Parametrization of a demand-driven operating model using reinforcement learning

Duhem, Louis; Ali, Maha Ben; Martin, Guillaume

doi:10.1016/j.compind.2023.103874

Cited by 14 publications

(5 citation statements)

References 22 publications

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“…Also exploring kanban, Puche et al (2019) use agent-based techniques to compare kanban with drum-buffer-rope in a supply chain resilience perspective. Suggesting that lean approaches are no longer sufficient given today’s complex, volatile planning environments, Duhem et al (2023) proposed ML (specifically reinforcement learning) as a means of optimizing a demand-driven production system. Similarly, Xia et al (2022) highlighted that ML and industrial big data can enable manufacturers to dynamically adapt to changing environments and respond quickly to market changes.…”

Section: Resultsmentioning

confidence: 99%

“…Results Ahmed et al, 2023;Antosz et al, 2020;Herwan et al, 2023;Hosseinzadeh et al, 2023;Küfner et al, 2021a;Mjimer et al, 2023;Shahin et al, 2023c;Shakir and Iqbal, 2018) Smart production planning and control(Bouzekri et al, 2022;Castej on-Limas et al, 2022;Duhem et al, 2023;Fanti et al, 2022;Herwan et al, 2023;ITO et al, 2020; Jan et al, 2023; Javaid et al, 2022; Khadiri et al, 2022; Küfner et al, 2021b; Kutschenreiter-Praszkiewicz, 2018; Paraschos et al, 2023; Puche et al, 2019; Rossit et al, 2019; Sordan et al, 2022; Tripathi et al, 2022b, 2022c; Ulhe et al, 2023; Vickranth et al, 2019; Villalba-Díez et al, 2020; Xia et al, 2022; Xin et al, 2015) Quality control (Bhatia et al, 2023; Duc and Bilik, 2022; Kumar et al, 2021; Park et al, 2020; Perera et al, 2021; Pongboonchai-Empl et al, 2023; Shahin et al, 2023b; Yadav et al, 2020) Towards Industry 5.0: Sustainability, Resilience, Human-centricity…”

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confidence: 99%

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Artificial intelligence in lean manufacturing: digitalization with a human touch?

Powell

2024

IJLSS

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Section: Resultsmentioning

confidence: 99%

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confidence: 99%

Artificial intelligence in lean manufacturing: digitalization with a human touch?

Powell

2024

IJLSS

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“…These algorithms pick up new skills via interaction with their surroundings and feedback in the form of rewards or penalties. Reinforcement learning algorithms might be used to weather prediction to improve resource allocation, evacuation planning, or other decision-making procedures pertaining to catastrophe preparation and response (Duhem et al, 2023). There are other specific algorithms and approaches that may be utilised for weather prediction in addition to these general types of machine learning algorithms.…”

Section: Types Of Machine Learning Algorithms For Weather Predictionmentioning

confidence: 99%

Leveraging machine learning algorithms for improved disaster preparedness and response through accurate weather pattern and natural disaster prediction

Jain,

Dhupper,

Shrivastava

et al. 2023

Front. Environ. Sci.

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Globally, communities and governments face growing challenges from an increase in natural disasters and worsening weather extremes. Precision in disaster preparation is crucial in responding to these issues. The revolutionary influence that machine learning algorithms have in strengthening catastrophe preparation and response systems is thoroughly explored in this paper. Beyond a basic summary, the findings of our study are striking and demonstrate the sophisticated powers of machine learning in forecasting a variety of weather patterns and anticipating a range of natural catastrophes, including heat waves, droughts, floods, hurricanes, and more. We get practical insights into the complexities of machine learning applications, which support the enhanced effectiveness of predictive models in disaster preparedness. The paper not only explains the theoretical foundations but also presents practical proof of the significant benefits that machine learning algorithms provide. As a result, our results open the door for governments, businesses, and people to make wise decisions. These accurate predictions of natural catastrophes and emerging weather patterns may be used to implement pre-emptive actions, eventually saving lives and reducing the severity of the damage.

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“…Employing data analytics and Python's Machine Learning (ML) libraries facilitates demand prognostication [30]. This dynamic procedure converges historical sales data, seasonal patterns, and other relevant variables to yield forecasted demand-a fundamental input for the MRP calculations [31]. However, demand forecasting presents some complexity, as some machine learning algorithms are diverse and more complex than others, requiring more data to be trained.…”

Section: Demand Predictionmentioning

confidence: 99%

Exploring Applications and Practical Examples by Streamlining Material Requirements Planning (MRP) with Python

Reis

2023

Logistics

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Background: Material Requirements Planning (MRP) is critical in Supply Chain Management (SCM), facilitating effective inventory management and meeting production demands in the manufacturing sector. Despite the potential benefits of automating the MRP tasks to meet the demand for expedited and efficient management, the field appears to be lagging behind in harnessing the advancements offered by Artificial Intelligence (AI) and sophisticated programming languages. Consequently, this study aims to address this gap by exploring the applications of Python in simplifying the MRP processes. Methods: This article offers a twofold approach: firstly, it conducts research to uncover the potential applications of the Python code in streamlining the MRP operations, and the practical examples serve as evidence of Python’s efficacy in simplifying the MRP tasks; secondly, this article introduces a conceptual framework that showcases the Python ecosystem, highlighting libraries and structures that enable efficient data manipulation, analysis, and optimization techniques. Results: This study presents a versatile framework that integrates a variety of Python tools, including but not limited to Pandas, Matplotlib, and Plotly, to streamline and actualize an 8-step MRP process. Additionally, it offers preliminary insights into the integration of the Python-based MRP solution (MRP.py) with Enterprise Resource Planning (ERP) systems. Conclusions: While the article focuses on demonstrating the practicality of Python in MRP, future endeavors will entail empirically integrating MRP.py with the ERP systems in small- and medium-sized companies. This integration will establish real-time data synchronization between the Python and ERP systems, leading to accurate MRP calculations and enhanced decision-making processes.

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Parametrization of a demand-driven operating model using reinforcement learning

Cited by 14 publications

References 22 publications

Artificial intelligence in lean manufacturing: digitalization with a human touch?

Artificial intelligence in lean manufacturing: digitalization with a human touch?

Leveraging machine learning algorithms for improved disaster preparedness and response through accurate weather pattern and natural disaster prediction

Exploring Applications and Practical Examples by Streamlining Material Requirements Planning (MRP) with Python

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