cognit Neurosci Durham NC USA

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When You Work with a Superman, Will You Also Fly? An Empirical Study of the Impact of Coworkers on Performance

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MANAGEMENT SCIENCE 2019年第8期65卷 3495-3517页

作者： Tan, Tom Fangyun Netessine, Serguei Southern Methodist Univ Cox Business Sch Dallas TX 75275 USA Univ Penn Wharton Sch Philadelphia PA 19104 USA

We examine a large operational data set in a casual restaurant setting to study how coworkers' sales ability level affects other workers' sales performance. We find that waiters react nonlinearly to their coworkers' ability. In particular, when coworkers' overall sales ability is low, increasing this ability may prompt waiters to redouble both upselling and cross-selling efforts. When overall coworkers' ability is high, however, further increasing their ability may trigger waiters to reduce sales efforts. Our empirical findings imply that, to maximize sales, managers should mix waiters with heterogeneous ability levels during the same shift. Through a counterfactual analysis, we find that considering the inverted U-shaped peer effects when optimizing current waiters' schedules without changing their utilization may increase total sales by approximately 2.48% at no extra cost.

关键词： peer effects behavioral operations management scheduling/rostering empirical labour decisions in service operations restaurant operations people-centric operations data-driven analytics

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Improved permeability prediction based on the feature engineering of petrophysics and fuzzy logic analysis in low porosity-permeability reservoir

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2019年第2期9卷 869-887页

作者： Wang, Xidong Yang, Shaochun Wang, Ya Zhao, Yongfu Ma, Baoquan China Univ Petr East China Sch Geosci Qingdao 266580 Shandong Peoples R China Qingdao Natl Lab Marine Sci & Technol Lab Marine Mineral Resources Qingdao 266071 Shandong Peoples R China SINOPEC Shengli Oilfield Co Oil & Gas Explorat Management Ctr Dongying 257000 Peoples R China

Permeability is difficult to evaluate in reservoir petrophysics property, especially in low porosity-permeability reservoir. The conventional permeability estimation model with establishment of the regression relationship between permeability and porosity is not applicable. This regression hypothesis based on the correlation between porosity and permeability (logarithm) is not available in low porosity-permeability reservoir. It remains a challenging problem in tight and heterogeneous formations' petrophysical interpretation. Feature engineering process, as the most significant procedure in data-driven analytics, indicates that accurate modelling should be based on the main control factor on permeability ignoring its concrete mathematical expression. To select the factors that influence the main function of the model, and use the appropriate model to carry out the model structure, fusion and optimization is the main task to permeability estimation in low porosity-permeability reservoirs. Fuzzy logic, as a widely used approach in estimation of permeability, can be used to estimate the permeability with the advantage of tolerance. Its good adaptation in objective contradictory concepts and false elements in computational processes outweighs the traditional method on permeability estimation which always lies in a wide distribution of orders of magnitude. The research takes the permeability estimation issue in Mesozoic strata, Gaoqing area as example. The area of study mainly contains reservoirs with low-to-ultra-low porosity-permeability. The relationship between porosity and permeability is somewhat certain but insufficient using the regression method to predict. The research combined specialized feature engineering process with the fuzzy logic analysis to predict permeability. First, this paper analyzes that the main control factors of permeability in the region is the homogenization by diagenetic with statistical multivariate variance analysis SNK (Student-Newman-Ke

关键词： Permeability estimation Low porosity-permeability reservoir Feature engineering Fuzzy logic data-driven analytics

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