The Key Role of the Software Backbone
The right balance between edge and cloud
Making Data-Driven Decisions
Industrial companies are often confronted with how to handle data correctly. This blog post intends to contribute to a better understanding of data management and strategy. As a superordinate data structure, the software backbone can help in this respect. However, a certain amount of knowledge and design is required to consolidate this in the company. The primary goal remains to gain context-specific benefits from the data.
The Potential of the Industrial Internet of Things Is Immense
However, industrial companies face the challenge of fully exploiting this potential. Suppose a connection from the machine to the cloud is secured with cyber security measures(phases 1 & 2 of the IIoT model). In that case, a data flow is possible, and the systems can be integrated. In this third phase of the IIoT maturity model, how the data infrastructure is controlled must be considered. This includes the concept of a software backbone coined by Arvato Systems. The separation of edge and cloud technology is of central importance due to their different characteristics to enable data-based decisions and continuously improve the efficiency of the machines. An essential first step is identifying the valuable data for the respective process. The question arises as to which information is relevant in which context. Which statement is for day-to-day operations, including machine management, and which is for business decision-making?
This differentiation results in the essential need for effective data management and a clear strategy. The selection of the proper processing and storage locations for the data, whether on the machine or in the cloud, should be well thought out and tailored to individual requirements. This strategic orientation is reflected in the software backbone. This backbone must integrate well to harmonize all relevant data, processes, and core systems, such as a company's ERP. A sensible distinction is made between edge and cloud technology. Both have specific advantages that need to be emphasized to achieve optimal overall results.
Edge computing
Edge technology is ideal for the direct execution of software for machine control due to the local proximity between the data source and its processing. The short-term, local storage of data is therefore crucial at edge level. Data is processed in real-time, which enables an immediate response in the form of control decisions for operations. In addition, running software directly on the machine minimizes any delays that could arise due to high latency times and broadband restrictions in the cloud. A vital example of this is the immediate identification of machine production problems. The total data can also be collected, evaluated, and reduced to the relevant data at the edge level. This saves costs for data transfer to the cloud.
Cloud computing
Compared to edge technology, the cloud has higher latency and is limited by broadband connections. In contrast, however, it also offers theoretically unlimited computing and storage power and scalability. Cloud structures are, therefore, ideal for training complex machine learning models. Among other things, this enables the development of predictive maintenance strategies or the optimization of machine performance over extended periods. In this context, cloud computing is the ideal place to store long-term and processed data and logs that are important for analyzing trends and long-term optimization. In addition, experience from processes, troubleshooting options, and centralized knowledge management can be built up and shared with all employees.
Finding the right balance:
The challenge is to find the optimal balance between edge and cloud computing. This balance enables data-driven decisions, maximizes the efficiency and performance of the machines, and minimizes the overall cost of the solution. Carefully choosing and implementing a software backbone helps companies optimize their processes, take fuller advantage of IoT integration, and position themselves in a data-driven industrial landscape. It is the company's reliable source for all information in the right place and quality.
Arvato Systems can contribute to this balanced software backbone in cooperation with its strategic partner, TTTech Industrial, and its platform solution, 'Nerve.' They achieve machine connectivity and secure connectivity to cloud structures with the necessary edge devices. Arvato Systems, as an expert in cloud environments, can map private and public cloud solutions through its data centers within Germany. This partnership creates a symbiosis of cloud and edge technology and the existing IT infrastructure in a software backbone tailored to your needs. The connectivity discussed in the last blog article in this series illustrates the collaboration in these areas.
The following article in the IIoT Maturity series will focus on the added value companies can derive from the IIoT context. New business models and exciting insights through data evaluation or machine monitoring are just some promising fields showing increased benefits and value.
The authors
Johannes Fuhrmann is Head of Strategic Business Development at Arvato Systems. In this role, he is responsible for the portfolio and product development for the manufacturing industry. His focus is mainly on the topics of digital twin, digital administration shell and the development of shared data ecosystems in the industry. Prior to his career at Arvato Systems, Johannes Fuhrmann held various relevant positions within the manufacturing industry. For example, he worked as a Senior Consultant for Industry 4.0 at Deloitte Consulting and as Head of Digital Operations and Business Development at VELUX. Johannes Fuhrmann completed his studies at the University of Warwick with a Master of Science (MSc.) in Information Systems Management. He also holds a Bachelor of Arts (B.A.) in Technical Business Administration from the Hamburg University of Applied Sciences (HAW Hamburg).
Konstantin Klein is Sales Growth Manager at Arvato Systems and responsible for customers in the manufacturing industry. In his position, his focus is on the digital transformation of companies, particularly in the areas of process optimization, digital twin, and cyber-physical systems. Before joining Arvato Systems, Konstantin Klein gained extensive experience in discrete automation as Product Manager Industrial IoT Network Solutions at B&R Industrial Automation (Member of the ABB Group) and as Business Development Manager at TTTech Industrial. After completing his schooling and training as an electrician, he completed a Master's degree with a Master of Science (M.Sc.) in electrical engineering and information technology with a focus on automation technology at the Leipzig University of Applied Sciences.
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