According to the World Economic Forum (WEF), the automotive industry is “dramatically overshooting its estimated carbon and resource budgets.” Automotive manufacturing is a major contributor to a global industry that requires the extraction and processing of more than 100 billion tons of materials every single year, which causes 90% of biodiversity loss and water stress, and about 70% of the world’s carbon dioxide emissions. 

Moving from a linear to a circular economy will reduce this burden on the planet’s resources, minimize waste, and give the most pioneering manufacturers a competitive advantage. The WEF predicts that circular economy will yield up to USD 4.5 trillion in economic benefits by 2030.  

However, to benefit from the circular economy, automotive manufacturers need to get ahead of the curve on future regulations, sustainability goals, and customer expectations.  

The circular economy replaces the traditional extract-create-dispose linear economy with a regenerative model, built around recycling and re-use of natural resources. By maximizing the lifetime of finite resources, it reduces consumption and waste. This is particularly relevant for the automotive industry as it moves to replace the internal combustion engine vehicle (ICEV) with battery electric vehicle (BEV). 

As the International Energy Agency (IEA) notes, BEVs require more than six times the mineral inputs of ICEVs. Considering the need to electrify the world’s existing stock of
1.3 billion ICEVs by 2050, with the total global vehicle stock projected to grow to 2.2 billion by 2050, such resource requirements are neither sustainable, nor any longer bearable.  

But the imperative to move to a circular economy is not exclusively an environmental one. Global supply chain challenges have created shortages in semiconductors, leading to sales losses of more than 30% for automotive manufacturers.  

Further pressure is approaching in the form of EU regulation. Brussels is set to impose minimum material recovery rates for battery raw materials such as nickel, lithium, cobalt, and copper in 2025, with increasing mandatory minimum levels taking effect in 2030 and 2035. According to the Nickle Institute, “Any company placing batteries on the EU market will have to comply with the manifold requirements in the regulation, independent of whether they operate in Europe or elsewhere in the world. All players will have to ensure that their upstream processes in mining and refining the materials, chemical processing, conversion into cells as well as assembling the batteries comply with the targets and requirements set by the legislation.” 

Manufacturers should not wait to implement circularity. The circular economy offers a means of gaining a significant competitive edge in a market where nearly two thirds of customers are willing to pay more for sustainable products. 

The time to kick off your journey towards circular economy is now. Buckle up. It may be a bumpy ride.  

By offering traceability through data transparency, digital transformation will provide the means to achieve true sustainability in automotive manufacturing. However, to achieve actual business value and leverage digital solutions in the best way, some key imperatives must be considered.  

1. Creating full transparency over your vehicle’s lifecycle  

Assets like the digital product passport, as introduced by the European Commission’s European Green Deal and Circular Economy Action Plan, will be essential tools for developing resource-efficient and environmentally friendly products in the future. These product passports will need to incorporate a wealth of data about how a product is built, used, and maintained, and how it may be altered throughout the different phases of a circular car’s lifecycle.  

Automotive manufactures must collect data from every aspect of the value chain to create data maps and a connected digital ecosystem. This will give manufacturers and other stakeholders a fully transparent view of emissions, resource use, and sustainability. 

Creating full transparency and traceability throughout the vehicle lifecycle with, for example, a digital vehicle twin will be vital in not only complying with regulations but also in reaching carbon net zero and in enabling new business models. To get there, organizations must capture digital twins for both their vehicles and the respective components and parts materials. This will help functions within an organization to extract the relevant data needed for R&D, compliance, procurement, and strategy to maximize the individual impact and make well-informed decisions.  

2. Innovating new and profitable ways to recover strategic materials 

The industry needs to find new ways of securing primary materials so that demand can be balanced, and materials are readily available. Shortages of semiconductors, magnesium, and wire harnesses have been exacerbated by recent challenges in the global supply chain, and they serve to highlight the difficulties that exist in guaranteeing the quality, security, and reliability of strategic materials.  

The way to overcome future shortages of strategic materials is to act within an ecosystem. Partnerships with downstream players, such as dismantlers, recyclers, or shredders, can help to secure components and materials for product manufacturing by increasing the ratio of secondary materials. If automotive players work together within their ecosystem, they can create digital solutions that enable efficient dismantling and recycling processes.  

Digitalization and automation have the power to transform the dismantling stage to enable product lifecycle extensions, as well as innovative strategies for remanufacturing, repurposing, or recycling. In short, these actions will help create closed loops of components and materials manageable on digital marketplaces, thus embodying the very idea of a circular economy.  

3. Identifying and implementing circular business models 

Companies need to evaluate both the opportunities and dangers of transforming into new business models. While circularity can help automotive players tap into new business, changing the mechanics and performance KPIs of revenue generation has its pitfalls and can often meet resistance within an organization. For that reason, automotive manufacturers must carefully design their transformation to circularity. 

The design must consider the entire lifecycle of the vehicle to identify new perspectives on value which requires increased transparency. Innovating your business model requires defining future customer profiles in detail as well as respective monetization details. In a next step, businesses need to evaluate what aspects within the business ecosystem can move towards a circular business model. New revenue streams might arise through extending the vehicle lifecycle, by building networks in platform models, or from “as-a-Service” (aaS) models to increase the usage of a single product. Extended vehicle lifetimes, for example, can help drive different perspectives across an organization. Assessing the effect of circularity actions on existing business models can help identify new opportunities to create value for customers across the automotive ecosystem. Partnering with organizations that offer access to existing circular business models and aaS models can be a good starting point when it comes to analyzing which new business opportunities make sense for customers. Automotive players can then prioritize use cases and define a respective roadmap.  

As an example of what’s possible, Chinese automobile manufacturer NIO has already pioneered the battery-as-a-service model through battery swapping. Under this model, customers don’t own the battery but pay for electricity and battery use either on a pay-as-you-go basis or by subscription. This means that increased battery range – by itself environmentally undesirable and more costly – is no longer an issue and opens the market for more economical and environmentally friendly BEVs.  

4. Leveraging Design-for-Circularity for products 

There is little doubt that a combination of future regulations and customer expectations will steer companies in the direction of secondary material quotas. This can only be achieved by reassessing existing product design and architecture.  

Original Equipment Manufacturers (OEM) must analyze the impact and footprint of a vehicle along the entire product lifecycle to gauge the current circularity of their products, components, and materials. Such an analysis of product lifecycles can help manufacturers formulate a product vision, value proposition, detailed product circularity requirements and future product concepts. 

By implementing a modular architecture for vehicles, manufacturers can enable efficient maintenance and dismantling processes encompassing reuse, remanufacturing, or recycling, all of which are prerequisites for a truly circular approach. The dismantling of components and the separation of materials – for example, from wire harnesses and interconnectors – will be facilitated by standardizing material compositions, which is likely to deliver higher quality secondary materials. 

It is worth remembering at this stage that innovations in product design and architecture have an impact across entire organizations, so senior management must lead the process for it to be successful. This leadership includes providing close support for changes in skill sets and job descriptions. Outside of an organization, these innovations also impact the whole lifecycle and, therefore, require cross-stakeholder partnerships with all partners and suppliers within the ecosystem. 

From what we have seen, circularity has the power and potential to deliver enhanced resilience and represents the key to a sustainability success story within the automotive industry. 

A circular model can provide value chain resilience and offer protection against the sort of supply chain dangers that have hit the industry so hard. Increased resilience will enable organizations to mitigate operational risk and business interruption by designing new strategies for sourcing material resources and components. Keeping recycled resources within the automotive ecosystem frees manufacturers from resource dependency and shields operations from the inevitable future scarcity of raw materials. 

The circular vehicle approach can deliver competitive advantages too. First movers will take a pioneering role in automotive manufacturing and will be in a position of thought leadership when it comes to use cases and business models in the automotive ecosystem. They will be more successful in attracting and retaining forward-thinking talent, and their improved sustainability will help to attract investment. Furthermore, circularity will allow manufacturers to develop new circular business models, access new markets, and diversify customer segments. 

A circular approach allows for a sustainable business set-up putting first movers ahead of the curve when it comes to future regulations around emissions and material use. Slow movers risk non-compliance and potential fines. 

It can also drive concrete business target setting and performance while giving OEMs the ability to leverage long-term visions to steer and monitor the relative success of circularity models in different business functions and regions. 

OEMs, suppliers, and ecosystem players need to make strategic choices to mitigate future obligations and disruptions. But they are well advised to take full advantage of the immediate and long-term benefits of transitioning towards a circular economy that digitalization can unlock.  

The goal is to close the end-to-end loop and maximize the full business potential of reusing materials and components, and to minimize industry’s impact on the planet. But to mitigate the risk, effort, and cost involved in something that has a deep impact on every aspect of a company’s operations, OEMs can kick off their transition to circularity by focusing on specific components or materials and building upwards from there. 

Right now, external momentum and stakeholder requirements are dictating the future of the automotive industry. Players still can shape their position in the race towards a sustainable future. A holistic circularity mindset is key to taking a proactive and progressive role and maximize societal and business value.  

Now is certainly not the time to apply the brakes.  

Additive Manufacturing and 3D printing are key enablers for more sustainable production, supply chains and services. In fact, the global additive manufacturing market is projected to grow by almost 24 percent between 2023 and 2025, and the market for 3D printing is expected to almost triple in size between 2020 and 2026. With everything from airplane parts to hearing aids created through additive manufacturing, it is clear that this technology is special and has the potential to dramatically impact business practices and address sustainability demands. 

Before looking more closely into how additive manufacturing is driving a more sustainable tomorrow, it is good to take a step back and examine current manufacturing challenges. At the core of the need for improvement is that customers want everything faster and with more flexibility in quantity, which puts a strain on traditional development, production, and distribution channels. Meanwhile, the business leaders need to balance that demand with a clear need for sustainable practices and processes. In general, taking care of resources, providing greater efficiencies throughout the entire supply chain, and developing a more circular economy are all things the manufacturing sector must pull together to achieve sustainability success.  

In addition, the new CO2 certification process in Europe puts additional pressure on manufacturers and supply chains. Paying for CO2 emissions clearly changes the cost-change paradigm. Producing emissions while manufacturing as well as shipping a product around the globe will become an expensive game. Tackling this issue will require a radical rethinking of the whole value chain.  

