Change is no longer just an occasional challenge for the global manufacturing industry – it has become a constant force. Today, manufacturers are facing a perfect storm of pressures that are reshaping how they operate and innovate. The industry is undergoing a seismic shift, driven by a combination of technological advances, increasing competitive pressure from emerging challengers, and global economic factors that can no longer be ignored. 

Technological breakthroughs like Artificial Intelligence (AI), digital twins, and 3D manufacturing are fundamentally transforming production processes and business models. Meanwhile, new competitors are catching up fast. These upstarts are achieving near-equal levels of quality while demonstrating greater agility and innovation, making it harder for established manufacturers to maintain their edge. At the same time, labor shortages, inflation, and slowing economic growth are further compounding the complexity of operating in today’s global market. 

To address these mounting challenges, manufacturing organizations are turning to the Industrial Internet of Things (IIoT) as a vital component of their strategy. IIoT offers a path forward by unlocking new efficiencies, enabling real-time insights, and creating opportunities for predictive maintenance. It helps companies do more with less. Additionally, the pressure to meet sustainability standards and increase transparency in operations only adds to the urgency to adopt IIoT solutions. 

For many manufacturers, successfully leveraging IIoT is no longer optional – it is crucial to staying competitive. By deploying IIoT solutions, companies can increase operational efficiency, improve customer engagement, and gain critical business intelligence that allows for more informed decision-making. However, despite these potential gains, successfully implementing IIoT is a complex endeavor. 

While some companies opt for ready-made solutions, an increasing number are developing proprietary IIoT platforms tailored to their specific needs. This approach offers numerous advantages: 

• Tailored systems that integrate seamlessly with existing workflows 

• Reduced reliance on third-party vendors, lowering operational costs 

• The ability to refine platforms based on real-time user feedback, maximizing ROI 

However, developing a bespoke IIoT solution also presents significant challenges. From integrating diverse technologies to ensuring data security and managing scalability, the road to success is paved with obstacles. Even more crucially, without a clear strategy and robust planning, companies risk turning these opportunities into pitfalls. Misaligning technology with actual work processes, underestimating the complexity of implementation, or failing to engage employees throughout the process can prevent businesses from realizing the full potential of their IIoT investments. 

This is where integrated change management becomes indispensable. It’s not just about managing the technical side of IIoT deployment; it’s about aligning people, processes, and technology to ensure that the transformation delivers its intended benefits. Change management ensures that every layer of the organization – from leadership to the shop floor – embraces the shift, ultimately enabling businesses to turn challenges into competitive advantages. 

Often looked at as the "soft and fluffy stuff," Change Management is also a technical discipline – reliant on fact-based research, analysis, and strategic thinking aiming at guiding an organization to sustainably introduce changes supported by the workforce. Change Management is essential for many reasons: minimizing disruptions, maximizing employee buy-in, and organizing training and development.

Bringing IIoT into any manufacturing organization requires both a shift in technology and a shift in mindset especially in relation to ‘how things were always done.’ “When organizations focus solely on the technical or operational aspects of a change initiative, they risk encountering substantial resistance and a lack of employee buy-in. This may also result in increased turnover, especially of key people. 

In addition to focusing on raising awareness, acceptance and commitment of the workforce relating to changes, Change Management also works very closely with the project, or in the case of large digital transformations, the Transformation Management team. This integrated approach is essential for successful and sustainable implementation. While the Transformation Management team is planning and managing the different work streams of a project, Change Management generates valuable input for when and where those plans need to be adjusted for the organization to follow the changes and generate the expected benefits. 

To sum it up, a successful Change Management program requires the acceptance of three factors:

• It is Integrated. Change Management oversees the technology and people involved in the transformation throughout the digital journey – from conception to implementation – and is connected to the Transformation Management team. 
• It is Equal. Change Management is often introduced as an afterthought and is limited to communication and training measures. Doing so reduces the effectiveness of Change Management tremendously. Its contributions must be treated equally to those of project management or solution development. 
• It is Iterative. Change Management anticipates and adapts to volatile and uncertain situations. “An iterative approach corresponds to developing flexible strategies and plans, and continuously adapting them while already implementing them. This contrasts with a traditional approach that would involve setting fixed goals, detailed upfront planning and rigorous implementation towards a fixed target state.

“The reality is that the biggest reason [I] IoT-led changes fail is the inability to reach adoption in core teams and via key stakeholders. With IoT in particular, adoption issues stem from the organizational tendency to force people and processes to accommodate a new technology rather than figure out how the tech will add value.”

A simple example is the issues that could arise if an organization forgets that there are at least five different demographic groups in the workforce right now. From the Silent Generation (1928-1945) to the Baby Boomers (1946-1964), to Generation X (1965-1980), to Generation Y/Millennials (1981-1996), to Generation Z (1997-2010) – each of these groups learn and accept technology at a different pace. 

As our colleague Britta Stutzman has written, “Successful Change Management boils down to understanding and relying on the diversity of our workforce; our job is to make sure we know as best as possible what keeps our employees motivated, how they like to learn and adapt to changes and finally what makes them engage in their job.”

But how do you create a transformation program in the right way to avoid the big risk of not having buy-in from the workforce? From our own experience, there are some aspects that contribute to a successful implementation. 

• Question if IoT is the right thing to do and how it helps: This might sound surprising, but what we encounter quite often is a situation where the solution is already decided, but the problem that it is supposed to solve, is still unknown. We know that everyone is eager to start and see the first features deployed, but not having clarity on why an IoT platform must be introduced and what should be achieved by it, will fall on the project’s feet later. 
• Design the IoT platform based on user feedback: Allowing those affected by the change to contribute to the solution significantly reduces implementation efforts. The people who will work with the IoT platform every day know best what features they need to achieve the expected benefits. This should also not be a one-time query at the beginning, but a continuous feedback collection throughout the rollout. Every deployment of new features will generate waves of valuable input influencing the course and outcomes of the project. Highly influential individuals must be identified and engaged early to minimize change resistance and develop influential change promoters. 
• Give clarity on how the IoT platform should be used: While it is always good to leave room for people to come up with their own ideas on how to make use of a solution, IoT platforms are very complex and for them to bring benefits, many different parties have to work together perfectly (e.g., to have correct data, actions can be triggered based on detected patterns). In one of our past projects, the client decided to forgo the focus groups and collaborative workshops on a new solution to let the workers show creativity in adjusting to the new tools. The workers did not have enough time to adjust to the changes causing frustration. Ultimately, the approach did not succeed, and the leaders realized that interviewing employees would provide the input to make the necessary changes.

Despite all the mentioned hurdles and risks associated with introducing an IoT Platform into a manufacturing setup, there is hardly a way around it for organizations that want to be prepared for the future. Fundamental changes in digitalization, automation, and demography need to be addressed proactively by any player in the manufacturing industry who strives to compete in the long run.  

By opting to take the proprietary approach to IIoT, manufacturers can focus on both technology and employee acceptance of digitalization. The journey to developing a proprietary IIoT platform, despite its high investment and associated risks, underscores the necessity for strategic and early implementation of integrated Change Management. Success depends on the people within the manufacturing organization believing in, accepting, and driving the changes necessary for a full organizational transformation and manufacturing optimization.  

Emerging manufacturers face immense pressure from EV makers, investors, and fierce competition amidst market volatility. The rapidly growing battery cell market demands speed and scalability. Those who can quickly ramp up gigafactories will lead the future of energy and transportation.

The battery cell market is gaining significant traction, presenting a booming opportunity for emerging manufacturers. To capitalize on this growth, these manufacturers must prioritize innovation and speed while navigating a complex landscape to dominate the rapidly evolving electric vehicle (EV) market in the long-term. They face numerous challenges: EV makers demand swift, reliable supply chains and high-performance batteries, investors seek rapid returns and high multipliers, and there is fierce competition from established players with advanced technologies and large-scale production capabilities. Market volatility further complicates the scene, with EV demand projections for 2024 falling short, creating uncertainty through 2030. In this high-stakes environment, the ramp-up of gigafactories is crucial, as the ability to scale quickly has become a decisive factor for new manufacturers determined to lead the future of energy and transportation.

The global EV market, valued at USD 388.1 billion in 2023, is projected to reach USD 951.9 billion by 2030 with a CAGR of 13.7%. Despite this, current delays in meeting the 2024 forecast create a volatile market. S&P Global reports that the anticipated EV demand boom for 2024 hasn't materialized, resulting in uncertain projections for 2030 growth. With global EV sales projected to grow by 36% in 2024, down from 62% in 2023, battery cell manufacturers face pressure to adapt. Economic challenges, including high interest rates in Europe and the US and a saturated Chinese market, contribute to this slowdown. Manufacturers must navigate tensions between EV makers, investors, government requirements, and industry players, showcasing their adaptability in a rapidly changing market.

1. Pressure from EV Manufacturers

EV manufacturers stress the need for quick, reliable supply chains, often setting strict deadlines for battery cell providers. Contracts typically include rigorous schedules, with delays leading to financial penalties and reputational damage. The demand for high-energy-density, fast-charging batteries in short development cycles adds additional pressure. Manufacturers must produce lighter batteries with shorter charging times, extended ranges, and reduced carbon footprints, all while meeting high-quality standards. The push for efficiency and excellence in production and quality underscores the intense demands on the EV supply chain.

2. Investor and Government Expectations

Battery cell manufacturers also face significant pressure from investors and competition for government support. Investors expect rapid production scaling within 3-5 years, demanding quick gains and substantial profits. This forces manufacturers to prioritize speed, efficiency, and cost-effectiveness. The need for considerable funding to scale up adds further pressure, requiring strong growth potential and adherence to demanding schedules. Competition for government incentives like tax breaks, grants, and subsidies is fierce. Emerging manufacturers must swiftly adapt strategies to meet eligibility criteria and align with investor and government demands to secure support.

3. Competition from Established Players

Leading companies dominate the battery cell manufacturing market with efficient, large-scale production facilities, setting high capacity and efficiency benchmarks. They use advanced techniques and innovative technology to produce high-quality batteries at lower costs, creating a cost-competitive advantage. This pressures other manufacturers to scale up rapidly while keeping costs low. To stay competitive, new battery cell manufacturers must streamline and accelerate their production processes to reduce costs without compromising quality.

Considering the triangular pressure, the role of gigafactories becomes critical in addressing the industry's demands for speed and scale. These production facilities are designed to meet the high-volume needs of the EV market, promising swift and efficient battery cell manufacturing to keep up with the fast-paced growth and innovation. Gigafactories, with their capability to ramp up production quickly, offer a strategic solution for emerging manufacturers to tackle the pressures of rapid scaling and market volatility.

Building a gigafactory and scaling up production involves overcoming intricate challenges. Emerging battery cell manufacturers must adeptly navigate each stage to achieve large-scale sustainable manufacturing.

