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As the global pace of digitalization accelerates, businesses are becoming ever more immersed in technology, regardless of sector or core activity. But the rush to establish a use case and demonstrate the Return on Investment (ROI) has tempted many to overlook the ethical implications of technology.

But what exactly is ethical tech?

The World Economic Forum defines it as technology with a clear moral dimension. It should be designed and deployed with sufficient forethought to account for and promote human and corporate values, ethics and norms with fairness and transparency at their heart.

First, it’s a golden opportunity to promote and safeguard a trustworthy reputation and avoid damage. It’s clear that there is a huge and proven potential of digitalization as a force for good within both business and society. However, mistakes happen as the field of Artificial Intelligence (AI), for instance, is fairly new and undefined, and many companies and institutions are still experimenting. These mistakes attract a lot of media attention, such as Amazon’s HR program that evaluated job applicants’ resumes to the detriment of women, or Facebook’s AI robots that had to be shut down after they started speaking to each other in their own language. Consequently, such cases raise general unease over the use of AI in decision-making and are cause for concern. Nonetheless, it’s important to state that these mistakes give a boost to development. It’s up to us to learn from them fast and establish improvements to build ethical tech in business and society.

Developing and embedding an ethical technology mindset in your organization when undergoing a digital transformation is crucial, since the consequences of an ethical tech incident can be substantial. With evolving digital technologies, ethical standpoints must evolve, too. What effect does it have on your customers, partners, shareholders, and employees? In the long term, an ethical tech company can enjoy competitive advantages by the chance to earn the trust of stakeholders by promoting human values.

How can you embed ethical tech into your digital transformation from the very start?

Conveying a clear message on how you use data, and why you use it, should be a priority for executives committed to transparent operations. With ever growing quantities of data collected and stored from an increasing number of devices and sensors, an organization’s capacity to interrogate and analyze that data to gain valuable business insights becomes more enhanced by the day.

For instance, consumers’ personal details are now routinely collected and combined with an array of information harvested from their digital footprint. This might include social media posts, the route driven to the office, the buying history, internet search histories, and so on. The importance of data should not be underestimated; with society claiming the right to shine a light inside business practices to confirm whether corporate values align with their own, proactively ensuring transparency in how data is used becomes a business priority. This is also true for companies operating in the B2B sector since stakeholders are increasingly interested in ethical tech in the entire supply chain.

How can you establish data transparency?

An organization’s approach must be guided by a moral and human compass. To become an ethical tech company, leaders first need to establish and publicize company values. These should be based on customers’ expectations of openness and consent; The guiding principles of the General Data Protection Regulation (GDPR) illustrate that for the European Union: lawfulness, fairness, transparency, integrity, and confidentiality. That gives some guidance for the use of new technologies involving data and AI to strengthen our democracies, give people a voice and make meaning out of data to create a better future. Second, it should be ensured that the way data is used aligns with the organization’s values.

The overarching imperative for digital organizations should be to work on building trust in the security of that data and promote transparency around its usage. And that starts with policy and procedure. That’s why it’s important to build a robust data usage. This involves categorizing data methodically and keeping close tabs on all employees who have access to it. Everybody can then be aligned around your company’s approach to accountability for data, how that data is used and how it’s protected.

As a crucial part of ethical tech, cybersecurity should be baked into your plans from the beginning, with digital transformation risk assessment undertaken as your project is being developed. It should feature at every stage and needs to be preventative, detective and defensive. This reflects the reality that security is a multi-faceted and on-going challenge, and one that responsible and ethical tech companies rely on to build trust in their use of data.

By gaining user trust, companies will be able to gather more and more data to enhance their specific offerings. Many argue that the real value for users and customers will be lifted when companies start collaborating more in ecosystems, rather than trying to reap small gains through acquiring more data than their competitors. However, moving from ego system to ecosystem doesn’t mean that you share your data indiscriminately with everyone. Ecosystem means that you share information only with trusted partners who have a common and aligned approach to collaboration, where it is clearly defined who contributes what to achieve a common goal – for the good of companies and its clients.

A commitment to an unambiguous approach to privacy will help to build stakeholder trust in the protection of data. Historically, big data has been characterized by a ‘catch-all’ approach to maximize data collection, allowing organizations to quantify an individual’s everyday life to the benefit of the company collecting the data. Because organizations have been keen to exhaust all its inherent value, it’s not been uncommon to sell on or share customer data in a secondary market.

But most customers don’t fully understand the specific purposes behind collecting and sharing personal data. A fear that many users have is that their information is accessed by unknown parties and that their everyday choices are being tracked, ultimately undermining their trust in the privacy of their data. Though being mostly illegal but hard to trace, algorithms designed to influence political opinion, or to promote “fake news”, are presenting us with ever more dangerous applications that have the potential to erode privacy.

How can you establish stakeholder privacy?

Go out of your way to demonstrate good behavior and compliance, and actively seek to banish opaque policies that technically tell the truth, but don’t quite tell the whole story. Carte-blanche to use data as you like is no longer acceptable, and if your organization has plans to sell personal data onto third parties, your customers need to understand that in full, actively give their permission, and be prompted to grant that permission at regular intervals.

Giving stakeholders control over their data builds transparency and aligns your organization’s everyday activities with the values it outwardly promotes. This is already a legal requirement in some jurisdictions; in the EU, for example, the GDPR outlaws any processing or use of data that an individual has not given consent for. In fact, it explicitly states that “data subjects” can withdraw consent whenever they want, and those who hold the data are obliged to comply.

A variety of more sophisticated technologies, including AI and machine learning (ML), continue to move into everyday life. According to The Harvard Gazette, global corporate spending on AI software and platforms is expected to top USD 100bn by 2024.

In addition to some of its most visible and benign uses in buying recommendations on websites or in translation tools, AI is also already used in a wide array of more critical scenarios, from helping medical diagnoses and assessing mortgage applications to keeping driverless cars safe. As a result, human oversight is instrumental, not just to ensure that disaster can be avoided in the event of technology breakdown, but to act as ultimate arbiter of a technology that is still likely to produce further ethical dilemmas. In short, ethical tech requires humans to be responsible and accountable for the final decision, not AI. Complex decisions should be more transparent, with the roles of AI and human intervention clearly visible in the process.

How can you establish technology trust?

Organizations must establish processes to ensure that they and their stakeholders know exactly what technology is being used, where it’s being used and how. Creating trust among clients, partners and employees is dependent on ensuring methodical and consistent human oversight of critical systems.

That’s why you should encode your organization’s values. Digital technology can be developed to take account of biased training data which results in algorithms and AI decisions so that the technology operates within the bounds of your company’s values. With so much riding on client and customer trust in the fairness and ethics of modern digital systems, encoding ethical values so that they can be assessed and measured against technological business operations will help bridge the potential trust gap.

One way around developed biases is to explain how AI decisions are made. For example, with medical diagnoses that rely on AI, healthcare companies have examined ways to give each diagnosis a confidence score based on a variety of contributory medical and lifestyle factors, allowing clinical staff to follow the workings of algorithms and introduce the benefit of their medical training and experience into that diagnosis if needed. AI is not replacing human beings, it’s enhancing them. But human beings are still in charge.

Taking it a step further, Explainable AI (XAI) is bringing further transparency to decision-making. Defined as a system that can explain its decisions, as well as the rationale for those decisions and some notion of how it might behave in the future, the self-learning AI platforms that power driver-less cars, for example, now have the ability to “explain” their choices. In this case, choices such as changing lane or accelerating or braking for no apparent purpose – and how factors influencing those decisions – are weighted. Yet, research on XAI is still fairly young and many questions remain unanswered, leaving applicants of AI with the sometimes tricky trade-off between the performance benefits of an unexplainable state-of-the-art model and an explainable, but lower performing, commodity model.

Ultimately, leaders must decide how to put their company values into practice to guarantee stakeholder welfare through ethical tech. The global tech industry can only build trust by using technology in a responsible way that promotes transparency and a purpose to that technology that focuses on the good it can bring about for all of society.

There is an opportunity for leaders to gain a significant competitive advantage by making trust a business-critical issue.

 How can you establish this?

It’s equally important to drive ethical tech both from the top of your organization, and from the bottom up, too. Leaders need to communicate their company’s values and ethics, formalize them in policies and make sure technology use is aligned with those values. That process can be set in motion by making sure employees understand the commitment and dedication to ethical technology. When leaders communicate that clearly, it helps others throughout the company to buy into that sense of responsibility, and will inform ethical decisions aligned with company values.

Digital technology use cases that are specific to your company can help employees test your decision-making framework against real-life scenarios, and that framework can be used to question and adapt the way individual employees make responsible decisions. In turn, employees are then more disposed to develop a natural ethical mindset of their own, one that is more likely to manifest itself in all facets of their roles.

