Self-organizing manufacturing network: A paradigm towards smart manufacturing in mass personalization

Abstract

Mass personalization is becoming a reality. It requires responsive and flexible manufacturing operations for producing individualized products in dynamic batch sizes at scale in a cost-effective way. Therefore, manufacturing systems should timely respond to meet changing demands and conditions in the factory, in the supply network, and in customer needs. However, current manufacturing systems fail to adapt to dynamic production environments via changing system configurations and production plans while maintaining stable production performance. Therefore, a manufacturing system is required to be capable of self-optimizing manufacturing operations to achieve flexible, autonomous, and error-tolerant production in the mass personalization context. In this article, we systematically reviewed the literature on Self-Organizing Manufacturing Systems (SOMS) and proposed a complete concept of Self-Organizing Manufacturing Network (SOMN) as the next-generation manufacturing automation technologies for achieving mass personalization. Our review started by tracing the roots, origin, and state-of-the-art research of SOMS and concluded that the existing SOMS work could not achieve the mass personalization goal. As a focus of this review paper, we systematically discussed self-organizing manufacturing’s functional requirements to achieve mass personalization and proposed Self-Organizing Manufacturing Network. The concept, functions and essential technological system components (i.e., system modeling and control architecture, peer communications, and adaptive manufacturing control) are discussed by reviewing existing work and highlighting transferrable knowledge from other disciplines. Future research challenges are also discussed. © 2021 The Society of Manufacturing Engineers

Publication
Journal of Manufacturing Systems
Zhaojun Qin
Zhaojun Qin
PhD Student

My research interests include smart manufacturing, self-organizing manufacturing system and reinforcement learning.

Yuqian Lu
Yuqian Lu
Principle Investigator / Senior Lecturer

My research interests include smart manufacturing systems, industrial AI and robotics.