Industry 4.0, also referred to as the fourth industrial revolution is envisioned to bring transformation in manufacturing and supply chain through the integration of physical systems with digital systems across the value chain. The global market size for Industry 4.0 is projected to be USD 165 billion by 2026 (source: marketsandmarkets-Industry 4.0). To achieve Industry 4.0 goals, technologies like the Industrial Internet of Things (IIoT), data analytics, ML and AI, and cyber security, can be leveraged to enable value potential of up to 30% improvement in productivity and throughput, up to 20% quality improvement, and up to 50% downtime reduction (source: report by Mckinsey- Capturing the True Value of Industry 4.0).
Proof of value solutions can be used to test the technology, functionality, user experience, and other desired goals while bringing agility and resilience to the business (source: "Industrial companies of the future").
McKinsey in their report — Capturing value at scale in discrete manufacturing with Industry 4.0, quotes “The hurdles resulting from limited resources, high cost of scaling, a lack of clarity about business value, and an overwhelming number of potential use cases leave the majority of companies stuck in “pilot purgatory”.
It is, hence, imperative to plan a pilot use case and its implementation strategically aligning with the enterprise vision. This ensures scalability and quick positive outcomes to truly derive value from pilot solutions. It is also important to follow a structured approach, as highlighted in Figure 1, before jumping directly to pilot implementation.
Figure 1 – Structured approach to pilot implementation
In this article, we will be deep diving into Step 1- “Evaluate the current state of maturity”
Evaluate the current state of maturity
To assess the current state of Industry 4.0 maturity for an organization, we leverage ‘Acatech Industry 4.0 Maturity Index’ (Figure 5) (Link - Acatech-Industry 4.0 Maturity Index) developed by research groups from RWTH Aachen University in collaboration with industry experts. This model functions on value-based developmental stages, providing a framework to assess and support Industry 4.0 adoption. The model has helped organizations on their transformation journey, and their 2017 publication gained wide acceptance. It identifies interdependencies and interconnectedness of multiple aspects of the organization including resources, information systems, organizational structure, and culture. We will focus on how you could evaluate the evolution of your organization’s information systems across different stages of digitization.
As per Acatech’s updated 2020 revision of the index, 58% of board members surveyed identified their organizations were lagging and unsuccessful in digital transformation. It is imperative to acknowledge, though, that the journey towards success is marked by various stages of maturity, each with its own set of challenges.
Stage 1 – Computerization
This is the first step in the development path because it sets the stage for digitalization. At this point, the organization uses digital and electronic control technologies to automate individual tasks. In most businesses, computerization is already quite advanced and is mainly used to carry out repetitive tasks more effectively. A perfect example is manufacturing metal parts using a Computer Numerical Control (CNC) machine and a Computer-Aided Design (CAD).
Stage 2 – Connectivity
In the second stage, the siloed computerized systems are connected to digital solutions and business applications through advancement in internet protocol technology. The connectivity of machines on the shopfloor with digital systems on the same network allowed to establish Operational Technology (OT) level connectivity at a nascent level. OT systems like Manufacturing Operations Management (MOM), Manufacturing execution system (MES), inventory management systems, and others were introduced with basic functionalities such as monitoring of current inventory and production process, production planning, and others. For example – a CAD file can be updated dynamically on a CNC machine from CAD software on the same OT network. This stage includes application- specific point-to-point connections between independent systems on the OT network to serve a specific application. The applications developed focus on collecting data from an identified machine or system to serve a specific purpose in the factory.
Stage 3 – Visibility - “What is happening”
In addition to OT connectivity achieved in stage 2, this stage allows convergence of OT with IT technologies. At this stage the Industry 4.0 adoption starts. This stage is identified by real-time state and event monitoring of processes throughout the shopfloor, allowing to create a digital copy of any process or machine. Hence, this stage answers ‘what is happening’ at the shopfloor at any given time. Acatech referred to this model as the company’s ‘Digital Shadow’. It enables the integration of sensor data and on-premises shopfloor systems, such as MES, with different enterprise applications such as PLM, ERP, and others. Thus, it allows the creation of a single source of truth for all data to represent a digital shadow in a single pane of view. These systems make it possible to remotely track key performance indicators in real-time, allowing data-driven dynamic decision-making.
Stage 4 – Transparency – “Why is it happening”
This stage uses the digital shadow of the organization created in stage 3 to identify the reason behind a particular behavior using engineering knowledge. This stage focuses on the semantic linking of machine and operational data to their corresponding asset to extract contextual information for holistic analysis. The contextualized information is then further aggregated and analyzed. The analysis of large volumes of data across datasets is done to identify correlations in events and dependencies. The analysis is then used to aggregate this data to generate patterns, trends, and insights to allow condition monitoring in real-time. These insights are complex and sometimes require big data applications on data obtained from ERP, CMMS, and others. Big data analytics of machine health parameters at the time of aberrant machine behavior can help identify the root cause of unexpected behavior.
Stage 5 – Predictive capacity – “What will happen”
Predictive capacity is the next developmental stage of this value-based model, building on the success of stage four. The complex trends, patterns, and correlations in historical data aid in extrapolating data points to create accurate forecasts and simulations. Statistical, machine learning or artificial intelligence-based algorithms are used based on the complexity of datasets and applications. Some examples of typical use cases at this stage are downtime or failure prediction, predictive maintenance, simulation of production or quality process output to identify areas of improvement, scrap reduction, and others.