And that’s where the bottom-up approach comes in; by taking your cue from the Tech for Life movement – which establishes a “code of honor” for the tech community regardless of a person’s status within a corporation – you can help foster this bottom-up movement in the direction of socially responsible tech based on a human moral compass. After all, although it needs to be guided from the top in the design stage, ethical tech is a responsibility shared between each and every one of your employees across all functions.

Remember, data regulations exist to enforce broad minimum standards of protection; for example, the EU’s GDPR can penalize data misuse and breaches with fines of between 2% and 4% of worldwide annual revenue, or flat fines of EUR 10m to EUR 20m respectively, whichever is higher. Similarly, for the use of AI, the EU is acting as a driving force to establish legislation and we can expect laws and regulations to come soon. But if ethical tech comes from the bottom up and from the top with conviction, those values and ethics will infuse every decision with responsible ethical corporate values, policies and procedures that guarantee even higher standards around data use.

Ethical safeguards, policies and procedures can only go so far. To truly master the art of building trust in a burgeoning tech landscape, you need to devise ways to equip employees with a mindset sensitive towards ethical standards. The role of leaders today is to embed those values into the culture of their organization, from the bottom, up and from the top, down. That way, you’ll gain trust in how you use and collect data, in how your technology is deployed to make decisions, and in the privacy of all stakeholders; top-down and bottom-up leadership will ultimately help weave an ethical technology mindset through the fabric of your digital transformation to prioritize the relationship between technology and human values. Everyone involved in tech should apply human principles guided by an ethical compass to ensure that tech is used in a responsible way.

So, set out today to make sure your organization is becoming an ethical tech company in parallel to embarking on your digital transformation; start at the planning stage, apply ethical tech standards iteratively depending on your industry and business, and your efforts can offer a competitive advantage in the form of the trust of stakeholders, clients, and investors alike.

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The number of connected vehicles around the world is growing rapidly. It's estimated that by 2025, when 100% of all new vehicles to market will be connected, there will be more than 400 million connected passenger vehicles alone. More connected cars require connected vehicle ecosystems in smart cities to function efficiently. The market is expected to see a compound annual growth rate (CAGR) of 17% between 2019 and 2027, amid increasing population density in urban areas. According to the UN, nearly 70% of the world’s population will live in cities by 2050. So, the performance of modern networks to support connectivity is becoming even more critical.

Whether automotive industry players will be successful or not depends on how they leverage ecosystems and combine technologies like edge and cloud computing. In tandem, they can deliver a wealth of benefits, including safe and reliable autonomous driving, energy optimization and real-time navigation, as well as new data and subscription services.

To pave the way for connected vehicle ecosystems, read on!

So what, exactly, is a connected vehicle? It’s one that can be linked to different services and devices via wireless networks. This includes other connected car technology such as software, entertainment and communications, or parts of infrastructure such as traffic signals, emergency centers and navigation aids, as well as other vehicles, and even bicycles, cyclists and pedestrians.

A connected vehicle ecosystem refers to everything that will connect to the car via smart-city platforms. It’s the sum of every fixed or mobile device or sensor that can connect to tomorrow’s cars. It will be able to collect and process information on everything from location, weather and driving conditions, to parking availability, congestion or hazards ahead.

In short, both will become part of tomorrow’s inner-city networking with a seamless and continuous exchange of information and data via fixed devices, sensors, and via the edge and automotive cloud services.

As 5G grows, and eventually gives way to 6G a decade or so later, those connected vehicle ecosystems will be key to providing state-of-the-art vehicles with the ability to become sentient machines. They will rely on the transfer of data through wireless communications for everything from situational awareness to predictive maintenance.

How will this benefit users?

Broadly speaking, the term “users” will include not just passengers: fleet owners, smart cities, original equipment manufacturers (OEMs) and original equipment suppliers (OESs) will all stand to gain, too.

Connected vehicle technology will offer services including driver assistance, safety features, entertainment, well-being, as well as vehicle and mobility management. Vehicles could receive rapid product iterations over-the-air to further improve software functions. Users will be automatically connected to ecosystems to enable easier road charging and reactive traffic monitoring and management – improving the driving experience significantly.

For businesses, connected vehicle technology will also open up the potential for monetizing connected-car data with big data analytics. Connected vehicle ecosystems will also soon enable the wide-spread use of fully autonomous vehicles and will offer predictive maintenance and decentralized fleet-management features, while significantly improving vehicle condition monitoring, predictive analytics and optimized fuel/hydrogen/electricity consumption. From a strategic perspective, many of these benefits will ultimately aid a gradual move towards a vehicle-as-a-service model, where subscription models based on usage rather than ownership gain market share.

The potential for businesses and passengers alike to unlock the future of mobility is clear. But how to untap it?

The automotive sector is awash with buzzwords relating either to the edge or to the cloud. So far, key players have tended to focus on just one of these technologies. The sweet spot, however, is in the combination of both edge and cloud: each will need to work together to truly enable connected vehicles.

Despite an on-going and increasing shift to the cloud, pushing everything to the cloud is expensive. And although current cloud connectivity cannot, without latency, support uploading and processing the 1 GB of data that connected cars will generate every second, the ability to do that isn’t far away.

So, from a strategic perspective, when many functionalities can already be handled on the edge, the key question for automotive players is: which particular service categories warrant the cloud and which can be processed on the edge?

Automotive edge computing involves data processing and analytics physically close to where data is generated and collected. At its simplest, it narrows the gap between data storage and the devices that rely on it to function within a pre-determined timescale. This means that latency problems can be resolved by meeting the sub-1 millisecond reaction time that safety-critical features demand. Not only is it more dynamic and flexible than the cloud for certain applications; for many service categories, it’s also more cost-effective. When combined with IoT technology, edge computing saves bandwidth, allowing you to allocate resources efficiently.

Seeing the car as part of the greater ecosystem is key, and building your ecosystem boils down to two issues:

1. Which partners do you include?
2. How do you combine both technologies to focus on the sweet spot?

Choose your ecosystem partners well:
Many providers claim expertise in either edge or cloud technology, but choosing the right partner is not as easy as it seems. Include your entire value chain – from the very beginning, think about hardware and software providers, developers, national government organizations, OEMs, OESs and beyond. After doing so, it’s crucial to pull the network together by fostering continuous interactions, enable seamless interaction based on an open technology architecture and utilize joint data to continuously maximize potentials of the collaboration within the ecosystem.

Decide where to combine edge and cloud technology:
Which vehicle features should be in the cloud and what makes sense on edge? The crux lies in differentiating between service categories to make those decisions more manageable. Driving assistance such as collision avoidance, for example, is currently better suited to automotive edge computing, where a low latency, or fast reaction time, is needed. The edge also supports other safety-critical features such as lane discipline or traffic-sign recognition. Furthermore, vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communication via Dedicated Short Range Communication (DSRC) and Cellular-V2X (C-V2X) can be used to transmit e.g., the previously detected red traffic light to another vehicle. Navigation, on the other hand, can be dealt with by automotive cloud solutions, as can features such as energy efficiency, vehicle security and multi-level authentication. In addition, some subscription and premium data services may warrant the higher costs of cloud. In short, automotive cloud services are suitable for use cases in which higher latency is acceptable or desirable, and where subscription revenue streams warrant the cloud.

How you maximize the benefits of each technology, once safety-critical features are assured, ultimately comes down to a strategic choice: which applications and services justify the higher costs of the cloud, and which ones can save bandwidth on the edge?

The key to developing an edge-cloud ecosystem is to view vehicles as an integral and integrated part of a much bigger system. A connected car is connected to a fleet, which is in turn connected to entire smart-city platforms comprising everything from highly dynamic and unpredictable road and city users to transient static features such as adverse weather, lane closures and accidents.

With demand for smart-city ecosystems that encompass passenger vehicles and their connected vehicle platforms as part of the whole system, it’s crucial to build a partner ecosystem along the whole value chain. Prioritize the integration across the partners and focus on easy technical connectivity. Here, it doesn’t have to be an edge vs cloud decision: It’s crucial to find a way to combine them efficiently for each individual use case!

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The transformative potential of IoT as an enabler of digitalization across all industries is increasingly seizing executives’ attention. However, our partner pulse survey with Harvard Business Review Analytic Services also identified certain challenges regarding IoT that potentially hinder business leaders to kick off their digital journey.

Although three quarters of respondents said they felt under some pressure to quantify and leverage the potential of IoT benefits before their competitors get there first, only 10% were able to measure the return on investment (ROI) on most of their initiatives. Nearly half were worried about change management challenges, 45% admitted to not knowing where to start with IoT, and a majority reported they were still trying to identify potential use cases.

