Industry 4.0

The Industry 4.0 vision of decentralized, autonomous networks of smart products and automated equipment collaborating in smart supply chains is the direction manufacturing industries must move to achieve intelligent, high-performance, resource efficient and fully predictive manufacturing.  Industry 4.0 shapes a future of agile, affordable manufacturing fueled by technology enablers such as the Internet of Things (IoT), Additive Manufacturing (3D printing), Augmented Reality, Cloud Computing, Mobile Devices, Autonomous Robots and Big Data Analytics. That future reality actually does have the potential to change the process of manufacturing. It’s a disruptive change in how companies and supply chains work, what people and software applications do, and what customers can expect and when. That does not mean all the processes, equipment, IT systems, and procedures a company uses today will disappear. They need to prepare for the following transformations:

  • Distributed Manufacturing – from centralized to decentralized decisions and control
  • Autonomous Machines (robots) – from people directing or even doing much of the operations work to automated intelligent mechanisms capable of acting independently
  • Vertical Integration – from isolated systems at each level (work centers, production lines and units, plants, enterprises) to vertically integrated information flows that enable full business processes. That includes IT/automation convergence of information technology (IT) systems used for data-centric computing with automation technology systems traditionally associated with industrial control systems (ICS) such as supervisory control and data acquisition (SCADA).
  • Horizontal Integration – from separate systems in each department and organization in the supply chain to horizontally integrated information flows among everyone in the organization and extended supply chain
  • Simulation – from somehow organized to fully predictive processes, which could be readily tuned to the best performance with the respect to specific but fast-changing requirements faced by complex manufacturing businesses
  • Augmented Reality – from drawings, instructions and manuals to context-sensitive interactions between people and technology.
  • Reliability Centered Maintenance – from reactive maintenance of assets and tools to smart predictive, condition-based ones in the environment of big data.
  • Mobile – from large companies and particular types of processes being connected to the widespread democratization of connectivity, mobility and location-sensitive technologies
  • Cloud Computing – from on-premises to cloud-based, service oriented computing
  • Big Data Analytics – from limited and localized analytics, to advanced plant-wide analytics, both real-time and offline

For most manufacturing companies, a disruptive approach to implementing new and thus unknown technologies is rather risky. Industry 4.0 technologies are at the heart of most manufacturing processes and influence critical steps within the value chain. The cost of production downtime per day is high, and thus manufacturing companies will carefully weigh the benefits of introducing new technologies against possible risks to process reliability. In addition, many companies had some concerns around having the right skills to capitalize on Industry 4.0. Digitalization skills are critical. The younger people coming through are digital natives but there are generations of existing workers who will need to rapidly reskill and retool. As a result, companies approach fundamental disruptions with caution, so that change will be rather incremental.

Agile Manufacturing and Industry 4.0

                      acatech Industrie 4.0 Maturity Index: Less latency helps improve faster 

Agility is a strategic characteristic that is becoming increasingly important to successful companies. In this context, agility denotes the ability to implement changes in the company in real-time, including fundamental systemic changes to the company’s business model, for example.
Consequently, the significance of Industry 4.0 lies in the key role of information processing in enabling rapid organisational adaptation processes. The faster an organisation can adapt to an event that causes a change in its circumstances, the greater the benefits of the adaptation. In this context, the umbrella term “event” may relate to a range of different business decisions. Events may be short-term in nature, for instance a production line breakdown, or medium- to long-term, for example a change
in product requirements and the associated modifications to the product design itself, to the manufacturing process and to related processes in purchasing, quality and service.
At present, when an event occurs in a company there is a delay before detailed insights about the event become available. This means that there is also a delay in taking the corresponding decisions and (counter-)measures . One of the reasons is that the relevant information systems are not sufficiently integrated to enable end-to-end data processing, from data capture to analysis. Industry 4.0 capabilities help manufacturing companies to dramatically reduce the time between an event occurring and
the implementation of an appropriate response.
In practice, this means that, for example, changes in customer requirements based on field data can be incorporated even during a product’s manufacturing process because the company possesses the agility to adapt to the new situation. As a result, the customer can be supplied with a product tailored to their exact requirements in a significantly shorter period of time and higher quality.