1. Strategy & Setup

The success of a gigafactory depends on thorough strategic planning and meticulous operations setup. Analyzing target customer adoption rates and battery cell demand is crucial to accurately predicting market demand. This ensures that the factory's strategy aligns with future needs. Securing adequate funding is equally important, as large-scale projects and expansions require significant financial resources to support construction and operations.

2. Product Design

Emerging battery cell manufacturers must balance advanced materials and innovative designs with cost constraints. This requires continuous innovation to maintain economic viability while optimizing energy density and thermal management to maximize storage capacity and minimize size and weight. These efforts align with Design for X (DfX) principles, emphasizing manufacturability, cost-efficiency, and sustainability. Ensuring cell safety and stability prevents risks such as overheating or short circuits. Additionally, achieving consistent performance over numerous charge and discharge cycles is vital for longevity.

3. Factory Design & Planning

Securing an optimal location requires a balance between proximity to raw materials, market access, and logistical efficiency. Access to green energy sources is increasingly important to enhance the sustainability of the batteries. This directly impacts the facility's operational efficiency and cost-effectiveness by reducing transportation costs, ensuring reliable supply chains, and lowering energy expenses. Additionally, creating a scalable and adaptable layout is crucial for responding flexibly to market changes and accommodating future technological advancements and shifts in demand, ensuring the facility remains competitive and efficient.

4. Build, Commission & Ramp-Up

The build, commission, and ramp-up phase involves several critical tasks. Integrating and stabilizing new equipment requires meticulous oversight and collaboration with machine builders to ensure efficient manufacturing processes and prevent delays and cost overruns. Hiring and training a skilled workforce is equally important, as advanced manufacturing processes demand highly qualified employees to manage sophisticated systems and ensure smooth operations.

5. Operate

Once operational, a gigafactory must manage ongoing complexities. Handling maturing processes is a significant task, with errors occurring from electrode production to module packing, often making stacking a bottleneck process that requires continuous improvement and careful monitoring. Scaling up production while ensuring process stability is crucial. As the gigafactory footprint expands, maintaining stable processes in new environments, such as additional production lines or new facilities, is essential to uphold product quality and operational efficiency.

Emerging battery cell manufacturers must take a holistic approach, integrating advanced planning, automation, and digitalization to overcome these challenges and ensure rapid scaling.

Advanced Planning

Advanced planning forms the foundation for successfully ramping-up and scaling a gigafactory. This involves several critical components:

• Integrated Market Analysis and Forecasting: Accurately predicting market demand and trends is vital. Manufacturers can accurately forecast EV adoption rates and battery cell demand using advanced analytics and predictive modeling. This ensures strategic alignment with market dynamics, reducing the risk of over- or undercapacities.
• Strategic Financial Planning: Securing sufficient funding and financial backing is essential for large-scale projects. Understanding the major cost drivers of a battery gigafactory enables better decision-making and reduces CAPEX and OPEX. Developing robust financial models, engaging with a diverse range of investors, and establishing clear financial goals and milestones ensures the availability of necessary financial resources.
• Digital Twin Technology: Enhancing gigafactory design and planning with digital twin technology allows manufacturers to create virtual models of the gigafactory. This enables detailed simulation and optimization of production processes, facilitating flexible and scalable factory designs. Additionally, virtual equipment commissioning accelerates new product introduction in a "safe" environment.
• Virtual Product Design: Balancing performance and cost through virtual product design is essential. Engineers can analyze thermal management, electrochemical performance, and mechanical stresses under various conditions by simulating physical behavior and rapidly validating designs. This approach identifies potential issues and optimizes designs before building physical prototypes, saving time and resources.

Automation

Automation is an essential component in enhancing production efficiency and scalability within gigafactories. Key aspects include:

• Advanced Manufacturing Systems: Implementing advanced automated production lines, robotics, and AI-driven quality control systems reduces manual labor, minimizes errors, and increases production speed and consistency. Transitioning from predictive to adaptive systems necessitates close collaboration with machine builders to achieve the target operating model.
• Automated Testing and Feedback Loops: Integrating automated testing systems that provide immediate feedback and adjustments to production processes, enhancing overall product quality and reducing waste.
• Energy Management Systems: Integrating automated energy management solutions optimizes energy consumption, reduces costs, and enhances sustainability. Smart grids, AI-based energy forecasting, and automated demand response systems contribute to more efficient energy use within gigafactories.

Digitalization

Digitalization is vital for enhancing operational efficiency and resolving bottlenecks in gigafactories. Key elements include:

• Data-Driven Decision Making: Implementing IoT sensors, real-time analytics, and AI-driven decision support systems at every production stage provides actionable insights for continuous improvement and quick issue resolution. Real-time data collection and analysis help identify inefficiencies, predict maintenance needs, and optimize processes, with AI offering predictive analytics and automated responses.
• Digital Twin of Production: This technology improves operational efficiency by simulating processes and predicting outcomes. The virtual model enables testing and optimization in a digital environment, leveraging real-time data and analytics to foresee issues and suggest improvements. Consequently, it boosts efficiency and reduces downtime.
• End-to-End Digital Integration: Integrating end-to-end digital systems across the supply chain, production, and distribution networks into a single cohesive digital platform enhances coordination, reduces information silos, and improves overall operational transparency.

Leveraging a holistic approach ensures streamlined operations, cost reduction, and a competitive edge in the rapidly evolving battery manufacturing industry. This comprehensive strategy encompasses all aspects necessary to ramp-up and scale a Gigafactory effectively.

A top-tier European supercapacitor cell manufacturer scaled its production capabilities, positioning itself at the industry's forefront through strategic planning, advanced automation, and digitalization. The company predicted EV adoption rates using advanced analytics and market research, aligning production with future needs and avoiding overproduction risks. Diverse financial models attracted investors, ensuring sustainable growth and operational efficiency. Virtual product design balances performance and cost, creating a sustainable supply chain that meets high market standards.

Implementing virtual commissioning and digital twin technology improved production line setup and testing, reducing planning and setup time by 30% and operational costs by 15%. Flexible factory designs enabled rapid market adaptation, ensuring a competitive edge. Additionally, automation, including robotics and AI-driven quality control, lowered scrap rates by 30% and increased production efficiency by 20 p.p. Data-driven predictions have improved logistics, ensuring timely raw material availability and reducing lead times by 25%. Automated training programs and VR simulations effectively prepared the workforce to operate sophisticated machinery. Digitalization has enhanced operational efficiency through IoT sensors, real-time analytics, and AI-driven decision-making, continually improving production processes and doubling cell output.

Meanwhile, end-to-end digital integration enhanced coordination and transparency across the supply chain, using digital twins to optimize operations dynamically. This strategic transformation allowed efficient production scaling, high-quality standards, and cost reduction, securing the manufacturer's position as a European leader in the supercapacitor cell market. Embracing innovation and strategic foresight, the company met market demands and set new industry benchmarks, solidifying its competitive advantage in the rapidly evolving EV landscape.

Data is everywhere and whether you are a CEO or business manager you need to know where it is and what to do with it. The amount of data created daily around the globe continues to increase at exponential rates. It is estimated that 163 zettabytes of data will be created worldwide by 2025. However, reporting by IDC indicates that “only 32% of the data available to enterprises is ever used and the remaining 68% goes unleveraged” leading to businesses missing millions in revenue. 

If used correctly, data is an asset to any organization. “Ninety percent of enterprise analytics and business professionals say that data and analytics are key to their organization’s digital transformation initiatives. However, there are many companies that are reluctant to pull the trigger because they aren’t sure about the advantages of becoming data driven.”  

A data-driven organization captures the power of this ever-growing asset (data) and makes strategic and tactical decisions based on that information, not on gut reactions, historical references, or personal opinions.  

Despite the reluctance of some leaders, there is proof that becoming a data-driven organization is beneficial: 

• “Data-driven organizations can outperform their competitors by 6% in profitability and 5% in productivity. Data-driven organizations are 162% more likely to surpass revenue goals and 58% more likely to beat their revenue goals than non-data-driven counterparts.”

• Through a data-driven cost modeling approach, an EV start-up business ensured the profitability of its product at scale. This collaborative, data-led engagement with a focus on the supply chain resulted in an overall reduction of over 25% in Bill of Materials (BOM) costs.

• BARC research surveyed a range of business leaders and found that those “using big data saw an 8 percent increase in profit and a 10 percent reduction in cost.”

• The correct data can also shape an organization’s sustainability success. For example, “One Life Sciences company has a state-of-the-art continuous manufacturing facility … Live operations data is fed into a virtual twin, enabling continual optimization through real-time analytics. This initiative has seen an 80% reduction in energy consumption and CO2 emissions, a 91% reduction in the facility’s water footprint, a 94% drop in the use of chemicals and a reduction of 321 tons of waste per year. What’s more, it’s 80 times more productive, making medicines for twice the number of patients in less time.”  

Data holds the power to improve decision making and create new opportunities for growth and innovation throughout the organization. To truly be data driven and leverage the value of enterprise information, organizations need a secure, holistic view of its digital and cloud strategy. This includes how and when to deploy powerful off-the shelf software or when to customize and rapidly apply new use cases built on existing IT infrastructure.  

For years the question businesses faced when applying new digital solutions to existing operations was – should we make or buy? Given the availability of open innovation especially through recent releases from AWS and Microsoft, the same question leads to a new answer: We need to do both - make and buy.  

The Xcelerator program from Siemens is an example of such a hybrid approach. The robust ecosystem of sellers and developers with integrated hardware and software leveraging a strong solution portfolio and interconnectivity of systems, companies can apply specific operations improvements via SaaS products. A custom-built add-on approach allows them to quickly apply the newest innovation or business requirement delivering significant business impact. 

The Digital Core serves as a remarkable illustration of Siemens Xcelerator's vision to enhance collaboration and co-creation. It demonstrates interoperability by seamlessly integrating with both Siemens and third-party systems. Moreover, it is intentionally designed to be flexible, allowing for the creation of tailored solutions according to customer-defined use cases, with the added benefit of expandability and scalability. Lastly, it embraces openness through the use of standardized application programming interfaces (APIs).  

The Digital Core operates not as a super platform dominating everything, but as an integral component running in parallel with existing systems. It seamlessly integrates necessary and minimal data to fuel new business logics and processes, leveraging outputs from various systems. Therefore, adding a customized Digital Core to the digitalization mix enables companies to use qualified data and existing corporate knowledge to transform into a powerful decision-making body. It also allows them to own their digital backbone with the option and availability to scale it over time. 

Having a Digital Core creates a new level of data ownership that is fully customizable and scalable to meet the company’s needs. This positions the organization to answer additional new business needs by repurposing data from SaaS/PaaS products landscape for advanced analytics and decision making. This will enable enterprises to create new IP or build their own data team, further accelerating digital transformation with the benefit of not relying 100% on third party vendors. 