With the underlying imperative that technology must serve business, people and our wider society, how should leaders overcome these specific hurdles to make their IoT initiative a resounding success?

Let’s have a closer look at the top four IoT adoption challenges.

90% of executives struggle to accurately measure the return on investment (ROI) of their IoT initiatives. This is one of many common IoT challenges and issues in large and complex organizations that raise executives’ doubts in the impact and value of IoT for their businesses.

The return on investment is defined “simply” by the ratio between profit and the cost of the investment based on discounted cash-flow calculations. However, it is still a huge roadblock for leaders to accurately measure and calculate it.

How to overcome this?

The first thing to remember is that leaders should only consider starting their digitalization and IoT journey once they have established a clear business goal. Companies tend to “fall in love” with a new technology but forget about the actual business value it should bring to them. The real value of digital initiatives can be seen and assessed more clearly if executives flip the question of ROI around: Instead of looking for the ROI of a specific IoT solution, they should be asking how to calculate the ROI when using IoT to solve a business problem and create a lasting impact. Only when defining the business goal of the initiative as precisely as possible, executives will be able to accurately measure whether it will pay off in the future. The way the solution solves the business problem defines the return of the initiative.

Therefore, the investment budget always needs to consider transition costs for change management, the adaptation of processes and tools and skill development next to technical costs.

Only when taking all of these aspects into consideration, the real return of your investment will become visible. More details about accurately calculating the ROI for IoT initiatives can be found here.

To view the Key Challenges for IoT graphic in full width please click here.

The failure rate for IoT projects remains high – between 60% and 85% – reflecting the many IoT challenges. Many of them refer to the people domain, like the need of engaging all employees in the digital transformation efforts.  

As pointed out earlier, one of the main causes of failure stems from a misconception of IoT as an initiative based primarily on new technology. In truth, IoT is a people business, and the success depends on taking the employees on the journey from the start. However, executives often don’t know how to prepare their employees for what is to come.

How to overcome this?

For employees and leaders alike, some of the most challenging aspects of IoT are characterized more by the need for a cultural transformation than a technological one – this requires a structured change management approach from the very beginning. First and foremost, an integrated approach across both tangible and non-tangible elements of the transformation is needed. That means bringing together technological and process elements with social factors such as corporate and team culture. Both aspects are equally important to create employee buy-in.

Additionally, an understanding and acceptance of evolving target states – part and parcel of any digital transformation – is crucial. Here, leaders need to foster an agile mindset. During the transformation, needs and overall objectives might change unexpectedly – that’s just natural. Flexible concepts and an agile approach are needed to constantly adapt to new requirements – while not losing people along the way.

In the context of IoT projects, classic change management levers – communication, leadership, team configuration and ongoing training – have to be adapted to the digital environment. One possible starting point for change management measures is to check the employees’ readiness for change in general and digital readiness in particular. This will help to identify the organizations change capabilities and address any gaps in digital core competencies and to prepare the workforce for new and changing job requirements.

To carry the digital mindset into the organization, the successful approach of one of our clients, a leading energy equipment supplier can be adopted: the client identified digital role models in their workforce to act as ambassadors and coaches. With full access to the digital transformation strategy, their role is to report back to colleagues and help lead them through the necessary changes, meaning nobody’s left behind. In our client’s case, it gave their engineers clarity on future roles and training, and resulted in their engineers proactively engaging in the successful launch of their AI design algorithm for their parts. However, this is only one specific action that can be taken to put your employees at the core of your digital transformation journey.

Business leaders are naturally worried about the risk of cyberattacks. With more and more devices and assets connected and networked, executives are becoming more wary of an increased digital attack surface and greater vulnerability. IoT security challenges can range from customer data theft to ransomware to industrial espionage and are driven by increasing professionalization of cybercrime. As the number of vulnerabilities increases, the attack surface becomes more important to defend.

Also, connecting the OT and IT world brings along several unknown cyber challenges as the number, diversity and complexity of connections amplify: a secure linking with the cloud, the industrial control center or third-party providers need to be guaranteed while availability and connectivity of the assets need to be ensured at any time. In general, the discrepancy between the OT and IT network logic needs to be considered to define what are the challenges of IoT here.

Nearly half of respondents in our HBR study rank cybersecurity as an issue that merits close attention, and over one third report that it’s among their top concerns. 

How to overcome this?

Step one is to understand that even if a company already has complex cybersecurity defenses in place, they usually only protect the IT world. In the IoT / OT landscape there are new problems which is why companies need to adapt their approach. A common pitfall is to threaten the IT landscape by neglecting the operational technology (OT) network. This brings additional risks which the companies need to mitigate.

Companies should use the planning phase to embed security into their IoT project and establish threat risks with a full IoT risk assessment while projects are still being developed. Therefore, a risk-based approach that prioritizes vulnerabilities and threats can be the answer: Starting with prevention is crucial to make it as difficult as possible for an adversarial to break into the systems. If the cybercriminal finds a way in, companies need to detect the threat actor as fast as possible.

Especially in the IoT environment, it is essential to have a risk-driven strategy in place and create transparency over assets. Cybersecurity departments need to make sure to know, manage and track all endpoints and devices during the entire lifecycle. Weaknesses need to be identified and therefore, effective threat monitoring solutions implemented. Applying new principles like “zero trust” to control and verify all actors in the network as well as every data stream can help to prevent disruptions before they occur.

By connecting assets with digital environments and data analytics, IoT can be a main enabler for asset monitoring and tracking, effective resource management and maintenance optimization – especially in the manufacturing environment. Measures of predictive maintenance can for instance prevent factories from unplanned production downtimes and therefore, can ensure production and supply chain functioning. At the same time, data-driven decision making can optimize maintenance schedules and improve investment decisions.

However, the big initial question is: how to actually realize the connection between operational (OT) and information technology (IT) – especially for aging, proprietary manufacturing assets? Due to this difficulty, this is often believed to be one of the main IoT implementation challenges as it requires greenfield sites which hardly exist in reality. This makes business leaders doubt the business potential of IoT. Also, IoT implementation leads to more connected devices in the ecosystem causing higher asset diversity, volume and in the end complexity – especially when trying to adapt many diverse IoT technologies at the same time. Localizing even more assets in real time might be challenging as well.

How to overcome this?

Companies should apply a step-by-step, structured approach: Sensors and connectivity should be added gradually to existing machinery, allowing to harvest data and gain valuable insights one machine at a time. This gradual implementation allows executives to optimize existing assets and control costs. A carefully tested and piloted path can equip machines with IoT capability causing only minimal impact on the production line. Doing so, executives can demonstrate the overlooked value of IoT, which lies in upgrading brownfield environments consisting of older equipment step by step. Sensor monitoring, data analytics and predictive maintenance, for example, can be deployed on legacy assets without widespread risk to the production lines.

To find the right sensors and data collectors, might even require a certain amount of creativity – especially in brownfield environments. Established machinery is often hard to connect to state-of-the-art sensor technology. For example, to obtain data from a shop floor with machines not originally designed to deliver operational data, a camera system monitoring the shop floor could provide valuable data that can be further processed and evaluated, thus creating a potentially valuable use case.

The complexity and diversity of data often requires major efforts in processing and evaluating the information generated from assets. To overcome this, implementation teams that include IT experts, domain experts, and data scientists are needed. Furthermore, in complex cases, no company can do this alone, so a co-creation between different partners of an ecosystem can be the answer.

After tackling these initial hurdles, the potentials of IoT for asset management are limitless: Optimizing the OEE (Overall Equipment Efficiency) makes it possible to not only manage the physics of assets, but even their usage itself. Track & Trace solutions can be the foundation of an e2e supply chain, connecting inter- and intra-logistic. 

IoT has little value without an established business case, benefitting people and society. Here we’ve identified what are the main challenges of IoT faced by organizations and their potential solutions.

What they show is that a change in perspective of the way the ROI is calculated can reveal the true value of IoT; that the employees should not be treated as an afterthought; and that the machine assets should be digitalized gradually and creatively to maximize output whilst controlling costs and optimizing utilization. And all insulated by effective cybersecurity defenses to shield the data, assets and networks from attack while ensuring availability.

What executives can achieve by overcoming these hurdles presents an even greater prize than merely the sum of their parts: A resoundingly successful IoT project that generates benefit to the company and its stakeholders.  

With the confidence to face the challenges head-on, executives are more likely now than ever to be able to seize the significant advantages of IoT to leverage these advantages ahead of their competitors.

Now’s the time to seize the significant opportunities that digitalization offers.

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By setting free the power of IoT, we will be able to transform our economy – making the way we produce more efficient and sustainable and helping economies reach their ambitious decarbonization goals. The Industrial Internet of Things (IIoT) plays an important role in this context as it acts as a key enabler for sustainable manufacturing, ensuring economic efficiency as well as helping to reach extensive environmental goals.