To dramatically overhaul the value chain, address customer demand, and reach sustainability goals, manufacturers must undergo a digital transformation. Connecting the real and the digital world provides manufacturers with the data and insights needed to make well-informed decisions on how to optimize processes and increase efficiencies.  

Digitalization offers multiple opportunities to gain a competitive advantage by improving product design, factories, supply chains, and after-sales services (spare parts) with digital twin solutions. By mimicking the product or whole supply chain in the digital world, manufacturers can simulate, test, and predict scenarios in real-time while optimizing production processes and operations. Digitalization creates the necessary transparency for better decision making through access to data. Consequently, digitalization is the foundation of many of the sustainable manufacturing processes that are already in use today – everything from low-energy robotics to modern additive manufacturing methods. 

Additive manufacturing is known to be a technology which is “digital by nature,” meaning that the process itself would not work without digital technology connected to it. At the process level, 3D printing has the power to alter production, supply chain, and post-sales support practices throughout the manufacturing sector.  

The sustainable importance of additive manufacturing is more obvious in some areas than others. Here are a few known benefits of this technology: 

1. Improving Resource Efficiency During Production Processes 

A key advantage of 3D printing can be experienced during production. Digital methods save natural resources through more efficient processes. The parts 3D printers produce are close to their final intended shape. Barely any material needs to be removed in contrast to grinding or milling production methods.  

The accuracy of today’s digital printing methods means that products are made right the first time with little need for rejections or modifications post-production. Since there are no molds or other tools required in 3D printing (compared to on-demand technology), the requested order size is made without any overproduction, waste, or additional storage costs. 

Overall, resource efficiencies are ensured thanks to a combination of simulated printing processes before production runs, optimized print pathway automation, and the potential to use digital warehousing techniques to validate which parts are – or will be – most in-demand.  

2. Saving Emissions By Reducing Supply Chain Lengths 

Supply chain length reduction means establishing a less costly and greener supply chain. Additive manufacturing enables the creation of parts and products closer to the end consumer and could even allow for printing in customers' homes. By decoupling manufacturers from global logistical operators, it also helps to make supply chains more resilient and less susceptible to international or weather events, as well as reducing the carbon footprint. The lower supply chain costs of additive manufacturing might even outweigh the higher costs in the location of production. Emissions associated with road haulage and international shipping will be vastly reduced. Localized 3D printing hubs will be able to bundle several production runs to make transportation to end users even more efficient and sustainable. 

Another key to additive manufacturing’s sustainability capabilities is that it produces fewer parts than traditional manufacturing. The thumb rule is that if a conventionally manufactured component consists of 100 parts, the same additively manufactured component can potentially consist of only 10 parts or even less. This shortens the value chain extremely including the complete energy consumption.  

Think of it this way – fewer parts and production steps require less logistical expertise to move goods around the world. Additionally, fewer machines need less space / storage, less resources, and in the end less energy.  

3. Enabling Smart Repairs and Upcycling Options 

The after-sales aspects of additive manufacturing should not be overlooked when considering sustainability. For one thing, additive manufacturing technologies such as cold spraying or laser melting can help to lengthen product lifecycles by reproducing worn-out parts or even worn-down sections of them. For example: With a local 3D printing service, customers can obtain the necessary part and replace it, thereby extending the product's lifespan, even improving the part’s performance, or enabling new features. Maintenance services can be strongly optimized, as parts can be produced whenever and wherever needed, making the process way more effective, saving a maximum of time. 

To enable this process, manufacturers need to have the relevant files available in a digital warehouse that allows end users to select the part(s) needed and print them directly. Since customers can access the parts in real-time, there is no need for warehousing of spare parts.

Most of us can understand how additive manufacturing drives sustainability by improving resource efficiency during production or how it can save emissions by limiting the length of the supply chain. But there is one area where additive manufacturing boosts sustainability that may not be as obvious – the product design and its performance when it comes to energy.  

3D product design can improve energy efficiency of products like no other technology can, not just in production but over the course of its lifecycle. Manufacturers may be surprised how easily 3D printers produce complex structures. This not only saves material resources, but it also saves the energy used to generate the material. With additive manufacturing it is also possible to produce significantly lighter products. Less weight means lower fuel consumption and lower distribution costs. 

The biggest energy savings are achieved through flow-optimized design and maximization of thermal energy absorption capability. This means that, simply by optimizing parts of the bigger product slightly through complex geometries only additive manufacturing is capable to produce, it might already improve its energy efficiency significantly.  

For example, with an aerodynamic part in a turbine produced through additive manufacturing you can manufacture near-shape cooling channels right below the part surface. With the increased efficiency in cooling the turbine can run at a high temperature creating lowering energy costs over its entire lifespan. In this situation, additive manufacturing optimized not only the part but the entire product, in this case the turbine.  

But additive manufacturing can even increase sustainability and energy efficiency for already sustainable technologies. Take the electric motor, for instance: by printing components more precisely, the effectiveness of the engine can grow even more – to new heights.  

A word of caution - leaders should not apply additive technology just to say they can. The technology needs to serve the business needs, solve problems, and entail significant benefits for production processes.  

Here are a few things to keep in mind for successful additive manufacturing: 

It’s not all about technology. While additive manufacturing can dramatically change a manufacturing business’ level of sustainability, it is not all about technology. In fact, the confidence and support of the people in your organization can make or break your success. Leaders are encouraged to remember that there is a human component regarding changes that will be inevitable when moving from legacy production methods to more sustainable 3D printing processes. Good change management programs are essential. 

Optimize and design new part(s) first…based on business purpose and then print the optimized version for actual value. There is no point in copying the initial traditionally produced part. In fact, doing so, might even be more expensive. Instead, the value of additive manufacturing lies in using the right simulation tools like digital twins and Artificial Intelligence to achieve application-optimized geometries and printing them very precisely.  

Invest in the infrastructure. Manufacturing enterprises must invest in the necessary infrastructure to benefit from additive manufacturing processes and the greater sustainability they can generate. Some businesses will inevitably decide to purchase 3D printers and the associated digitalization tools that go with them. However, it is also extremely convenient for manufacturers to outsource 3D printing requirements at a lower cost due to shared resources. Sharing the resources of an expert in additive manufacturing with other organizations means even more sustainable outcomes can be achieved because fewer resources need to be retained in-house for printing products, parts, and prototypes. In some cases, depending on the individual situation (location, ecosystem, and customers), there will be no need for a factory in the first place. 

Shape business strategy around sustainable goals and scale accordingly. Senior managers and executives must reassess corporate strategies if they are to find the success of additive manufacturing. Furthermore, meeting the demands of customers will require the adoption of services in a portfolio more closely aligned to 3D printed solutions. Producing 3D printed product designs is the first step, but manufacturers also need to consider material choices carefully because that will directly impact future sustainability and distribution.  

Additive manufacturing successes and advocates have reframed the story from “the technology reducing machines in production” to focusing on its potential to expand manufacturing opportunities. It may become a competitive advantage for some manufacturers. It may change the rulebook for production. It may be the answer for radically improving your business strategy. It could be the critical action your business needs to address the societal imperative to deliver a more sustainable future. What’s stopping you from finding out? 

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For CEOs of industrial manufacturing firms, one of the biggest concerns arising from the COVID-19 pandemic has been supply chain disruption. One study put the average cost to large businesses (those with revenues above USD 1bn) at around USD 184m in 2021.

Throughout 2020, the number of organizations experiencing 20 or more supply chain disruptions increased by a factor of six, according to the Business Continuity Institute’s 2021 Supply Chain Resilience report. This ranged from the Suez Canal blockage to container pileups in Shanghai and other major ports. If this wasn’t enough, ongoing climate change issues as well as geopolitical changes were the missing cherry on top. The same report found that the proportion of senior management showing a medium or high commitment to supply chain risk rose by nearly 10 percent to 82.7%. This goes to show that these global influences have the effect of transforming supply chains into revenue assurance drivers rather than cost reduction pillars.

Faced with this increasing volatility, uncertainty, complexity and ambiguity, the number one priority for sector leaders is to build resilience into the supply chain. Due to the pandemic leaders’ priorities shifting to business continuity and their realization that connectivity is the golden ticket, the challenge lies in a successful way to accomplish this.

The global supply chain market is expected to experience a compound annual growth rate of 11.2% from 2020 to 2027 – a market value surge from USD 15bn to USD 37bn. With the added speed bump of supply chain disruptions, it’s time to build resilience into your supply chain, and business leaders are reaching for plentiful and readily available technological developments to do so. Currently, the IIoT-based solution showing the most potential when creating a resilient supply chain is the use of digital twins to mimic supply chains and productivity networks.

In recent years, the digital transformation of supply chains has made them increasingly dynamic and complex. Catalyzed by the pandemic and other previously mentioned supply chain hurdles, manufacturers have sought out new ways to optimize supply chains to ensure transparency. To do so, in-depth understanding and visibility of the entire end-to-end ecosystem of suppliers and customers – their needs, strengths, weaknesses and potential risk factors – is the Holy Grail.

This ideal of a fully connected ecosystem providing real-time data starts by connecting your own supply chain. Only once you have complete visibility of your own end-to-end operations, connection with the wider ecosystem can be achieved, enabling you to create a holistic overview and transparency from customer to supplier.

Based on this holistic overview, you need to decide on your own business goals and adjust your digitalization strategy accordingly. Easier said than done? Not necessarily. The important thing is not to drown in the ocean of possibilities and to keep your eye on your business goals, letting them dictate the level of detail your digital solution requires.

It’s estimated that up to 93% of all IoT Platforms will contain some form of digital twin capability by 2027. So what exactly is a digital twin? And how does it tie in with a connected supply chain? To put it simply, a digital twin is a virtual representation of a physical product or process. By feeding it with data, it can be used to model the current situation in real time, test ‘what if’-scenarios for the future and, in some instances, even provide ‘best practice’ to the modelled disruption. As such, it provides predictive insights, highlights areas of weakness or potential failure and enables timely preventative action.

