I think sustainability is really growing on the corporate agenda these days, primarily because the problems that are happening around us in the world are growing. So as we see issues like climate change or the global pandemic really coming to the forefront, we're realizing that we can't actually solve these problems if we don't have the private sector and solutions that only the private sector can bring as part of the equation. In previous chapters, we have discussed the need to go from a product perspective to understand the value of the product as it is used. We have also pointed out that singular products are not enough to take on larger, sustainable changes. As it is the usage of a product that generates value and not the product itself, we can decouple growth and value from resource usage. This is clear in many circular business models, enhances the value of services, shared resources, and digital solutions. President, Scania. To do this sustainable transformation, I think you need to find a number of accelerators that you can use. It gives you the X-factor that is giving you an exponential sort of rapid development. For us, once we had looked through our products and services offering to make sure that they are becoming sustainable, that we've looked through our own footprint that, that is sustainable, the next step was to try to see what sort of accelerating leavers do we have? What sort of x-factors do we have? One of them for us was apart from building a lot of knowledge based on science that we needed to do studies on how to make a long-term journey. We signed up for science-based targets for 1.5 degrees and reliability was utilization and actually utilizing since 2010, we have connected all our trucks and buses that have left the factory now. Today we have active around 500,000 connected vehicles sending information every minute where they are, how they're loaded, how much fuel they use, how much CO2 they emit. That data lake has been tremendously important for us to take out the waste in our customer's operation. When it comes to optimizing logistical flows, make sure that you lower it as much as possible when you go into vehicles. We see that the average truck in Europe, which has the most sophisticated logistical system in the world, the average truck roughly is loaded around 60 percent, 40 percent is air, waste and that we need to address. How can we bring that 40 percent down? That we can do with connecting all these vehicles for our customers. But it's also information for us on how we can design and develop products for the future to reduce CO2 emissions and fuel economy. It is also internally for us a way to create a more sufficient service system where we use the digital information from the vehicle before it comes into the Workshop two days before we download information, like a health report of the vehicle. The vehicle actually books itself into the workshop, not on 100,000 kilometers or 120,000 kilometers, but actually one when it's needed for that specific individual vehicle. Then when it comes to the workshop then we have able to use this information to plan our work. So instead of maybe the customer has to wait four hours to get out on the road again, we can do it in one hour. Just to put it into perspective, our customers, they earn around two percent EBIT margin. They run operation 24-7, 365 days. That means that they make their profit from Christmas to New Years', the last seven days of the year. So is two hours extra on the road important for them. Yes, it is. If we focus on digital technologies, the design phase, and the ways we integrate these solutions in our offerings are essential. Value proposition is of central importance in order to articulate the value to customers and stakeholders. Traditionally, this is something that we tied to products and services. However, in a circular economy, value often has a broader perspective and creates benefits for multiple stakeholders. As we reflect on how new technology can be valuable from a circular and sustainable perspective, we need to understand both the benefits and dis-benefit of different technologies to sustainability. The exponential road-map highlights the following aspects. IoT is the technology that connects devices and everyday objects to one another and to various services. We can use it to optimize systems to save energy, materials, or to enable circular economy. It can also enable distributed demand and responsed in electrical grids. It also increases the efficiency of oil and gas extraction and production of high-carbon products, which delays the shift to low carbon alternatives. AI, machine learning, and deep learning, the ability of a computer to think, learn, and respond from high-volume data structured in intelligent solutions can enable the continuous improvement of energy system, factories, buildings, and vehicles. It can reduce cost and carbon footprint while improving functionality and performance. We can link technologies with social media support and nudge people to act in a sustainable direction. But AI can also be used in fuel production in an unsustainable way and it can accelerate high-carbon production and consumption. 5G networks, the cellular network that is now being out-rolled provides high-speed connectivity. The mass scale connectivity of grids, buildings, industries, cities, vehicles, and things combined with AI or machine learning enables efficiency of systems, research manufacturing flows, and autonomous services, which can support value creation with fewer resources. An example is virtual meetings, which reduce traveling. However, of course, it can also drive demand of high-carbon industries and solutions. A digital twin is a digital replica of a living or non-living physical system, which allows designers and engineers to test how products or systems can be improved, optimized into full. To create truly scalable industrial sustainability offerings, I think there's great potential in data, and data analytics, in the machine learning, and AI. There's a lot of untapped opportunities. In the AI Sustainability Center where I'm working now we talk about exponential benefits and exponential risks. So that's the dual nature of AI. I started working with this maybe 20 years ago and working with a platform that we call technology for good. That means that technology can be a powerful enabler of inclusiveness in society, of offsetting CO_2 emissions, of transforming the way we live and connect, and engage with family, and friends, and medicine, and education, and the whole list of things there. But at the same time, as we come into an increasingly data-driven and machine learning or AI world, we need to think what are the exponential gains that we can make, but how do we make sure that we're not amplifying risks in the same way? Most companies would go into AI for two main reasons. You either want to transform the way that you're doing something today in order to get those cost savings or optimization, or you want to have totally new revenue streams, closer relationships, more tailored relationships with your clients. So you're either going after the big bets on revenue gains or cost savings. But I believe there's a third part of that picture, which is about society. As we do that and we go after all the promise of AI and machine learning, how do we make sure that we're not creating any unintentional harms? For example, algorithms, you can have bias of the creator, you can have inherent data bias. Bias is one issue that leads to discrimination and you could make a lot of faulty conclusions based on the information if you're not aware of the potential risks. Let's make it clear. Digital technologies don't solve sustainable problems in themselves. Used without sustainable caution, they can increase the carbon footprint. In order to make them sustainable, sustainability should be a part of the design phase and it should have a stakeholder perspective. Each stakeholder should be able to influence the outcome of the technology application in both design and usage. Moreover, the design should also assess the sustainable dimensions of a given solution. Design research from RMIT University in Melbourne suggest a process with the following steps in order to make a sustainable profiling of projects. First, start with a requirement list including everything that you want to accomplish. Second, make a stakeholder analysis, mapping out the needs and perspectives. Third, process the assignment with a sustainable questionnaire covering the individual dimension, economic dimension, technical dimension, social dimension, and of course, the environmental dimension. Next is to rate their requirements and then to analyze responses based on the order of preferences. Finally, design on sustainable profiling requirements. This process just like most other sustainable design tools is not very different from ordinary design tools and instruments. However, if not applied from the beginning, you won't get the results you aim for.
