Exploring Cloud Adoption Possibilities for the Manufacturing Sector: A Role of Third-Party Service Providers

As the manufacturing sector strides towards digitalization under the influence of Industry 4.0, cloud services have emerged as the new norm, driving change and innovation in this rapidly transforming landscape. This study investigates the possibilities of cloud adoption in the manufacturing sector by developing a conceptual model to identify suitable cloud-based solutions and explores the role of third-party service providers in aiding manufacturers throughout their cloud adoption journey. The research methods consist of a comprehensive literature review of the manufacturing industry, digital transformation, cloud computing, etc., followed by qualitative analyses of industrial benchmarks case studies and an investigation into an application of the developed model to a hypothetical food manufacturing company as an example. This study indicates that cloud adoption can yield substantial benefits in the manufacturing sector, including operational efficiency, cost reduction, and innovation, etc. The study concludes that the developed conceptual model provides a practical framework to identify the most suitable cloud-based solutions during the cloud adoption process in the manufacturing context. In addition, third-party service providers like Capgemini are capable of not only filling the technical gaps but also consulting strategic directions and innovations for their client organizations, hence playing a vital role in driving the industrial digital transformation process. With an extensive mapping of their capabilities, a set of recommendations intended to assist Capgemini in enhancing capabilities and improving competitive performance in the market has been offered

Manufacturing as a Data-Driven Practice: Methodologies, Technologies, and Tools

n recent years, the introduction and exploitation of innovative information technologies in industrial contexts have led to the continuous growth of digital shop floor envi- ronments. The new Industry-4.0 model allows smart factories to become very advanced IT industries, generating an ever- increasing amount of valuable data. As a consequence, the neces- sity of powerful and reliable software architectures is becoming prominent along with data-driven methodologies to extract useful and hidden knowledge supporting the decision making process. This paper discusses the latest software technologies needed to collect, manage and elaborate all data generated through innovative IoT architectures deployed over the production line, with the aim of extracting useful knowledge for the orchestration of high-level control services that can generate added business value. This survey covers the entire data life-cycle in manufacturing environments, discussing key functional and methodological aspects along with a rich and properly classified set of technologies and tools, useful to add intelligence to data-driven services. Therefore, it serves both as a first guided step towards the rich landscape of literature for readers approaching this field, and as a global yet detailed overview of the current state-of-the-art in the Industry 4.0 domain for experts. As a case study, we discuss in detail the deployment of the proposed solutions for two research project demonstrators, showing their ability to mitigate manufacturing line interruptions and reduce the corresponding impacts and costs

Data Processing for IoT in Oil and Gas Refineries

This paper summarizes and gives examples of the using of IoT in Industry 4.0, especially in Oil and Gas Refineries. Industry 4.0 and Industrial Internet of Things (IIoT) technologies are driving digitalization driven by software and data solutions in many areas, particularly in industrial automation and manufacturing systems. Global refineries are currently all heavily instrumented, and process regulated in real-time to the millisecond. To meet the ever-increasing needs of operational demands, SCADA, Distributed Control Systems and Programmable Logic Controllers (DCS & PLCs) have grown significantly. On the other hand, certain assets and operations in a refinery are still not being monitored or evaluated in real-time. If an error occurs that causes production to be hampered, the company must bear large losses even though production stops in just a matter of minutes. This is one of the reasons why the oil and gas sector is starting to implement the Internet of Things (IoT). The overall aim of this paper is to give and summarize several papers to provide solutions for a simple process monitoring system that would enable process operators to identify any sources of abnormality quickly and easily in the process. A system is being made so that it can be accessed and transmit data remotely via a computer network and will display conditions in real-time without being limited by distance, space, and time. This will allow all previously disconnected assets and processes to be linked and monitored in real-time in a simpler, cost-effective, and easy-to-implement manner

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

2020 NASA Technology Taxonomy

This document is an update (new photos used) of the PDF version of the 2020 NASA Technology Taxonomy that will be available to download on the OCT Public Website. The updated 2020 NASA Technology Taxonomy, or "technology dictionary", uses a technology discipline based approach that realigns like-technologies independent of their application within the NASA mission portfolio. This tool is meant to serve as a common technology discipline-based communication tool across the agency and with its partners in other government agencies, academia, industry, and across the world