By incorporating the latest technical innovations (eg multi-physics simulation, AI, data analytics and machine learning), digital twins can demonstrate the impact of usage scenarios, environmental conditions and other variables to improve the resilience of a supply chain. The digital twin is therefore the ultimate goal in connecting your supply chain, yet you need to learn to walk before you can run. Start by determining the digital journey most adequate for reaching your business' set goals and drivers. Luckily, there are a few maturity levels you can reach before chasing the Holy Grail of the digital twin, and there is likely a data solution for every organization and budget.

To provide visibility and transparency on an operational level, applying a digital map or model might suffice, as it provides basic insights into your operations. However, if you want to leverage data to simulate and explore various possible scenarios and the effects of changes before implementing them in the physical supply chain, a digital shadow might be the best approach. However, only a digital twin can create enough insights to enable real-time connection and provide concrete simulations for operational, tactical as well as strategic decision making.

The key to realizing this ideal scenario is leveraging the correct data in the correct way by the correct people. However, as with any form of digitalization, there are several factors to consider when starting the journey to executing a digital twin.

Firstly, it’s crucial to always have your goals in mind: the business outcome you want to achieve should be the foundation for your decision as to which data model to use. For certain organizations, it may not be necessary to apply an end-to-end digital twin at the very beginning of your digital transformation journey. Trying to navigate through the buzzwords and promises from solution providers and staying true to your core needs is therefore an important first consideration. Applying technology just for the sake of applying technology is never the solution!

Secondly, a digital twin is only as good as the data it gets fed. Therefore, you need to gather useful data and that’s the tricky part. When creating the digital twin, connecting your internal data is advised, as this is the data that’s readily available to you. However, it’s important not to fall into the trap of data overload, which can result in too many insights slowing down and clouding the decision-making process. Once you have an internal connected supply chain in place, you can start reaching out to your ecosystem to further increase connectivity. It’s essential to take the time to understand relevant data management solutions for your business goals and ecosystem.

Everybody knows that data is the new gold in business, and companies are often reluctant to give it up lightly. This results in a potentially complex process of connecting your full ecosystem, as you need to convince all stakeholders about the joint value that can be created through sharing knowledge and creating transparency. As soon as they’re fully connected and sharing their data, you can collectively create the most detailed digital model possible, which will benefit everybody along the chain.

However, in reality it’s unlikely that every member of your ecosystem will share the data needed to simulate a complete supply chain digital twin, but that does not mean the end of the line for your digital twin journey. The good news is that there is a sophisticated workaround to help you overcome this particular roadblock: artificial intelligence (AI). Through AI, it’s possible to model the missing data and thus fill in gaps in your data lake. Furthermore, other data sources can be fed into AI to derive expected impacts to the supply chain from striking events, for example the news of a flooding in a particular region in the world can be overlaid with your supplier base and the digital twin can preempt potential impacts and solutions.

Now that your data is in place and connected, the true benefit of a digital twin emerges. Real time connection and seamless data flow will enable direct transparency of your data, and customized visualization, dashboards and alerts will allow for rapid decision making. The only question is, will your decision-making processes be able to keep up with this increased speed of knowledge?

Data digitalization and process optimization need to be harmonized, as data without a process is useless, and a process without data is inefficient. Future supply chains will be a combined learning network of humans and technology, so alongside building your digital twin, it’s important to invest in the creation of a flexible workforce willing to adapt its skillset to facilitate a bionic supply chain. After all, the most powerful intelligent network is still the human mind, and it’s fundamental in deriving and acting on the benefits of a supply chain digital twin.

When applying connected supply chains in the industrial manufacturing environment, it is important to shed light on how their connection to the green, lean, and digital factories of the future will look like. In this specific context, the successful creation of supply chain digital twins depends on the quality of data from every factory, warehouse, or nod within the supply network. This means that it is best practice to successfully develop digital twins of your factory in the first place. This will make the trick for the whole network as data could be provided seamlessly.

At the same time, it is crucial to understand that the factory of the future and its successful operations heavily depend on seamless data flows, processes, connectivity, and agility on a large scale in order to ensure individual productivity, robustness, and delivery excellence. Ever changing markets, customer requirements and supply disruption can only be managed sustainably by building agility and reactiveness into the mix. The supply chain digital twin can be the dealmaker in this context enabling the transparency needed beyond one’s own facilities. In the end, the factory of the future, being green, lean and digital in nature, is both enabler and benefactor from the creation of a supply chain digital twin. Seamlessly, connected supply chain can build the basis for factories and whole networks to decarbonize operations.

Increased connection and digitization open the possibility of creating supply chain digital twins, but it’s not actually the digital twin that creates the value for your business. A digital twin is simply the vehicle you need to enable a fully connected supply chain, and the strategic connection with your individual business goals is the key that makes your vehicle perform. As a result, your entire ecosystem will gain a competitive advantage.

Siemens Digital Industries Logistics (DI LOG) has already paved the way towards a comprehensive digital supply chain twin with their end-to-end resilience solution. This platform helps both Siemens and its partners to proactively manage supply chain and operations based on real time tracking and data-driven alerting functionalities for all involved parties within the network.

What started as mere science fiction is now becoming a reality. Fully autonomous driving capabilities and even Robo-Taxis and Shuttles are already being introduced in some markets, complementing the more basic Advanced Driver Assistance Systems (ADAS). The future is here, now, and we can grasp it with our bare hands.

After a relatively long period of hype, the technology is now rapidly maturing and becoming increasingly prevalent. Many drivers already rely on ADAS features, such as automatic emergency braking and lane departure warning and correction systems. While this technology makes driving even more convenient for consumers, most automotive players are still uncertain about customers’ specific autonomous driving needs and concerns.

To market autonomous driving technologies effectively, automotive players need to understand a wide range of factors relating to questions such as: How are customer expectations evolving? What are the specific pain points, and how can they be effectively mitigated? How much do customers value autonomous driving features and what is their willingness to pay for different solutions? How relevant are established brands to market these solutions to customers and are there regional or other demographic differences?

To shed some light on these questions, we recently conducted the Autonomous Driving Consumer Survey 2022. We surveyed more than 2000 participants from three regions, namely the US, Europe, and China, through a representative online panel. Our findings reveal sentiments that may raise some eyebrows but will undoubtedly give valuable food for thought to all kinds of stakeholders involved in making autonomous driving a reality. This includes, for example, car manufacturers, technology companies, but also (local) authorities, and research institutions.

Our results show that, without a doubt, people still love cars. 74% of consumers surveyed globally expressed a preference for cars over alternative modes of transport under current conditions. Even under ideal conditions (e.g., perfect availability and reliability, safety and cost, of alternative modes of transportation), the majority (59%) of respondents prefer cars. Cars will, therefore, likely stay relevant in the foreseeable future.

However, the role of currently leading car manufacturers in the future of mobility has often been questioned over recent years. Many have predicted that traditional OEMs will become increasingly irrelevant. Our results contradict this notion by showing that customers have high perceptions of traditional OEMs’ abilities to introduce AV technologies to the market. Chinese consumers are especially confident (82%), followed by Europeans (49%) and Americans (42%). Furthermore, most consumers have more confidence in traditional OEMs than in new tech companies in this regard.

On the contrary, new electric vehicle (EV) makers such as Tesla, divide regional opinions. In the US, consumers are more confident in these companies (60%) than in traditional OEMs (38%). Chinese consumers have comparatively more balanced perceptions, assigning 68% and 63%, respectively. European consumers, however, have similar faith in both new EV OEMs (52%) and traditional OEMs (52%). Contrary to common perception, these findings highlight the high relevance of both cars and traditional car manufacturers to consumers.

Chinese consumers are especially interested in ADAS, with 83% intending to purchase these features with their next car. Furthermore, 87% of them would go so far as to switch to a different car brand if that meant securing better ADAS implementation.

In Europe and the US, however, enthusiasm for ADAS is substantially lower. 56% of Europeans and 57% of Americans are interested in purchasing ADAS features, while 44% of Europeans and 49% of Americans would consider switching car brands in exchange for improved ADAS features. Still, the results highlight that ADAS features are a key differentiator for OEMs, even in these markets.

More importantly, manufacturers in Europe and the US need to establish why consumers are reticent. When asked about the reasons, the most common responses revolved around the arguments “unawareness of features” or “the system is too expensive.” These findings imply leeway for a better explanation of ADAS features and their benefits to consumers and/or finding alternative bundles or pricing models to address respective concerns.

Addressing such consumer concerns and reducing uncertainty should be a top priority for manufacturers before marketing the technology.

There are essential factors to consider when trying to overcome obstacles for vehicle or feature purchases: pricing and payment models and differing preferences between consumer groups.

The attitudinal differences between China and the West are also evident in pricing and payment models. For connectivity services, 78% of Chinese consumers accept new payment models (e.g., pay-per-use or subscription-based), compared to just 42% of American and 34% of European consumers. However, even more striking is the difference in uncertainty in this regard, with 9% of Chinese, 29% of American, and 31% of European consumers who are not sure of whether to accept such payment models. The acceptance of new payment models for functions on demand diverges similarly (China 75%, US 36%, Europe 30%) and shows comparable rates of consumer uncertainty.

There are also important preference differences between consumer groups for other demographic variables, such as age. In the US and Europe, acceptance is lowest in the over-60s and highest in the under-30s. While this is not surprising, it’s the acceptance rates among the over-60s in China that are striking. A massive 65% of these older consumers accept new payment models, compared to only 22% of American and 15% of European over-60s. OEMs need to be aware of such demographic differences while ensuring that each payment model’s benefits are crystal clear to their consumers.

What do OEMs need to take away from the messages consumers are conveying? For us, it nails down to three different imperatives.

1. Develop products that fit customers’ needs

OEMs must sharpen their focus on products that circle around customers’ needs. Developing autonomous driving features for the sole purpose of doing it won’t bring a competitive edge. Manufacturers need to realize how to make autonomous driving technologies as comfortable, easy to handle, and therefore as desirable as possible. Manufacturers should ensure their product strategies reflect consumer needs on, for example, smartphone integration and connectivity services, especially in China.

