Automated Lead Time Estimation for Anomaly Detection using a Machine Learning Algorithm

1
Shivakumar Nagarajan
Shivakumar Nagarajan
2
Divya T
Divya T
3
Prasanna S
Prasanna S
1 Anna University

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The increasing complexity of modern business operations demands efficient and accurate lead time estimation to enhance decision-making processes. This study proposes a novel approach to automate lead time estimation using machine learning algorithms. Traditional lead time estimation methods often rely on manual calculations and historical averages, leading to inaccuracies and inefficiencies. In contrast, machine learning algorithms leverage historical data, contextual factors, and patterns to predict lead times dynamically. The automation of lead time estimation not only improves accuracy but also facilitates real-time decision-making. The system continuously learns from new data, adapting its predictions to changing business environments. A user-friendly interface is developed to allow easy input of relevant data and to visualize the lead time prediction.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

Shivakumar Nagarajan. 2026. \u201cAutomated Lead Time Estimation for Anomaly Detection using a Machine Learning Algorithm\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 24 (GJCST Volume 24 Issue D1): .

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Automated machine learning algorithm for anomaly detection in computer science research.
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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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August 28, 2024

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English

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The increasing complexity of modern business operations demands efficient and accurate lead time estimation to enhance decision-making processes. This study proposes a novel approach to automate lead time estimation using machine learning algorithms. Traditional lead time estimation methods often rely on manual calculations and historical averages, leading to inaccuracies and inefficiencies. In contrast, machine learning algorithms leverage historical data, contextual factors, and patterns to predict lead times dynamically. The automation of lead time estimation not only improves accuracy but also facilitates real-time decision-making. The system continuously learns from new data, adapting its predictions to changing business environments. A user-friendly interface is developed to allow easy input of relevant data and to visualize the lead time prediction.

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Automated Lead Time Estimation for Anomaly Detection using a Machine Learning Algorithm

Shivakumar Nagarajan
Shivakumar Nagarajan Anna University
Divya T
Divya T
Prasanna S
Prasanna S

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