Design-Based Supply Chain Operations Research Model: Fostering Resilience And Sustainability In Modern Supply Chains
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915594710810624 |
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| author | Anumula, Sathish Krishna |
| author_facet | Anumula, Sathish Krishna |
| contents | In the rapidly evolving landscape of global supply chains, where digital disruptions and sustainability imperatives converge, traditional operational frameworks often struggle to adapt. This paper introduces the Design-Based Supply Chain Operations Research Model, a novel extension of the Design SCOR framework, which embeds operational research techniques to enhance decision-making, resilience, and environmental stewardship. Building on the foundational processes of DSCOR such as Design, Orchestrate, Plan, Order, Source, Transform, Fulfil, and Return DSCORM incorporates predictive analytics, simulation modelling, and optimization algorithms to address contemporary challenges like supply chain volatility and ESG (environmental, social, governance) compliance. Through a comprehensive literature synthesis and methodological approach involving case-based simulations, we explore DSCORM's hierarchical structure, performance metrics, implementation strategies, and digital modernization pathways. Results from simulated scenarios indicate potential efficiency gains of 15to25 percent, reduced carbon footprints by up to 20 percent, and improved agility in dynamic markets. Discussions delve into practical implications for industries like manufacturing and logistics, highlighting barriers such as data integration hurdles and the need for skilled workforces. By humanizing supply chain management emphasizing collaborative, adaptive strategies over rigid automation DSCORM positions itself as a blueprint for sustainable growth. Conclusions underscore its role in advancing digital transformation, with recommendations for future empirical validations in real-world settings |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_01878 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Design-Based Supply Chain Operations Research Model: Fostering Resilience And Sustainability In Modern Supply Chains Anumula, Sathish Krishna Other Computer Science In the rapidly evolving landscape of global supply chains, where digital disruptions and sustainability imperatives converge, traditional operational frameworks often struggle to adapt. This paper introduces the Design-Based Supply Chain Operations Research Model, a novel extension of the Design SCOR framework, which embeds operational research techniques to enhance decision-making, resilience, and environmental stewardship. Building on the foundational processes of DSCOR such as Design, Orchestrate, Plan, Order, Source, Transform, Fulfil, and Return DSCORM incorporates predictive analytics, simulation modelling, and optimization algorithms to address contemporary challenges like supply chain volatility and ESG (environmental, social, governance) compliance. Through a comprehensive literature synthesis and methodological approach involving case-based simulations, we explore DSCORM's hierarchical structure, performance metrics, implementation strategies, and digital modernization pathways. Results from simulated scenarios indicate potential efficiency gains of 15to25 percent, reduced carbon footprints by up to 20 percent, and improved agility in dynamic markets. Discussions delve into practical implications for industries like manufacturing and logistics, highlighting barriers such as data integration hurdles and the need for skilled workforces. By humanizing supply chain management emphasizing collaborative, adaptive strategies over rigid automation DSCORM positions itself as a blueprint for sustainable growth. Conclusions underscore its role in advancing digital transformation, with recommendations for future empirical validations in real-world settings |
| title | Design-Based Supply Chain Operations Research Model: Fostering Resilience And Sustainability In Modern Supply Chains |
| topic | Other Computer Science |
| url | https://arxiv.org/abs/2511.01878 |