How Manufacturing Companies Transform Their Business
Focus on data and machines
Article 1 on the gradual increase in digital maturity using the IIot maturity model
Is the digital transformation of the industry stuck in PoC status?
Is it not yet possible to use production data from manufacturing machines?
Are in-house developments not advancing the self-imposed IoT strategy as quickly and effectively after all?
The use of technology to optimize business processes is nothing new, but digital transformation is taking this to a new level. It enables the development of innovative products, changes operational processes and creates added value. However, companies in the manufacturing industry are wondering where they should start - with optimizing their IT applications, using new payment models or setting up cybersecurity?
In this article, we explain the IIoT maturity model as a useful tool for checking your company's digital progress.
A total of six blog articles will guide you through the digital transformation of the manufacturing industry. We use an IIoT maturity model to explain how production companies can increase their digital maturity. You will find links to the blog articles that build on each other at the bottom of each page.
The IIoT Maturity Model
The IIoT Maturity Model is a concept that helps companies assess and plan their progress and goals in the area of the Industrial Internet of Things (IIoT). The IIoT refers to the application of IoT technologies in the industry to increase efficiency, reliability, and innovation. The IIoT Maturity Model consists of five phases that map and highlight different aspects of the digital transformation.
1. Machine Connection
The five points from the IIoT model build logically on each other and are indispensable precursors for the change in Digital Transformation. First, all data-generating machines must be connected to the IIoT network via edge devices. For this, the hardware and software must be set up appropriately to ensure that the machines can capture the relevant data and transmit it correctly to the network.
2. Connectivity via the Cloud
The second step is connectivity via the cloud. The focus here is on reliable and efficient connectivity of the various devices (edge devices, sensors, machines, etc.). The aim is to achieve a smooth flow of information to use the collected data effectively in the following phases and to derive real added value from it.
3. The Right Software Backbone
Once this is ensured, the secured data can be integrated throughout the existing systems. The third phase is the control of the data infrastructure in the form of the proper software backbone. One differentiation criterion is the sensible separation of cloud and edge computing. Edge computing is ideally suited for real-time processing of the accumulated data directly at the machine. Due to restrictions (e.g., latency and broadband), the cloud is more suitable for training ML models and long-term data storage tasks. Striking the right balance between edge and cloud computing in our software backbone is crucial to reap the full benefits of IoT integration.
4. Value-Added Services Through IoT
In phase four, the necessary conditions are achieved to concentrate on developing added value through the collected data. Based on aggregated data analyses, it is a matter of creating (future) applications and services around the machines. In concrete terms, this can involve software for data visualization as a basis for decision-making, the monitoring of function and behavior, predictive maintenance of the system, or support in the event of service.
5. Deep Benefits From Data Through the Use of AI
Integrating artificial intelligence into the software also opens up new and more profound possibilities in phase five. For example, AI algorithms can evaluate machine data even better or optimize general operating processes. The customer-oriented services also benefit from AI. On the one hand, the existing services are improved and more closely adapted to the needs, and on the other hand, completely new services can be developed.
A holistic approach is of fundamental importance for the smooth interaction of various systems with the ultimate goal: customer satisfaction. Regardless of the different industrial communication protocols (e.g., OPC UA, MQTT, or Modbus), the entire data is transferred to the backbone via the edge connection of the machines, which is then stored, processed, and forwarded. Thus, with a good and balanced software backbone, the foundation for all value-added IIot functions and services is given.
According to the business cases demanded by industrial companies, production machines are integrated into the IT world across the board. This includes the topics that will become increasingly important in the future:
- Sustainability: in terms of energy management, resource tracking, and the mandatory ESG reporting
- AI & digital twin: For more efficiently designed production and logistics processes
- Cross-company ecosystem: As a basis for developing new business models.
Here, existing (partner) solutions and customized solutions can be used. The primary objective is to solve customer-oriented problems and expand the potential of Industrialisation 4.0. Edge technology forms the link between the IT and OT of your production company. If you follow the IIoT model, your company can integrate digital services into everyday business and offer your customers new possibilities.
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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