A non-conformance rate prediction method supported by machine learning and ontology in reducing underproduction cost and overproduction cost

Ji, Bongjun; Ameri, Farhad; Cho, Hyunbo

doi:10.1080/00207543.2021.1933237

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…Barron and Hermel (2017) considered different decision policies under shortage, while Najid et al (2011) investigated an integrated production and maintenance planning model with time windows and shortage costs. Ji et al (2021) developed machine learning techniques for reducing underproduction costs and overproduction costs. Grillo et al (2016) supports the importance of shortage planning.…”

Section: Discussionmentioning

confidence: 99%

Equilibrium Supply-Demand Allocation in a Single-Commodity Network

Krylatov¹,

Raevskaya²,

Li³

2022

Contributions to Game Theory and Management

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This paper is devoted to the recent findings in the analytical research of supply-demand allocation in a single-commodity network with distant (in space) suppliers and consumers. The allocation problem is formulated as an equilibrium flow assignment problem with affine functions of demand, supply, and logistic costs in a network represented by a digraph with suppliers and consumers located in nodes. We offer a brief overview of supply-demand relocation patterns obtained for elastic, shortage, and overproduction cases. Such kinds of results seem valuable since they allow one to develop different competitive distribution models to facilitate the decisionmaking of supply chain managers. In particular, supply chain managers can use available patterns to design decision-making strategies that mitigate risks concerning disruption or ripple effects.

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Section: Discussionmentioning

confidence: 99%

Equilibrium Supply-Demand Allocation in a Single-Commodity Network

Krylatov¹,

Raevskaya²,

Li³

2022

Contributions to Game Theory and Management

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“…The application of RCA machine learning to interrogate quality problem solving has been suggested to support schemes, but may introduce new uncertainties, e.g. understanding data outputs from such machine learning [59].…”

Section: B Practical Implicationsmentioning

confidence: 99%

Simplifying Complexity? On Quality Decision-Making and Nonconformance Outcomes of Megaprojects

Ford,

Gosling,

Naim

et al. 2024

IEEE Trans. Eng. Manage.

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Research in quality management has provided much insight into the challenges construction projects face with non-conformance and rework. However, most rework research has focused on most prevalent and costly avenues that desire improvement, rather than the capabilities of quality problem solving and appropriate decision-making in uncertain situations. Quantitative method is adopted whereby 1205 non-conformance report (NCRs) from a £1.45bn highways megaproject are analysed using a cognitive decision-making framework to determine real-time and retrospective action pathways employed to rectify non-conformance problems. We identify that the interventions to address quality problems are typically premature and do not fully consider the wider picture of nonconformance failure. The findings revealed many cases of oversimplification, resulting in premature quality problem solving outcomes. This causes NCRs to be ineffectively addressed and does not eradicate future occurrence. We show that with the assistance of a cognitive decision-making framework and a categorisation ruling, projects can improve decision-making by determining when to switch intervention pathways to ensure the correct outcomes. There cannot be a one-size-fits-all approach to quality problem solving. Project teams must be more aware of the differing complexities with NCRs that require different courses of action. We close with the limitations of the paper and suggest avenues for further research.

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“…Ontology has also been shown to assist in feature and model selection, causal inference, 211 and automated reasoning 208 in research related to other industries. [211][212][213][214] So far, in the biomedical field and pharmaceutical industry, several prominent ontologies have emerged that cover various aspects of product development and manufacturing. For instance, Ontology for biomedical investigations (OBI) can be used to log biological and clinical investigations.…”

Section: Data Contextualization and Metadata Managementmentioning

confidence: 99%

“…Hence, the utilization of ontologies enables knowledge to be standardized, transferable and both machine and human usable. Ontology has also been shown to assist in feature and model selection, causal inference, 211 and automated reasoning 208 in research related to other industries 211–214 …”

Section: Data and Knowledge Management In Continuous Biomanufacturingmentioning

confidence: 99%

Current challenges and recent advances on the path towards continuous biomanufacturing

Drobnjakovic

Hart²,

Kulvatunyou

et al. 2023

Biotechnology Progress

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Continuous biopharmaceutical manufacturing is currently a field of intense research due to its potential to make the entire production process more optimal for the modern, ever‐evolving biopharmaceutical market. Compared to traditional batch manufacturing, continuous bioprocessing is more efficient, adjustable, and sustainable and has reduced capital costs. However, despite its clear advantages, continuous bioprocessing is yet to be widely adopted in commercial manufacturing. This article provides an overview of the technological roadblocks for extensive adoptions and points out the recent advances that could help overcome them. In total, three key areas for improvement are identified: Quality by Design (QbD) implementation, integration of upstream and downstream technologies, and data and knowledge management. First, the challenges to QbD implementation are explored. Specifically, process control, process analytical technology (PAT), critical process parameter (CPP) identification, and mathematical models for bioprocess control and design are recognized as crucial for successful QbD realizations. Next, the difficulties of end‐to‐end process integration are examined, with a particular emphasis on downstream processing. Finally, the problem of data and knowledge management and its potential solutions are outlined where ontologies and data standards are pointed out as key drivers of progress.

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A non-conformance rate prediction method supported by machine learning and ontology in reducing underproduction cost and overproduction cost

Cited by 8 publications

References 35 publications

Equilibrium Supply-Demand Allocation in a Single-Commodity Network

Equilibrium Supply-Demand Allocation in a Single-Commodity Network

Simplifying Complexity? On Quality Decision-Making and Nonconformance Outcomes of Megaprojects

Current challenges and recent advances on the path towards continuous biomanufacturing

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