Industry 4.0 Maturity Index
A new index provides companies with guidance for carrying out the transformation to Industry 4.0
Businesses increasingly recognize the growth opportunities offered by digitalization and interconnectedness. These technologies are enabling new business models, efficient use of resources, and cost-effective production of highly customizable products. These developments are collectively referred to as “Industry 4.0.”
Numerous studies have explored companies’ attitudes towards digital transformation and the opportunities and obstacles associated with it. The latter are rarely confined to a lack of technologies or standards. In many cases, faltering implementation of Industry 4.0 is due to rigid organizational structures and a conservative culture where people lack courage to do things differently.
Our experience implementing lean has taught us that it is not enough simply to ring the changes. Successful implementation also requires an understanding of the organization and a widespread willingness to change among its members. Just as lean is about more than eliminating waste, Industry 4.0 is not merely a matter of connecting machines and products via the Internet.
Industry 4.0 will inevitably lead to new types of work and ways of working. This will necessitate changes to company structures and the relationships between companies. The ability to analyze corporate culture will be critical to success. Businesses must understand what Industry 4.0 means to them and develop a corresponding implementation strategy.
Our Industry 4.0 Maturity Index provides companies with guidance for carrying out this transformation. The index was developed by the German Academy for Science and Engineering, together with German universities and industry partners, among them TÜV SÜD.
The index comprises a six-stage maturity model in which the attainment of each stage delivers additional benefits. The goal is to generate knowledge from data to enable rapid decision-making and adaptation.
The capabilities outlined in our model were validated in real-world scenarios. This confirmed the model’s principles and highlighted the fact that companies are not focusing enough on the full implications of their strategic thinking.
Indeed, many organizations lack a basic understanding of key aspects of Industry 4.0. For instance, companies often wrongly limit Industry 4.0 to digitalization or full automation. Moreover, rather than pursuing a common goal, many actions have been implemented as standalone measures. Our index can be used to develop a digital roadmap tailored to the needs of each company to help them make the most of Industry 4.0.
Our index helps companies determine which stage they are at in their transformation into a learning, agile company. It assesses them from a technological, organizational and cultural perspective.
The road to Industry 4.0 will be different for every company. It’s therefore necessary to begin by analyzing each company’s current situation and goals. What are the company’s strategic objectives for the next few years? What technologies have already been implemented?
Every company must make strategic decisions about the benefits it wishes to achieve, its priorities, and the sequence in which Industry 4.0 measures will be implemented. The goal is to produce a step-by-step roadmap that will reduce investment and implementation risks. The roadmap will also underscore the importance of developing a common digital strategy for the whole business.
Our approach is based on a succession of stages. Since a company’s target state will depend on its business strategy, each company must decide which stage represents the best balance between costs, capabilities and benefits, taking account of how these requirements might change over time in response to changes in the business environment.
Industry 4.0 involves significantly upgrading a manufacturer’s digital capabilities, and it will affect large parts of the organization. Since the process can take several years, it should be implemented so that positive impacts on profitability occur at each stage. Benefits should be made visible at each point in the process to support overall success. This approach enables quick wins while keeping an eye on the overall transformation goal.
We developed an Industry 4.0 development path that starts with basic requirements and supports companies throughout their transformation. The path comprises six stages. Each stage builds on the previous one.
Since many companies are still confronting the challenge of creating the basic conditions for Industry 4.0, the path begins with digitalization. Although digitalization does not itself form part of Industry 4.0, computerization and connectivity are basic requirements for implementation.
Stage One: Computerization
Computerization provides the basis for digitalization. In this stage, different information technologies are used in isolation. Computerization is already well advanced in most companies and is primarily used to perform repetitive tasks more efficiently.
Nevertheless, it is still possible to find many machines without a digital interface. This is especially true of machinery with long life cycles or machines that are manually operated. In these cases, terminals often provide the missing link between business applications and machines.
One example would be a CNC milling machine. Although it can machine parts with great precision thanks to computer numerical control, the CAD data detailing which actions should be performed is often still transferred to the machine manually. The machine is not connected.
Another example involves business application systems that are not connected to the company’s ERP system. This can lead to a situation where quality assurance is carried out at a semiautomatic test station, but the data is not associated with a corresponding work order. This makes it harder to determine which quality issues occurred in which orders.