Roadmap to Industry 4.0

  1. Computerisation: While the use of IT and process automation has already become the standard, companies still use insular information systems at this stage.
  2. Connectivity: Once the individual components are connected, companies have reached the maturity stage of connectivity and implemented digitization as defined in this guideline. However, they have not yet achieved full integration between information and operational technologies.  Stages 1 and 2 are defined as Industry 4.0 preliminary steps. Because a good portion of organizations are at the beginning of the journey, the index had to include existing levels of maturity in the current industry.
  3. Visibility: At this stage, companies are starting to use sensors for real-time recording of conditions and processes. They produce a digital model of production, a “digital shadow” or “digital twin” that shows what is happening at any given point in time.
  4. Transparency: Once companies use the digital shadow to identify and understand interactions, they have reached stage 4. To do so, they need to interpret the recorded data in the relevant contexts by applying engineering knowledge. Big data applications are deployed in parallel to business application systems – such as ERP or MES – to provide a common platform for data analysis.
  5. Predictive capacity: To simulate scenarios and evaluate them in terms of their likelihood and consequences, the digital shadow is projected into the future. As a result, companies can anticipate future developments and make the necessary decisions.
  6. Adaptability: At the highest stage of maturity, the IT systems will make these decisions independently. At this stage, Industry 4.0 has been realised in full. IT systems initiate the necessary alignment measures automatically and without delay. The extent to which IT systems will be allowed to act autonomously depends on two aspects: first, on the complexity of the decision, and second, on the cost-benefit ratio of automated versus human actions.

RAMI 4.0

RAMI 4.0, which stands for Reference Architecture Model Industrie 4.0, is a conceptual framework and reference architecture used in the context of Industry 4.0. Developed in Germany, it serves as a blueprint for designing and implementing Industry 4.0 solutions in manufacturing and industrial settings.

RAMI 4.0 consists of three primary layers: the Asset Layer, Communication Layer, and Information Layer. The Asset Layer deals with physical assets, each represented by an Asset Administration Shell (AAS). The Communication Layer facilitates standardized communication, while the Information Layer focuses on data processing and management.

AAS serves as a digital twin of physical assets and contains detailed information about them. RAMI 4.0 emphasizes interoperability, security, and scalability. It supports both horizontal and vertical integration, enabling assets and systems to work seamlessly together at different hierarchical levels.

Security measures are integrated at every layer to protect data and ensure communication integrity. The framework encourages the use of data analytics for process improvement and decision-making. Standardization is key, leveraging protocols like OPC UA to enable interoperability.

RAMI 4.0 considers the entire asset lifecycle, from design to maintenance, and promotes openness and collaboration among stakeholders to drive innovation in Industry 4.0.

Standards ANSI/ISA 95 and ISO/IEC 62264

ISO/IEC 62264 (ISA-95) is an international standard that guides the integration of process control systems and enterprise systems in manufacturing and industrial automation. It establishes a hierarchical model with defined levels within an organization, ensuring consistent data exchange between control systems and enterprise systems. The standard includes functional models to describe manufacturing processes and operations, promoting standardized communication and interoperability. IEC 62264 aims to improve operational efficiency, reduce manual data entry errors, and support data-driven decision-making. Compliance with this standard is crucial in regulated industries, and its global recognition makes it valuable for enhancing manufacturing operations and aligning them with business processes.

In summary, IEC 62264 is an international standard that provides guidelines for integrating process control systems with enterprise systems in manufacturing and industrial automation. It emphasizes interoperability, efficiency, and standardized communication to enhance the integration of control and business aspects within an organization.

ISO/IEC 22400 specifies an industry-neutral framework for defining, composing, exchanging, and using key performance indicators (KPIs) for manufacturing operations management (MOM), as defined in IEC 62264‑1 for batch, continuous and discrete industries.

B2MML (Business to Manufacturing Markup Language) is an XML implementation of the IEC 62264 (ISA-95), Enterprise-Control System Integration, family of standards. B2MML consists of a set of XML schemas written using the World Wide Web Consortium’s XML Schema language (XSD) that implement the data models in the ISA-95 standard.


OPC DA (OLE for Process Control – Data Access) and OPC UA (Unified Architecture) are communication standards widely used in industrial automation and control systems.

OPC DA, an older standard, primarily focuses on real-time data exchange and is known for its simplicity and efficiency in connecting data sources such as sensors and devices to applications and systems.

On the other hand, OPC UA, a more modern and versatile standard, offers improved flexibility and security. It not only supports real-time data exchange but also provides extensive capabilities for information modeling, complex data structures, and advanced security features. OPC UA is designed to facilitate communication not only within local networks but also over the internet, making it suitable for the evolving requirements of Industry 4.0 and Industrial Internet of Things (IIoT) applications.

Both standards aim to enable interoperability and smooth communication in industrial environments, but OPC UA’s enhanced features make it a preferred choice for many modern industrial automation applications.