2. Develop clear pricing models that fit customers’ needs

As previously mentioned, customers’ wishes towards pricing models differ strongly, for example between regions and generations. Customer centricity and proximity are vital to addressing customer preferences and reducing uncertainty in this regard. OEMs and other automotive players should offer tailored pricing models to relevant customer sub-groups to maximize their chances of successfully positioning their solutions in the market.

3. Educate!

One of the key challenges identified in this survey is that consumers, especially in Western markets, are often somewhat reluctant to the new offerings and indifferent in their preferences. Therefore, automotive players need to advance their efforts to explain the benefits of the technology and the advantages of new payment models. This will be crucial to ensure high adoption rates and stable revenues early on. If people know “what is in it for them,” they will be more likely to press the purchase button.

Similar recently conducted surveys show that the ‘lack of consumer demand’ is not a barrier for autonomous driving technology adoption. Our findings support this notion. The demand is there, it just needs to be untapped. The process of getting there is simple: stay close to your customers and educate them as thoroughly as possible to tackle potential obstacles along the way.

With increased awareness, the potential to gain business value from autonomous driving is not something from a sci-fi movie anymore. It’s just around the corner.

Who knows? Maybe flying cars or time machines are next!

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In the discrete manufacturing, plant engineering, food & beverage, and automotive sectors, applying lean principles and automation has been and will remain evergreen when striving for efficiency gains. The golden rule appears to be: the more efficient and flexible the manufacturing processes are, the more profitable and competitive a company will be. Holding true, keeping your processes as lean as possible will build the foundation for the factory of the future, but the best process is still the one that doesn’t exist. The next big milestone on the path towards efficiency and flexibility is digitalization. Production companies already started transitioning from analog to digital information as well as collecting and connecting data throughout the value chain. Through digitalization, production complexity becomes manageable, behavior of machines and assets predictable, disturbances in supply chains transparent, production systems quickly adaptable and as a result, productivity overall rises.

However, the growing importance of sustainability as well as decarbonization of supply chains and own operations is adding additional requirements and challenges for the digital transformation of factories. There is a new goal to reach now that making production sustainable is no longer an option – it’s a must. Once your factory is lean and digital, what does it take to make it green as well? What are the key components of the factory of the future?

Digitalization and technology will heavily accelerate by 2030. However, according to the World Economic Forum (WEF), over 70% of the global production sector is still languishing in pilot phases for the Industrial Internet of Things (IIoT). As a consequence, only very few companies have effectively deployed advanced digital and sustainable production at scale.The potential to untap is still vast. Simultaneously, we see digital solutions and services becoming increasingly ready-to-scale, unleashing tangible benefits of digitalization for shopfloors and manufacturing environments in all industries.

In fact, establishing a factory that is at the same time green, lean, and digital will enable companies to outperform competition in both profitability and sustainability. On the other hand, there are several key challenges to address when kick starting your factory digitalization journey.

In manufacturing, as in any other business, potential transformations will continue to take place in a VUCA (volatility, uncertainty, complexity, ambiguity) environment, which becomes even more visible in today’s turbulent times. Manufacturers will need to operate in a world that is rapidly changing. Events will become less predictable and therefore less reliable for planning. The world’s complexity and ambiguity mean risking poor decision making based on ambiguous information, especially when failing to look beyond the horizon of one’s own operations and integrating the entire ecosystem in decision making. Despite this, if one thing is certain, it's uncertainty, and it is here to stay. We just have to find a way to cope with it.

There is also a certain probability of getting lost in digitalization – a commonly observed pitfall. Companies tend to rush into the idea of implementing IIoT solutions at high speed. However, in most sectors, executives often lack the knowledge to select, adopt and scale the right technologies that could be most beneficial for the company. They tend to become infatuated with a technology without considering the impact and benefit for their business. Worst case scenario, this technology doesn’t fulfill its purpose. Furthermore, most plans and roadmaps lack prioritization, also resulting from a lack of knowledge in digitalization and automation. In short, we see plenty of organizations missing consistent knowledge to elaborate, prioritize and evaluate the benefits of factory digitalization. These organizations will always under- or overestimate the potential impact of these solutions when scaling-up. Without understanding the business value, companies might become entangled in proof of concepts and pilot phases that will never scale sufficiently. The more complex the digital environment gets, the more crucial it becomes to keep a compass with clear direction to avoid getting lost in the digitalization jungle.

In addition, there is another major challenge that shares the spotlight. An ageing population and society means that the labor market will dwindle mid-and-long-term, especially in Europe and the Americas, but also already in some Asian countries. This demographic shift will decrease the available workforces significantly. Therefore, existing staff members must be encouraged and supported by automation and digitalization to facilitate their operations and productivity for a longer period. There is also a need to relieve the shopfloor operator from physical exertion e.g., by handling robotics and automation in conveyor technology, while nourishing their intelligence and creativity supported by digital solutions e.g., through worker guidance or quality assurance. Digital transformation will not work without the right people, so keeping and training them is of highest priority.

Fourth and finally, executives must determine how to implement stricter emissions regulations to achieve sustainability targets for their supply chain and manufacturing processes. ‘Carbon neutral’ is mostly, while still perplexing, measured and realized for the process. In Europe, manufacturers are beginning to tinker with the concept of BAT (best available techniques) to limit emissions from manufacturing. As defined by the OECD; “[…] ‘best available techniques’ means the most effective and advanced stage in the development of activities […] to reduce emissions and the impact on the environment.” However, utilizing data to accurately determine the carbon footprint is highly challenging, especially as supply chains become increasingly complex.

Nevertheless, vertically and horizontally increasing connectivity within entire supply chains allows, based on common digital solutions and platforms, to create this transparency to identify the levers needed to continuously monitor, balance and reduce the carbon footprint.

Addressing those challenges takes more than just a plug-and-play solution or deploying applications from an App Store in the systems and assets. The digital transformation in factories comes with a package of unique requirements and ongoing disruptions, which must be opened and addressed to reach the Factory of the Future vision. We need to realize that transformation applies to more than just the factory line or floor. Digital transformation encompasses the whole of your organization, comprising your processes and technology, with people being the stars of the show.

Before transforming your company, you need to identify your north stars. In manufacturing, there are three specific goals to shoot for to create the Factory of the Future.

Productivity is the most obvious one, and it has been there from the start. Even without digitally transforming the factory, a yearly increase of 10 percent is optimistic for many manufacturers. Without a doubt, this increase in productivity is the bottom line to creating lasting commercial success and ensuring competitiveness. These productivity boosts can already be achieved by applying classical lean processes and administration as well as connectivity and automation.

Achieving flexibility is another important destination for digital transformations. The factory of the future will need to meet market uncertainties head-on. Variations in demand, smaller lot sizes and increased uncertainty in complex supply chains will have to be tackled while avoiding, or even better, cutting waste in factory processes. Automation and cutting-edge technologies like IoT and AI, as well as the transparency achieved through their implementation, can help executives reach that flexibility destination.

Sustainability is not only critical for meeting BAT-compliant production models, but methods in manufacturing that support sustainability goals, achieved with digital transformations, are crucial for combatting climate change. Additionally, public opinion on climate change is shifting towards a demand for more sustainability. As a result, signaling the achievement of your sustainability and decarbonization goals, through digital transformation and energy efficiency, will also improve your corporate image.

To create a sufficient backbone for the digital factory of the future, you need to foster changes in the areas of processes and organization, people and culture as well as scaling technologies. It is crucial to understand that digital transformation affects every aspect of an organization.

Adapting processes and organizational setup to lean principles

Peeking into one our client’s sites, cost pressures demand that companies streamline operations and add more digitalization to achieve higher efficiency in manufacturing processes. Siemens’ Lean Digital Factory approach offered the necessary transparency to demonstrate to our client how to successfully use digitalization in a discrete industrial manufacturing environment. What we often still see are missing interfaces for horizontal and vertical integration, overcomplex processes and systems, and many uncoordinated or even competing initiatives that over work the staff, resulting in a chaotic working atmosphere.

Companies have always been aiming for process improvements that lower costs and time-to-market. By doing so, efficient, waste-free processes are (still) the way to go to maximize the value that modern technology offers. Digital technology can be applied to all processes in production, logistic, administrative processes, and even the organizational structure. The organizational structure itself helps to efficiently organize people’s responsibilities and communication channels throughout the process.

In the factory of the future, processes are designed to be simple, built on direct communication and fewer interfaces. Building and adapting processes with a lean and digital mindset means avoiding uncertainty and unclear responsibilities. Processes and organization need to be programmed to consider the opportunities and limits of digitalization, e.g., for communication and general information flow. As a result, you can develop touchless processes that are built on data-driven decisions.

In the end, digitalization will enable progress, and it will foster innovation by giving transparency on current processes and scenarios and making data-driven decisions based on these insights. This approach will boost your productivity to new dimensions to reach your sustainability and flexibility goals.

Enabling people’s digital mindset and a learning culture

The pursuit of technological progress, efficiency and sustainability can foster a culture of continuous development, but siloed knowledge, that has traditionally rested with experienced and qualified individuals, will need to be extracted and shared company-wide. It needs to become institutional knowledge.

Broadly speaking, what needs to be fostered is an environment in which employees appreciate your company’s vision and translate it into action. There needs to be a shared vision in place to achieve the common goals across your enterprise. As a result, your digital factory can be a place where employees see a particular problem as an opportunity to improve the entire enterprise. You need to make your people part of the digital solution from the get go.

To successfully progress in digitalization, your organization needs to motivate and engage employees to build a culture with digital elements. Initiating the change to an employee digital mindset will coincide with raising curiosity of the new digital work environment. This can range from building expert communities, that work together across organizations and hierarchies, to re-thinking your collaboration in interactive online events and workplace planning.

This change requires constant training and aiming towards attaining and building the right mix of competences. In order to do this, you need to identify your individual skills gap and areas for improvement. In many cases, this means re-thinking job profiles and offering individual learning opportunities. You must also make sure to foster digital learning and give employees the stage to share their knowledge. Mentoring and coaching can be a great addition to the set methods of learning from within and from outside your organization. Remember, people make digital transformations happen while technologies are only the tools needed to get there.