Establishing a Digital Core and becoming a data-driven organization are not overnight processes. It is important for leaders to grow the Digital Core on a case-by-case basis and for critical business reasons. The biggest leap leaders will take is simply to start designing and building the Digital Core framework.  

Here are seven points to consider when establishing the Digital Core:  

1. Align the company’s data strategy with corporate strategy and involve champions at the highest level of the organization. 

2. Secure enterprise-wide buy-in and understanding of the changes involved in becoming data-driven including the impact on the company culture.  

3. Demonstrate the potential value of your data by selecting the most valuable use cases where data can drive strategy for success.  

4. Create an IT/OT blueprint and detailed roadmap of the expected journey. 

5. Design the Digital Core with consideration of the company’s existing digital infrastructure and software products in use. 

6. Scale the Digital Core into new areas of the organization and increase its functionality for success, even consider how the data could be used in multiple ways. 

7. Consider implementing an expert team who own the organization’s data and Digital Core to create accountability, to continuously incubate new innovations, and to improve data-driven decision making.  

Sustainability and Artificial Intelligence (AI) provide the perfect business cases to leverage data across the organization because their importance can be quantified quickly due to recent developments and the speed of innovation.  

The importance of harnessing enterprise data can be seen in the worldwide effort for better, more accurate sustainability reporting. For example, in Europe, organizations must comply with the Corporate Sustainability Reporting Directive, providing details on sustainability-related impacts, opportunities, and risks. The US and the UN have other reporting requirements that are also dependent on good data sources. Without access to the correct sustainability information organizations may struggle to complete the requirements.  

And the next challenge is already on the horizon – optimizing – operations based on data driven decisions. This can only be achieved with qualified, reliable data which includes new business logics and advanced analytics to understand and predict impacts. 

Data-driven organizations measure their environmental impact such as CO2 emissions and waste and water consumption across a large infrastructure. Then with the right data quality they optimize operations to reduce their ecological footprint. Finally, they augment their ability to make faster, more effective decisions and support new ways of working with more diverse teams and communities. 

Meanwhile, AI is gaining more attention around the globe. 

Satya Nadella, CEO of Microsoft, explained that the rate of AI adoption depends upon each specific firm, rather than which industry sector it belongs to. “I think whether it’s in financial services, retail or even healthcare, it’s very exciting to see the broad swathe of industries being reshaped [with AI].”  

AI is important because it helps manage large and complex data sets to extract information usable for the company. AI-powered tools like Microsoft Copilot allow users to interact with complex systems using natural language, making it easier to extract valuable information. AI also benefits labor-intensive activities such as cataloging, cleaning, integrating, and organizing the data that goes into the Digital Core.  

The Digital Core opens opportunities for companies, but it also generates debates within the C-Suite with challenges from finance to security, and IT. Let’s debunk some of these pain points: 

Security Measures in the Digital Core: In the digital landscape, security is a paramount concern for CXOs. The Digital Core is custom-built on hyper-scaling cloud technology and incorporates robust security measures (i.e., advanced encryption protocols, multi-factor authentication, and continuous monitoring for potential threats). The architecture prioritizes data integrity and confidentiality, meeting industry compliance standards. 

Cost-Efficiency Strategies: Cost-effectiveness is a constant challenge for CXOs. The Digital Core optimizes resources through cloud scalability, allowing organizations to pay only for the resources consumed. The absence of a subscription model further empowers clients by providing transparency and predictability in budgeting, eliminating unexpected financial burdens, and allowing for efficient allocation of resources. 

Seamless Integration Capabilities: For CIOs a pain point is the complexity of integrating diverse IT and OT systems. The Digital Core is designed with openness and scalability in mind, facilitating seamless integration with various systems and applications. Through well-defined APIs and standardized data formats, it ensures interoperability, allowing organizations to adapt and expand their technology stack without major disruptions. This addresses the concerns of CIOs regarding system compatibility and ease of integration. 

Scalability for Future Growth: CFOs often worry about the scalability of digital solutions and impact on long-term budgets. The Digital Core, built on hyper-scaling cloud technology, is inherently scalable. It grows with the organization, accommodating increased data volumes and expanding operational complexities without compromising performance.  

Ownership and Control of Data: CXOs are concerned about data ownership and control. The Digital Core addresses this by making the organization the sole proprietor of its data. Unlike subscription-based models where intelligence might be hosted externally, a custom-built core ensures know-how stays with the corporation as is not embedded in an external software which might not be available as necessary and is not part of the digital backbone of its daily operation.   

The technical architecture of the Digital Core not only empowers organizations to harness the benefits of digital transformation but also directly addresses the key concerns of CIOs, CXOs and CFOs. By providing a secure, cost-effective, and seamlessly integrable solution, the Digital Core becomes a strategic asset in driving the success of modern enterprises. 

Once a business reason / use case is defined to create new insights (e.g. sustainability challenges) or innovation infusion (AI), a Digital Core needs to be created which involves key components including:   

• Data Structuring: This component not only manages data ingestion and acquisition but ensures efficient storage, retrieval, and transformation. It plays a pivotal role in structuring data to align with organizational needs and standards, enhancing its overall utility. 

• Front-end single pane of glass: Serving as the main user interface for operational data, the web application is designed with a user-centric approach, providing an intuitive experience. It fosters collaboration by centralizing data analysis, featuring dashboards for multiple KPIs, other metrics, and an alarms and notifications system. 

• Analytics: The analytics application offers flexibility with customizable reports, providing insights into various parameters. Its adaptability and scalability make it a robust tool for evolving data needs and technologies. Additionally, it facilitates diagnostics, high-level management, and control capabilities across connected subsystems. 

• Integration Layer: Operating as more than just a storage area, this component serves as a dynamic communication backbone, facilitating seamless data flow within and between data centers. Its versatility in supporting various data formats and protocols enhances its role in integrating data from defined subsystems. 

• Process Automation and Event Processing: Working alongside the Integration Layer, this component excels in real-time capabilities, managing automated processes and pipelines for data processing. It ensures data quality, consistency, and reliability, contributing to the overall efficiency of the Digital Core. 

• Work Order Management: Derived from insights provided by the web application and reports, the work order management component streamlines operations by initiating actions. It plays a crucial role in fostering a proactive approach, addressing issues promptly, and optimizing workflows based on analytics and data-driven decision-making. 

Each of these components works together allowing the Digital Core to enhance strategy implementation through data driven insights generated from combining external market data with the company’s own performance, lower operations and maintenance costs, reduce failure rates, and achieve the highest return on investment. Overall situational awareness is provided for connected infrastructure, serving as a single point of reference for all involved in joint decision making. 

For example, a Port wants to create situational awareness of its operating entities, with the goal of delivering services to all stakeholders to improve the operational performance and efficiency. This will at the same time increase supply chain transparency or full emissions footprint through the implementation of the different views on an integrated control center. By creating a Digital Core and a Common Operational Picture (COP), the Port Operator would meet the need for awareness across its operation of a single port or multiple locations it is managing across the globe. The COP will replicate information from related sub-systems, and be shared between the command, control and coordinating groups serving as a single point of reference for all involved parties for joint decision making. The Digital Core creates additional insights by further processing the data from the subsystems to create prediction models for operations. 

Leaders may arrive at this point in this post and feel overwhelmed by the importance of the Digital Core and how complex the transformation is for their organizations. There are still so many business leaders who do not see how data and powerful innovations will radically change their businesses. Successful companies will run a hybrid strategy build (Digital Core) and buy (SaaS), leveraging great software products for specific operations. Optimization and an owned Digital Core create enterprise intelligence and control of their digital transformation. To achieve this, leaders will need a guide and engaging the right partner with extensive IT/OT and digital transformation experience is critical for success.  

And remember – the goal is not to connect everything with everything. The Digital Core runs on minimal data with meaningful use cases embedded and can be created based on individual issues or use cases that a specific company faces. It underpins total enterprise reinvention by providing agility, flexibility, easy interoperability, and resilience.  

Sustainability reporting has come a long way since the publication of the first environmental reports in 1989. But so has that amount of data that companies generate. 

In a company's data lies a spectrum of information, spanning from product development details to environmental measurements. However, this diversity in data is often spread across multiple departments, complicating the task of gaining a comprehensive understanding of the whole picture. Breaking down silos or accessing data in divergent systems is a challenge. All of this leads to companies not knowing exactly what data they have access to daily.

As a Harvard Business Review article notes: “Increasingly, companies must come to recognize and appreciate that data is a business asset that flows through an organization. Data cuts across traditional organizational boundaries, often without clear ownership.” With the increased amount of data and the lack of visibility across the business, for most companies, corporate sustainability reporting and monitoring is a tedious and time-consuming task. It often involves manual data collection, data processing, and interpretation. Data constitutes the foundation for various use cases in sustainability reporting (e.g., value chain mapping).

Sustainability reporting and monitoring is becoming more important to customers, politicians, employees, and investors. In fact, “Businesses are not gauged through their financial performance alone. Instead, stakeholders weigh in more on the sustainability aspect of the business than just the profitability.”

Several new reporting rules require data and transparency from companies no matter where they are in the world. For example, in Europe, organizations must comply with the Corporate Sustainability Reporting Directive, a part of the Green Deal reforms, requiring details on sustainability-related impacts, opportunities, and risks. Germany has its Supply Chain Act. And in the U.S. companies are responsible for reporting via the United States Security and Exchange Commission’s (SEC) new climate risk disclosure rule. The UN has additional reporting obligations that also rely on high-quality data sources.

Research by ESG Book found that “1,255 ESG regulations have been introduced worldwide since 2011, compared to 493 regulations published between 2001 and 2010. Since the turn of the millennium to the present day, there has been a 647% increase in ESG regulations.” Without access to the correct sustainability information organizations may struggle to complete requirements.

“Put simply, noncompliance is not an option and could have significant consequences.” Errors or avoiding any of the regulations can result in fines and damage the overall reputation of the company. According to Harvard University Law School’s Forum on Corporate Governance’s report, The State of U.S. Sustainability Reporting, “There has never been a more important time to ensure that company sustainability disclosure is robust, clear and credible—while also keeping pace with the rapidly evolving demands of stakeholders.” 
 

Sustainability reporting creates internal accountability as well as transparency and accountability with external stakeholders. Reporting on sustainability can be complex. It is often made more difficult because leaders do not know specifically what information to report. They are also unaware of the data they have within their grasps leading to one of the largest challenges to achieving sustainability reporting requirements – the way companies gather data for those reports. 

In these situations, the employees do the best they can to interpret the data manually which often leads to errors. A recent Qlik survey showed, “… just 24% of the global workforce claimed to be fully confident in their ability to read, work with, analyze, and argue with data.” In addition, “85% of organizations believe they are ahead of their peers concerning sustainability reporting, but almost half (47%) still use spreadsheets to aggregate their data.” 