With the following examples, we hope to give you one or the other inspiration on how you can actually make your manufacturing process more sustainable.

In a world of ongoing change and new challenges around every corner, it is crucial for innovators to always stay one step ahead. What is society concerned with? How are investors and markets adopting to new circumstances? How can I prepare my business for the next big transformation? And how can I prepare for the next digital disruption?

With climate change and scarcity of resources knocking on our doors, there is ample motivation to take action. So let´s explore the issue of sustainability in manufacturing and why it matters for seminal businesses!

React to growing awareness

In recent years, a growing awareness on the environmental impact of our economy can be seen in both public and economic arenas. Especially in the manufacturing world, it becomes clear that due to massive decarbonization goals and limited natural resources, continuing to manufacture in an unsustainable way can only go on for so long. This could particularly affect traditional companies that do not convert to sustainable manufacturing processes, as they run the risk of falling out of favor with public opinion and by that lose the high degree of trust with customers they have built up over many years.

Be prepared for policy initiatives

In addition, Green New Deal initiatives in both Europe and North America will increase the responsibility on all businesses to adopt more sustainability in manufacturing. Supranational decarbonization guidelines are paired with concrete growth and innovation strategies, making businesses that adopt sustainable ways of producing more attractive towards investors and markets. At the same time, businesses unwilling to shift to more sustainable manufacturing processes are likely to be left behind. So be prepared for governmental restrictions and face them as a chance for innovative smart manufacturing processes.

Make your business future-proof

We have now learned that businesses simply cannot turn their heads when it comes to improving sustainability in manufacturing processes. It is a chance to react to the growing environmental awareness of clients and public, contribute to supranational decarbonization goals, and meet new demands of investors and markets. It is also a chance to gain a competitive edge when incorporating smart manufacturing solutions to reach these goals. Why? Let´s find out!

In their 2018 report into IoT manufacturing, the World Economic Forum not only found that the inter-connectedness of manufacturing processes and plants would add some $14 trillion of value to the global economy by 2030 – IoT solutions will also act as key drivers for sustainability in manufacturing as well over four-fifths of IoT deployments in industry are currently addressing, or have the potential to address sustainability goals as set out by the United Nations.

With these promising numbers in mind, let´s explore three steps to increase sustainability in manufacturing by using the power of IoT. As already stated, we are aware of the simplification of the concepts presented in the following. However, we would assume they may be the starting point into your personal sustainable manufacturing journey.

Step 1: Use your data wisely

First of all, you can start treating your data as one of the most valuable resources you have. The main benefit of using IoT and IIoT for sustainable manufacturing lays in collecting exactly this data in order to work with it. Use a thought out, vertically integrated IoT solution including software, automation, and sensors to easily connect, monitor, and analyze your entire manufacturing process. Now you can start to measure, compare, and analyze your current input in order to improve your output, making it more efficient - and greener. Digital Twins of your production and your products may help to run what/if scenarios and decide for the optimum process combining economic and environmental sustainability.

Step 2: Use your energy wisely

In 2018, 37% of global energy use could be traced back to the industrial sector. Looking at the enormous challenges in front of us, it becomes clear that energy efficiency in industrial manufacturing will play a major role when reaching ambitious sustainability goals. IoT-based solutions can help manufacturers optimize their entire energy system, transforming it to become both more efficient and sustainable. As already mentioned, one of the prime advantages of IoT manufacturing is the real-time collection and processing of data. By connecting, monitoring, and comparing this set of information, you can easily identify irregularities and patterns that make your manufacturing process waste or lose energy – and take suitable measures right away. For example, intelligent devices and sensors on machines can independently recognize when they are needed, helping to prevent over- or underuse, and by that, saving energy and actively reducing your environmental footprint.

Step 3: Use your other resources wisely

A thought out IoT manufacturing solution will help connect, share, and analyze all the data running your production process and by that, will help you use your given resources more wisely. By knowing exactly the right number of resources – your raw materials, commodities, and construction materials – you can start using what you have more mindfully. Cutting down on the use of fluids, especially oil and water, is just one sustainable manufacturing example that can be expanded to include many more. Let your IoT solution help you decide which materials or spare parts to use, how to reduce over- or underproduction, or how to reuse resources or waste. This not only means less consumption, it will also run hand-in-hand with longer lifespans for production equipment and tools and make your production line leaner and more productive. Sustainable usage of resources also spans the complete product lifecycle, starting with product design over product operations until decomposition of the product and the recycling of its parts in a circular economy.

There is no doubt that great challenges lay ahead of us. Complex tasks require bold and innovative solutions. This is where IoT steps in. We have already witnessed how the Internet of Things helps us embrace and shape change. Now, we can incorporate IoT manufacturing solutions, in order to transform the way we produce in many ways, making our manufacturing process smarter, more sustainable, and compatible with ambitious and necessary decarbonization goals.

Smart manufacturing solutions thereby deliver the concrete technology to do so, resulting in various sustainable manufacturing examples, like the mindful and economical use of valuable resources like material, energy, or water. Introducing IoT into our economy also allows us to make it future proof by reacting to new societal and political requirements while also securing economic growth.

Now it´s time to face the challenges ahead of us. Luckily, we have just the right toolset to do so. So let´s embrace digital transformation, unlocking the full potential of IoT and reach true sustainability in manufacturing.

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Let's face it – the world needs to overcome a big challenge with regards to energy: After all, the energy consumption of cities and industries is ever-growing. According to World Bank data from 2019, the proportion of the global population that resides outside of cities and towns has fallen by two-thirds compared to the start of the 1960s. At the same time, academic studies have noted that urban areas account for about three-quarters of global energy use, something that will undoubtedly cause a problem for ongoing sustainability efforts.

Even with more and more green energy sources coming online with every passing month, there is still an extensive challenge to lower carbon dioxide levels in the atmosphere and for the nations of the world to meet their Paris Accord climate change commitments. Many businesses have committed themselves to lowering their carbon footprints to meet regulations and cover new customer demands. Siemens, for instance, was actually the first DAX company to commit on carbon neutrality by 2030 and reduced emissions already by 54% since the start of the program. Consumers increasingly turn to companies that focus on sustainable energy generation and consumption and realize that a profitable future is only possible in a sustainable world. Either way, I already see a world in which decarbonization is considered to be of huge importance in the urban realm. However, many companies still lack a holistic approach to decarbonize their energy systems.

In this article, I will emphasize the relevance of decarbonization for the energy sector and the related energy-intensive industries as well as for sustainable urbanity. Then let me show why digital twins should have a part to play in any decarbonization roadmap. Finally, I will share six identified principal success factors on how to apply digital twins to decarbonize energy systems.

Sustainable energy use is at the heart of the fight against climate change, as energy generation is responsible for a huge amount of carbon dioxide (CO₂) and other greenhouse gases that get emitted into the atmosphere each year. Only for the purpose of illustration, according to the Energy Information Administration (EIA), electrical generation accounted for some 4.13 trillion kilowatt-hours (kWh) of energy in 2019 in the United States alone. The EIA estimates that some 1.72 billion metric tons of CO₂ was released into the atmosphere as a result of all energy sources. To put it simply, that's approximately 0.92 pounds of CO₂ emissions per kWh!

Numerous decarbonization trends, such as the switch to biofuels from fossil-derived ones, already try to tackle that problem today. Others include the increasing use of wind energy, both on and off-shore, as well as the development of hydrogen-based fuel cells, or the roll-out of carbon capture and storage (CCS) technologies. These truly are groundbreaking times for decarbonization!

Nevertheless, significant quantities of greenhouse gases are still being produced every day due to “business as usual” operation of energy systems in business, housing and smart life, not to mention the mobility and transportation sectors in cities. The good news is we can keep a cap on these emissions. With the fundamental optimization of the entire energy system to known decarbonization solutions, the world will come closer to the goal of meeting its climate agreements and lower cost pressures to bear on climate goals. This is especially the case with energy systems that offer huge potentials for CO₂ reductions. Energy systems are basically interconnected networks that deliver energy to end users – both industry and consumers – on an on-demand basis. Sometimes demand may be high and sometimes it may be low, that’s just natural. Any energy system must account for this and be run in an optimized way so that over-generation of electrical energy is minimized or even avoided. In this regard, digital twins represent a powerful ally in modern decarbonization technology today bringing together two major goals for businesses: sustainability and cost effectiveness.

To view the Digital Decarbonization graphic in full width please click here.

