Using Maturity Model and Discrete-Event Simulation for Industry 4.0 Implementation

Abstract

Production environments worldwide transform themselves in order to take the best advantage of the Industry 4.0 guidelines. Automation, data exchange, cyber-physical systems, the IoT, cloud and cognitive computing represent a step in the unknown to these companies, associated with high risks and also the need to restructure their culture. If the execution route is not clearly defined and understandable to all levels of employees, the renovation is too long. The maturity models can be used for the assessment of current Industry 4.0 maturity level, but the practical use of scores and assessed level often requires the involvement of consulting firms. Companies can avoid the involvement of consulting companies with the use of complementary tools. In this paper, we propose a new methodology that combines the Industry 4.0 maturity model and discrete-event simulation tools in the case of steel production company with the possibility of generalization. The combination of these tools in the first step helps the company to assess its current level of maturity for Industry 4.0, and in the second step helps to consider about strengths and weaknesses of possible scenarios for transition to a higher level of maturity.

Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process

“…The industrial and production processes will be transformed into intelligent factory systems [ 2 ]. Production will be controlled autonomously and dynamic, with a high degree of automation [ 3 ]. Smart systems are replacing human decisions.…”

Sustainable Move towards Flexible, Robotic, Human-Involving Workplace

“…It is, therefore, particularly important that the right methodological approach is taken when considering the technological upgrade of a manually managed workplace to a flexible, robotic, human-involving workplace. The best methodology proved to be a combination of the maturity model, process mapping with flowcharts, and critical analysis [30], rounded in content with a customised evaluation model. Further studies on the flexibility of workplaces combining collaborative robots, AR, and humans are proposed and presented in the laboratory case study on collaborative screwing by HoloLens programming.…”

Multi-Scenario Simulation of Subway Emergency Evacuation Based on Multi-Agent

“…Safety evacuation of urban rail transit is more of a decision-making method under discrete circumstances [1]. Jiang et al studied evacuation modes in 23 scenarios by building Exodus software, compared the evacuation capacity in each group with the requirements in the current subway construction specifications, and explored and compared the effects of different possible evacuation management measures under each passenger capacity [2].…”