Big data analytics-based approach for robust, flexible and sustainable collaborative networked enterprises

Networked enterprises (NEs) in the current business are constantly under pressure from stakeholders and government restrictions to encourage ethical and transparent behavior in using natural resources, and their impacts on nearby and global ecosystems, people, and communities. In addition, NEs face vulnerable economical challenges including, market changes, personalized consumer trends, as well as, environmental and social restrictions. In this context, this paper addresses the problem of sustainable NEs vulnerabilities. To do so, a big data analytics-based approach is developed to drive sustainable NEs flexibility and robustness. More specifically, flexibility refers to the network's ability to respond quickly to changes and risks. While robustness concerns the development of optimum and long-term strategies enabling the network to cope with severe environmental risks and economical costs. Moreover, even if the literature is rich with Big Data models and frameworks developed for sustainable enterprises, there is a real need to scale and extend existing models to cover all sustainability pillars (i.e., social, environmental, and economical) and sustainable value creation (SVC). Accordingly, flexibility and robustness coupling with big data analytics (BDA) levels (i.e. descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics) will enable NEs to grow sustainability in order to create sustainable value. Finally, to demonstrate the applicability of the developed approach, the corporate environmental impact (CEI) database is used to evaluate the sustainable development goals (SDGs) of NEs. The obtained numerical results show the efficiency of our approach.