There are several technologies that already exist which contribute to the reduction of CO₂ – and other greenhouses gases – from being released into the earth's atmosphere. Among these technologies, digital twins can be a way to bring all technologies together and model energy flows and changes of parameters in real time. Simply put, a digital twin is a virtual version of something that usually resides in the physical realm and is used to mimic real systems or products in industrial environments. It was once used to help designers and engineers with new product concepts but, strengthened by the Internet of Things (IoT) and Artificial Intelligence (AI) systems, it has proven itself to be an even more valuable tool. This is because it can model a raft of interconnected systems through big data analytics.

In general, one can now model energy systems and infrastructures within smart districts and utilities – and even across entire national networks, if up-scaled – by creating digital twins of the as-installed or as-planned grids. The basic idea is that digital twins help with the decarbonization of energy systems by mapping all of its aspects – from electrical production to distribution losses and localized demand – in the digital realm. It is already proving itself to be particularly effective in energy management as well as district heating, electrically powered public transport infrastructure management, and sector coupling, to name just a few areas. Therefore, it is important to know which factors in the deployment of digital twins will lead to the most successful outcomes. I identified the top six success factors to successfully decarbonize energy systems using digital twins.

1 - Don’t start without a decarbonization strategy in place

To begin with, any successful decarbonization program must commence with clear goal-setting. The modeling used in the digital twin of the effective electrical system should offer a clear decarbonization roadmap with carefully thought-through criteria about the intended outcomes. First and foremost, the monetization of the current versus the targeted carbon footprint comes into play.  Also, current and future business models focusing on either private or public goods or services will get conceptually challenged based on their inherent carbon intensity. Subsequently, the question of the future energy mix along the entire value chain of production or servicing has to fully include all suppliers and business partners – and this can even raise new questions related to the own location strategy. Finally, sustainability reporting should not be underestimated, as it even exposes management to personal liability.

2 - Always consider the whole system!

Secondly, you always need to consider the entire energy system when starting your journey and how to best optimize it for favorable outcomes. It regularly includes electricity, but also heating & cooling and transportation for a thorough end-to-end analysis of the initial situation and therefore the entire technological framework conditions. To name only a few, each site or entity calls for a multidimensional consideration of e.g., technical lifetime, maximum capacity, minimum versus maximum operating and down time, investment and maintenance cost, and of course its energy demand and relative efficiency. In other words, this attention to detail and completeness is needed to unlock the full decarbonization potential.

3 - A technology-neutral approach is vital

Having a technology-neutral approach to the decarbonization of energy related systems in place means that you should have no preconceptions about which model or configuration will work when adopting digital twin technologies. You need to start the journey with an open mind. A technology-neutral approach allows to draw flexibly from a variety of existing ideas and possible technology solutions. In short, predetermined technological pathways tend to end up with sub-optimal outcomes. Never commit too early to a single technological approach when examining all of the conceivable approaches to decarbonization because your digital modeling and AI number-crunching may surprise you!

4 - Data is the new gold

Preparing your dataset before you build your digital model(s) is something I have learned first-hand to be crucial to the entire process. In short, this means gathering, cleaning and structuring the data, but it will also require that the plausibility of the required data in real-world settings is checked. If you put garbage data into your digital twin model, then expect low-quality outputs. All too often, we have experienced these challenges when analyzing for example meteorological data being crucial for renewable solutions next to data sets about electrical consumption in buildings or feed-in profiles from photovoltaic, solar thermal, concentrated solar, wind, and hydro power plants at locations worldwide. In general, some patience paired with professional experience is needed to find, prepare and make the best use out of your data. There can never be enough data preparation and checking!

5 - Take your time!

Since digital decarbonization is obviously such a highly multi-faceted approach, better avoid looking for quick wins. Preparation of the energy system model needs care because it will be more complex – often much more complex – than you had initially bargained for. For sure, it all depends on the size of the endeavour: it takes rather a few months than weeks when adding renewables into an existing fossil power generation system as this might require spatial decoupling of power generation and consumption to handle imminent grid constraints and finally, a flexible use of battery storage systems. It might also take some time to onboard the right experts especially the ones with field expertise – but believe me, it’s worth it. In general: Success doesn’t come overnight, after all!

6 - Know your technology well

Although the decarbonization of energy systems obviously requires know-how with electrical gerneration, supply, and distribution, the final factor I recommend prioritizing if you want to achieve successful decarbonization is expertise with digital (twin) technology itself. Put simply, you require a high-level understanding of data modeling as well as expertise in smart data technologies if you want digital twins to help decarbonize your energy system. If you don't possess these skills in-house, then prioritize outsourcing them before starting your decarbonization journey. Digital decarbonization always need these most experienced practitioners coming from multiple disciplines to optimize the specific decarbonization path.

When applying a strategical approach, the impact of digital decarbonization can be quite impressive in numbers: for instance, in one of our projects with a German city with about 200,000 residents, we modeled its energy demand and infrastructure and found that 70 percent fewer emissions by 2035 would be perfectly feasible. This project also demonstrated that a 25 percent reduction in heat demand could be achieved in the same period which is a fantastic result by any standards.

In general, the main advantages of utilizing digital twins in future decarbonization strategies will be felt in basically all public and private sectors. Given that buildings account for about 40 percent of all current carbon emissions, focusing on the digital decarbonization of energy systems will make a huge difference to the climate of tomorrow. While decarbonization resonates best with the utilities sector as a whole, high energy demanding sectors such as the chemical and similar process industries, deserve highest attention as well. Benefits like running cost-minimized energy systems designed for specific locales, such as district heating systems, both conventional and renewable local utilities production facilities, community energy storage systems, and even better managed electric vehicle charging station capacity, will offer concrete saving potentials in municipalities and smart city environments.

While reducing wasted electricity – and, therefore, saving significant expenditure on daily operating costs – businesses will be able to strengthen their brand image as truly sustainable commercial entities. In the end, I would argue that there is a significant win-win for businesses that want to remain competitive and to be seen as a trusted supplier. Digital twins are, of course, only a part of achieving a best possible decarbonized future but they will become an increasingly important one.

Are you looking for more ways to decarbonize your smart district? Find out here!

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Digital transformation is becoming an ever more urgent business imperative, with executives keen to capitalize on the significant benefits it offers. According to research from Gartner, 87% of business leaders report that digitalization is their company’s top priority. Yet we regularly see executives who don’t know how to prepare their organizations to enable and embrace it. All too often, excessive focus on the technology itself means many leaders simply overlook the importance of planning and preparation with people in mind. And it’s one of the most common causes of failed digital transformations.

Of course, digitalization must have a business case, but ultimately its aim is to benefit people and society as a whole. Successful digitalization projects demonstrate an elegant, harmonious and interdependent interaction between technology and people for the wider benefit of everybody in mind. One element alone can’t succeed without the other. That’s why the human aspect is crucial. Leaders must strive for twin goals: early employee buy-in through effective change management. Let me break down how this works in practical terms.

What many fail to appreciate is that digital transformation is a people business in which human factors and culture are the predominant influencers. And the cost of ignoring the human factor is eye-watering; the failure rate for digital transformations stands between 60% and 85% (HBR Pulse Check Survey, 2019). As a leader, once you understand that it’s as much a cultural transformation as it is a technological one, you’ll find it easier to develop an overarching strategy that has your personnel at its heart from the beginning. 

As our CEO, Aymeric, reports, he visited a customer factory in Asia that had implemented a digital transformation. Its production machinery automation was successful. But management hadn’t adequately considered the workforce from conceptualization; employees remained oblivious to how digitalization could improve the processes for them. Because they hadn’t been involved from the start, floor managers were disengaged and resisted change, reverting to experience over automation to assign incoming jobs. New systems designed to track jobs through the production cycle were by-passed and the technology wasn’t able to learn.

Initiating cultural transformation

How, then, could this situation have been better managed so that employees recognized the benefits? If you put your workforce at the heart of any transformation, employees become agents of transformation rather than casualties. So, establish clear two-way communication up-front so everyone’s aware of the direct benefits, impacts and improvements you expect.

Part of this approach is to ensure new technology and user interfaces are instinctive for all generations represented in your company. They must be intuitive for generation Z (born post-1997) as well as baby boomers (1946-1964). Workshop your digital processes, encourage feedback and be prepared to redesign them if employees find new processes harder than legacy ones. Human-centered design has a helpful analogy in today’s music-streaming apps; most of them offer pretty much the same access to music but are all slightly different in UX design. People choose to use Spotify, Apple Music or Deezer, for example, based on how they interact with them. What digital leaders need to address is: what kind of technology and processes will a wide variety of employee groups and generations need and use, and still all find intuitive, attractive and engaging?

