Production Flow Analysis in the Era of Industry 4.0 : How Digital Technologies can Support Decision-Making in the Factory of the Future

Abstract

In the context of Industry 4.0, manufacturing companies have been increasingly adopting digital technologies such as Internet of Things, data analytics and cyber-physical systems to seize opportunities for productivity improvements. At the same time, established manufacturing philosophies such as Group Technology have assisted companies in managing the complexity of production processes for decades. To support manufacturing management with more informed decision-making tools, the literature has been proposing new approaches that exploit the potential of digital technologies to enhance the effectiveness of traditional manufacturing techniques. This study focuses on Production Flow Analysis (PFA) as an established approach for Group Technology. Although the existing literature has been focusing on Artificial Intelligence (AI) based approaches to plan the change to Group Technology for decades, few studies rely on production data directly extracted from the factory floor. This is partly due to the fact that technologies such as sensors and data analytics have been increasingly adopted in recent years, and this has led to an increasing amount of data that can be exploited to develop models that can support decisions. In particular, in the context of Industry 4.0, process mining has gained increasing interest, as it provides a data-driven methodology to capture real production processes. The goal of this study is to explore how PFA has evolved in the last decade thanks to the adoption of digital technologies, and to investigate potential synergies between PFA and process mining. This study uses a structured literature review to map advances in industrial applications of PFA in relation to digital technologies, as well as process mining applications in manufacturing to present a future research agenda. This provides manufacturing managers with a structured overview of existing industrial applications and the digital technologies adopted to enhance decision-making tools.