Scaling and combining Industry 4.0 and green technologies with IoT

Technological advancements have been the main driver of past industrial revolutions and are still essential for industry 4.0 development. Modern technologies such as cloud computing, machine learning and the industrial internet of things (IIOT) create a connected organizational environment that allows constant exchange and analysis of data between systems and physical assets.

To ensure a consistency of successful implementation, project companies require scalable and proven solutions that build on interfaces and scalable design. This is facilitated by using reference architectures and choosing from existing best practice solutions from an established solution library. To achieve net zero, this library should also include solutions that serve the main purpose of neutralizing the carbon footprint.

The crux lies in the successful implementation of these digital solutions. You need to make sure your strategy allows you to collect accurate and continuous data, and this is only realistic when building on blueprints and design principles for infrastructure and leveraging scalable solutions.

Connecting the worlds of information technology (IT) and operations technology (OT) will do the trick to build the factory of the future. While these areas did not particularly overlap in the past, the full integration of IT and OT will create unique insights generated from plant and operations data. With data-enabled technology platforms, the organization can gain transparency and smooth access to existing and planned digital solutions. In the end, choosing the right green and digital solutions, connecting them based on existing blueprints, and making them scalable will be the success factors for your factory of the future. The technology to tackle the climate crisis is here – we just need to leverage it correctly.

By doing so, cloud-based networks, AI, and automation will cause more disruption in the next five years than anything we’ve seen before, like in the Automotive sector.

The factory of the future will realize its full potential for sustainability, productivity, and flexibility by transforming not only processes but people, organization and technologies. Scalable digital solutions will play a key role in this transformation. Only with a holistic factory digitalization approach in the palm of your hands will your factories be able to address the challenges of tomorrow.

Digital solutions will also be our only hope when trying to tackle the climate crisis. Producing goods in the most resource efficient and sustainable way will only be possible if you have the data prepared and analyzed to make the right decisions. Remember, there is no sustainable transformation without a digital one.

Siemens Digital Industries has launched an ongoing program that has been in place for four years. Involving over 30 plants, Siemens has taken more than 800 measures in five key areas resulting in an 8% increase in productivity. The program is currently scaled up to incorporate more factories, regions, and businesses. This way, Siemens will reach its ambitious goal of staying competitive in the market and paving the way for its sustainable and carbon neutral future: Green, Lean and Digital – the Factory of the Future! 

All data is not equal and data access alone will not deliver value to your enterprise.

For a simple statement, it is one that carries significant consequences. For every bit and byte of data exchanged from central, enterprise platforms to edge and connected devices or assets, there is a specific cost and value to the business and its stakeholders.

At a unit level the TOTEX (total expenditure, including upfront capital outlay and ongoing operational costs) associated with each byte of data may be small; however, with the exponential growth of data, related costs will continue to rise.

While that rise may not be as steep as the growth in data volume overall, the continuing fall in the cost of data storage is flattening out, meaning businesses need to look more closely at how they manage their data to keep costs under control.

The accumulation of data in isolation does not yield outcomes or deliver business returns. Data must be consolidated and consumed to turn it into value.

So how should data be viewed strategically, if not as a direct source of value but one with an inherent cost? Ultimately, data is an enabler – it enables use cases and applications, which solve the business challenges of today and support the ongoing advancement of an organisation for its stakeholders.

The potential value of data is equally reflected within the art of the possible – the emerging topics and innovative ideas that are yet to be conceptualised by the best and brightest of our global society yet to be brought to market which will increasingly rely on data to disrupt and progress.

Clearly data and information provide a basis to construct value and insight, which would be inaccessible without it. The emerging data-driven solutions of today and tomorrow will be a key facet within the necessary evolution and transformation of our utilities, cities and societies, as part of a net carbon zero future.

However, Rome was not built in a day and digitalisation represents more of a journey than a destination. It is vital, therefore, that we acknowledge that there is no silver bullet when it comes to digital and data enablement.

Challenges will be witnessed throughout the journey of individual organisations, evolving based on the maturity and capability of the existing systems, strategy and personnel at their disposal.

As we uncovered during my last blog, global spend within the Industrial Internet of Things (IIOT) is rising. In 2020 it was valued at USD 216.13bn, 20% of which is directly attributable to the energy sector. Investment at this scale will create transitional risks for utilities, but these can be mitigated with a prudent approach to business digitalisation strategy, planning, implementation and management.

As data volumes grow throughout the energy sector, there will be an unequivocal need to provide robust cyber security, auditability, standards adherence etc, and to control processes for stakeholder access to data. With individual platforms and systems, this can be challenging enough; when considering the wider direction of enterprise IT/OT integration, these headaches will be compounded.

A greater focus upon platform and system integration will increase the pressures on legacy systems to scale and meaningfully participate within an increasingly open digital environment. It is not too far-fetched, therefore, to suggest that there will be a push from both internal stakeholders and external suppliers to procure new, digitally native, application rich products and systems which can critically scale.

This increases the risk of stranded assets throughout the enterprise estate, both today and tomorrow. For example, how confident are businesses that software and products procured today will scale and grow with your organisation, aligned to your respective digitalisation strategy, throughout the mid-to-long term?

Stepping away from technology, challenges also emerge when considering the sunk cost of data and its management, not just in storage and communications, as noted previously, but also in terms of the digital fluency of a workforce.

Tellingly, 47.3% of European transmission and distribution operators questioned in IDC's  'Utilities Core Business Transformation Survey 2021' confirmed that a lack of digital skills amongst employees was the largest internal challenge when investing in new technology. In comparison, the second highest challenge was budget and financial constraints, capturing 29.7% of the respondents’ concerns.

Then consider the challenge associated with determining the business impact of these investments. How frequently is Return on Investment (ROI) calculated and evaluated within digital procurement? How is this tracked throughout the operational lifecycle of the system?

Furthermore, how are the associated Environmental, Social and Governance (ESG) benefits monitored? These are aspects that are becoming increasingly important within the push for meaningful change in terms of reduced in carbon consumption over the coming decade. If businesses are unable to accurately document determine these required outcomes and benefits ahead of procurement up front, it is highly likely such KPIs will not be monitored nor realised during the lifecycle of the solution.

These points contextualise some of the typical challenges faced by utilities as part of digital transformation programmes. It is increasingly clear that the success of digitalisation initiatives depends on the provision of a client-orientated solution and outcomes, rather than potentially siloed digital products and offerings.

Regardless of macro similarities, no two utilities are the same. Each business requires results consistent with its strategic direction, encompassing the underlying enterprise estate.

A client-driven solution goes some way to addressing nascent risk, though equally important is that solutions are underpinned with best-in-class common values – an effective set of guiding principles. Guiding principles offer consistency in approach, incorporating decades of best practice to minimise inherent risk within complex delivery.

Focus on business impact and ROI – All things data and digitalisation have inherent cost but this need not be a barrier to progression. The supporting business case for each investment should demonstrate where and how value can be created throughout the organisation.

Transparency in terms of target ROI and ESG benefits is critical because these elements provide justification as part of a robust business and investment case. Documenting how a utility company will benefit against its individual operational and strategic KPIs is a must.

Maximising the use of existing client investments – Throughout the operational lifecycle of any system there will be the need to maintain and even extend or enhance – activities which bear consequential cost. Driven by client need, building around and upon existing core systems to focus efforts within the enterprise IT estate can maximise existing capabilities and skills throughout the business, whilst limiting investment in new technologies.

If, for example, core systems can be enhanced to sanitise valuable data for wider use, this is likely to reduce investment cost in the enabling efforts and associated spend, thus improving ROI when creating new data-driven value.

Target Minimum Viable Data (MVD) – If not all data is equal, the task of prioritising which data offers most utilisation potential and consistency to support impactful use cases and applications is a vital activity. Determining what data exists, and what is accessible, creates a bespoke data landscape for your business – the MVD baseline.

Grow with your data landscape – As digitalisation is a journey rather than a destination, generating the MVD baseline to rapidly enable value with accessible data is critical. As your business and its ecosystem of increasingly connected technologies and digital personnel develop, so can the MVD baseline itself.

Therefore, providing a mechanism through open and scalable APIs for example, to integrate additional data from existing or emerging sources is essential to enrich and unlock additional digital value.

Coordinate data and increase accessibility – In collating a reusable MVD baseline, the opportunities to increase transparency and structurally open access to internal business stakeholders (subject to relevant access and governance requirements) becomes increasingly compelling.

How could the business benefit with an increasingly transparent approach to inter-departmental data access? What business challenges could be solved in new and innovative ways? In post-COVID operations, could data access and transparency support social cohesion for remote staff and increase digital fluency of the workforce?

The recent Gartner CDO Survey suggests it could. According to the survey, data and analytics leaders who increased data sharing led teams that were 1.7 times more effective at showing demonstrable, verifiable value to stakeholders.

Get your people on board – Early buy-in from employees, from the conceptualisation to implementation stages, helps to mitigate the scepticism often felt within a business in times of change. As presented within this Siemens Advanta blog, it is important to consider that digital transformation can often be as much a cultural transformation as it is a technical one.

Early engagement of your staff in the journey itself will help determine native digital fluency throughout the business, supporting the identification of internal experts, plus those areas that require future training and development.

These principles are constructed upon time-proven best practice and decades of solution implementation experience, underpinned by common sense. They are designed to directly reduce the risk and effort associated with data-led value enablement.

Embedding these principles supports the goals of early identification of business value based on common and existing data, and minimising enablement cost and business disruption in terms of systems and personnel alike. Consequently, these principles can be directly mapped within a wider strategic digital transformation approach, known as the ‘Digital Core’.

Digital Core is a term occasionally used in the industry to describe technology platforms and applications that support business transformation, enabling organisations to address the needs of nascent and evolving markets considered within the digital economy.

Central to our Digital Core thesis is the consideration that existing and legacy systems cannot and will not be replaced overnight. Certainly, there will be times when operational systems reach the end of their working life, fail to integrate or even scale, at which point strategic product replacement will lead the way.

However, it is reasonable to assume that the constellation of operational systems in place throughout today’s modern utility landscape is likely to broadly reflect the enterprise environment in operation towards the end of the decade.

Therefore, to our team at Siemens Advanta, the Digital Core represents a mindset and approach, as much as technology and solution.