Successful change management boils down to understanding the diversity of our workforce; our job is to make sure we know exactly who our employees are, how they learn and what makes them tick. Efforts to align your corporate culture will dictate the success of any cultural transformation and change management. There are three aspects of cultural alignment on which the success of your change management depends:

• Information & functional silos
• Corporate immune system
• Generation diversity

Information & functional silos – how to mitigate isolation

Certain functions, like legal or finance, demand specific skills and qualifications, and employees within those groups consequently pursue vertical career paths. These individual function groups tend to value qualifications and skills over agility, and the privilege they tend to enjoy as a result of their expertise, influence and resources can create a functional silo.

These groups in turn can create information silos. Function-specific business imperatives within vertical groups that are, by their very nature, functionally oriented can ignore the broader company-wide advantages of collaboration. As a result, the organization can find itself in a situation where functional silos – in which many employees already feel isolated and disincentivized to collaborate – actively resist the sort of collaboration and data-sharing essential for digital transformations.

Look for opportunities for collaboration across different functions where isolated employees can break out of their silos to form new groups. Essentially, you’re looking to instill a shared culture across a diverse group of silos to develop common values and expectations. Once you achieve that, cross-functional teams can form and disperse quickly in response to commercial demands. The result? A more agile, coordinated operation.

The corporate immune system – how to use it

Let’s be clear; an immune system, like that of the human body itself, exists to protect the functioning status quo. Like the human body, that’s great if you don’t want to change anything. But disruption and innovation in an organization can be seen as a threat in the same way that a human immune system will see a viral infection, for example; individuals within your corporate structure may act to resist and neutralize anything that threatens to change any part of your company’s past and present.

Your corporate immune system – a key element of the human factor – can halt your entire digital transformation in its tracks. And it uses three key traits of human nature – resistance to change, skepticism and suspicion – to decrease innovations like digitalization. It’s important to recognize each of these three traits, so let’s have a closer look at the detail.

Resistance to change is partly rooted in a natural fear of the unknown, as recent experience with one of our clients shows. Engineers at a major energy equipment supplier feared losing their jobs to an AI design algorithm. The engineers were skeptical of an automated process. As a result, despite the benefits in time-to-market and reduced costs, the company experienced widespread resistance to proposed changes in design processes.

The solution was to pick out a handful of coaches from among the engineers, selected on their willingness to adopt change and their ability to act as role models to colleagues. These coaches were given access to the organization’s entire digital strategy, allowing them to see how important the transformation was to the company’s continued success. This gave the coaches the opportunity not only to identify new emerging roles as a result of the transformation – roles that would give engineers valuable extra skills – but to plot their development within the organization. Once satisfied that change was desirable, the coaches acted as intermediaries to explain the potential to their colleagues. As a result, engineering teams that had previously feared the unknown and were therefore resistant to change sensed the urgency and benefit of the change; in the end, those teams actively pursued change to bring about a successful launch of the algorithm.

Skepticism is insidious, so it’s important to keep an eye on it. Skeptics may have experienced failed transformations with previous employers, and this can strengthen a pervasive narrative that digital leaders may be untrustworthy. One way to overcome this is to identify prevailing skepticism in the team. It’s important to get a feeling for employees that might not possess core competencies that are critical to the success of any transformation: collaboration, disposition to change and agility. Conversations, support and internal or external training can be useful to upskill employees and address any gaps in core competencies that are identified.

Finally, the immune system deploys suspicion. Few employees simply tend to dislike the need for change. If they feel being left behind, they’ll look at the evolving target state and take that as justifiable grounds for suspicion. In reality, due to inevitable ongoing developments, the target state of digital transformations can continuously evolve – and change can happen quickly. If a small number of skeptics latch onto this, this tiny minority might already think the transformation will fail. If left ignored, the attitudes of that small minority could gain wider traction across the organization.

Generation diversity – how to identify & harness behaviors

It’s common for many organizations to play host for three to four generations of employees. Broadly speaking, your workforce will fit into one of four categories: baby boomers (1946-1964); generation X (1965 – 1976); millennials, or generation Y (1977 -1997); and generation Z (post-1997). Successful leaders know that they all think and feel differently.

That’s not to say these generations don’t share common ground, and we should beware of stereotypes. But leaders need to know unique attributes of distinct generations in order to take everyone of the transformation journey. Include the baby boomers, for example, and leaders are getting valuable knowledge, expertise and experience.

To make your transformation effective, the use of training to embed cross-generational mentoring is worth considering. The thinking behind this suggests that pairing different generations creates complementary strengths. One possible approach is to pair millennials with baby boomers, helping employees in their 50s and 60s understand exactly where their accumulated wealth of experience fits into your digital transformation. Another possible way is to follow the example of Italian fashion brand Gucci and establish a shadow board, made up of non-executive millennials to expose older executives to broader perspectives. Since it was established in 2015, its internet and digital strategies have been largely responsible for a 136% growth in sales.

Those two clear goals - early employee buy-in and effective change management – are at the heart of a successful transformation. Aligning your corporate culture to lay the ground for effective change management involves planning with people in mind and communicating those plans clearly; understanding their digital preparedness and anticipating employee reluctance will help ensure nobody is left behind.

So, before anything else: put people at the very heart of your transformation now. If you enable them as agents and advocates of change, together they will help you unlock the power and full potential of digitalization for your business.

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The year 2020 was a living demonstration of how fast and dramatic supply chain disruptions could become. Ninety-four percent of Fortune 1000 companies still haven't recovered. Over half of them are forced to downgrade their growth projections in the foreseeable future, as a result. Only a few businesses have achieved the supply chain agility which is necessary to tackle these new challenges. “Future-proofing” has shown to be critical to ensure a company’s resilience in times of crisis. To build a future-proof supply chain, a few crucial imperatives need to be considered. Luckily, you don’t have to be a fortune teller to see the future – just read on to do so.

In the sporting world, you're only as agile as you are alert. It takes extreme awareness to react to unexpected swipes and throws on the field. In supply chain management it’s similar, the faster you can detect opportunities and threats, the sooner and better you can respond to them. Therefore, one huge goal is to guarantee supply chain visibility and transparency. The benefits of supply chain transparency lie in the possibility to identify potential risks and vulnerabilities at an early stage. This identification may require a bit of digging – looking deep into your network to identify strong dependencies on e.g. one single supplier – but believe me: it’s definitely worth it.

The most adaptive supply chains are able to predict shipment disruptions long before they occur. Strategic planners are increasingly turning to predictive analytics as an enabler for forecasts once transparency is guaranteed and data is collected. Building on observations and learnings of the past as well as actual data, analytics can spot for example weather extremes, material shortages, and demand fluctuations before they become influential to supply chains. Of course, Artificial intelligence (AI) also has its part to play. The more information you have, the more proactive measures can be initiated. So, it's time to haul out those crystal balls. It can save you a fortune in supply chain costs.

Naturally, not every potential disruption of your supply chain can be predicted – this would be magic. However, creating supply chain transparency while controlling vulnerabilities will reduce risks significantly.

A strong ecosystem of partners is essential to collaboratively tackle supply chain threats. The importance of ecosystems to ensure future-proof value chains is steadily growing. Companies need to break up internal as well as external silos to ensure faster data sharing and value creation. They need to decide which stakeholders (suppliers, business partners, organizations, etc.) to include and how to collaborate efficiently. If single suppliers have been identified to be of high risk for your supply chain, diversifying the network can be the answer. To give a concrete example: Multitier supply chains are becoming more popular in numerous sectors as they rely on several single-level collaborations instead of one multi-level one. Multitier collaboration platforms aren't always easy to use, though. One weak link in the collaboration annihilates the entire chain, means they're only as effective as their least adept participant. Digital platforms can be a great enabler in this area creating consistent transparency and improving connectivity. Platform-based ecosystems can help by integrating all supply chain tools and synchronizing timelines and therefore optimize your processes sustainably. To unlock the benefits of supply chain resilience, you have to invest in not only building up but also managing a strong ecosystem.

We live in a world where automated processes and robotics become more and more part of modern life – basically a world that science fiction authors pictured decades ago. The technology landscape changes rapidly. Characteristics of supply chain 4.0 like digital twins, big data, augmented reality and the Internet of Things (IoT) revolutionize processes and organizations and can bring enormous benefits for your operations. Automation can increase efficiency for repetitive and high-volume tasks, freeing up time for your staff to concentrate on more value adding activities. Technologies like process mining increase transparency, can be used to track process quality and process deviations in real-time and allow you to be ahead of your competitors by immediately taking corrective actions. Also, on a more operational level, technologies like automated guided vehicles (AGVs), picking robots and more can speed up your warehouse processes and support you in satisfying your customer needs faster and more reliable.

Implementing new technologies turbo-charges your analytics by gathering more data than ever before, benefitting your predictive power enormously. However, technology needs to serve the business – not the other way around. Make sure to prove that the selected solution brings long-term business value before applying it.

