By Charlie Norz, WAGO automation product manager
This feature is from our eHandbook focused on edge computing. Click the cover to get your copy.
The networked, intelligent, self-controlling, self-optimizing, and resource-efficient production is the central scenario of the Industrial Internet of Thing (IIoT)—the smart factory. Despite the alleged advantages that IIoT appears to offer, questions remain for many companies, such as: What steps should be the first on their path to the smart factory? In what ways is edge computing beneficial for digitalization? Why is it even necessary? What level of their own production must attain 4.0?
It is undeniable that an intelligent, resource efficient, and cost-efficient production is gaining significance against a background of increasing international competition. The advantages of IIoT will therefore produce results and will drive competitive advantages for factories and process plants in North America.
Companies are looking to maintain their core competences when it comes to manufacturing. To be competitive in the global economy, there are significant efforts to reinforce production operations using a maximum level of automation to reduce costs while increasing product quality. With the use of an increased level of automation, modern infrastructure, IT expertise, and proximity to regional markets, companies are beginning to relocate their production base back to North America. In this context, there are those in industry who are already talking about a re-industrialization.
Data transparency for the smart factory
The essential criteria, which characterize a smart factory, are those that enable the measuring, networking, and evaluation of data:
- Sensors at all levels, including down to the product level and the product itself
- Networking all components and internet connection
- Maximum IT security
The first step along this path is transparency across all production and system data. Only when the data have been brought into context with one another, suitably processed, and consolidated into information, can measures be introduced to improve the production process. For this to succeed, sensors must record product- and production-relevant data at the field level. These sensors have to be considered in the system architecture or incorporated into the product itself, for example, in the form of RFID chips.
With regard to production-relevant data, which is recorded via sensors on the machines and systems, the challenge consists less in the mere collection of data, but instead in bringing information securely and error-free from the field level into a higher level, for example, a MES (manufacturing execution system) or the cloud. With the relatively high expect to transfer and store data in the cloud, it does not make sense to have raw sensor data sent directly higher level systems.
So how does this work?
Automation edge controllers can provide a decisive contribution as signals can always be reliably collected from the field level and managed locally on the plant floor. Edge controllers can also be incorporated into already existing automation systems as scalable nodes and gateways, which can be retrofitted without having to interfere with the actual automation process; the data can then be aggregated into abridged information that is transmitted to a higher level, an MES or the cloud. In this context, the advantages connected with a cloud link initially appear quite promising: cloud solutions are flexible, scalable, are highly available, and provide the opportunity for centralized access.
Individual ideas for tomorrow’s production
In order for North America to remain an attractive location for manufacturing companies, these corporations must also be in a position to manufacture profitably, even in the context of individualization and globalization. The ability to manufacture products according to lot size without substantially increasing production costs will be an indicator for the smart factory with companies. The future success of producing companies will be determined by their production changeability and the ability to network to a high degree along the entire value chain—right up to the end product. How this will be specifically implemented in production essentially depends on the existing underlying conditions. The smart factory cannot be imposed as a solution. Instead, it must be the smart version of an extant production line but must be as individual at the processes of the producing company itself.
Before considering how IIoT can be technologically introduced into existing production, there must be consideration as to which ideas, methods, or approaches can lead to an improvement in the existing individual production processes. These improvements may lie in more efficient use of resources during production, preventing duplication of applications along the value-added chain, or significantly shortening system-engineering times. For example, there are potential advantages for machine and system designers in observing a system after the sale and obtaining as much data from the lifecycle as possible. This would allow them to draw conclusions to apply to refinements in their own work, or would enable them to provide recommendations to their clients about operating the machines.
Measure first, then manage
No matter which method is applied for transitioning from the merely extant to the smart factory, networking of existing processes and operations remains a prerequisite. This networking includes the vertical, namely from the control system to the field level, as well as the horizontal, which extends beyond the various steps in the value-added chain. The only opposition to this type of complete networking today is that the data cannot be consistently generated and used. Diverse media and system discontinuities, which occur in both the vertical, but primarily in the horizontal integration, introduce difficulties in correlating data logically and sensibly across processes. As a rule, each IIoT approach initially proposes recording data, digitizing them, and linking them to one another in a profitable way.
This step is precisely the central thought driving IIoT: collecting, networking, and evaluating data from the production process in order to exploit them profitably such that a sustainable added value is generated for the corporation.
Creating added value
In order that one does not drown in the resulting data flow, applications for local data analysis and control with edge controllers play a decisive role. If they are incorporated correctly and make use of the individually relevant key performance indicators (KPIs), then the existing process can be fundamentally improved, depending on where one places the focus, i.e., time, resources, or energy. Thus, more is accomplished on the road to a smart factory than a single step.