The Digital Core enables facilitates transformation; its adoption enables businesses and their stakeholders to build upon existing investments and extract data from operational core systems with focussed investment, to create new business value, underpinned with a clear business rationale. This, in essence, is harnessing the data that is being created and stored today throughout your organisation.

From a methodology and engagement standpoint, the Digital Core requires a baseline of technologies and systems which are strategically relevant over the mid-to-long term for a client. The theory is that each will represent the anchor for digitalisation initiatives over the coming years.

We consider the data from these core systems to be of principle value, as it is likely to be increasingly consistent and even enhanced, throughout a given time horizon, thus worthy of accessing for repetitive re-use.

Analysis of these core systems enables the creation of the MVD baseline, an activity that identifies what data and information exists and documents what is subsequently accessible today from these core systems. This environmental analysis of strategically and operationally relevant systems documents the data landscape and digital maturity of each core system.

Additionally, robust engagement sets out to understand and document the challenges faced by clients. In discussing business pain points and framing strategic and operational hurdles, it is feasible to derive data-driven resolutions in the form of new use cases and applications. Each of these can be subjected to cost-benefit analysis to calculate independent ROI.

Furthermore, this analysis includes consideration of the typical input data expected to bring individual use cases to fruition, tying the value directly back to the enabling, underlying data itself.

A gap analysis follows, assessing generated/accessible data from the business against highlighted use cases and input data. Any significant gaps can be either addressed via business change management to trigger additional data discovery or, where relevant, with targeted access to third party data, such as Metrological data. Armed with this insight, use cases could be reprioritised based on the MVD baseline and cost to supplement or extend. The outcome of this consultative process is to derive a common MVD landscape, which encompasses the following elements:

• MVD baseline – an overview of the data generated within the business from identified key systems, acknowledging which is and is not accessible to the business.
• Business challenges and resolutions – highlighting defined use cases alongside the common data required to enable each.
• Data gap analysis – available data versus use case comparison.
• ROI review – framing cost-benefit of key use cases from existing business data

As a consolidated activity, this gives a robust insight into the digital environment of any client, documenting how current data can address key challenges and create new value when enabled via the digital core.

The Digital Core is not only a consulting activity but also the provision of a client-orientated solution. Tasked to access the MVD baseline from core business systems, it facilitates the creation of impactful use cases via a reusable and accessible integrated solution.

In creating a common, reusable and accessible data framework or MVD baseline, the intention is to enable client stakeholders to rapidly ideate, test and develop parallel data-led innovations. At every step of the Siemens Advanta Digital Core journey, value is created, either in analysis and understanding or with rapid prototyping and testing of ideas based on common data.

Depending on the respective requirements of each business, the form of the digital core solution will change. This is to balance the need for on-premise versus cloud technologies and for creating a single source of truth, like the master data of the business data versus a reflective data extract from core systems.

Once available, this promotes wider internal access to useful data, away from critical business systems, with governance factored into the solution. Thus, the need to really understand what data you have, where it came from, where it goes, security implications and its quality is satisfied. Such technical considerations will be explored further in a later blog.

As the digitalisation journey progresses, the MVD baseline can be extended with additional, richer data and information, compounding the enabling value of data held within the digital core.

In some regions, such as the UK, there is even a push towards ‘presumed open data' from the energy sector. This involves requiring that utilities, amongst others, reveal data to external parties, with the ambition to foster whole systems thinking and innovation to address sustainability and energy-related challenges.

The digital core methodology and solution accommodates even such a progressive take on data sharing, whilst providing governance and internal value creation simultaneously.

Delivering value from digitalisation should not be held to ransom over big data aspirations. Using a common or emerging MVD baseline to demonstrate quick wins in the form of a Proof of Value and Minimum Viable Products will promote longevity and acceptance of the broader digital transformation initiative.

According to a Siemens Advanta HBR pulse survey, 80-90% of respondents can’t or don’t accurately measure ROI for digitalisation initiatives. Given this, it is somewhat unsurprising that between 60% and 85% of digital transformation initiatives have failed. You can find more detail in our white paper, Internet of Things: From Buzzword to Business Case.

The paper spells out the direct costs associated with IoT/digitalisation initiatives, including applications, platform/solution, communications and devices/assets. It also quantifies the transition costs that align to the business and its adoption of new technologies.

Transition costs include the processes and tools, skills and personnel development that go alongside change management – all critical factors when considering the true cost of digitalisation.

A realistic ROI assessment will not yield a singular outcome. However, when considering variable uncertainty ranges within the calculation, the credibility of the results increases dramatically, as shown in the white paper.

The ROI analysis can, therefore, be enhanced when adopting a specific lens towards global utilities, offsetting the direct and transition costs against benefit created for the business.

As part of an activity called ‘360 business impact reflection’, analysis considers how data access and new use cases will impact the business. It addresses individual or shared challenges throughout internal departments with, for example, cost savings, efficiency benefits and improved KPI adherence.

This task includes the effort to prioritise and analyse common KPIs consistent with many utilities worldwide, based on the respective organisations. Examples include:

• Planned network interruptions
• Unplanned minutes lost
• Number of faults
• Inspection and maintenance cost
• Network reinforcement and refurbishment
• Asset health
• Customer satisfaction and complaints
• Cost to serve
• Time to connect
• Environmental management

These KPIs align within broad strategic pillars for utilities, which commonly include:

• Delivering a safe and reliable network
• Improving environmental impact
• Improving network connections
• Enhancing customer satisfaction
• Supporting vulnerable customers

To explore the concept of 360 business impact further, let us take a specific example: heightened vulnerability of network assets due to sub-optimal network planning. In this example we’ll consider three key departments of the utility: 1) network planning, 2) network operations and 3) customer services.

• Suboptimal network planning can intrinsically increase the risk profile of the asset fleet throughout the network. 360 business impact reflection considers the effectiveness of asset intervention programmes, how asset risk is determined and the replacement/upgrade of each asset managed.
• The outcomes of this shortfall roll into network operations. Higher asset risk is likely to increase the number of faults and related costs of the network, potentially decreasing the operational lifetime of fleet assets and in turn increasing asset replacement cost.
• With increased faults come increased outages, impacting the customer in terms of minutes lost and supply interruptions. These factors are likely to come back to the customer services department, which must respond to stakeholders, both customers and regulators, as to the network performance.
• Such topics can additionally be reflected in the time to quote and connect of the connections department.

The cause and effect of decisions and activities is a constant within any operational environment and, as noted previously, digitalisation is not a silver bullet. However, what can be provided is richer, more powerful insight, using data from existing systems, manifested within new use cases, to solve current and future business challenges. As an inclusive and accommodating methodology, understanding the specific circumstances of each client, the Siemens Advanta Digital Core ensures a financially appropriate outcome is realised with clear investment rationale.

By linking the enabling and value creation activities, built upon existing client systems, and addressing specific business challenges using current and available data, it drives and unlocks impactful use cases as part of a tailored solution for your business.

According to the Harvard Business Review, Siemens is among the top global players for digital transformation along with firms such as Amazon, Netflix, Microsoft, and Intuit, to name just a few. Since the dawn of the digital age, Siemens has been at the forefront of digital automation, vertical software leadership, and, more recently, the Internet of Things (IoT). This means that as an organization, Siemens offers unique insights into the challenges that need to be met for digital transformation to succeed, because the company already stumbled over several roadblocks itself. If digital transformation strategies are not put in place, or enacted poorly, then companies will fail. After all, around half of Fortune 500 companies have disappeared since 2000. The same fate could befall any organization that fails to meet the challenges for digital transformation today. What are they, and why are some not as obvious as others?

First, it will be best to look at some of the most obvious business challenges with digital transformation. They are a good benchmark of what every organization will need to overcome to succeed in the digital future. They are also a good starting point to think about digital transformation before the less obvious challenges start to become more evident.

One – Developing new services and products

Perhaps the most obvious of all challenges for digital transformation involves identifying new services and products to support a business's core portfolio. New customer demands force businesses to develop new products and services. However, companies often struggle to find the right solutions that fit their original business strategy and product portfolio. To take a highly successful example, Netflix began as a DVD and Blu-ray rental-by-mail firm in 1998, a service it still offers. Netflix's commitment to digital transformation in the 2000s meant it initially offered an online ordering system for its physical products. Soon, however, it had completed its digital transformation into one of the world's most popular digital streaming services, not just offering on-demand access to its customers but producing exclusive content for it as well. New business models that go beyond the traditional core business, such as Anything as a Service (XaaS), can be one digitally enabled evolution for a traditional business. A holistic data strategy can support the evaluation of the value of data for a business and identify new untapped business potentials.

Two – Digitizing internal processes and new tool implementation

Another quite obvious business challenge with digital transformation addresses the goal to optimize internal processes and procedures. While the development of new digital products and services is very much outwardly focused, transforming processes are about focusing on internal structures. In many organizations, this might boil down to adapting internal processes so that they can function in a newly implemented digital environment. This might mean, for example, analyzing where exchanging traditionally used tools to digital ones will work, such as human resources (HR) tools. For instance a digitized self-service portal for booking leave or assigning availability for shifts could make it much easier to track worker numbers in real-time rather than relying on old-fashioned paper-based systems.

Equally, leveraging customer data to simplify and accelerate business processes can help firms to run more efficiently. For example, Artificial Intelligence (AI) systems might be deployed in manufacturing businesses to ensure supply chains are one step ahead of the game. AI-driven sales forecasting might help with inventory and financial planning, but it could also inform buyers that they'll need to order certain components to meet future orders even before they are done deals. In short, process automation is about operating with less wastage and greater efficiency, something that affects every company's bottom line. However, not every process should and can easily be digitalized. The secret lies in identifying the right areas and tools in which digitalization provides value not only to the organization itself but also to its customers.

There are also some less obvious challenges organizations need to face today during their digital transformation. Although many digital transformation challenges faced by organizations and their potential solutions are, in fact, relatively clear, some might not be. What are those hidden challenges, and why are they sometimes not so easy to identify within certain organizations?

