Artificial Intelligence for Production Management and Control Towards Mass Personalization of Global Networks

Companies operating in global production networks should handle the complex, uncertain, and volatile environment, making them more vulnerable to disruptions. The Mass Personalization (MPe) paradigm is already a reality and has increased the involvement of end-users in the product lifecycle. It requires responsive and flexible manufacturing operations to produce cost-effective individualized products in dynamic batch sizes at scale taking into consideration the unique preferences of each customer. Therefore, modern manufacturing and production systems and networks must be capable of responding quickly to (i) the alteration of demand and conditions in the supply chain, and (ii) the volatile customer demands. By extension, in the context of MPe, manufacturing and production systems must be capable of self-optimizing manufacturing operations in order to achieve flexible, autonomous, and error-tolerant production. On the other hand, Intelligent Manufacturing (IM) is a key concept that has evolved during the last five years and is, currently, gaining momentum thanks to the potential offered by the Industry 4.0 vision. Thus, the ability of a company to setup an effective data gathering and processing strategy, orchestrating data flows, and then draw meaningful and actionable insights from them, is critical to MPe success. As such, the technological drivers of MPe are the Big Data Sets and Artificial Intelligence (AI) among other pillar technologies of Industry 4.0. The scope of this essay is to identify and highlight the state-of-the-art on how the integration of AI and Big Data technologies and techniques will contribute towards the efficient personalization of each customer’s experience under the framework of Industry 4.0 and beyond.