The IIoT needs IT composers

Published: March 29, 2022 · Last updated: April 22, 2022 · 9 min reading time

Efficiency, sustainability, innovative strength – companies are achieving their major strategic goals through smart production. The prerequisite for this being digital solutions that can be modularly composed into individual architectures. This composability factor has many advantages. First and foremost being that companies can achieve their transformation goals at their own pace.

Market requirements are changing, customer wishes are becoming more individualized, and production cycles are increasingly shorter: To remain competitive, companies are pursuing three major goals in production.

First, is increasing efficiency to ensure profitability and value creation. Second, are the relevance importance of sustainability and CO2 reduction issues – organizations need to produce in a climate-friendly manner and comply with political requirements. Third, companies are training their innovative strength in order to react flexibly to innovations in customer and technology markets.

For all three goals, the Industrial Internet of Things (IIoT) era offers a toolbox of many digital technologies. The complexity of IIoT opportunities is growing. To keep track, it is advisable to take the digital transformation journey in small steps. A smart master plan for technology and roadmap is needed.

The concept of composability

What might such a master plan look like? The analyst firm Gartner proposes the management concept of “composability” as a lesson learned from the Corona pandemic. “Composable business means creating an organization made from interchangeable building blocks. The modular setup enables a business to rearrange and reorient as needed depending on external (or internal) factors like a shift in customer values or sudden change in supply chain or materials.”
Source: The Future of Business Is Composable – Gartner Keynote

The three building blocks for this, according to Gartner, are:

Composable thinking based on the principles of modularity, autonomy, orchestration and discovery
Composable business architecture to be faster, more agile, stronger in leadership and more resilient
Composable technologies are the tools for today and tomorrow

Composability needs modularity and openness

Gartner continues: “Composable business requires a foundational change in business thinking, architecture and technology.” At the same time, composable technologies are fundamentally nothing new to CIOs, according to Gartner, but exist “in familiar technology, from APIs to containers”. Nevertheless, composability of technology needs systematic management.

The two key elements of a composable IT architecture are modularity and interoperability of solutions, and openness to free data flows. To put it in an image: An orchestra also only plays together successfully if everyone has agreed on an artistic work and a key and everyone can hear each other.

IT composers are needed to fully exploit the possibilities of the industrial IoT: They help themselves to a pool of modular solutions that allow free data exchange among themselves in a wide variety of deployment scenarios such as on-premise, edge, and cloud.

Three technological stages can be identified along this concept, which technology must be able to master in the era of the IIoT:

Digitally connect all machines – existing equipment as well as new ones

Every manufacturing company has its own starting conditions: Machines from a wide variety of manufacturers of different ages with different control systems are in use in every production facility. The digital connection of such heterogeneous machine parks is considered the central challenge. This is especially true for global manufacturing companies with international production networks.

The supreme discipline of any successful IIoT strategy is therefore connectivity. “Nothing works in the digital space without connectivity,” states the McKinsey study “Industrial IoT and leading technologies as drivers of digital transformation in production.”

Ideally, machines are connected using plug-in concepts, which should support all common proprietary protocols of machine manufacturers as well as industry standards (Fanuc, Heidenhain, Siemens S7 and many others). Connection paths or supported protocols should be diverse and range from simple connections via 24V cables (I/O controllers) to PLC plug-ins and standards such as MTConnect, OPC/UA, cloud protocols (MQTT) and DNC.

With a standardized concept, connecting three pilot machines, for example, takes one to two days. Then further networking with factory software solutions or the planning level (ERP) can start via adapters.
For large companies with global manufacturing networks, the networking of machines and plants represents a particular challenge. It should be possible use a standard to scale the machine connection in order to save time and costs. This is what a machine repository offers. Such a template library enables companies to quickly connect and deploy the same types of machines worldwide.

A machine repository should be organized as a learning system. In other words, the knowledge from existing machine connections is available quickly and in a standardized form in all of company’s factory networks. This provides companies with a growing library for the smooth networking of the same machine types.

FORCAM offers a freely available collection of templates for machine configuration. These can be downloaded from www.forceedge.io and then imported into the company’s own Machine Repository.

2. Harmonize data – convert signals into Smart Data and use them

Digitizations should not however be the sole target. Rather, it represents a powerful toolbox that companies can use to best achieve their strategic goals, such as efficient and sustainable production, integrating manufacturing processes into global supply and service chains, and establishing new business models.

