In the manufacturing industry, the success of companies increasingly depends on gaining a complete overview and in-depth understanding of their own production and logistics. This is the only way they can plan and control processes optimally, even in the face of uncertainty. In addition, the European Union's Corporate Sustainability Reporting Directive (CSRD) presents many companies with the challenge of having to report comprehensively on their sustainability. Direct insight into all relevant data is urgently required for this.

Companies are increasingly turning to digital twins for greater transparency. The technology is already considered by many to be indispensable for optimal production planning and control and is gradually becoming the new standard in the industry. As a study commissioned by the digital association Bitkom shows, 63% of the German industrial companies surveyed see digital twins as a competitive factor and 44% are already using them.

Digital twins not only depict machines and systems, but also processes, competencies, relationships with customers and suppliers as well as intellectual property. They are used to explain the current situation or provide an outlook for the future and identify risks. It is even possible to depict an entire value chain. All of a product's data across its entire life cycle is recorded and structured. Employees use digital twins for complex simulations, for example to optimally plan processes based on test runs and always make the right decisions.

Combine with prescriptive analyses

Companies can achieve even more if they pair digital twins with artificial intelligence or machine learning (ML). This allows them to efficiently analyze the data on which the representatives are based at the same time. This gives employees new insights for their decisions more quickly.

Prescriptive analysis is a versatile approach that uses artificial intelligence methods. Prescriptive analysis solutions make concrete proposals for action to control processes - with full transparency about the factors and effects of the individual alternatives. This includes, for example, the time required and the costs that would be incurred for each of the possible decisions. For the calculations and simulations, solutions for prescriptive analyses access historical and current data from internal and, if required, external sources. Employees actively use the functions, or the corresponding solution reports automatically as an early warning system. They then weigh up the best course of action from the selection of options themselves and then initiate the process. Companies can also completely automate the decision-making and control of processes in certain areas.

But how does all this relate to digital twins? They provide an overview of all relevant data within their area of activity. Depending on the implementation, the individual areas can be merged into a higher-level representation of the entire value chain, creating a complete overview. This simplifies the introduction of prescriptive analyses enormously. Employees can also test the recommendations for action provided in simulations. Risks and potentials resulting from changes to process chains can thus be identified in a secure environment.

Prerequisite: Modern data management

InterSystems partner Ortelius from Sweden can represent a company's entire value chain with digital twins. At the beginning of a project, the focus is always on the question of which data is important in order to achieve a specific business objective and implement the right solution for it. It often turns out that crucial data is missing. The question is then how and where it can be found, generated or procured. Companies need to combine all relevant data from their production and logistics to create a digital twin of their entire value chain.

The next step is to link and process data of any format from various sources. A digital twin that covers the entire value chain should store the data in a data model that can flexibly change and expand over time. "A modern data platform such as InterSystems Iris is the key to this. It closes gaps between systems, applications and services, breaking down data silos," says Daniel Lundin, Head of Product and Service at Ortelius. To ensure interoperability between internal and external systems, the solution masters all common standards, protocols and profiles for data exchange in the industry. It can also see whether any relevant data is missing. This creates a standardized and always up-to-date database for digital twins and prescriptive analyses. Lundin continues: "We create digital twins and define the relationships between the data. We don't store large amounts of data, that's not our core competence. That's why we work with InterSystems, from whose many years of experience in data management we benefit."

Meet all requirements at all times

The digital twin of an entire value chain also provides a comprehensive overview of a company's sustainability. In addition, Ortelius can break the relevant figures down to individual products and services through clear allocation and end-to-end data models. This results in an accurate_{CO2 balance}. Companies need this in order to comply with a range of new regulations. Only through greater transparency can companies report correctly on their_{CO2 emissions} and provide reliable data quickly. This is why many industry representatives are also considering the concept of the asset administration shell. It is a standardized digital twin that clearly displays all available information on an asset within a company.

The transparency created by the digital twin of an entire value chain brings yet another advantage: companies can continuously monitor all of their production and logistics processes and make informed decisions based on this in order to optimize them and reduce their_{CO2 emissions}, for example. According to the aforementioned Bitkom study, digital twins contribute to sustainable production for 59% of respondents. Prescriptive analyses promise even greater efficiency in decision-making. The recommendations for action help to quickly initiate the right measures. Modern data management platforms offer integrated AI and ML functions to carry out prescriptive analyses.

Find further use cases

Creating a perfect digital twin of an entire value chain requires preparation and therefore time. The digital twin can serve as a blueprint for companies to achieve their goals. This is all the more important as the legal regulations on sustainability reporting (CSRD) have been in force for larger companies since January 1, 2023 and will be extended to many more companies by 2026. The situation is similar for the German Supply Chain Duty of Care Act, which initially came into force at the beginning of this year for organizations with more than 3,000 employees. As early as January 1, 2024, this directive will also apply to companies with more than 1,000 employees. In addition, there are other European legislative initiatives such as the Data Governance Act, which aims to simplify the sharing of data. In connection with this, it is becoming apparent that the EU Data Act, the second pillar of the European digital strategy, will also soon become law.

Many challenges await companies along the way. However, interim successes can be achieved, for example in the form of increased transparency and optimized sub-processes. Once the option to create digital twins and introduce prescriptive analyses has been implemented, there are also many other possible applications. For example, prescriptive analyses are used to ensure greater quality and efficiency in production planning and supply chain management. They enable companies to reduce costs and fulfill orders with greater reliability. All of this guarantees long-term competitiveness and sustainable success.

Werner Reuß, Manufacturing Solutions Executive at InterSystems