High Speed Simulation Analytics

Abstract

Simulation, especially Discrete-event simulation (DES) and Agent-based simulation (ABS), is widely used in industry to support decision making. It is used to create predictive models or Digital Twins of systems used to analyse what-if scenarios, perform sensitivity analytics on data and decisions and even to optimise the impact of decisions. Simulation-based Analytics, or just Simulation Analytics, therefore has a major role to play in Industry 4.0. However, a major issue in Simulation Analytics is speed. Extensive, continuous experimentation demanded by Industry 4.0 can take a significant time, especially if many replications are required. This is compounded by detailed models as these can take a long time to simulate. Distributed Simulation (DS) techniques use multiple computers to either speed up the simulation of a single model by splitting it across the computers and/or to speed up experimentation by running experiments across multiple computers in parallel. This chapter discusses how DS and Simulation Analytics, as well as concepts from contemporary e-Science, can be combined to contribute to the speed problem by creating a new approach called High Speed Simulation Analytics. We present a vision of High Speed Simulation Analytics to show how this might be integrated with the future of Industry 4.0.

PDCA 4.0: A New Conceptual Approach for Continuous Improvement in the Industry 4.0 Paradigm

“…In this context, a digital twin of the manufacturing site that uses a simulation can be used towards CI [51]. This requirement can be further supported by on-demand cloud computing resources that allow high-speed simulation analytics [85], as well as IoT to transmit data between machines and sensors to software tools [9]. Regarding practical use cases, this requirement is present in scheduling solutions based on both real-time simulations [69] and the combination of VSM with simulation models in order to validate current and future states, aiding in decision-making processes [68].…”

“…Enabling data transmission and access [9,47] Data interchange between machines, systems, and sensors in order to end user software tools [9] Cloud-based data storage [12,42,83] Information sharing across organization [9,12] Cloud computing [35,42,83,85] Collection of production data [12,82] Table A1. Cont.…”

“…Enabling data transmission and access [9,47] Data interchange between machines, systems, and sensors in order to end user software tools [9] Collect and access real-time data [9,54] Predict machine failures [52,55] Data analysis [9,12] Predictive analysis [11,12,52,53] Data mining [53] Correlation analysis [47] Machine learning [11,43,53] Cloud-based data storage [12,42,83] Information sharing across organization [9,12] Cloud computing [35,42,83,85] Collection of production data [12,82]…”