The Impact of Machine Learning (ML) Optimization of supply chain management domain

M V S V, Kiranmai; Panduro-Ramirez, Jeidy; Dhyani, Anubhuti; Reddy, G. Swamy; Al-Taee, Mustafa; Alazzam, Malik Bader

doi:10.1109/icacite57410.2023.10182817

2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) 2023

DOI: 10.1109/icacite57410.2023.10182817

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The Impact of Machine Learning (ML) Optimization of supply chain management domain

Kiranmai M V S V,

Jeidy Panduro-Ramirez,

Anubhuti Dhyani

et al.

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2024

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Cited by 3 publications

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Hybrid intelligence – systematic approach and framework to determine the level of Human-AI collaboration for production management use cases

Sauer,

Burggräf

2024

Prod. Eng. Res. Devel.

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Artificial intelligence (AI) and machine learning (ML) have emerged as transformative technologies in production management, enhancing decision-making and operational efficiency across a range of use cases. The challenge of determining the optimal level of Human-AI collaboration in decision-making processes persists for many organizations, despite the potential benefits of such integration. However, the current systematic approaches frequently lack a structured approach to determine the level of Human-AI collaboration in production management use cases. This results in inconsistent applications and suboptimal outcomes. This study presents a first and superior systematic approach for the systematic evaluation, development and implementation of AI in production management and introduces a structured framework that can be employed to assess and determine the optimal level of Human-AI collaboration for a range of production use cases. The framework classifies use cases based on critical factors such as data availability, process variability, error susceptibility, and decision complexity. This classification assists managers in calibrating the balance between automation and human intervention. The application of this framework could enhance the efficacy, productivity, and accuracy of Human-AI collaboration in production management. Furthermore, the systematic approach incorporates continuous evaluation and adaptation, ensuring long-term success in dynamic production environments. These findings provide a practical tool for managers to strategically implement AI, improving decision-making processes and operational outcomes. The proposed framework offers immediate opportunities to enhance production management through a structured, scalable, and human-centered approach to AI integration.

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Hybrid intelligence – systematic approach and framework to determine the level of Human-AI collaboration for production management use cases

Sauer,

Burggräf

2024

Prod. Eng. Res. Devel.

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Improving efficiency and sustainability via supply chain optimization through CNNs and BiLSTM

Dalal,

Lilhore,

Simaiya

et al. 2024

Technological Forecasting and Social Change

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Optimizing e-Commerce Supply Chains With Categorical Boosting: A Predictive Modeling Framework

Sayyad,

Attarde,

Saadouli

2024

IEEE Access

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The Impact of Machine Learning (ML) Optimization of supply chain management domain

Cited by 3 publications

References 21 publications

Hybrid intelligence – systematic approach and framework to determine the level of Human-AI collaboration for production management use cases

Hybrid intelligence – systematic approach and framework to determine the level of Human-AI collaboration for production management use cases

Improving efficiency and sustainability via supply chain optimization through CNNs and BiLSTM

Optimizing e-Commerce Supply Chains With Categorical Boosting: A Predictive Modeling Framework

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