A framework for data-driven computational experiments of inter-organizational collaborations in supply chain networks

Internet + ecosystems, big data applications, customers' specific demands, and internal and external values integration of enterprises pose new challenges to inter-organizational collaborations in supply chain networks. To confront these challenges, this paper integrates computational experiment and data analysis, and proposes a methodology for data-driven computational experiments for inter-organizational collaborations in supply chain networks. It explores a paradigm shift to the development of data-driven computational experiments that supports decision making in the domain of inter-organizational collaborations. A basic principle for integrated solutions generation in the parallel worlds of virtual reality interaction is studied and the corresponding key issues in the paradigm are analyzed. To support the paradigm and solve key issues, a six-layered framework with four viewpoints for data-driven computational experiments is proposed. This framework systematically presents conceptual and technical solutions for data-driven computational experiments and decision support in the domain of inter-organizational collaborations in supply chain networks. The effectiveness of the framework is verified by theoretical analysis and a case study.