Successfully implementing future supply chain strategies must mean that there is an operational fit in your organization that puts sustainability at its core. True, certain aspects of digital transformation in supply chain management can help to overcome some of the short-term pains associated with organizational change, but the transformation must always focus on how sustainable business practices will become. In this regard, organizations must have a clear vision of what sustainability means to them and how they will put this at the heart of their supply chain systems going forward. Acting responsibly requires integrating sustainability criteria in daily decisions and assessing environmental and social risks at any stage. Designing a sustainable supply chain is about looking beyond the next quarter, year or decade and act upon a long-term vision of the future. We all know: the time to act is now!

In many ways, supply chain resilience is an aggregation of your greatest strengths. When you focus on the personal assets you bring to the table, you can push your resilience to unparalleled heights. It's you who tweaks your strategy and you who decides on your business values. Your supply chain might be marching into the future, but it still needs some traditional human values. Similarly, your corporate culture and brand determine the strength of your ecosystem. Business leaders need to foster new digital-driven mindsets. If the individuals in your supply chain are allowed to flourish, so will the greater system. The modern supply chain is overwhelmingly reliant on digital technology, but the more human its strengths, the more adaptive its future.

Ultimately, you might think of your supply chain as an attempt to control the future, so don't forget your vision along the way. Having a clear vision in mind, based on your business strategy, fitting your operational processes and cultural environment is core for success. Your goals will determine the principles you espouse in your everyday transactions. In the end, making your supply chain future-proof is about implementing long-term change. Failing to do so will mean that your more forward-thinking competitors are beginning to outstrip your organization already. And yet, change for change's sake will not work either.

Think big. Think clear, and your ultimate supply chain strategy will come to the fore. To make your supply chain future-proof, you will need to see the holistic picture and make your supply chain not only transparent, but sustainable, data-driven and focused on a strong ecosystem while leveraging your own strengths. Avoiding future disruptions won’t come easy (as long as you are no fortune teller) – but will make your company navigate the digital future.

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The global cloud market is expected to surpass US$600 billion by 2023. Edge computing, although presently commanding considerably less market value, is growing equally as fast. Analysts project that the edge computing industry will generate revenues of more than $15 billion in 2025. It might be a relative newcomer on the scene, but it's already changing the way the world handles and processes data.

There are now over 50 billion connected devices in the world, so modern networks have an enormous load to bear. Today's wireless connections must support everything from self-driving cars and data storage systems to warehouse robotics and video analytics. Edge computing addresses those bandwidth challenges by carrying computing topology closer to its source. At its simplest, it narrows the gap between data storage and the devices that need it so that latency problems can be resolved.

At first glance, the cloud's basic premise of creating a centralized data source that can be accessed anywhere in the world looks like the opposite of edge computing's local data handling idea. However, in many ways, it's the cloud itself that created edge computing. Without centralized data storage, the big data movement would never have achieved such scope. Many online payment providers wouldn't exist, for example, and brand titans like Microsoft and Amazon would be different from what they are today.

If you're working on your IT infrastructure, you've probably spent some time trying to sort through the benefits of edge and cloud computing. Which is best? The answer isn't as clear-cut as you might think.

The IT industry came up with the word "cloud" for its amorphousness. Its name is the perfect metaphor to define its function. Much like a floating cirrus cloud, the data or "water" it provides can reach people all over the world.
So, in contrast, what is edge computing? To extend the cloud metaphor, it would be more like a faucet. It brings data (or water) right to your doorstep but supplies nothing to your neighbors. The differences don't end there, though.

Cloud Computing Definition

Cloud computing relies on a remote server network to store and use data off-site. Like our figurative cirrus cloud, it can supply data to a large number of people at once. The cloud doesn't require you to maintain your own infrastructure; thus, no capital investment or staffing costs are needed in that area.

Edge Computing Meaning

Any edge computing definition should emphasize that this model doesn't rely on data centers or the cloud. Instead, it brings computing closer to a data source to minimize potential distance-related challenges. Much like our figurative faucet, it delivers its resources quickly and cheaply through fairly basic infrastructure. When things go wrong, it's also straightforward to troubleshoot.

The cloud was designed to overcome the limitations of local storage. It brought the world on-demand data storage and new levels of computing power. That changed the way businesses and individuals approached their IT assets. Dropbox launched an era of floating file access, while service providers like Amazon Web Services (AWS) brought the business world a new approach to software. Both companies turned data storage and computing into a sailing cirrus that you could access anytime from anywhere, no matter which device you had on hand. Remote work became easier to achieve than ever before, and apps could be accessed and developed by multiple specialists simultaneously. In short, the cloud's benefits include:

• Access to masses of storage space without the costs involved in storage infrastructure.
• Speeds that would be prohibitively expensive to achieve on your own.
• Remote data access that allows workers to collaborate from any country or device.
• The potential for Software as a Service (SaaS) pricing structures, which makes expensive software scalable and remarkably affordable. SaaS lets businesses pay a regular premium to "rent" software instead of buying it. It costs a fraction of a software product.
• Reduced risk of data losses.
• No need for a large in-house IT department.

You need the cloud if you use remote teams, need to extend local data storage capacity, or want to streamline your IT operations. In addition, key digitalization technologies like IoT (Internet of Things) depend on the cloud as a central location to store, process, and analyze data. It's also ideal if you expect rapid growth and need access to certain innovative software that is being offered through cloud-based subscription models. By contrast, edge computing benefits include:

• Reduced latency, so your apps usually function smoothly when working with real-time data.
• Data privacy and security is more straightforward to implement locally.
• Edge computing combined with IoT technology saves you bandwidth, thereby allowing you to choose where to best dedicate your resources.

Edge computing allows you to analyze your devices before sending data to the cloud—and that's where the magic happens. If your industry requires adherence to strict privacy laws or you have a tight IT strategy, for example, then edge computing gives you the right blend of benefits. That said, the best solution to the cloud-vs-edge debate is to use both. Separately, each solution is a lone voice. Together, they're a choir.

Fog computing blends both edge and cloud computing. By doing so, it stretches the cloud to the edge of the network so that it's easier to connect IoT devices in real-time. By incorporating the benefits of both edge and cloud technology, it achieves a high-level network environment. It can connect two disparate ecosystems without losing local storage benefits. Fog computing reduces latency between devices while simultaneously reducing bandwidth requirements. It opens up exciting possibilities for several industries. Autonomous self-driving cars, smart cities, and real-time analytics are all at their best with fog computing. Its capacity to transfer data right at the edge of remote areas makes it suitable for roaming use cases as well. Fog computing is, effectively, edge computing with wheels.

Edge computing has plenty of untapped potential. Analysts predict that it will account for 75% of enterprise data by 2025. In the coming years, it will deliver insights faster than ever before. But as more and more devices with ever greater numbers of sensors will be producing even more data with higher sampling rates in the near future, a centralized model, such as cloud computing, will be placed under greater stress. Even with optimizations, the bandwidth required will become a bottleneck. This doesn't mean there's an end in sight for the cloud, though. After all, many devices designed for edge computing have strictly limited computing power. Both solutions offer important benefits to the business world but used in tandem unlocks additional versatility for advanced digitalization approaches.

Let’s dig a little deeper to illustrate this: The transmission costs of providing trivial sensor values to a centralized location will often not be worth it for ordinary measurements, such as confirming the normal state of a device. The value in knowing an IoT-tracked 24/7 production asset is switched on, for instance, is much less than knowing when it is off for whatever reason. In the end, edge computing's benefits are limited by the fact that edge-enabled devices only know what is going on locally - the ‘big picture’ can only be assembled centrally and is not readily available at the edge. Consequently, AI for a fleet of such devices can only be realized centrally and not at the edge.

What will the future look like?

Although no one can say for sure, fog computing is already shaping up as an added value driver of digitalization initiatives, bringing benefits both in the direction from edge to cloud and vice versa.

From Edge to Cloud

It is likely that data will be pre-processed on edge devices, and their first actions will be triggered in a decentralized manner on the basis of some centrally specified analytics. This model should allow for the automation of systems while maintaining low latency. Only selected data – information that is particularly interesting or potentially important for others to know about - will be collected centrally via cloud services.

From Cloud to Edge

Fog computing will facilitate fleet recommendations to IoT devices derived from the centrally collected data held in the cloud. For this to work, new analytics models will need to distribute centrally computed insights back out to edge devices where they can be utilized. As such, adopting a fog computing digitalization strategy now appears to offer organizations the greatest level of versatility going forwards.

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The speed of change often blows my mind. It took 75 years to get 100 million people using the telephone, but in 2016 it took just one month to get 100 million people playing the online game Pokémon GO.