The generation and use of unified data – smart data – is crucial. After all, one of the core requirements of Industry 4.0 is to generate information digitally at the machine level and make it available in a user-friendly way at all levels – on the shop floor and on the top floor. The aim is to close the gap between IT and OT, between information and operational technology.

Operationally, this means: After the foundation – the comprehensive digital networking of all factory equipment – the collected machine and sensor signals must be converted by the software into relevant and usable information. The goal is the digital twin of production, which maps all processes in real time in all desired systems. In this way, waste and errors can be analyzed virtually in real time and optimized in real terms.

The digital twin of production is born as soon as the unified signals from the machinery (machine twin) are brought together with the order and input data.

The digital twin then provides all IT systems and apps with the smart data stream they need for a wide variety of real-time analyses. With this precise information, analyses of all kinds can be run in specialized programs – for example
- Efficiency and quality analyses (overall equipment effectiveness OEE)
- Traceability (Track & Trace)
- Resource efficiency and CO2-reduced production (energy monitoring)
High-performance, cloud-based computing solutions for data modeling and validation are necessary for the digital twin of production (semantic layer). In addition to this validation competence, an IIoT solution should offer comprehensive networking competence, i.e., both horizontal networking on the shop floor and vertical networking with the planning level – from the shop floor to the top floor (ERP – Enterprise Resource Planning).
Real-time analyses are the first step toward greater efficiency. For sustainable success, comprehensive historical analyses should also be possible. Historical evaluations answer central questions for sustainable optimization such as:
- In which production area was the highest quality achieved in the past month?
- Which machines had the highest energy consumption in the last six months?
- Which machines are likely to have increased maintenance needs in the next month?
For historical analyses and forecasts, all information must be stored and available at the push of a button. This is ensured by the so-called data lake. Put simply, it contains the digital twins of every single machine. All the real events on each machine are recorded: raw signals collected at machine and event-level (current, strokes, pressure, weight, consumption, etc.), all semantic interpretations of the signals, sorted in precise chronological order.

In addition, a data lake should be able to store configuration changes, write operations, and transferred NC files. Among other things, this makes dispatch logs possible:
- When was which signal sent from which machine and how?
- Was a particular signal translated into its correct meaning?
- Who changed what in the configuration of the machine connection and when? (Revision security)

3. Compose IT solutions – and ensure seamless interaction

The age of learning IT systems has begun. In this context, manufacturing companies face an apparent dilemma: On the one hand, they want to be able to use innovative solutions such as predictive, machine learning and AI apps in the future. At the same time, they own existing assets and IT systems worth many millions or billions of euros, which should still be able to run for years and continue to operate undisturbed by IT innovations to increase efficiency.

Companies need stability in production with simultaneous flexibility in order and capacity planning. Such flexibility in production can be enabled by modular IT platform solutions that, through open interfaces and connectors, enable a step-by-step digital transformation of a company’s own production IT – and, in the longer term, the path to a global world of digital supply and service chains.

In terms of IT strategy, many companies want a clear separation of which data is hosted in which systems. One emerging trend is for data collection and machine control to be more decentralized at the “edge” of the network, while software provisioning is centralized in a cloud.

Hybrid cloud/edge solutions are therefore the future. The technological challenge here is to translate the versatility of the machine connections and machine signals – to “wrap them up” – and to forward them as standardized events to higher-level systems.
In the FORCAM FORCE EDGE solution, such downstream systems can include MES or MOM systems (Manufacturing Execution / Manufacturing Operations Management) such as SAP DMC (Digital Manufacturing Cloud), SAP ME or SAP MII.

Technologically, the retrieval of machine master data and the configuration of the machine connection is done via the EDGE API as a RESTful API. The EDGE API event service is used to forward machine data in the form of standardized events to higher-level systems. The connection of higher-level systems can be done via
- OPC/UA (Open Platform Communications Unified Architecture),
- MTConnect (Manufacturing Technology Connect) or
- MQTT (Message Queuing Telemetry Transport)
- HTTP/REST

Connect, harmonize, compose – bringing all three steps under one hat

If companies also take the concept of composability into account at the technological level, they have three key advantages: They ensure that

IT innovations can be integrated at their individual speed – step-by-step instead of big bang,
IT innovations can be tested successively in pilot projects, while main production continues undisturbed
a later rollout of IT innovations or scaling can take place seamlessly.

The best solution for this is a central and powerful IIoT platform solution that offers connectivity, the digital twin, and the composability of software systems: the connection of all machines, smart data and digital twin, all common interfaces for data use in all desired apps and systems, and in different hybrid cloud/edge infrastructures.