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June 2023 (published: 31.05.2023)
Number 2(53)
Home > Issue > A systematic review of modeling approaches in supply chain management:
past, present, and future perspectives
Rogulin R.S.
Supply chain management plays a vital role in modern business operations, encompassing the coordination and optimization of activities across multiple entities. Effective modeling of the supply chain problem is crucial for making informed decisions and improving overall supply chain performance. This systematic review aims to analyze and evaluate the current and historical approaches used in modeling the supply chain management problem. The review categorizes selected articles based on the modeling techniques employed, such as mathematical optimization, simulation, network analysis, and machine learning. It analyzes and summarizes the findings of each modeling approach, highlighting their strengths, limitations, and applicability to different SCM problem domains. Furthermore, the review identifies trends, common themes, and gaps in the literature, providing insights into the evolution of modeling techniques over time. It also discusses emerging trends, novel methodologies, and innovative approaches identified during the review. The implications of the findings for supply chain practitioners, researchers, and policymakers are discussed, along with potential future directions for modeling the SCM problem. The review underscores the importance of integrated approaches, dynamic decision-making, behavioral considerations, sustainability and resilience modeling, advanced data analytics and AI techniques, industry-specific modeling, and risk management strategies. The outcomes of this systematic review contribute to the understanding of past and future approaches in modeling the SCM problem, guiding future research efforts and supporting evidence-based decision-making in supply chain management.
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Keywords: Supply chain management, modeling approaches, systematic review, mathematical optimization, simulation, network analysis, machine learning, trends, implications, future directions.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
UDC 338.001.36
A systematic review of modeling approaches in supply chain management:
past, present, and future perspectives
Supply chain management plays a vital role in modern business operations, encompassing the coordination and optimization of activities across multiple entities. Effective modeling of the supply chain problem is crucial for making informed decisions and improving overall supply chain performance. This systematic review aims to analyze and evaluate the current and historical approaches used in modeling the supply chain management problem. The review categorizes selected articles based on the modeling techniques employed, such as mathematical optimization, simulation, network analysis, and machine learning. It analyzes and summarizes the findings of each modeling approach, highlighting their strengths, limitations, and applicability to different SCM problem domains. Furthermore, the review identifies trends, common themes, and gaps in the literature, providing insights into the evolution of modeling techniques over time. It also discusses emerging trends, novel methodologies, and innovative approaches identified during the review. The implications of the findings for supply chain practitioners, researchers, and policymakers are discussed, along with potential future directions for modeling the SCM problem. The review underscores the importance of integrated approaches, dynamic decision-making, behavioral considerations, sustainability and resilience modeling, advanced data analytics and AI techniques, industry-specific modeling, and risk management strategies. The outcomes of this systematic review contribute to the understanding of past and future approaches in modeling the SCM problem, guiding future research efforts and supporting evidence-based decision-making in supply chain management.
Read the full article
Keywords: Supply chain management, modeling approaches, systematic review, mathematical optimization, simulation, network analysis, machine learning, trends, implications, future directions.
DOI 10.17586/2310-1172-2023-16-2-164-179