Our world’s level of complexity is increasing tremendously. In today’s world of digital transformation, circumstances dictate that we make weekly if not even daily decisions of strategic importance. Nowadays, it’s vital to turn organizations into future-sensing organisms with the capability to probe, sense and respond continuously. IoT is a major lever for this transformation, serving both business and community. In fact, the best examples of IoT in action show that this double objective is clearly being met.

IoT is transforming the way we work – and the way we live.

Imagine the scene: a strong storm over an offshore facility. Grid operators are warning of outages. The challenge is to manage the gusts exerting excessive wind loads on the turbines of an offshore wind farm and to avoid potential interruptions to clean energy generation.

When storms like this exceed the operating limits of certain wind speed ranges, the turbines apply a brake and stop operating. However, there is an operational and commercial requirement for all turbines to be available as much as possible.

Here’s the solution: turbines with sensors and an IoT cloud-based platform incorporating for example machine learning (ML) that allow increased remote monitoring and troubleshooting. Using data from different sources, such as weather or operational data, allows to utilize the maximum potential of the wind farm. As a result, turbulence can be managed as optimal revolutions-per-minute are maintained and specific loads across the turbines are balanced.

How is this changing the world?

With assets maximized to deliver in extreme weather conditions, we’re less likely to suffer power cuts. So, the electricity in our hospitals, schools and homes stays on and our communication networks remain operational and robust. All with clean, zero-carbon energy utilized to its full potential – no matter how big the storm.

Manufacturers are usually eager to improve factory set-ups and assembly line design to attain greater worker safety and better resource efficiency. But how can that happen without taking the factory off-line for an expensive physical run-through?

The solution is a digital twin: A digital simulation of a real-life process that allows the factory to trial any proposed improvements or changes. Created with plant simulation software, the facility can develop a digital twin of the assembly line design based on a 3D material flow simulation model. In other words, it builds a factory simulator.

How is this changing the world?

What this example shows is how we can plan changes to any part of our physical world without exposing humans to unquantified risk. And we’re no longer subject to so many downtimes in our real-life systems; they march on while their digital twin ponders a more productive future for them.

IoT allows us to identify unknown pitfalls without any risk to assets, while business-critical systems remain online. Its primary human benefit here lies in reducing risks exposure during trial runs, for example on new factory line design. Further, it opens the way to improve workplace health and safety.
 

When we walk on the sidewalks of our streets, we rarely think about what is happening underneath us. But there’s an IoT story buried down there that is much more than just a legacy heating system blowing off steam.

A city underground network hosts a typically extensive array of critical infrastructure, including miles of electrical cables, water systems, and gas pipelines. If any part of it fails, everything can grind to a halt in seconds. That’s why it’s essential to monitor the underground network constantly to ensure the city’s functions are never interrupted.

Previously, maintenance would have involved work that was expensive, time-consuming, and dangerous. But by equipping critical infrastructure with smart sensors – predicting maintenance issues long before anything goes wrong – it’s possible to watch, detect and transmit the condition of fibre-optic cables and gas pipes in real time. Meanwhile, with the potential threat to public safety, IoT-based early warning systems are able to reduce incidents significantly.

How is this changing the world?

These technologies increase safety and reliability. They deliver efficiencies, save time and reduce costs. That means our broadband is less likely to fail; it ensures a safe water supply; and it minimizes the risk to human life and property from leaking gas pipes and manhole covers.

Think of how many factors determine whether our urban lives are happy or miserable. Can we move around the city with ease? Will the air we breathe impact our long-term health?

Air quality is one important consideration among many that challenges citizens and planners alike. In order to reduce the impact on people’s health, city authorities are searching for new ways to manage and reduce air pollution in metropolitan areas. One possible solution is leveraging the potential of cloud-based solutions and Artificial Intelligence (AI). Comprehensive data analysis and forecasting enable decision makers to improve emergency response times and lower emissions of carbon dioxide, carbon monoxide and nitrogen oxide. How does it work? IoT gathers emissions data in real-time and simulates immediate future actions designed to improve air quality for example by adapting speed limits or offering free public transport.  

How is this changing the world?

The forecasts allow cities to simulate measures to avert or mitigate breaches in air quality standards. The result? Lower greenhouse gas emissions that support a happier and healthier metropolitan life where we can move with ease and enjoy the full potential of cities.

Yes, the world is becoming increasingly complex, and so how we deal with it must mirror that. But underneath this complexity is a relatively simple philosophy – to adopt a growth mindset and learn to react effectively with solutions that genuinely benefit the individual, the organization, and society at large.

As we have seen, IoT has already affected us in many positive ways. Businesses are seeing both cost savings and greater revenue, and governments are finding better ways to provide essential services relating to security, infrastructure, environmental protection, and beyond.

And we are just starting. Now is the time to shape the future world we want to live in – using technology with purpose which serves both business and society!

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The last decade was about connecting the consumer world. This decade is about connecting infrastructure and industry to the Internet of Things (IoT)—and the pandemic has only accelerated this trend.

So, at the Web Summit 2020 in December we brought together eight leaders across the private sector, government, and academia. We then asked: How we can unlock the full potential of the IoT to transform infrastructure, industry, and society?

And one takeaway was clear: No one's company can develop and offer these end-to-end solutions on its own. We will need to reassess longstanding ways of thinking and redefine competition. The pandemic, in particular, has demonstrated the value of working together as part of an IoT ecosystem – not an egosystem – tapping into the full range of expertise and perspectives to solve problems and innovate new solutions.

Here are four key elements to building the connected world that emerged during our discussion.

Collaboration isn’t just necessary to building the IoT—it's an accelerator. That’s because no one person or company is an expert at everything. Creating the new systems and developing the new markets for the IoT ecosystem will require a lot of collaboration between companies and organizations. The software developers need a data scientist who needs a marketing communications expert, and so on. The IoT is going to involve tons of R&D, coding, hardware, intellectual property expertise, and communications at a new scale. That assembling of talent and intelligence will make things move faster.

This does not mean an end to competition. The IoT ecosystem will need that, too: innovation will be the name of the game. But we must move beyond the old models of competition to a much more integrated practice of collaboration and cooperation. Most, if not all, the companies with a major stake in developing the IoT will come to see collaboration as a core practice. Collaboration will also be inevitable as we realize the opportunity of using the IoT not just to create business value, but to keep the end-user in mind and solve shared societal challenges.

Moving from a total competition model to a new era of cooperation will rely greatly on a global diversity of thought, workforce, and leadership. Put simply, diversity sparks innovation—the overlap of multiple cultures, experiences, and backgrounds in the act of innovating is where the magic happens.

We’re already seeing that magic happens in engineering programs at universities and colleges across the world, where a new emphasis on multidisciplinary thinking, collaboration, and diversity is helping to accelerate innovation. Today’s young, diverse engineers are highly aware how technological advancements can exacerbate inequities, and they also know that the possibilities of the IoT ecosystem are endless and can transform every single aspect of modern society to create a more sustainable, secure and safe future.

To be sure, business managers need to provide real, measurable results and value for shareholders and stakeholders—that’s the here and now. But as we consider how to build the IoT ecosystem, we realize that we must develop a combination of technologies that over the long term will bring about all kinds of changes at great scale, so we must think long term about how those technologies will work together, how they will adapt and evolve, and how they will change industry and society. 

Exponential technologies like artificial intelligence (AI), digital communications, quantum computing, and blockchain are becoming more and more integrated. This integration of technologies will play a critical role in the digital transformation of just about every legacy business we can think of and will create business synergies we don’t yet know. These combined technologies will also be central to decarbonization and reversing climate change. The IoT will allow us to integrate services, predictive maintenance, efficiency, and innovation that will deliver results for customers in ways that we need to plan for now. That’s why we need to plan for the long term and keep an eye on the big picture: the value of IoT to society.  Embracing open source technology and the power of ecosystems will help us tackle global challenges together.
 

If there’s one barrier to developing the IoT ecosystem, it’s the tendency in business to do things the way they’ve always been done. Breaking that barrier will require a cultural change in every business and organization, and it starts with leadership. Business leadership must be willing to revise business models, align their people on new key objectives, and accelerate toward new outcomes, with new ways of measuring success. This will generate value for customers, for the business and for societies.

Cultural change is also essential for companies to create the sales force, service managers, and engineering teams they will need to build their IoT ecosystem. Once they have such new teams in place, companies will quickly find out how much value they can create through transformation and how to tackle the global challenges.

And what brings all four of these elements to life? People and trust. People control the tech and navigate using the tech, and we will learn to trust ourselves and trust each other, knowing that we’re advancing IoT in the right way: replacing the egosystem with an ecosystem that, five, 10, and 20 years from now, will be a people-driven network expanding what’s humanly possible. If we do, we will be unlock the full potential of IoT both to our businesses and to advancement of a more resilient, sustainable, and equitable world.