There is hope for improving the process for obtaining required information for sustainability reports. In fact, leading organizations have turned to technology to automate the harvesting and interpretation of data. 
 

Achieving the technical automation necessary to assist in data gathering for sustainability reporting requires both a clear business strategy and an appropriate structure to unite IT/OT and other various data sources within the organization. 

Let’s look at the strategic component. There are four questions that the executive team needs to ask:

1. What data are we already collecting?
2. What data is required for reporting regulations?
3. What do we want to collect in the future?
4. Who is responsible for capturing and harmonizing the data?

Once there is a clear and documented approach capturing the data required for automated collection and report generation, then the company can move to building the appropriate IoT framework to make this solution a reality.

“An IoT framework is a set of protocols, tools, and standards that provide a specific structure for developing and deploying IoT applications and services.” It enables collection, standardization, and processing of data. It also connects with IT/ OT where data from various sources within the company (e.g., in ERP systems or on operational/shopfloor level) are assimilated, hosted centrally, and interacted with through a dashboard. The entire process is done in real-time, is cost efficient, reliable, and easy to use. Ultimately, the IoT Framework generates a holistic overview of the company and its progress on ESG initiatives.

A critical part of the IoT framework is the interplay of the semantic layer and processed data because it is the foundation for future processing to aid in certification, reporting and visualization. Since the data is from various places within the organization and may not follow the same format or criteria, “it is necessary to implement a method of sophisticated data integration … it is challenging to efficiently fuse a large amount of probably noisy data and then infer an accurate result.”

“Incorporating data fusion, the process of merging multi-source data to increase integrity, allows companies to effectively deal with noisy data of dynamic environment, and helps the decision-making process based on the available information.” This means the heterogeneous data of an organization is conveyed, stored, and accessed in the same way. “The data structure is expressed through the links within the data itself, it is not constrained to a structure imposed by the database and does not become obsolete with the evolution of the data. When changes in the data structure occur, they are reflected in the database through changes in the links within the data.”
 

Automatically generated sustainability reports provide several advantages compared with manual processing, including: a reduction in errors, allowing employees to focus on more value-added work, and faster, more accurate production. 

In addition to ensuring accurate data is available and referenced in automated sustainability reports, the IoT framework delivers even more benefits to the organization. For example:

• It allows for improved data quality and interpretation because it can be queried easily, and calculations are based on real data and not average or estimates. 
• The interactive dashboard used in many IoT framework solutions is automatically updated allowing the sustainability manager (and others) to manage process on goals.
• IoT frameworks offer advanced visualization and modeling. The information is shareable in several views and dimensions (technical, commercial, geographic).

Spreadsheets and manual data entry aren’t made for the era of Big Data and IoT. Changes are necessary across industries and around the globe to move to more automation such as IoT frameworks, especially for gathering data to demonstrate sustainability growth and achievements for companies. 

Leaders must accept technology’s role because sustainability reporting is a necessity. In fact, “Regulators and investors hold sustainability reporting to a higher standard: Companies making sustainability claims without defensible data [supporting information generated through a data-driven approach] put themselves at risk of greenwashing lawsuits and other liabilities.”

Therefore, “Companies … need robust reporting technology to help ensure organizational alignment while meeting complex reporting requirements. A focus on data consolidation and using an IoT framework are essential for businesses to “achieve their strategic goals, comply with regulations, and become a leader in sustainability.”

Embracing technology and implementing robust reporting systems are imperative for companies to navigate the evolving landscape of sustainability reporting, mitigate risks, and position themselves as leaders in sustainability, all while ensuring transparency, credibility, and organizational alignment.

Urbanization is on the rise. More than half of the world’s population now live in urban areas and this is expected to increase to circa 70% by 2050, according to the United Nations.  Around the globe, and particularly in the Middle East, there are many plans underway to develop entirely new urban living areas, to accommodate the population rise and expatriate influx.  Both Abu Dhabi and Dubai were recently ranked as the smartest cities in the MENA region.  With many new infrastructure plans underway, this ranking is expected to climb even higher globally.  

These trends set a pressing need to build and expand services, infrastructure, city management, and controls within these increasingly populated areas. With these numbers expected only to grow, strategic planning is of utmost importance to ensure long-term efficiency, safety, and quality of life. As the technological landscape continues to develop at a rapid pace, and digitization now touches nearly every industry, a key component of current and future planning will be the integration of smart and cognitive solutions.
 

Before continuing, it is useful to set some parameters around what we mean when we say ‘smart’. The word itself is central to so much innovation, and as a prefix, it has migrated away from being attributed to personal devices (such as a smart watch or smart phone), to increasingly becoming a feature of many target systems and services that will be provided by governments and private companies, forming the interface between users, customers and service providers with the end goal being to create more efficient and optimized services.  A ‘smart’ system will be able to operate in its own silo, typically without the need for human intervention. It will be able to track, recognize and produce data or responses according to its purpose. 

But where are we today? Across the globe, we have been edging towards a more integrated digitized experience for some time now. With new smart features being introduced that form the initial buildings blocks of fully integrated smart cities in the future. The Middle East has already long been a proponent of innovation and leveraging the best of disruptive technology. In particular, the UAE and Saudi Arabia are setting a new benchmark when it comes to smart technology for customers and residents. Smart systems are becoming more common across many government and private buildings and processes and have been for several years now. They provide responsive interfaces for users and consumers and relieve much of the manual efforts, as well as costs to services, and provide significant reductions on environmental impacts. Dubai has over 100 Smart initiatives and over 1,000 smart services - many as a result of collaboration and cooperation between government and private companies. This number is expected to grow significantly, in line with the number of exciting new infrastructure products as part of the agenda to become ‘the happiest city on earth’. This vision is underpinned by the objective of leveraging technological advancements to optimize resources, integrate services seamlessly, and protect people and their information. 

Siemens Advanta recently worked with Dubai to complete an Internet of Things (IoT) strategy for the city to help identify, prioritize and define smart initiatives, which included an assessment of central computing platform options to create an integrated citywide command center. Implementation of these initiatives is expected to lead to substantial benefits, including operational cost savings (up to 30%), improvement in overall resident and customer satisfaction (up to 20%), reduction in emergency response times (up to 80%), and reduction in non-revenue water use (up to 35%).
This is an excellent case study for demonstrating that buildings and environments that adapt to human needs, create safer and more harmonized living and contribute to a more sustainable future. Overall, folding smarter and sensory technologies into urban planning, and architectural and interior design will not only increase competitiveness but ensure that present and future generations’ economic, social, and environmental needs are also met.
 

Smart security systems, smart access, and smart metering (to name a few) are now becoming part of common jargon. More recently, and in line with the accelerated growth of technology, the term ‘cognitive’ has also entered the lexicon.

What’s the difference between smart and cognitive systems? While a smart system is used to measure, track, communicate, and collaborate with its users, a cognitive system independently intuits and anticipates. 

To put it another, simpler way: imagine a meeting that is held in a conference room at the same time every week. The heating system for this room is accessible and controllable from an individual mobile device. Given the number of attendees within the meeting room the temperature of the room can increase, so the users are able to adjust, using their devices. That is a smart system. But now imagine that that same system would recognize both the pattern of the meeting cadence and the increase in temperature and then adjust itself at the same time the following week to accommodate the extra body heat. That is a cognitive system.

Both systems are fundamentally intelligent but as this example shows, while a smart system is able to be digitally controlled, a cognitive system goes one step further and is able to adjust and self-regulate according to user and customer needs. Through prediction and enablement, cognitive systems offer the most sophisticated, human-centric solutions that exist today.

The concept of cognitive is still considered emerging, but it is a trend that we can expect to see growing in the very near future and ultimately, replacing its ‘smart’ predecessor. With the use of self-learning artificial intelligence (AI), a cognitive infrastructure will be able to provide fully integrated solutions that move away from siloed systems, and communicate and learn across a network of enabled services. It is the ultimate vision for a fluid end-to-end process that benefits both the user/customer base and the provider through seamless experiences, while enabling long-term cost savings and significant reductions in environmental impacts for the provider. Simultaneously, its sophistication will only continue to grow with machine learning technology at its core.

But getting it right is key: from the very first point of data, a cognitive system needs to be able to accurately register and understand the user requirements. We have seen minor challenges with this in the recent past, with many of the machine learning technologies that make up cognitive solutions. Given that many of them are still at a comparatively nascent stage, the rate of pace at which they are developing and improving is extremely fast. 

Smart and cognitive infrastructure is not just about increasing the end user’s human comfort level and ease of effort. From an economical point of view, they can deliver long-term cost savings by reducing the wastage of services and energy. There is a wider imperative and obligation to ensure that we are investing in longer-term sustainable solutions that will help reverse the negative impact on the environment. As the topic of ESG (Environmental, Social and Governance) rises on the agenda for both government and private companies alike, both are looking to technologies to enable their core objectives.  The valuable data collected by these systems can enable city and building management to make informed and strategic adjustments. This covers all three layers of a city and building’s infrastructure:

1. The sub-layer: includes water, gas and electricity delivery and waste disposal
2. The surface level: engages directly with users, residents and customers, such as transportation, retail, in-person services and recreational activities
3. The vertical layer: includes energy consumption

Applying a digital layer across all three of these provides a more detailed insight into gaps and required adjustments than has ever been accessible before.

Cognitive systems will provide a competitive advantage across multiple industries, including healthcare, manufacturing, mobility, and buildings. Through customer and user-centric data gathering, these systems can continuously update and amend their operating structures based on customer and user preferences and interactions. Businesses will benefit from ongoing optimizations to their human services, environmental impact, safety & security, utilities, and operational intelligence. Stay ahead of the curve with the limitless potential of cognitive systems.

There is no doubt that technology adds to the desirability and attractiveness of residential properties, communities and services, as well as tourist experiences. From a user and customer perspective, smart and cognitive systems can improve quality, ease of use and safety and security, among many other benefits. Meanwhile, cost reduction, ESG considerations, improved data management, and enhanced governance all stand as pivotal advantages for service providers, thereby futureproofing their business and ensuring continued growth through investments in smart technology.

In the imminent future, the most prosperous communities will have technology at their core, equipped to facilitate growth and development. All businesses, both public and private, should start to consider how their ways of operating will tie in with a larger, more cognitive-based infrastructure that will underpin future ways of living.  

The movement of products from point A to point B is a crucial part of commerce no matter where you are in the world. Commercial vehicles (CV) or trucks that are part of fleets comprised of one to several thousand vehicles transport these products. Post- COVID, the global market for freight trucking continues to grow with a projected expansion to US $2.9 trillion by 2027.

