Managing complexity of assembly with modularity: a cost and benefit analysis

Abstract

Industry 4.0 is characterized by a modular structure of the production process that consists of cyber-physical systems. These cyber-physical systems provide interoperability, information transparency, and decentralization of decisions. The modular structure, according to Industry 4.0 principle, creates intelligent networks of machines, work pieces, and systems that can predict failures, self-organize themselves, and react to unexpected events. In this paper, we consider the complexity of assembly processes and propose modular structures for assembly processes based on probabilistic formulation. Despite the reliability and precisions that the use of cyber-physical systems such as robotics and automation in assembly processes have introduced, and because of the increasing complexity, there is a need for probabilistic process characterization models for smart assembly planning purposes. First, a new framework for assembly complexity measurement based on processes’ probabilistic and Markovian characters is suggested. Then, two effects of modularization, namely stabilization of components by boundary creation and application modular interfaces, are analyzed. For each case, a probabilistic formulation for assembly formation and analysis is presented. The effect of task sequencing and component modularization on assembly time and cost is considered simultaneously by the Bayesian formulation of the assembly problem. Several heuristics are derived from simulation examples, and the modularization cost is studied through utilization of design structure matrix.