These use cases allow the organization to predict future events by analyzing real-time data, hence allowing the organization to make appropriate decisions or take actions. While manual measures still need to be implemented, early detection of potential failure allows for just enough time to prepare and plan for it in cost- effective and efficient manner. Thus, it also helps to prevent unexpected disruptions in operations.
Stage 6 – Adaptability – “How can an autonomous response be achieved”
Predictive capacity is a pre-requisite for this stage. At this advanced stage, the decisions are expected to be taken autonomously based on predictions and real-time conditions. The goal of this stage is to make decisions quickly and accurately without the need for human intervention. The return on investment, risks and impact associated with decision-making drive the adoption percentage of adaptability in the organization for any process. For example – A self-learning predictive maintenance model is a great example of how AI can help with asset management. This model can adapt to comprehend the wear and tear of a machine and adjust its predictions and expected asset life based on real-time parameters. Another example is autonomous adaptive demand forecasting solutions that can help businesses optimize their operations by predicting demand in real- time. Both use cases have low risk and can provide significant benefits to businesses that adopt them.
Our perspective on the Visibility stage of the maturity index
While Acatech’s maturity index provides a high-level guiding framework to evaluate Industry 4.0 maturity of an enterprise, we believe describing it as a digital shadow of the company is an oversimplification. Based on our experience, Stage 3 can be divided further into 3 levels for better control over adoption, wherein each level builds upon the foundation of the previous level:
- Level 1 – Foundational level: Real- time monitoring
- Level 2 – Intermediate level: Integration and control
- Level 3 – Advanced level: Unified digital shadow
Level 1 - Foundational Level: Real-time monitoring
At this level, organizations have started the journey on the visibility stage of their maturity index, connecting essential equipment and assets to gather fundamental data. The primary focus at this level is on remote monitoring and basic raw data analytics. Hence, key objectives include enhancing remote visibility of shopfloor assets, production data, alarms, events, downtimes, and so on.
We have developed a Cluster of Industrial IoT Accelerators(C-IoTA) that facilitate this level including –
- Device Management accelerators that allow secure authentication to provision and onboard edge gateways and linked machines, sensors, and their corresponding data parameters in a seamless and secure manner. As part of C-IoTA modular accelerator solutions, data can be collected from shopfloor systems and data sources over widely used protocols such as OPC UA, MQTT, HTTPS, TCP, FTP, and SMB.
- Data management accelerators allow to collect, store, and manage data securely for different machines.
- Tenant and user management accelerators allow an ability to define users and roles for a specific plant, ensuring data isolation and secure storage while allowing archiving of data across multiple plants simultaneously.
- Timeseries data visualization accelerators allow real-time raw or aggregated data monitoring through different widgets on configurable and personalized dashboards.
Level 2 - Intermediate Level: Integration and control
At this level, the focus is on expanding the visibility through integration with other shopfloor and enterprise systems. These enterprise systems are usually applications such as MES, ERP, PLM, and CMMS.
As part of C-IoTA, we have developed protocol connectors as accelerators for integrating with such enterprise systems at the shopfloor and enterprise level, to pull relevant data. In addition to data collection from enterprise systems, C-IoTA also has accelerators to acquire data over widely used protocols such as OPC UA, TCP, FTP, SMB, and others from different on-prem data sources such as file servers, distributed control systems, and data servers. These accelerators can be hosted on edge gateways or servers in plants enabling real-time data preprocessing, rule-based automation, asset control, and critical alert notifications. This results in a comprehensive picture of the complete factory regarding the current state. The data can be securely transferred to the cloud for further storage and analysis.
Level 3 - Advanced Level: Unified digital shadow
At this level, the focus is on interlinking data and information acquired from machines, shopfloor systems, servers, and enterprise applications to create correlations and context for business applications. For example – Asset A manufactured at a certain timestamp on machine Z by operator B belongs to X order number. These correlations can be built in a point-to-point manner, but this method is challenging to implement in an efficient manner that is easy to operate, maintain, and scale across the factory with several different systems. One key drawback of such an approach is that any change in a parameter has a multifold impact on engineering effort and cost if executed in a point-to-point integration.
Nagarro can help to develop a unified namespace (UNS) solution that can integrate with C-IoTA accelerators to help solve the above challenge. UNS acts as a single source of truth or digital shadow at the factory and enterprise levels by maintaining the current state of each asset. It acts as a software component of your solution that uses a hub and spoke model to communicate with all systems. Its design principles allow it to be open, lightweight, and edge-driven. It can uniquely identify assets and correlate data across different systems.
Additionally, as part of C-IoTA, we offer a drag-and-drop digital shadow interface that can integrate with the single source of truth (UNS) to show the current state of your machine, cell, line, plant, or organization. The combined solution will act as a digital shadow of the organization that can be enhanced to view on the web, a 3-dimensional model or virtual reality, and even a metaverse-based business application.
Inspired by our Fluidic Enterprise vision, we have created frameworks and toolkits to assist in the discovery, development, and implementation of the use case. As part of discovery frameworks, we can help you assess the current state of maturity of your organization in Industry 4.0. Depending on where you are in your journey, we can assist you in identifying use cases that will add value based on your current state, evaluating readiness to adopt a use case, crafting a strategic plan, and implementing an identified use case. In the implementation phase as well, we have extensive experience in creating a faster, cost effective and scalable proof of value, a minimum viable product, or a complete solution using our suite of accelerators, as part of C-IoTA. These accelerators can be finetuned as per the use case, leading to faster solution delivery. If this interests you, please reach out to us firstname.lastname@example.org