A data-driven workflow for evaporation performance degradation analysis: a full-scale case study in the herbal medicine manufacturing industry

Zhang, Sheng; Xie, Xinyuan; Qu, Haibin

doi:10.1007/s10845-021-01816-w

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…Clinicians can better manage patient cases by applying ML algorithms to learn the most effective treatment plans that are tailored to patient-specific information by analyzing patient data, resulting in favorable clinical outcomes and a boost in patient satisfaction. [20] Zhang et al ( 2023) presented an approach to evaporation performance degradation analysis in herbal medicine industry through data-driven workflow. Their research shows how machine intelligence is used, specifically in the identification of factors determining the functioning of evaporation and also in making the predictions of irreversible decline of performance over time.…”

Section: Iirelated Workmentioning

confidence: 99%

Data-Driven Decision Support Systems for Drug Discovery and Development: A Case Study in Pharmaceutical Management

Rohit R Dixit

2024

jes

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This paper covers the use and influence of data-driven decision support systems (DSS) on drug management, particularly in the areas of drug exploration and production. The study, which employed a mixed-methods approach, involving literature review, qualitative interviews, quantitative assessments, and case study analysis, reveals the AI, machine learning and big data analytics ability to drive drug discovery and development processes revolution in the pharmaceutical industry. The study shows that handling of DSS allows sound decision making, consequently resulting in improved efficiency, decreased expenses and better innovation throughout the drug development process. Areas like data integration, algorithm robustness, and regulation are identified as the major issues, which offers an insight into the importance of these areas of concern in the effective application of a data-driven approach in pharmaceutical management. Such contrast with the like works underlines the role of our findings as an essential link in the AI-driven healthcare innovations chain. The research provides a foundation for further improving the state of knowledge and understanding of DSS in drug management, guiding future research aiming at expanding conceptual frameworks and designing practical implementations.

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Section: Iirelated Workmentioning

confidence: 99%

Data-Driven Decision Support Systems for Drug Discovery and Development: A Case Study in Pharmaceutical Management

Rohit R Dixit

2024

jes

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Hybrid intelligence failure analysis for industry 4.0: a literature review and future prospective

Mokhtarzadeh,

Rodríguez-Echeverría,

Semanjski

et al. 2024

J Intell Manuf

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Industry 4.0 and advanced technology, such as sensors and human–machine cooperation, provide new possibilities for infusing intelligence into failure analysis. Failure analysis is the process of identifying (potential) failures and determining their causes and effects to enhance reliability and manufacturing quality. Proactive methodologies, such as failure mode and effects analysis (FMEA), and reactive methodologies, such as root cause analysis (RCA) and fault tree analysis (FTA), are used to analyze failures before and after their occurrence. This paper focused on failure analysis methodologies intelligentization literature applied to FMEA, RCA, and FTA to provide insights into expert-driven, data-driven, and hybrid intelligence failure analysis advancements. Types of data to establish an intelligence failure analysis, tools to find a failure’s causes and effects, e.g., Bayesian networks, and managerial insights are discussed. This literature review, along with the analyses within it, assists failure and quality analysts in developing effective hybrid intelligence failure analysis methodologies that leverage the strengths of both proactive and reactive methods.

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Topological Data Analysis in smart manufacturing: State of the art and future directions

Uray,

Giunti,

Kerber

et al. 2024

Journal of Manufacturing Systems

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A data-driven workflow for evaporation performance degradation analysis: a full-scale case study in the herbal medicine manufacturing industry

Cited by 4 publications

References 37 publications

Data-Driven Decision Support Systems for Drug Discovery and Development: A Case Study in Pharmaceutical Management

Data-Driven Decision Support Systems for Drug Discovery and Development: A Case Study in Pharmaceutical Management

Hybrid intelligence failure analysis for industry 4.0: a literature review and future prospective

Topological Data Analysis in smart manufacturing: State of the art and future directions

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