Design choices for next-generation IIoT-connected MES/MOM:An empirical study on smart factories

The role of enterprise information systems is becoming increasingly crucial for improving customer responsiveness in the manufacturing industry. However, manufacturers engaged in mass customization are currently facing challenges related to implementing Industrial Internet of Things (IIoT) concepts of Industry 4.0 in order to increase responsiveness. In this article, we apply the findings from a two-year design science study to establish the role of manufacturing execution systems/manufacturing operations management (MES/MOM) in an IIoT-enabled brownfield manufacturing enterprise. We also present design recommendations for developing next-generation MES/MOM as a strong core to make factories smart and responsive. First, we analyze the architectural design challenges of MES/MOM in IIoT through a selective literature review. We then present an exploratory case study in which we implement our homegrown MES/MOM data model design based on ISA 95 in Aalborg University's Smart Production Lab, which is a reconfigurable cyber-physical production system. This was achieved through the use of a custom module for the open-source Odoo ERP platform (mainly version 14). Finally, we enrich our case study with three industrial design demonstrators and combine the findings with a quality function deployment (QFD) method to determine design requirements for next-generation IIoT-connected MES/MOM. The results from our QFD analysis indicate that interoperability is the most important characteristic when designing a responsive smart factory, with the highest relative importance of 31% of the eight characteristics we studied

Cloud-Based Architecture for Production Information Exchange in European Micro-Factory Context

In a constantly changing world, information stands as one of the most valuable assets for a manufacturing site. However, exchanging information is not a straightforward process among factories, and concerns regarding the trustability and validation of transactions between various stakeholders have emerged within the context of micro-factories. This work presents an architecture designed to enable information exchange among heterogeneous stakeholders, taking advantage of the cloud infrastructure. It was designed to enable the use of several tools, connected through a middleware system deployed on the cloud. To demonstrate the potential of this architecture, a platform was instantiated, and two use cases—designed to accurately represent real manufacturing sites—were implemented.© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

An End-to-End Big Data Analytics Platform for IoT-enabled Smart Factories: A Case Study of Battery Module Assembly System for Electric Vehicles

Within the concept of factories of the future, big data analytics systems play a critical role in supporting decision-making at various stages across enterprise processes. However, the design and deployment of industry-ready, lightweight, modular, flexible, and low-cost big data analytics solutions remains one of the main challenges towards the Industry 4.0 enabled digital transformation. This paper presents an end-to-end IoT-based big data analytics platform that consists of five interconnected layers and several components for data acquisition, integration, storage, analytics and visualisation purposes. The platform architecture benefits from state-of-the-art technologies and integrates them in a systematic and interoperable way with clear information flows. The developed platform has been deployed in an Electric Vehicle (EV) battery module smart assembly automation system designed by the Automation Systems Group (ASG) at the University of Warwick, UK. The developed proof-of-concept solution demonstrates how a wide variety of tools and methods can be orchestrated to work together aiming to support decision-making and to improve both process and product qualities in smart manufacturing environments

Industrial Transformation Roadmap for Digitalisation and Smart Factories:The Danish SMEs Model

Today only some sections of the supply chain are digitalized, but some companies are also already far with Industry 4.0, where the virtual factory and the physical factory work closely together (digital twin). Industry 4.0, which started in Germany among the large OEMs, seems to have not resonated much with SMEs. There is an imminent challenge of coming up with a feasible transformation roadmap which will resonate effectively and efficiently with SEMs as they are the core backbone of every performing economy. This research investigates Smart Factories/Industry 4.0 in the Danish SMEs model perspective. This research's main objectives are to develop a feasible roadmap in the form of a conceptual framework for easy industrial transformation to the digitalizing and smart way of (doing things) developing products and/or services. This research employs quantitative research methods such as surveys and interviews where applicable as well as a literature review in the SMEs perspective. Previous research has shown that the digital evolution coined as Industry 4.0 was started among large companies. However, this initial precedence has not resonated very much with all-inclusive industrial evolution, especially within the SMEs perspective. The main industrial implication will be the definition of a clear feasible roadmap for what this research terms as an industrial transformation process - "digital change management process - Industry 4.0/Smart factory" in the industrial SMEs perspective - the Danish Model. This research seeks to propose a conceptual smart factory roadmap in an Industry 4.0 perspective, which could be adopted among manufacturing SMEs to effectively, and efficiently transform their production operations. The Danish model perspective or angle of Industry 4.0.</p

INDUSTRY 4 . 0 : LEGACY DEVICES INTEGRATION WITH OPC UA AND THE DIGITAL TWIN

Over the years, the constant evolution of the industry has led to many advancements in factories and manufacturing systems. The terms "Smart Factories" and "Smart Manufacturing Systems" have been used to describe the latest wave of technological innovations that have transformed how factories operate. One of these innovations is the concept of the Digital Twin, which is a realistic virtual copy of a physical object. This technology allows entire manufacturing shop-floors to be digitalized, and physical processes to be tightly intertwined with their cyber counterparts. The development of Digital Twins encompasses several challenges, including model accuracy, security, and the integration of different devices and systems, including interoperability and standardization across them. The goal of this work is to develop key applications to support the implementation of Digital Twins in a Smart Factory environment, by describing an example of the development of an Industry 4.0 enabling application for a legacy device, as well as the design of a Digital Twin for a real industrial system from the ground up. A key result of this work is a successful use case of creating a Digital Twin for a quality control cell in the industry, using RobotStudio as the simulation environment and OPC UA as the communication protocol between the devices in the cell. The developed Digital Twin is capable of simulating the behaviour of the devices in the cell, as well as performing the cell’s control logic. It is also capable of storing historical process data, which could be analyzed and used to perform process optimization. Another relevant result is related to the use of a device’s Digital Twin to support the development of an application, performing tests and validation, while eliminating the need of accessing the real device. It shows that this technology can be used to speed up development and reduce downtime of industrial devices, thus reducing costs and improving the production process.POCI-01-0247-FEDER-04608

Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations

Published by Informa UK Limited, trading as Taylor &amp; Francis Group. The technological advancements of recent years have increased the complexity of manufacturing systems, and the ongoing transformation to Industry\ua04.0 will further aggravate the situation. This is leading to a point where existing systems on the factory floor get outdated, increasing the gap between existing technologies and state-of-the-art systems, making them incompatible. This paper presents an event-based data pipeline architecture, that can be applied to legacy systems as well as new state-of-the-art systems, to collect data from the factory floor. In the presented architecture, actions executed by the resources are converted to event streams, which are then transformed into an abstraction called operations. These operations correspond to the tasks performed in the manufacturing station. A sequence of these operations recount the task performed by the station. We demonstrate the usability of the collected data by using conformance analysis to detect when the manufacturing system has deviated from its defined model. The described architecture is developed in Sequence Planner–a tool for modelling and analysing production systems–and is currently implemented at an automotive company as a pilot project

Integration of Cutting-Edge Interoperability Approaches in Cyber-Physical Production Systems and Industry 4.0

Interoperability in smart manufacturing refers to how interconnected cyber-physical components exchange information and interact. This is still an exploratory topic, and despite the increasing number of applications, many challenges remain open. This chapter presents an integrative framework to understand common practices, concepts, and technologies used in trending research to achieve interoperability in production systems. The chapter starts with the question of what interoperability is and provides an alternative answer based on influential works in the field, followed by the presentation of important reference mod4els and their relation to smart manufacturing. It continues by discussing different types of interoperability, data formats, and common ontologies necessary for the integration of heterogeneous systems and the contribution of emerging technologies in achieving interoperability. This chapter ends with a discussion of a recent use case and final remarks