Three – Developing a holistic digitization strategy and north star

Today, every digital transformation strategy needs to be based on a holistic digital approach that does not play around on the sideline. By adding a 'off-the-shelf' digital offering here or there, some organizations might feel as though they have done enough to adapt to the digital age. However, it is only by adopting a comprehensive strategy that true transformation will be achieved. Of course, any transformation, digital or not needs to fit the overall business strategy. Otherwise, business goals will not be achieved. This doesn't mean that everything other than a root-and-branch adoption of digital technologies will be enough if an organization wants to succeed. This means that its core business, as well as its organizational structures, processes, and technologies, need to be part of the transformation strategy.

Think of it like following the north star in the night sky. Although there might be a lot of change happening around it, progress in one direction is possible by sticking with the fixed point in the distance. This is why a holistic approach is often less obvious to organizations that want to change. Many will try to alter one or two things and question why a more comprehensive approach is needed at all. However, unless digital technologies are deployed throughout the organization, from the business management team to the people and organizational structure as well as the operational processes, competitors are likely to outperform further down the line. In short, for digital transformation to work, it needs to be truly transformative in every aspect.

Four – Gaining the commitment of top leaders

In fairness, digital transformation leadership challenges come in many forms. The most essential one to grasp will be gaining the commitment of business decision-makers to go about transformational change. Unless the management team is on board with a holistic approach to organizational transformation, it will be much harder to achieve the desired outcomes. In other words, the key decision-makers need to be committed to all of the initiatives that will form the digitization strategy. Even more crucial, they'll need to see it as their highest business priority.

For those driving digital transformation from below, keeping the top management on board is vital. Therefore, chief information officers will need to continue extolling the virtues of digital transformation in commercial terms. Once digital transformation begins, there should be no turning back or wavering among the top level of management. Equally, company bosses need to foster enthusiasm among their middle management teams if the project is to come to fruition in a timely manner. Business owners will need to ensure they have the buy-in of lower management levels as well as ordinary employees. Sometimes appointing champions or ambassadors of digital change can make all the difference in ensuring this sort of buy-in occurs.

Five – Obtaining external capabilities by strengthening your partnerships and ecosystem

The next hidden challenge that needs to be met is how to obtain the external capabilities you'll need for your organizational transformation to succeed by leveraging your partner ecosystem. Some organizations will have some of the digital competencies they will need to transform in-house already. Others won't. Therefore, you need to understand your own core competences first and complete it with strong partnerships afterwards as even the largest organizations cannot do it alone. Regardless of the number of recruits an organization takes on board to complement its current skillset, outside partners will need to be brought in as well. This may mean a transformation in management culture too, of course, if the organization in question is not used to outsourcing some of its key processes. There are alternative strategic approaches some organizations might also like to consider. For example, companies don't always need to outsource to secure the capabilities to strengthen their ecosystem. Instead, they could look at joint ventures with partner businesses or even analyze their options with regards to mergers and acquisitions. If the skills a business needs are already abundant in a competitor, it might be worth considering to adapting it.  

Six – Altering employee mindsets and ways of working

Although it is fair to say that systems, processes, and managerial buy-in are all crucial to meeting the challenges of digital transformation, there is something else businesses need to consider: people. After all, it is people who will be at the core of any successful digital transformation exercise. Unless your own staff gets on board with the concept and understands its benefits from a very early stage, it will not be the success it could be. What does this mean in practical terms, though? To begin, education is at the core of explaining why a digital transformation strategy will be better for the strength of the organization as a whole. People may feel threatened by adopting a new way of working or of increasing automation, potentially putting them out of work. However, if they are taught to realize that without a digital transformation process, the business does not have a viable future, then they'll soon come to understand why such change is needed. In other words, they need to realize that digital transformation is in their best interest and beneficial for all stakeholders.

Organizations can create better awareness about digital transformation by being open about the process. Secrecy rarely helps in meeting the challenge of persuading people about this sort of transformation. As transformation plans begin to roll out, organizations will want to alter their employee's mindsets. This won't happen unless staff members feel empowered with the digital tools they're now expected to use. In short, it means investing in high-quality employee training so staff can adapt to the new way of working. In some cases, decentralizing decision-making to a departmental level can help people to feel more in control of the change process. Agile ways of working may be introduced thanks to digital technology, but it may still take a period of adjustment to ensure full commitment. People still often want human interaction when they're at work, so it's important to give employees time to adjust their mindsets and habits. The bottom line is that employee buy-in is crucial. Therefore, without educating your people about the new technologies and processes they'll be expected to use, your transformation simply won't work.

Seven – Keeping hold of the ownership of business digitization

In short, digital transformation needs to be driven by the corporate decision-makers themselves. It is not a project that leaders can fully delegate to their business units, IT department or regions. The responsibility can’t lie with a single department but needs to be realized cross-departmentally to avoid silo thinking, power struggles, following departmental goals and losing the overall picture. The transformation will alter every aspect of the organization, not just specific business areas, products, or functions. Equally, there is no one-size-fits-all transformation process businesses can purchase off-the-peg. Every step along the way needs to be tailored not only to the whole organization involved but also to the individual business. Every business needs to take care of adapting the corporate initiatives to their individual needs.

Eight – Developing an implementable transformation program

Finally, it is insufficient to have a theoretical framework for digital transformation alone. Organizations can plan ahead, but what they'll actually need is something concrete that can be fully implemented. Therefore, a holistic program for digital transformation is needed. A key challenge for all organizations, both large and small, is to have a program that is not only far-reaching but also realistic at the same time. The program needs to be adaptable to any changes in real-world situations. Business priorities might change as the digital transformation program is underway. Setting up trackable key performance indicators (KPIs), overarching goals, and other measures is essential to keep track of progress. It is also fundamental to identify lighthouses, namely already running and successful projects and blueprints as best practices to set an example for all parts of the organization.

Digital transformation should not be done for its own sake. Organizations that do not follow their business imperatives on their journey will not see successful outcomes. All business transformation projects need to be adapted to their individual organizational needs and digital transformation is no exception to this rule.

Although kicking off a digital journey is unique, the challenges identified above are key to every commercial enterprise and organization when it comes to digital transformation, regardless of its core business. However, some companies will face further challenges specific to their region or sector. Of course, identifying the key obstacles to success is one thing, but doing something about them is another.

The factors that will help large and complex organizations to overcome these challenges are also varied. Key among them is the integrative and multi-dimensional approach that should be taken throughout the organization. This must be based on a holistic strategy, because scratching at the organization’s surface will not be sufficient to drive real change. Accepting that there will be resistance to transformational change, especially at the beginning of the project, will be of huge benefit. Organizations that try to force the pace of change or that make too many demands of employees tend to suffer.

That is not to say that digital transformation projects should not be ambitious. The transformation will require organizations to challenge every aspect of their processes, technologies, and structure. The key is momentum. Once change is happening, keep the ball rolling and don't let it stop. You don't need a wrecking ball approach, however. Think big but start small. All organizations will find there is better buy-in from stakeholders once the proof of concept surrounding the strategy has been established. Therefore, even small early wins can have a big impact on change down the line. In many situations, a strong partner company is all that is needed to tackle the initial challenges, as well as the harder ones that businesses will sometimes face as the pace of change picks up.

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From traditional industrial technology, such as carbon-neutral synthetic eFuel to an increase in distributed cloud computing, innovators are leading the way to a more sustainable world, and they’re showing huge potential to remove carbon dioxide from the atmosphere measured by the gigaton. Advances in cloud technology alone could remove 1.6 gigatons of CO2 from current emissions.

With everyone from small and medium enterprises, corporate giants to national and local governments committed to reducing emissions, both conventional and digital technologies continue to play a crucial part in developing strategies that offer a sustainable future.

Nevertheless conventional technologies are increasingly being augmented and aided by digital solutions that enable smart interconnections to reduce emissions through digitalization and industrial Internet of Things (IIoT).

Let’s look at a few examples across different sectors to examine the potential of digitalization, driven by the need to curb energy consumption.

Cities account for the majority of energy demand and resulting CO2 emissions; more than half the world’s population is urban, accounting for 78% of the world’s energy consumption and creating over 70% of all CO2 emissions. Whilst there is clearly some overlap between overall urban emissions and emissions from other sectors such as manufacturing and transport, buildings represent the largest portion of urban emissions, with non-residential buildings being 40% more energy-intensive than residential dwellings.

The heating, cooling and ventilation elements of buildings account for the lion’s share of their energy demand. In addition, energy-intensive embodied-carbon materials such as concrete, steel and glass often used in the construction of high-density urban buildings account for one-quarter of all buildings emissions.

To make cities smart – in other words, to create systemic efficiency for a sustainable, low-CO2 future – we have to look at the city as an ecosystem of urban features that is essentially one big energy demand center, and then figure out how to get those elements talking to one another.

How do you create scalable interconnections between buildings, energy infrastructure and transport?

Guidance on how cities can achieve environmental targets is key. Siemens has developed the City Performance Tool, a tool that can give an indication on how each infrastructure-related decision might influence employment and growth in the infrastructure sector.

“By leveraging the power of life cycle engineering and data analytics,” says Klaus Heidinger, Head of Sustainable City Solutions at Siemens Advanta, “the City Performance Tool helps to identify the best technologies in the energy, buildings and transport sectors for reducing greenhouse gas (GHG) emissions, improving air quality and creating new jobs in the local economy. The tool,” he goes on to say, “compares the performance of over 70 technologies and generates a shortlist of the most cost-effective solutions to help cities meet their environmental targets and has been validated by the world’s best academic institutes such as MIT, TU Berlin and London School of Economics.”

Arup and Siemens studied five cities across Europe to develop a methodology for demonstrating the long-term benefits of grid digitalization. In London, for example, where the grid operates close to capacity, they found almost half of its substations are under stress. Digitalization allows for smart systems like flexible AC transmission so that operators can squeeze more capacity out of the existing infrastructure without the need for further investment.

When coupled with digital measures that address systemic efficiency – such as Siemens’ joint venture with Vienna’s network operator – digitalization can help create connected elements of a smart energy infrastructure. These will help reduce emissions by modelling energy demand across the world’s urban areas. Vienna alone cannot solve the world’s problems, but as an example of digital measures that can be scaled up, it has huge potential.