Stage Two: Connectivity
In this stage, isolated deployment of information technology is replaced by connected components. Business applications are connected to each other and mirror the company’s core business processes. Parts of the operational technology (OT) system provide connectivity and interoperability, but full integration of the IT and OT layers has not occurred.
Internet Protocol (IP) is becoming more widely used, even on the shop floor. Since IPv6 allows for much longer addresses than IPv4, all components can now be connected without the need for network address translation. This is a key requirement for the Internet of Things. Connectivity means that, for example, once a design has been created in engineering, its data can be pushed to production. Once a manufacturing step has been completed, confirmation can be provided automatically and in real time via a manufacturing execution system (MES). It also allows manufacturers of production equipment to perform remote maintenance on their products via the Internet.
In most factories, assets are kept in production for as long as they produce quality products. It’s not unusual to see machines that are more than 50 years old still in use on the shop floor. Since IP enables standardized communication on the shop floor, these machines can easily be retrofit with sensors so they, too, can provide data.
Stage Three: Visibility
Sensors enable processes to be monitored from beginning to end with large amounts of data. The status and performance of equipment can be recorded in real-time throughout the company and beyond, rather than just in individual areas. This makes it possible to keep an up-to-date digital model of a factory. We refer to this as the company’s digital shadow. The digital shadow can show what is happening in the company at any given moment so management decisions can be based on real data. It is a core building block for later stages.
Producing a digital shadow is a challenge. One problem is that there is usually no single source of truth; data is often held in decentralized silos. Furthermore, for functions such as production, logistics and services, it’s often still the case that very little data is collected at all, even in centralized processes. In addition, the data is only visible to a limited number of people. Wider use of data is prohibited by system boundaries.
To become an agile, learning enterprise, comprehensive data capture companywide is essential. For instance, this will make it possible to rapidly determine delivery date variances through real-time key performance indicators and dashboards. As a result, managers can adjust production planning, and customers and suppliers can be kept informed.
This is one area where companies must change how they think. Instead of only collecting data to support one specific operation, companies must instead maintain an up-to-date model of their entire operation at all times. Integrating PLM, ERP and MES systems provides a comprehensive picture that creates visibility regarding the status quo. Moreover, modular approaches and apps can help build a single source of truth.
Stage Four: Transparency
Stage four is about understanding why something is happening and to produce knowledge through root cause analyses. To identify and interpret interactions in the digital shadow, data must be analyzed by applying engineering knowledge. Semantic linking and aggregation of data will provide knowledge to support complex and rapid decision-making.
New technologies that support the analysis of large volumes of data can be helpful. “Big data” is a buzzword that is frequently mentioned. It is used to describe mass data that can no longer be processed using conventional business analytics.
As a rule, big data applications are deployed in parallel with business systems, such as ERP or MES. Big data applications provide a common platform for carrying out extensive stochastic data analysis to reveal interactions in the company’s digital shadow.
This can be used, for example, to carry out condition monitoring of machinery. Recorded parameters are searched for mutual events and dependencies that are then aggregated to reflect the condition of the machine. Such information is required for predictive maintenance.
Stage Five: Predictive Capacity
In this stage, the company uses data to help predict the future. This involves projecting the digital shadow into the future to depict scenarios that can then be assessed in terms of how likely they are to occur. As a result, companies can anticipate future developments and take appropriate countermeasures in good time. Such countermeasures may still be carried out manually, but the longer lead times help limit negative impacts. Reducing unexpected events caused by disruptions or planning variances enables more robust operation. For example, the system can warn of logistics issues, such as carrier failure, before they even occur, so they can be prevented.
Stage Six: Adaptability
Predictive capacity is a fundamental requirement for automated actions and decision-making. Continuous adaptation allows a company to delegate certain decisions to IT systems so it can quickly respond to changing business conditions. The degree of adaptability depends on the complexity of the decisions and the cost-benefit ratio. It’s often best to automate individual processes.
It’s important to carefully assess the risks of automating business interactions with customers and suppliers, such as changing the sequence of planned orders because of expected machine failures. The goal of adaptability has been achieved when a company can use data from the digital shadow to make decisions that have the best possible results in the shortest possible time and to implement responses automatically.
To download a copy of the report, click https://tinyurl.com/y83zfc7s.