According to our recent research report focused on the European trucking and fleet management industries, out of the 36 million CVs on European roads, 6.4 million are trucks with >3.5 tons. France, UK, and Italy have the biggest fleets in total; while Poland, Germany and Italy have the largest fleets of medium and heavy commercial vehicles. In addition, we found 3.9 million trucks operating in the Road Freight Transport sector in the EU and more than 500,000 Enterprises registered with the same government organization. (1)

That’s an impressive number of trucks and drivers on the roads. 

The businesses that own these CVs must have a view of where the trucks are at any given time, which usually falls upon the Fleet Manager to oversee.

In recent years, Fleet Managers turned to digital solutions for efficiency. Fleet management systems (FMS) or professional software that allows the tracking of operations, driver management, and incident management as it relates to the fleet are vital to successful oversight of a company’s logistics, trucks, and profits. 

Unfortunately, while FMS are often seen as the number one profitability lever for Fleet Managers to improve operational efficiency of their truck fleets, there is no single, end-to-end, comprehensive solution available on the market today. 

According to an article in Forbes Advisor, the complete end-to-end FMS solution would have “core features that allow you to manage your drivers, vehicles, operations and… integrates with other business software tools like inventory management and CRMs. The vehicle management portion of the system should allow you to track your drivers and vehicles, such as through a GPS tracking system. It should also make operations management run smoother by helping you manage fuel and labor costs and provide proper trip planning for your drivers and customers.”

Today, Fleet Managers must review multiple platforms to obtain an accurate picture of their organization’s operations, safety, and other critical data. Despite having a large and significant role, Fleet Managers find themselves stuck with an inefficient process. 

Our research confirms that the FMS market in Europe is very fragmented. For example, the top 10 FMS providers collectively hold a market share of approximately 30%, with an estimated 13.2 million active units in Europe. These providers include Original Equipment Manufacturers (OEMs), Original Equipment Suppliers (OES), and Software Solution Providers. Today, each works in their own area. For example: 

• OEMs build their own FMS solutions with some collaboration initiatives. 
• OESs start building ecosystems by acquiring software solutions providers (ex. Bridgestone purchased Webfleet, formerly known as TomTom Telematics, for €910 million).
• Telecom providers such as Verizon and Deutsche Telekom build the backbone of FMS by offering IoT modules, terminals and network services while also offering their own FMS. 

While the breadth of services offered by these three groups seems thorough, we still must ask: ‘Why is there no single solution to this clearly obvious industry challenge?’ 

The answer is that no company has been able to connect all required information into one system. This is a tremendous opportunity for leaders looking to transform the FMS industry. 
 

Against the backdrop of an inefficient FMS solution, the global FMS industry is experiencing market consolidations through M&A, a push to innovate, and an increased number of partnerships formed. 
Recent significant partnerships include:

• Renault’s June 2022 partnership with telematics supplier Geotab to integrate its models into the MyGeotab fleet management platform. The collaboration will enable fleet managers' decision-making through enriched data and access to connected solutions for fleets of any size. Any Renault model built in 2010 or later is compatible with the integration and can receive a factory-fitter or retrofitted telematics solution for vehicles in the field. The new telematics solution is available for fleets in 21 European countries, including France, Germany, Spain, Italy, the UK, and Nordic countries.
• Geotab Inc., a global leader in IoT and connected transportation, entered into a partnership with Sygic in December 2021 to provide customers with offline GPS navigation and route planning for trucks and light commercial vehicles.
• Webfleet, Bridgestone’s FMS, enables the integration of its telematics solutions in all MAN trucks with the existing OEM hardware RIO Box from MAN (May, 2023)

Additional partnerships and M&A activities will have a significant impact on the landscape of the market – from defining the market leaders to potentially creating the FMS that Fleet Managers need to do their jobs. 
 

There is always room for industries to improve. We offer recommendations for change in the European market specifically it’s time for European FMS providers and data owners to prioritize a new organizational mindset. This mindset promotes integration and embraces innovation to stay competitive in the highly fragmented, and dynamic market. Let’s look at each of these a bit closer.

Organization Mindset

• Apply venture approach: Establish independent digital builders with adequate funding by applying venture capital principles.
• Initiate mindset change: Establish fail-fast culture, iterative design-thinking, and persona-focused product development.
• Push comprehensive thinking: Focus both on “pure” digital revenues and their “pull-through” role as drivers of hardware sales.

Integration

• Build ecosystem partnerships: Partner or acquire industry players to accelerate product development and third-party marketplaces. For example, the recently announced partnership between Webfleet by Bridgestone and RIO by TRATON.
• Guarantee FMS integration: Fully integrate FMS in existing IT-landscape of fleet manager including CRM and carrier Transportation Management support. 
• Become hardware agnostic: Decouple hardware from service portals to guarantee low switching costs and multi-brand fleet support.

Innovation

• Use big data: Differentiate through innovation by being at the forefront of big data analytics and machine learning.
• Offer data brokerage services: Leverage power over data interfaces by offering commercialized data sharing to industry players.

With these elements in place, we can envision a future where trucks are fully connected, and fleet management is optimized and highly automated with state-of-the-art digital solutions. 
 

As the global trucking industry is expected to grow, the FMS industry anticipates expanding to reach US$55 billion by 2030. (2) Combined with the three megatrends of market consolidation, technology innovation, and developing industry ecosystems, it is time to capture the full potential of this market while not losing competitiveness. There are many opportunities for those companies in Europe (and beyond) that rethink strategies and leverage innovative digital solutions. 

Consider these next steps to enable fleet management success: 

• Building or participating in one-stop-shop fleet management ecosystems addressing all the needs of fleet managers in their daily operations
• Using data sharing principles in ecosystems to enable big data analytics and machine learning (predictive maintenance)
• Growing connected services business by keeping up with the competition and profit from strong FMS market growth in the next decade
• Being part of a more sustainable future by driving operational excellence in truck fleets
• Driving hardware/truck sales with state-of-the-art FMS and multi-brand fleet compatibility features

Leveraging digital solutions, creating ecosystems, and simplifying processes will transform this essential market. 
 

“In our current economy, we take materials from the Earth, make products from them, and eventually throw them away as waste – the process is linear. In a circular economy, by contrast, we stop producing waste in the first place.”

The concept of circular economy is not limited to only manufacturing or waste management sectors – it is crucial for every industry, especially MedTech and healthcare. Owing to increasing environmental damage and pollution rates caused by large corporations, other equally responsible sectors have been overlooked in the public domain.

Despite being 28 percent smaller than the automotive industry, the global MedTech sector contributes to 13 percent more greenhouse gas emissions.

MedTech is any technology used to save the lives of people suffering from a wide range of conditions. It can range from syringes and latex gloves to heart values and pacemakers or replacement joints for knees and hips to total body scanning machines. 

The future of MedTech lies in the power of networks and data ecosystems, enabling the industry to build a circular and resilient healthcare ecosystem. Data ecosystems include various actors, like services, and software applications that use data to share and utilize it economically or socially. In many circular economy scenarios, this involves the network or the networking of companies. “By making these changes, transformation of the medical device industry to a more circular economy would advance the goal of providing increasingly complex care in a low-emissions future.”

This scenario would mean that the companies stop looking at the life of a device as linear (production through consumption). Instead, these same organizations would look at the “consumed” product as the raw material for another product. End-of-life is extended, device-cost-per-use goes down, and there is more control of the supply chain. The device does not end up in the landfill after only one use.

In the following article we will provide experience-based insights on why MedTech companies must embrace networks and data ecosystems to navigate this complex landscape. Furthermore, we will show how companies managed to harness the power of connectivity and data-driven insights to meet the demands of a circular economy and beyond - ultimately thriving in a sustainable future.

Barriers to circularity in the MedTech industry include perceptions regarding infection prevention, behaviors of device consumers and manufacturers, and regulatory structures that encourage the proliferation of disposable medical devices.

Even with these barriers, our experience shows that there are several significant reasons why MedTech companies need to invest in circularity, including:

• Regulatory compliance: By closing raw material loops and thereby reducing the product carbon footprint, the circular economy is a crucial element in a wide range of relevant frameworks and regulations (e.g., UN Sustainable Development Goals, Paris Agreement, Green Deal, etc.) and therefore an imperative to avoid fines and penalties.
• Resource Efficiency and Cost Savings: In times of rising material prices, using recycled or reclaimed materials leads to cost savings in raw material procurement, production, and waste management thereby improving the company's profitability and operational efficiency.
• Resilience and Supply Chain Stability: By reducing the dependence on scarce resources and minimizing vulnerability to price fluctuations or supply chain disruptions, circularity contributes to mitigating the risks associated with decoupling, climate change, resource depletion, and economic instability.
• Access to Sustainable Financing and Investors: Companies that embrace circularity are more likely to attract sustainable financing options and gain the interest of investors seeking companies with strong ESG performance, ultimately providing access to capital for growth and expansion.
• Development of new business models: Circularity drives companies to innovate in product design, business models, and operational processes leading to the generation of new revenue streams and services that provide ongoing revenue instead of relying solely on one-time sales.

Let’s explore how circularity is improving industries like Automotive and ways in which we can apply these insights to the MedTech industry.

Increasingly stricter environmental regulations regarding electric mobility, rising prices of raw materials, and supply chain shortages provided an impetus/incentive for the German Association of the Automotive Industry (VDA) to start the development of Catena-X in 2020. This became one of the first and biggest circularity-focused digital ecosystems that connects companies throughout the automotive value chain. 

The mission of Catena-X is to enable the digital flow of information across the entire supply chain from manufacturers to suppliers and service providers. Catena-X develops a digital map of the circular economy in the automotive industry - with more than 2,000 partners and across all levels of the value chain. This digital infrastructure enables secure and trusted end-to-end data exchange, facilitates supply chain optimization, supports new business models, and promotes innovation.

Data is a fundamental building block of the circular economy. It refers to the structured and standardized information exchanged within the ecosystem. This data includes product information, supply chain data, production data, vehicle data, and customer-related information. The data is collected, stored, and shared in a standardized format, enabling interoperability between different systems, and facilitating seamless collaboration between partners.

Another essential building block for the digital mapping of value-added processes over the entire life cycle in Catena-X is the European Union’s Digital Product Passport (DPP). This tool creates transparency and unlocks circularity proposed by the European Commission that will share product information across the entire value chain, including data on raw material extraction, production, recycling, etc. Digital product passports function as a holistic digital twin and play a crucial role in closing the information loop by providing actionable insights on ownership, materials, and lifecycle events.

The Battery Passport, a version of the DPP, represents the first use case of collecting information across different companies starting with the extraction of raw materials which was vital for Catena-X as it laid the groundwork for key issues in transparency and data availability. It even captured individual battery cells composition and high voltage storage. Moreover, it collected dynamic data from the use phase to the dismantling and recycling processes until the final life cycle stage.