Cities become smart cities when they offer interconnections that enable different segments of a city to communicate with each other for maximum efficiency in energy production, consumption and cooperation. The potential is huge; in Europe alone, the decarbonization of cities could cut CO2 emissions by an estimated 243 megatons. That’s the equivalent of permanently removing 188 aircrafts from the world’s commercial jet fleet, or nearly half of Lufthansa’s entire Airbus passenger fleet.

The worldwide industrial sector in isolation accounts for 37% of global energy use and 24% of all global emissions. Of course, the sector is also responsible for a proportion of city emissions (above), because some manufacturing and industry is sited in urban industrial zones and demands energy. However, to align with a sustainable development scenario (SDS), regardless of its siting, the sector’s emissions must fall by 1.2% annually, to 7.4GtCO2 by 2030.

Nonetheless, how can we go about reducing that impact?

Digitalization can help manufacturing and industry become smarter and more sustainable through better capture and use of data, more efficient energy use, and tighter resource management. Digitalization helps with real-time collection of data to identify problems that cause waste or unnecessary energy loss.

Spanish automotive supplier Gestamp worked with Siemens to save energy through data analytics. Siemens’ cloud-based application collects data every one or two seconds from plant machinery in individual production plants maintained by Gestamp around the world, from devices that collect around 800 million data points daily. This gives the company access to data on electricity and gas consumption in real time.

Easily scalable IIoT manufacturing solutions such as these can make manufacturing processes smarter, more reliable, more sustainable, and more likely to contribute towards decarbonization goals.

As a result of its digitalization, in its first year with the application, Gestamp’s improvements led to energy savings of 50 gigawatt-hours of energy. With over 15 networked production plants, those savings grew by 55 gigawatt-hours in its second year. With additional new plants opening in China, that are networked to the cloud-based application, more savings can be made.

Thanks to reduced energy consumption, the company has been able to lower its CO2 emissions by 15%. In 2016, Gestamp emitted 14,000 fewer tonnes of CO2 than the previous year, and by 2017, it had saved a massive 16,000 tonnes. That’s the equivalent of taking approximately 3,200 cars off the road.

Each 1-percentage-point reduction in the emissions of the global industrial sector, as a proportion of global GHG emissions represents a potential saving of around 350 megatons of CO2.

Currently, 80% of G20 carbon dioxide emissions come from energy consumption. As discussed above, cities – particularly their buildings and the manufacturing and industrial sites they host – are responsible for a large tranche of that consumption. When coupled with the fact that 60% of the way we handle energy is inefficient, the scale of the challenge can sometimes seem daunting.

On the contrary, that also means that the potential for savings through digitalization is huge. Deploying Digital can help save up to 65% in Energy Use, depending on what area you look at.

The global Distributed Energy Resource (DER) capacity is expected to more than double in the next ten years. With the transformation of our energy system into a decentralized system requiring constant balancing with minimal baseload, this will significantly increase the complexity of the distribution grid.

How can we overcome this?

Smart meters, for example, represent a scalable digital technology that provides real-time information to energy providers and consumers, reducing emissions as well as energy costs. With a digitalized grid, if you need to charge your EV, you can schedule it to demand energy when supply is at its most plentiful and affordable, with the maximum component of clean and renewable energy.

Siemens recently supported a European utility to determine the aggregated load profiles for individual low voltage stations. This consisted of a digital twin of the low-voltage grid, which allowed the utility to simulate all possible network states caused by the volatility of DER and electric vehicle (EV) charging stations. Armed with that data, digitalization could offer additional levels of freedom for distribution grid operators to maximize existing network usage and become proactive in managing DERs.

The World Economic Forum (WEF) estimates that 15.8 billion tonnes of net avoided CO2 emissions could be achieved in the electricity sector by enabling the integration of additional renewable capacity through digitalization and smart grid technology. The benefits don’t end there; utilities that have undergone digital transformations have already seen revenues grow by over 4% in the first two or three years, jumping to as much as 25% in the longer term.

WEF also believes that energy companies could unlock up to USD 1.3tr in value by 2025 by taking advantage of the data-driven decision making, as well as the active asset and resource intelligence that scalable digitalization offers.

Digitalization is providing the energy sector with opportunities that have never existed before; opportunities for minimizing emissions whilst maximizing efficiency in scalable solutions, all leading to a more sustainable future that benefits everybody.

It’s time to move to a digitalized future that can truly deliver the GHG emissions savings we need and set us on a path to true sustainability. With these three scalable examples alone, digitalization offers the potential to save CO2 emissions measured in the tens of gigatons.

Siemens Xcelerator is our open digital business platform and marketplace enabling efficiency, resiliency, flexibility, experience, and sustainability.
Making our customer’s digital transformation faster, easier and at scale.

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Expo 2020 Dubai is proving to be an event of epic proportions. With representation from across the world and a focus on innovation and collaboration, the six-month event is a platform for important global conversations. One of the core themes at Expo 2020 is sustainability, a concept that’s been literally built into the event’s DNA. 

The Expo site, which covers a massive area of four-square kilometers and takes up twice the size of Monaco, was built as an exemplary city putting urban sustainability and resilience to the test. It’s a showcase of what a sustainable city can look like and a place where city leaders can exchange ideas on how to transform the way we envision cities in the future. 

The world has been talking about smart and green cities for a while now, but in order to truly understand what that means, we need to take a deeper look into the ever-evolving relationship between cities and technology. How do we utilize technology to design living environments that best serve the needs of their citizens, and what’s the role of data in that process?
 

Let’s start with a definition. A sustainable city is one that incorporates environmentally friendly practices, green spaces and supporting technologies to reduce its negative impact on the planet. However, the term ‘sustainable’ goes well beyond the environmental impact. A city is truly sustainable when it offers its citizens services that improve their short and long-term wellbeing – while fostering its own resilience.

This approach is exemplified by the city of Dubai, which was recently named the most resilient city in the world by the United Nations Office for Disaster Risk Reduction. Supported by Siemens Advanta, the municipality has been strengthening its digital infrastructure and service set-up to better address environmental sustainability requirements and improve the experiences of its citizens and visitors. As of now, the city’s refined digital landscape has led to a 30% cut in costs as well as a 20% improvement in resident satisfaction. 

The city of Dubai represents many core elements that should be at the center of a city’s sustainability strategy: increasing quality of life, making the city more attractive and competitive, reducing carbon footprint, and improving operational efficiency. So, how do we utilize the right digital technologies to meet these sustainability goals? 
 

It’s important to highlight that a sustainable city is inherently a smart city. In fact, cities using smart technologies can significantly improve key quality-of-life indicators, which in turn leads to reductions in accidents, crime, as well as carbon emissions.  

That being said, ‘smartness’ is not just about installing fancy digital interfaces on your transport system or streamlining city operations. It’s also about using the right technology and data to make better decisions.

In order to balance stakeholders needs, existing infrastructure, different technologies like platforms, sensors or connectivity protocols, cities need a holistic technology strategy as well as a concrete implementation roadmap to follow. 

This is the path to unlocking the high sustainability potential across several domains of a (smart) city in which leveraging digital technologies can significantly improve operations. Let’s look at four of those domains:

A - Energy Grid Optimization

In a sustainable city, smart grids measure usage and deliver energy across a system of power generating sources, decentralized energy networks, smart buildings and electric vehicles. With the right data, the grid could make automated and informed decisions around the distribution of energy resources. 

In times of high energy demand, the grid is able to automatically prompt building systems to reduce their energy consumption. Meanwhile, periods of excess energy production could use smart vehicles or other players of the ecosystem as stores. 

With the support of big data and a vast network of digital data collection points, as well as measures of AI and Machine Learning, the grid can continue learning about generation and demand needs, adjusting input and output in real time. This way, energy production can be regulated most efficiently while energy consumption can be minimized with the effect of lower cost as an additional benefit.

B - Water Management

One of the biggest urban sustainability challenges today is providing access to clean water. Even though clean water is a basic human need, it remains a persistent challenge for cities due to suboptimal or outdated water infrastructure. It’s a key building block of a truly sustainable living space, making efficient and safe distribution an imperative. 

Within a smart city, smart water networks are equipped with optimized water distribution systems that are characterized by lower energy consumption. With smart endpoints across the whole supply chain, disruptions like leaks can be identified quickly or even preempted, reducing water loss. The data cultivated across the distribution network is also indispensable for observing trends and making accurate projections to ensure people have water when they need it. 

At Expo 2020 Dubai, for instance, Siemens technology monitors and controls a smart irrigation system with its MindSphere platform. It uses sensors to measure weather and soil data, and the data collected informs smart irrigation systems where, when and how much to water a certain area. This correlation of data is designed to optimize water consumption at the Expo site. 

C - Waste Management

As an area that requires significant infrastructure, investment and planning, waste management offers a prime opportunity for optimization. Not to mention if cities don’t manage waste properly, they risk contaminating the environment even further. It’s a problem we need to address now — as annual waste is expected to grow another 70% from its 2016 levels by 2050. 

With smart, digitally-enabled systems, waste management departments can monitor and optimize the end-to-end value chain. As part of this process, waste can be categorized and segregated earlier in the process, collection routes can be made more efficient, and recycling programs can be better used to reincorporate usable materials into the consumer lifecycle.

D - Transportation

While public transport is sustainable in nature - it reduces cars on the road and ensures that everyone can get where they need to - there are still more opportunities for optimization. 

Smart mobility systems can help reduce congestion, centralize public transportation payment methods and minimize the health impact caused by emissions. With intelligent traffic infrastructure, cities can do more to make their public transport routes more efficient, responding to usage data and the needs of their passengers. With the right technologies, the opportunities are endless!

Technology has an important role to play as cities become smarter and more sustainable. However, the steps we take towards urban sustainability have to be embedded within a larger, holistic vision that connects the dots between the technology and the people.

As we embark on this challenge, cities have a responsibility for bringing their citizens along on the journey, equipping them with the right tools and knowledge, and turning them into key stakeholders that make actionable changes at the individual level.

In the end, we have to recognize that individual choices compound to a greater societal impact, which means that the future of our cities depends on individual sustainable action. From our choices of vehicles and energy sources to the smallest of our everyday habits – they are all pieces in the puzzle of sustainable living. So, both as a society and as individuals – let’s choose green.