Cyber-physical systems for performance monitoring in production intralogistics. (April 2020)

• Record Type: Journal Article
• Authors: Mörth, O.; Emmanouilidis, Christos; Hafner, N.; Schadler, M.
• Is Part Of: Computers & industrial engineering. Volume 142(2020)
• Publication Date: 2020-04
• Subjects: Cyber-physical systems -- Intralogistics -- Internet of things -- Context awareness -- Data value chain -- Performance monitoring; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2020.106333
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Overview of performance measurement indicators in production intralogistics. A conceptual approach to IoT-enabled CPS for performance monitoring in production intralogistics. Development of demonstrator instance of the CPS concept for performance monitoring in intralogistics. Discussion on IoT, CPS, and context awareness for intralogistics. Abstract: The realization of a higher business value in manufacturing requires optimized internal logistics systems in terms of operational performance, uptime and sustainability. This paper deals with the introduction of Internet of Things (IoT) to unlock new capabilities for enhancing the performance of intralogistics. Specifically, it introduces a design perspective for IoT-driven analytics in intralogistics, within a Cyber-Physical Systems (CPS) approach. Such an approach enables the creation of data process chains linked to performance measurement for intralogistics, a prerequisite for optimizing logistics operations within production environments. An overview of key performance indicators for this domain is offered, followed by an outline of recent research on IoT and CPS and the role of context information management for IoT-enabled data process chains. The conceptual model is illustrated through a representative use case of a CPS demonstrator for performance monitoring in intralogistics. The application implements a simple data process chain, starting from the acquisition and processing of data from a conveyor testbed, followed by the determination and visualization of appropriate performance monitoring information on a dashboard.