The Battery Passport was key in facilitating the rapid scaling of sustainable, circular, and responsible battery value chains to meet the targets of the Paris Agreement through electrification of the transport and power sectors. And these accomplishments happened during the development state of the Battery Passport. In fact, a Battery Passport prototype was officially released by the Global Battery Alliance during the World Economic Forum’s Annual Meeting in Davos in January 2023. 
The information collected is used for more precise calculation of reference values, the optimization of production processes and data-based decisions, (e.g., whether a battery can be used as stationary storage unit at the end of a vehicle's life cycle or which measures are suitable for achieving the highest possible recycling rate).

The infrastructure of Catena-X consists of the technical framework and digital infrastructure necessary to support data exchange and connectivity. It includes standardized communication protocols, Application Programming Interfaces (APIs), cloud computing resources, and security measures. This infrastructure enables the seamless flow of data and information across different stakeholders in the automotive value chain.

With transparency along the value chain and the associated knowledge regarding the origin of materials as well as the product carbon footprint, Catena-X members are well prepared to tackle future regulations and are already seeing savings per year by avoiding import duties and penalty payments.

Catena-X also encompasses a wide range of value-added functionalities and applications built on top of the infrastructure and data components. These services include analytics, predictive maintenance, supply chain optimization, digital twin simulations, and other digital tools that enhance operational efficiency and decision-making. By leveraging shared data and infrastructure, these services enable stakeholders to gain insights, optimize processes, and create new business models.
In addition to the effects on productivity/savings, Catena-X enabled the pioneers of the battery passport to communicate with one voice to the government institutions and thereby influence the establishment of standards. 

Our experience with Catena-X demonstrates that it is not a question of if - but rather of how quickly - one can start building comparable circular structures. It will indeed take time. In fact, it took three years to establish Catena-X as a case study for the circular economy despite this project having the highest prioritization at the board level, the establishment of cross-divisional teams, and the empowerment of IT systems. It is critically important to not underestimate the time needed to adequately ensure the network connectivity and data availability required to implement circularity in an industry.

Catena-X should serve as an example to the MedTech sector that companies can only succeed in meeting stricter requirements (such as CO2 emissions standards, recycling quotas, etc.), with the help of circular material and raw material cycles by working together with partners along the value chain.

The automotive industry shows that cooperation between OEMs and suppliers can happen and that continuous data flows can be established along the product life cycle thereby creating value for all involved. At the same time, it is evident that technologies such as digital twin or product passports play a decisive key role along with the alliance of companies.

Importantly, Catena-X serves as an example and lessons and proof of concepts learned from it, in our opinion, can be transferred and adapted to other industries. . Even though Catena-X has a strong focus on current activities in the EU, we believe the findings can also be applied to other regions such as the USA or APAC. Technologies and solutions such as the digital twin or product passports can be established and used independently of regional markets.

According to a recent Health Affairs report, “Transition to a circular economy begins with a commitment to high-value care. This broader framework can drive efficiency of facilities operations with respect to energy and waste management and can nudge clinicians to be mindful of resource consumption and to select environmentally preferable drugs and devices where choices exist. Adoption of such high-value principles in procurement will foster circular and ethical supply chains.”
But the transformation to a more sustainable or circular economy can’t just be for philanthropic reasons. There is a strong business case for these changes to ensure competitive advantages for MedTech companies.

Even if all the regulations and adoption drivers are not applicable to your organization today, turning a blind eye to the need to change will have adverse effects in the future. Decisions made today influence whether MedTech companies can contribute significantly to a sustainable healthcare system while remaining at the forefront of innovation. 
 

The Metaverse is the next chapter of digitalization and offers enormous possibilities. 

But what is the Metaverse? As of today, there is no universal definition. 

Siemens Technology provides this perspective: "The Industrial Metaverse (IM) is a space to experience Digital Twins, enabling faster, more efficient and less resource intensive problem solving. Real-time data and predictive information made available to people collaborating in a virtual, immersive environment, facilitates smart and even autonomous decision-making across the entire product lifecycle."

A second view presented by Gartner is that the Metaverse is “A collective virtual shared space, created by the convergence of virtually enhanced physical and digital reality. A Metaverse is persistent, providing enhanced immersive experiences.”

Meanwhile, the author of an IOT World article stated: "The Industrial Metaverse is an ecosystem for industrial innovation, integrating new technologies with the real economy. In this way, the digital world is free of physical space constraints, forming a highly dynamic digital world with abundant resources. The Industrial Internet infrastructure is the core of the Industrial Metaverse, but not its only part. Despite its similarities to the Internet, it’s crucial to understand how this new ecosystem can transform the industrial world.”

Adding to the complexity about defining the Metaverse, ABI Research notes three Metaverse “worlds”:

• Consumer Metaverse: Providing digital services and media (gaming, social media)
• Enterprise Metaverse: Using benefits of increased collaboration and immersion in the context of enterprise application (education)
• Industrial Metaverse (IM): Providing benefits to industries core processes, specifically tapping improvement potentials with Digital Twins and simulations (manufacturing).

Today, many associate the Metaverse with the world of games, online retail, and social media. But one could argue that the most significant and biggest potential of the Metaverse lies with industries that make up the backbones of our economies – manufacturing, buildings, grid and infrastructure operators, or the transportation sector.

In fact, according to Michael Inouye, principal analyst at ABI Research, the Industrial Metaverse will grow faster than both the Consumer and Enterprise Metaverses.

Why will the IM be larger than the Consumer or Enterprise Metaverse?

• The IM is ahead on 3D simulations and Digital Twins. 
• The IM is ahead on standards like Nvidia’s Omniverse platform. 
• The consumer space is fragmented, with multiple companies claiming to have established “Metaverses,” most of which are not interconnected or interoperable. Digital assets are typically locked to a particular ecosystem, servicer or game. 
• The IM are also better grounded in ROI, meaning more trials and initial deployments have a higher potential to succeed or lead to more adoption compared to consumer efforts.

Whether you see the Metaverse as a consumer, enterprise, or industrial game-changer, the truth is that the market is growing. According to Statista, “the Metaverse market will be worth $678.8 billion by 2030.”

The Industrial Metaverse is the next evolution of Digital Twins. Digital Twins are representations of real-world processes and assets in the digital world. The IM differentiates itself from a pure Digital Twin by fulfilling three essential key characteristics (2):

• Immersion: Being “pulled into” a persistent environment by (photorealistic) 3D visualization on screen and/or virtual/augmented reality (XR) devices
• Interaction: Receiving instant feedback from real-world assets and their Digital Twins, as well as between different types of Digital Twins and simulations
• Collaboration: Working simultaneously on the “single source of truth” with multiple contributors, disciplines, and stakeholders. 

Once the Digital Twin is established, there are several other technologies that need to be explored and integrated into a company’s digital toolbox and leveraged for IM. These tools and technologies driving the IM include:

• IIOT: Sensors, controllers, and other devices that collect data and allow for remote monitoring and control of industrial processes and assets, as well as high-speed, low-latency networks will be crucial for seamless communication between the virtual and physical systems.
• AI: AI technologies, such as machine learning and natural language processing, can automate and optimize industrial processes and decision-making. 
• Augmented and virtual reality: AR and VR tools provide an immersive experience of industrial processes, allowing users to interact with and visualize data in 3D. 
• Cloud computing/Edge Computing: The challenge is to find the optimal mix of cloud and edge computing. Cloud computing provides a way to store, process, and analyze large amounts of data, making it easier to manage and access information from different locations. While edge computing supports the IM by enabling much leaner, lighter headgear by offloading a large part of the computer from the device to an edge infrastructure—while also providing superior speed and low latency capabilities. Without edge computing, there will simply be no Metaverse.
• Cybersecurity/Blockchain: Cybersecurity tools secure and protect the IM. Blockchain technology secures and manages data in the IM ensuring that it is tamper-proof and easily accessible to authorized parties.
• 3D modelling/Scanning: Realistic, eventually photorealistic replicas of the real-world support management, operational leaders, and project teams in decision making as they can intuitively feel the change in an environment before it happens in real life. Improvements in scanning technology are needed to quickly generate a realistic 3D model of an environment on the move. This is important to achieve the “Immersive” element of IM.

While the IM may not be a reality just yet, waiting for it to arrive before starting to embrace it could be a costly mistake for organizations. The evolution of the Digital Twin has already begun, and the next logical step is to build an immersive and collaborative digital world that interacts with the physical world through the IM. Solution architecture is a crucial element of this transformation and data management is the backbone of it all. 

Here is a five-step roadmap to embrace the IM:

1. Assess current digital capabilities: Evaluate data infrastructure, analytics capabilities, and scalability of digital solutions to identify gaps. 
2. Develop a strategy for the IM: Identify the use cases that the IM will enable, defining the scope of the initiative, and setting goals and objectives – most importantly understand which IM use cases are relevant to the business and bring a value add – no digitalization for digitalization's sake.
3. Upskill the workforce: Generate the necessary expertise to operate in the IM including investing in training programs to develop skills in advanced analytics, AI, and Digital Twin technologies. Ensure that you have the right talent to drive the initiative forward. 
4. Build partnerships with key technology vendors: Partnerships with key technology vendors and service providers ensure access to the latest tools and expertise required to operate in the IM. 
5. Invest in technology and infrastructure: Upgrading data infrastructure, adopting new technologies such as 5G and edge computing, and investing in advanced analytics and AI capabilities are essential.

The industrial metaverse will be an interface between the real and digital worlds and will transform how we work, live, and interact. Let’s look at three early adopters of the IM and what could be on the horizon as they continue the digitalization journey. 

Coca-Cola HBC, a partner of the beverage giant, used the IM to enhance the sustainability and resilience of its supply chain. By working with Microsoft, Coca-Cola HBC created an immersive digital replica of its bottling facility in Edelstal, Austria, ultimately minimizing waste and increasing sustainability while boosting operational efficiency. To reduce the carbon footprint associated with transportation, Coca-Cola HBC implemented automated yard management and vision picking which improved resources and availability checks, as well as guiding trucks into loading docks, and minimizing errors. Ultimately, Coca-Cola HBC aims to achieve zero carbon emissions by 2040.

