A big data based architecture for collaborative networks: Supply chains mixed-network

Abstract

Nowadays, the world knows a high-speed development and evolution of technologies, vulnerable economic environments, market changes, and personalised consumer trends. The issue and challenge related to enterprises networks design are more and more critical. These networks are often designed for short terms since their strategies must be competitive and better adapted to the environment, social and economical changes. As a solution, to design a flexible and robust network, it is necessary to deal with the trade-off between conflicting qualitative and quantitative criteria such as cost, quality, delivery time, and competition, etc. To this end, using Big Data (BD) as emerging technology will enhance the real performances of these kinds of networks. Moreover, even if the literature is rich with BD models and frameworks developed for a single supply chain network (SCN), there is a real need to scale and extend these BD models to networked supply chains (NSCs). To do so, this paper proposes a BD architecture to drive a mixed-network of SCs that collaborate in serial and parallel fashions. The collaboration is set up by sharing their resources, capabilities, competencies, and information to imitate a unique organisation. The objective is to increase internal value to their shareholders (where value is seen as wealth) and deliver better external value to the end-customer (where value represents customer satisfaction). Within a mixed-network of SCs, both values are formally calculated considering both serial and parallel networks configurations. Besides, some performance factors of the proposed BD architecture such as security, flexibility, robustness and resilience are discussed.