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Analysis and optimization based on reusable knowledge base of process performance models
Open accessAbstract
In this paper, we propose an architectural design and software framework for fast development of descriptive, diagnostic, predictive, and prescriptive analytics solutions for dynamic production processes. The proposed architecture and framework will support the storage of modular, extensible, and reusable Knowledge Base (KB) of process performance models. The approach requires developing automated methods that can translate the high-level models in the reusable KB into low-level specialized models required by a variety of underlying analysis tools, including data manipulation, optimization, statistical learning, estimation, and simulation. We also propose an organization and key structure for the reusable KB, composed of atomic and composite process performance models and domain-specific dashboards. Furthermore, we illustrate the use of the proposed architecture and framework by prototyping a decision-support system for process engineers. The decision support system allows users to hierarchically compose and optimize dynamic production processes via a graphical user interface.
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There are several attempts for designing conceptual architectures addressing real-time data analytics with a particular focus on prescriptive analytics.
“there are several attempts for designing conceptual architectures addressing real-time data analytics with a particular focus on prescriptive analytics (Brodsky et al., 2017;Deshpande, 2019;Pospieszny, 2017;Tan, Ang, Lu, Gan, & Corral, 2016).”
From Prescriptive analytics: Literature review and research challenges (2020) · cites this paper as Brodsky et al., 2017
The Introduction proposes an architecture for analytics of dynamic production processes that explicitly includes prescriptive analytics. The Required Analysis and Optimization Capabilities section describes continuous testing of sensor data, but the paper does not clearly characterize the proposed architecture as one for real-time data analytics. This source supports one example of such an architectural attempt, not the broader claim that there are several. (LLM, conf=0.82)
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Prescriptive analytics: Literature review and research challenges
Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.04.003 · Cited by 501
“In addition, there are several attempts for designing conceptual architectures addressing real-time data analytics with a particular focus on prescriptive analytics (Brodsky et al., 2017; Deshpande, 2019; Pospieszny, 2017; Tan, Ang, Lu, Gan, & Corral, 2016).”
Metadata
- Authors
- Brodsky, A., Shao, G., Krishnamoorthy, M., Narayanan, A., Menascé, D., & Ak, R.
- Affiliations
- George Mason University (US)
- National Institute of Standards and Technology (US)
- University of Maryland, College Park (US)
- Corresponding author
- Shao, Guodong
- Venue
- The International Journal of Advanced Manufacturing Technology
- Published
- 2016-04-23
- Volume
- 88
- Issue
- 1-4
- Pages
- 337-357
- ISSN
- 0268-3768 (ISSN-L)
- 1433-3015
- PMC
- PMC6604630
- OpenAlex
- W2340852864
- Cited by
- 23
- References
- 38
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 1 · 2021: 3 · 2020: 4 · 2019: 7 · 2018: 2 · 2017: 3 · 2016: 1
- FWCI
- 7.78
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- The International Journal of Advanced Manufacturing Technology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Daewoo Shipbuilding & Marine Engineering
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Business Process Modeling and Analysis
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- knowledge base, descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics, model translation, decision support system, process engineers, optimization
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