In this example of supply chains enhanced by the IM, Coca-Cola HBC achieved greater operational efficiency, sustainability, and profitability while meeting the evolving demands and expectations of its clients and stakeholders. According to our research, Coca-Cola HBC could expand its IM capabilities in three ways:

1. Establishing an integrated set of Digital Twins for real-time, collaborative communication among suppliers, distributors, and retailers to analyze sales projections, production schedules, and potential supplier restrictions to optimize supply chain management. Additionally, they could monitor inventory, capacity, and shipment information on a 3D supply chain network map (immersive) to identify potential shipping delays and model workarounds to ensure efficient delivery.
2. Introducing a smart packaging system that enables real-time tracking of a can‘s or bottle's position and inventory status (interactive). By using edge computing and data analytics, Coca-Cola HBC could optimize replenishment and routing decisions, shape demand by dynamically altering prices based on customer preferences, and improve overall profitability by reducing waste and maximizing resources.
3. Leveraging AI-generated synthetic data to create more precise and responsive forecasting models to balance supply and demand. The use of synthetic data allows for adaptation to unforeseen occurrences like pandemics, natural disasters, and geopolitical shifts. By selecting the most environmentally friendly modes of transportation and routes, Coca-Cola HBC may improve production scheduling, transportation costs, and decrease carbon impact.

As a second example, General Motors (GM) has been using Process Simulate from Siemens to create an ergonomically efficient production line in a short period of time. GM must update its production line on a regular basis to accommodate for design changes of existing vehicles and production of new cars. For efficiency, engineers work remotely with a virtual reality device to immerse themselves in the designs. It helps understand manual assembly, hand clearances, operator movements, and operator’s line of sight. With this information, engineers can identify a problem at an early stage and solve it before the issue occurs in real life.

The team at GM is leveraging the motion capture possibility with Process Simulate where a line design engineer wears a suit and performs the activity that an operator will do in real life. The captured motions help the engineer understand what the awkward positions are and for how long an operator needs to be in that position. Engineers can ergonomically optimize the production line and reduce work related health problems.

Can GM advance in other areas with IM? According to our research, yes there are additional improvements to consider.

1.  All the motions captured will be combined with biomechanics (study of how the bones, muscles, tendons, and ligaments work together and have an impact on the fatigue of the operator). Future software will simulate the biomechanics of a specific operator performing tasks over a long period of time. Based on the simulation health issues can be identified accurately and solutions like customized Exosuits or tailored Personal Protective Equipment can be created for the operator.
2. All these 3D models of operators can be converted into a Digital Twin and used to simulate realistic factories. GM has a large production workforce and a high number of robots in the line, making it important to check that operator movements would not be hindered by the robots. GM can make sure that operators and robots work in perfect synergy before commissioning the production line.
3. Simulate and track the operator tasks in real-time. By tracking biomechanics live, precautionary measures can be taken before any work-related disease or accidents occur. The IM will help companies secure the health and happiness of their most important resource: HUMANS.

A third example of using the IM is at automotive OEMs. This industry has been using virtual reality and other digital technologies to optimize manufacturing and improve designs for some time. But now, these companies are faced with taking transformation to another level. Here is where the Digital Twin of Planning comes into play because it can simulate an entire production line accurately. Ultimately, it will help virtually plan entire factories before a single brick has been laid.

One OEM set its sights on creating an environment where the Digital Twin of Planning is neither based on trial and error nor on manual calculation or human experience but rather based on real life, real time, and accurate measurements from the factory shopfloor. The data from virtual simulations and the real production data run in parallel with all nonconformities being captured and assessed.

To support this, an IM architecture was developed ensuring that all authoring tools (as data sources) were connected to layers that allow for joint and connected simulation and visualization. The heart of these connections is a data layer and the management thereof in between authoring tools, simulation and visualization layers.

With the capability of simulating entire productions prior to any real undertakings, the OEM reduces the risk of new technology introductions, has stricter adherence to ramp-up curves, earlier concept validations, and overall, a more stable production process and better understanding of the behavioral model of a full factory. The Digital Twin of Planning and IM architecture also support lower levels of energy consumption, thus supporting sustainability, and to drive a more flexible, modular production where it becomes feasible to automatically, at the click of a button, select the optimal plant to produce a certain part or model.

Our research shows that this automotive OEM can further grow and create the Digital Twin of Operations which has the potential to improve simulations, including predictive maintenance and real-life digital control functionalities.

While the Industrial Metaverse in its fully evolved state is still some way off, the time to start preparing is now. By embracing the evolution of the Digital Twin and taking proactive steps to prepare for the IM, organizations can position themselves to succeed in a future that will undoubtedly be shaped by these emerging technologies.

The IM is set to enhance the way companies operate. Improvements targeting the design and planning processes will drastically shorten product life cycle times, accelerate and simplify new product introductions, and increase productivity in planning processes. Not only will the Industrial Metaverse disrupt the planning of future business processes and production environments, but it will also optimize existing ones by reducing process times, improving product quality, freeing up cash by reducing inventory and completely changing the way we train and empower employees.

Those who don’t prepare for these changes, risk being left behind in the VUCA world. 

Television shows and sci-fi movies have brought us Artificial Intelligence (AI) and 3D simulations for decades. While some of these creations were out of this world, ideas such as digital twins (used in movies such as Mission Impossible and Her) are very real and have demonstrated value in many industries. Digital twins are simulations of real-world processes and assets in the digital world. 

With the need for faster, better, and more accurate treatments for patients, the healthcare and life sciences industries are turning to what a few years ago would have been unimaginable – personalized medicine powered by technology like digital twins to ensure patient treatment. In fact, according to one survey, 75% of U.S. consumers wish their healthcare experiences were more personalized … Patients would visit their healthcare provider more often if the experience was catered to them as individuals. This level of care can range from personalized treatment plans, to implants, to new drugs based on individual personal needs. As per market research by TMR, the digital twin in healthcare industry size stood at US$ 448.9 Mn in 2021 and is likely to surpass US$ 5.3 Bn by the end of 2031.

Digitalization, especially digital twins, can become a major enabler of personalized medicine. However, implementing digital twins is not easy. It requires substantial investment combined with a different digitally enabled operating model. As leaders in healthcare and life sciences are learning, it is unfeasible to introduce personalized medicine on a large scale, without changing the traditional engineering and manufacturing regime while utilizing digital twin technology. Additional costs and efforts for engineering, production, documentation, release management and post-market surveillance lead to significant investments for personalized medicine that are not cost-effective at scale within the current structures. Therefore, a targeted approach and an experienced technology partner are necessary to adapt traditional approaches and enable digital twin technology so that they create real business value.

To enable personalized medicine, reality needs to be simulated using digital twins along the whole value chain - from product development to production to supply chain management to performance / operations. This approach will increase efficiencies in engineering, enable small batch production scenarios at reasonable extra cost, allow for a faster time-to-market, and increase the security and quality of usage.

The digital twin integrates all data, models, and other information of a physical asset generated along its life cycle to predict and optimize performance. They can be demonstrated in three ways for healthcare and life sciences – product, production, and performance. Let’s look at these areas more closely.

Whether you are designing and engineering a personalized / customized medical device (e.g., implant) or creating a personalized drug as part of a personalized treatment plan, two key factors are important:

1. The effectiveness of the device or drug based on the personal conditions of the patients; and
2. The safety of the patient. 

With a digital product twin, both requirements can be simulated in the digital world through a digital representation of both the product and the key conditional factors of the patient. It then allows for an effective design, a fast and cost-effective design process, and immediate quality assurance. It represents the as-designed, as-built, and as-maintained product model along with properties and behaviors. 
As new products are developed, time consuming and costly clinical studies are required to prove effectiveness and patient safety before regulatory approval and market introduction. However, by simulating the product and its functionalities during the R&D process, we can help focus on the most promising products to allow for targeted clinical studies and to avoid unnecessary real-world testing for non-effective or unsafe product variants.  

Producing personalized medical devices or drugs requires a different production environment. Gone are the large batches of drugs or devices and adapting to different batch setting requests. The new world is leading towards of a batch size one where the product doesn’t exist until the clinical and manufacturer requirements define what it should be. By using a digital production twin connected to the digital product twin, virtual commissioning of production allows for a cost-effective way to adapt production while enabling automated release documentation.

The digital production twins represent the entire plant layout and production process. In some cases, they might even enable a holistic digital product passport as an end-to-end fingerprint over the supply chain capturing key production data, and data of the pre-production steps of suppliers including the source and condition of the raw materials. Being able to trace a medical device’s condition back along the entire supply chain can contribute to greater transparency and product safety. Digital production twins allow companies to tackle supply chain and regulatory risks. 

Once a personalized medical device is used by the patient, a flawless operation as well as perfect usability need to be ensured to enable the highest possible treatment and the best patient experience. A digital performance twin enables continuous monitoring of the device performance and usage with real-time validation against the intended design parameters. Anomalies in device performance or usage can be detected and corrected before harm occurs to the patient or the treatment becomes ineffective. A digital performance twin ultimately represents behavior and properties of a product during operation, maintenance, repair, and recycling.

Digital twins open avenues for improved and targeted care and efficiency in healthcare or life sciences operations.

For example, by creating a digital twin of a hospital, operational strategies, capacities, staffing, and care models can be observed to determine what actions to take in any situation. Virtual models can assist in managing bed shortages, stopping or mapping the spread of germs, modifying staff schedules, and overseeing operating rooms schedules and maintenance. Digital twins can virtualize the hospital to create a safe environment, which tests the influences of changes on system performance without risks. This is important in healthcare as it enables informed strategic decisions to take place in a highly complex and sensitive environment.  

At the same time, advances in medical imaging and wearables will have a great impact on the development of digital twins in healthcare. Medical imaging tools help to capture the state of the patient, its anatomy and physiology, and are one of the main inputs for mechanistic models. Wearables will be key to capture real-time patient data for monitoring and statistical models set up. This approach is already a reality with tools like Babylon Health’s Healthcheck App that captures health data into digital twins. It works with manually entered data such as health histories, a mood tracker, symptom tracker, and automatic capture from fitness devices and wearables like the Apple Watch. With this level of knowledge, the digital twin can provide basic front-line information or help guide priorities and interactions with doctors to address more severe or persistent conditions.

The pandemic also demonstrated that digital twins of a supply chain are critical to healthcare organizations to model relationships to understand better how to plan around new events, shutdowns, or shortages. For example, Siemens is working with Global healthcare company GSK to digitalize its vaccine development and production process. A key benefit will be much shorter development times for vaccines, allowing them to reach people faster and with the optimum quality. The digital twin plays a big role. 

With digital twins, it is now possible to collect data to understand exactly what is happening in real time during vaccine production, enabling optimization of operations. It allows not only monitoring of complex processes, but also predicts how changes would affect them. In short, turning to digitalization helps speed things up at GSK.

Just as the Human Genome Project advanced our understanding of the human genome to 99.9%, digital twins can dramatically overhaul our abilities in discovery, research, and ultimately the way we treat patients. Some key learnings vital to successful digital transformation include:

1. Life Science and healthcare companies need to understand which parts of their portfolio show potential for personalized medicine and which parts of their value generation (e.g., production, engineering) have possibility for digital twin applications.
2. Parts of the portfolio and value generation that show potential need to be fully assessed to see which technology can work and what limitations exist.
3. Once a promising application area that is technically viable is identified, a thorough assessment of required changes to processes, tools and the operating model in engineering, production, and the service department are required.
4. Integration and smart scaling are key. You do not only need to technically integrate your tools but you also need to transform the whole organization and integrate the new technology into your processes, procedures, and the way people work.

