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International Journal of Enterprise Modelling
Published by International Enterprise Integration Association
ISSN : 16939220     EISSN : 29878713     DOI : https://doi.org/10.35335/emod
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation
The International Journal of Enterprise Modelling serves as a venue for anyone interested in business and management modelling. It investigates the conceptual forerunners and theoretical underpinnings that lead to research modelling procedures that inform research and practice.
Arjuna Subject : Matematika - Matematika Terapan
Articles 5 Documents
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Search results for , issue "Vol. 13 No. 2 (2019): May: Enterprise Modelling" : 5 Documents clear
Exploring the Impact of Artificial Intelligence on Enterprise Modeling Denis Denunciar Otros; Vistos Otras
International Journal of Enterprise Modelling Vol. 13 No. 2 (2019): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v13i2.8

Abstract

This research investigates the impact of artificial intelligence (AI) on enterprise modeling, with a specific focus on supply chain network design. The objective is to explore how AI techniques can enhance decision-making, improve efficiency, and drive cost reduction in enterprise modeling processes. The research utilizes case examples and numerical simulations to demonstrate the benefits and implications of incorporating AI techniques in enterprise modeling. The findings reveal that AI-enabled approaches in supply chain network design lead to cost reduction, improved customer service levels, accuracy improvement, efficiency gains, enhanced decision-making, and collaboration facilitation. The research highlights the importance of data availability, ethical considerations, organizational readiness, and interoperability in realizing the full potential of AI-enabled enterprise modeling. However, the research acknowledges the limitations, such as simplified examples and the specific context of supply chain network design. Future research is needed to validate the findings in diverse industry settings and address challenges related to data availability, ethical considerations, organizational readiness, and interoperability. This research contributes to the understanding of the positive impact of AI on enterprise modeling, providing valuable insights for organizations seeking to leverage AI techniques to optimize their decision-making processes and drive operational improvements.
Enhancing Decision-Making in Enterprise Modeling: A Comparative Analysis of Artificial Intelligence Techniques in Supply Chain Network Design Fianarantsoa Rakotoarisoa; Tanana Matsiatra
International Journal of Enterprise Modelling Vol. 13 No. 2 (2019): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v13i2.9

Abstract

This research focuses on enhancing decision-making in enterprise modeling through a comparative analysis of artificial intelligence (AI) techniques in supply chain network design. The objective is to provide decision-makers with insights into the application, performance, and implications of different AI techniques in this domain. The research conducts a comprehensive comparative analysis of AI techniques, including machine learning algorithms, optimization algorithms, and expert systems. Performance evaluation metrics such as computational efficiency, accuracy, scalability, interpretability, and adaptability are established to assess the performance of these techniques. Real-world case studies are also presented to showcase the practical implementation and impact of AI techniques in supply chain network design. The findings contribute to informed decision-making by guiding decision-makers in selecting and implementing appropriate AI techniques. The research also identifies future research directions, including hybrid approaches, dynamic environment considerations, and the integration of AI with big data and the Internet of Things. Overall, this research provides valuable insights and guidelines for leveraging AI in supply chain network design, enabling decision-makers to optimize facility location, transportation routing, and inventory management, leading to improved operational efficiency and customer satisfaction.
Optimizing Supply Chain Network Design through Hybrid Artificial Intelligence Approaches: A Comparative Study Fernández Turay; Azzolini Matthew
International Journal of Enterprise Modelling Vol. 13 No. 2 (2019): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v13i2.10

Abstract

Supply chain network design plays a critical role in achieving operational efficiency and cost optimization. This research focuses on optimizing supply chain network design through the use of hybrid artificial intelligence (AI) approaches and presents a comparative study of different methods. The objective is to evaluate the effectiveness of combining machine learning, optimization algorithms, and expert systems in enhancing the design of supply chain networks. The research begins by formulating a mathematical model that captures the key decision variables, objectives, and constraints associated with supply chain network design. The model aims to minimize overall costs while considering factors such as facility selection, transportation routing, and product flows. To evaluate the performance of the hybrid AI approaches, various methods are compared, including genetic algorithms, particle swarm optimization, and reinforcement learning. Through extensive testing and analysis, the comparative study assesses the strengths and weaknesses of each approach in terms of solution quality, computational efficiency, and robustness. The study also considers the scalability of the methods to handle large-scale supply chain networks. The findings of the research demonstrate the benefits of integrating hybrid AI approaches in supply chain network design optimization. The hybrid AI methods outperform traditional optimization techniques, providing more accurate and efficient solutions. The comparative analysis highlights the specific scenarios in which each method excels, aiding decision-makers in selecting the most appropriate approach for their supply chain network design challenges. The research identifies opportunities for further enhancements and advancements in the field. Future research directions may include incorporating real-time data, considering uncertainty and risk factors, and extending the analysis to industry-specific applications. This research contributes to the optimization of supply chain network design by leveraging the power of hybrid AI approaches. The findings provide valuable insights for supply chain managers and decision-makers, enabling them to make informed choices that enhance operational efficiency, reduce costs, and improve overall supply chain performance.
Enhancing Supply Chain Network Design: Integration of Hybrid Artificial Intelligence and Real-Time Data for Dynamic Optimization Ahderom Rengga
International Journal of Enterprise Modelling Vol. 13 No. 2 (2019): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v13i2.11

Abstract

This research focuses on enhancing supply chain network design through the integration of hybrid artificial intelligence (AI) and real-time data for dynamic optimization. The objective is to develop a mathematical formulation and model that minimize costs while meeting demand and capacity requirements. The research proposes the integration of hybrid AI techniques, such as machine learning and optimization algorithms, with real-time data to enable data-driven decision-making and adaptability to changing market conditions. The implementation involves collecting and processing real-time data from various sources and utilizing AI algorithms to optimize facility locations, transportation routes, and inventory allocation. A numerical example demonstrates the application of the model, showcasing cost savings and improved customer service. However, the research has limitations, including simplified assumptions, data quality concerns, scalability challenges, and the limited scope of considered factors. Despite these limitations, the findings highlight the potential benefits of integrating hybrid AI and real-time data in supply chain network design, offering insights for practitioners and future research directions.
Optimizing Sustainable Supply Chain Network Design using Hybrid AI and Real-Time Data Mocombe Celucien; Eécoles Notre
International Journal of Enterprise Modelling Vol. 13 No. 2 (2019): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (0.474 KB) | DOI: 10.35335/emod.v13i2.12

Abstract

This research focuses on optimizing sustainable supply chain network design by leveraging hybrid AI techniques and real-time data integration. The objective is to minimize costs while considering carbon emissions, transportation modes, supplier selection, and inventory allocation. The research proposes a mathematical formulation model that incorporates these variables and constraints, enabling companies to make data-driven decisions and enhance their sustainability performance. Real-time data from various sources, including suppliers, transportation providers, and inventory systems, is collected and processed using AI techniques. The model is then solved using advanced optimization algorithms to determine the optimal supply chain network design. Sensitivity analysis is conducted to assess the robustness of the model and evaluate the impact of changing parameters and constraints. A case example illustrates the practical application of the research findings, highlighting the benefits of the hybrid AI and real-time data approach in achieving cost efficiency and sustainability goals. The research contributes to the field of supply chain management by providing insights into the integration of real-time data, AI techniques, and sustainability considerations in supply chain network design. It also identifies limitations and suggests areas for future research to enhance the applicability and scalability of the proposed approach.

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