A Platform Architecture for Industry 4.0 Driven Intelligence Amplification in Logistics

Abstract

The aim of this doctoral consortium paper is to introduce my doctoral research proposal in the field of enterprise computing. The scientific problem that I address in my research is the limited usage of real-time data, originating from Industry 4.0 (I4.0) technologies (e.g. smart IoT devices and sensors), by Small-and Medium sized Enterprises (SMEs) in the logistics industry. I argue that the development of an industry platform for real-time data streaming and analytics would allow SMEs to benefit from such data and help them streamline their operational processes and overall performance. The main contribution of my research is a reference architecture for such a platform, geared for the needs of SMEs, and incorporating: 1) a logistics canonical data model to collect and harmonize I4.0 data, 2) an automatic schema matcher to map SME data to the logistics canonical data model, 3) autonomous data mining agents, 4) an adoption strategy based on the concept of intelligence amplification and 5) key performance indicators to measure adoption effects on operational and decisional performance.