Digital twins can significantly improve enterprises’ data-driven decision-making processes. They are linked to their real-world equivalents and businesses use the digital twin technology to understand the state of the physical asset, respond to changes, improve operations, and add value to the systems. 

Healthcare and life sciences are complex and closely regulated industries. Personalized medicine is one trend that is not going away. Therefore, companies need to consider a strong partner that understands not only the technology, but the specialties of the industries, and how to make this digital transformation a success.  

Digital transformation does not end with digital twins. After all, a healthcare metaverse could combine the best elements of modern digital systems and traditional physical interactions. For example, using a virtual reality headset to practice surgical techniques using anatomical holograms – the physical training remains the same, but the virtual system allows for multiple attempts without creating harm.

Joining the digitalized world is not a question of why but it is a question of why not? That’s why leaders shouldn’t wait... acting today means staying competitive and profitable while meeting the growing patient needs.

Are you ready to take the next step in the age of digital health?

The real estate sector is in a period of reckoning in which digital transformation holds the key to the future.  

Why is digitalization in buildings, campuses and districts so important – if not vital – to the wellbeing of the people who live and work in these? There are many reasons: increased flexibility, greater efficiency, enhanced communication, greater employee satisfaction – the list goes on. 

So digital transformation has become a must, but this poses a number of challenges. According to a recent study, only 33% of commercial real estate companies feel they have the skills needed to operate a digitally transformed business. And just 45% plan to increase their investments in key technologies – like the cloud and digital channels.  

Many organizations find it difficult to identify and track the ROI of digitalization initiatives and only 10% of executives state that they are able to measure the ROI of their digital initiatives, whereas 90% can’t. This is largely because teams will focus more on the ‘how’ and ‘what’ of the transformation e.g. the technology, which on their own might not amount to much, instead of the all-important ‘why’ – which is to unlock business value and future-proof operations. Digital transformation needs to have a holistic approach to lift a business to the next level of e.g. efficiency and prove that it’s worth the investment. 

In the midst of this change, many new, digital-first Prop Techs are entering the market with a boosting venture capital spend of 31 billion euro alone in 2019 and technical innovation across the entire digitalization stack. This poses a challenge for established organizations to determine where to prioritize their digital transformation efforts and which technology to be used to reach their anticipated goal. And as they adopt new technologies and gather vast amounts of data from their customers, building sites, and more, companies aren’t always sure what to do with that information, or which potential they already have in their own pocket. 

There are additional layers of change and complexity, like the shift to mixed usages buildings, creating living environments in cities with a lot of new functionalities at one location, or the increasing pressures for the real estate market to improve their impact on the environment where buildings alone contribute to 40% of carbon dioxide emissions globally. If they want to meet global net zero targets by 2050, real estate developers, managers and operators have to do everything they can to decarbonize real estate – that might include retrofitting existing buildings and rethinking how they build new ones and most important gain the transparency needed to define interventions of their existing asset portfolio. 

In addition, Covid adds pressure on the real estate market to maximize usage of their property and put the highest priority on people who live, work and visit their assets and bring back life into the buildings via e.g. hybrid models, through hygiene concepts and new work space convenience which will become a default to increase productivity of the workforce and to attract new talents to grow the business.     

To address these challenges head on, as well as the very real challenges posed and highlighted by the current global crisis, real estate leaders need a more holistic strategy to transform their business with new stakeholders on the decision table across the C-Level of the organization like HR, Operations, IT, CDOs and other executives to play out the advantage of data rich technologies that provide a clear, big picture able to zoom into precise (near) real time data to enable effective decision making. This is where digital twins for intelligent environments like buildings, campuses and districts can play an important role – one that sets the real estate industry apart as it steps into the future. As the environment is diverse and customer needs are different, the shape of the twin always differs and can range between simple KPI dashboards to interactive 3D self-learning building representations.   

In the real estate sector, digital twins act as a bridge between the real and digital world, where smart buildings and campuses feed information to a virtual environment, the digital twins enabled by the right digital strategy, IOT devices, system integration, cloud computing and analytical models. The digital twin can then use near real-time and historical data to simulate thousands of scenarios and help assess the impacts of different decisions at each stage of the real estate life cycle. As those intelligent ecosystems are continually evolving, the digital twin needs to adapt to new requirements, functionalities and KPIs, to scale through the digital transformation journey of each individual user.  

Digital twins present a wealth of opportunities for the real estate sector, addressing many of the challenges that are changing the way they operate buildings to increase the ROI and to address the business properly. Each of the below listed categories needs to unfold in multiple use cases (applications, new processes, KPIs) to aggregate to a powerful tool supporting the business and its transformation journey.  

• Comprehensive insight. A digital twin for a building, campus or district provides a holistic view into the collection of assets, and visibility on how well they work together.  
• Enhanced health and wellbeing. Understanding tenants, visitors and employees also enables building managers and developers to focus on improving their comfort and wellbeing. This way, the building responds to the humans, and not the other way around. Having a strong IOT backbone and a scalable digital twin helps to enable such a living place by combining multiple data sources needed for a seamlessly functional intelligent building environment. 
• Sustainability & move to NetZero. Digital twins offer insight into for example energy and water consumption to uncover optimization opportunities which enable real estate stakeholders to move towards carbon neutrality and create a positive impact for the environment and their own bottom line.  
• ROI generation. With the right data, decision makers can choose approaches that enhance the return on investment and increase the value of the assets. This can become increasingly comprehensive through capturing all transition costs like process and tools, skill development or change management. But also keeping in mind costs that come with the technology stack in form of SaaS, support & maintenance, software development costs for applications, IoT platforms, communication & networks and IoT devices. 
• Predictive maintenance. By analyzing data such as from the BMS, weather forecast, energy usage and other key factors, a digital twin can enable a proactive approach to maintenance across the building portfolio. 
• Improved space usage. With a robust understanding of tenants and their habits, real estate managers can optimize how their space is used. This is particularly important for office buildings and mixed usage buildings in a post-COVID era.  

As they address each of these elements, digital twins offer a tangible approach to digitizing process-driven operations – while also establishing a foundation for the real estate sector to address long-term global challenges.  

As well as large-scale challenges that digital twins are helping the sector address, many of the obstacles that real estate professionals face day-to-day can be remedied by this data-rich technology.  

Lack of insight to achieve ESG goals 

With increasing pressure for the real estate sector to tackle ESG, a digital twin can offer full transparency into what processes and functions have the largest impact on environmental, sustainability and governmental KPIs.  

With this information, owners and facility managers can define interventions to reach their anticipated goals across their portfolio. By integrating additional non-real estate data like traffic and weather, additional correlations can be drawn to improve their ESG KPIs. 

Data silos everywhere 

Segregated data is a challenge in most industries, but it’s especially true in real estate. With multiple, decentralized buildings, different years of construction, and even more stakeholders, insights are stored in different formats and locations, making it difficult to get a clear picture of the portfolio.  

From BIM to Digital Twin 

Taking a Building Information Modelling (BIM) of an individual building and taking it to the next level with a digital twin which connects several buildings and campuses involves the amalgamation of dynamic and static data from multiple sources. This enables the company or organization to access real-time data on how the building is performing according to KPIs from the strategic to the functional.   

Consolidating data into a single pane of glass, a digital twin platform with multi-vendor system integration allows full asset transparency and advanced analytics that for example enables real estate owners to benchmark buildings, prevent system failure and enhance performance. 

Unplanned asset downtimes 

More often than not, building managers are responsive – not proactive – when it comes to addressing issues in their buildings. This negatively impacts the tenant experience and can prove a costly approach.  

A digital twin can use machine learning models to predict when something will fail, prompting managers to book a contractor and giving them all the information they need about the item being updated. This approach saves money and time while also protecting the manager’s reputation.  

Untracked asset performance 

Real estate owners and managers will often set out objectives and targets for their portfolio of real estate, but it’s not always easy to track against those. Traditionally, they use static data to retrospectively measure performance — but that makes it difficult to adjust systems and processes in a proactive way. 

Combining the real and digital world, new information can be extracted in near real-time alongside other external factors like weather conditions, occupancy scheduling, and human impact. This way, business owners can stay on top of performance and use it to make decisions around the rest of their portfolio. 

Technology is inspiring every industry to realize the art of possible and high-tech seems to solve every problem. Introducing ‘a digital twin’ and its embedded functionalities and applications without a clear business purpose is costly, will take time and in the worst case will not be accepted by operators, tenants or investors as it stays a digital tool without real business impact. 
 
The most important goal is to define the human and economic benefits and balance those against technical possibilities to create the right digital twin solving the anticipated business needs via individual software development, pre-defined SaaS products /solutions or a mix of both. 

Digital twins can be simple digital representations of one business problem or technically complex when covering a diverse ecosystem and a comprehensive set of KPIs, plus they can be resource intensive to build. In all cases they’re worth the investment when having the right strategy laid out for the business improvement needed.  

Getting the digital twin right requires a few critical steps:  

• Setting clear business goals that identify tangible use cases for the digital twin, clearly defining why a digital representation is needed and what it shall improve, by incorporating the existing data landscape to build up on an existing environment.  
• Defining the ROI for the anticipated goal. This step is critical and includes a back-and-forth balancing business and technical requirements which leads to digital twin ‘strawman’. 
• Designing the digital twin. Now high-tech comes in place to select the right technology for the right usage with a clear budget in threshold to ensure that the digital twin covers the business goals and gives the solution experts the boundaries needed to suite the digital twin into the business.  
• Implement the twin. A critical factor for success is the first go-live of the twin in an agile mode to ensure all technical interfaces are set, but also to prepare the organization for their first transformational step potentially changing the way how operations are run, how transparency has been gained and decisions are made. 
• Scale the solution. A digital twin is not a one-off. It’s a digital representation of the business and therefore needs to have the adaptability to scale and adapt along with the business itself. In step 1 this has been defined properly so scaling the twin across the real estate portfolio will need to be a natural step in the business strategy to start the journey of a digital transformation of the business.  

Digital twins are a market differentiator. As the real estate sector faces a new era of digital transformation, this innovative, intelligent technology helps address a lot of the challenges that established organizations are navigating. This data-rich approach is key to proving the ROI of any change – digital or physical – in real estate. It also levels the playing field, bringing legacy and disrupting businesses together as they collectively tackle necessary changes in how complexes are built and operated. To top it off, digital twins have a significant impact on efficiency, cost reduction, operational intelligence, and decarbonization – all factors that will future-proof the sector as we head into the next decade.