Keywords

7.1 Introduction

Sustainable production has been the focus of researchers and practitioners for more than two decades. In the beginning, the research largely addressed aspects such as increasing resource efficiency or avoiding hazardous materials in isolation; however, a common understanding exists between academia and industry that sustainability covers a broad range of economic, ecological, and social aspects. This approach is also reflected in the 12th goal of the sustainable development goals (SDG), which is “responsible production and consumption” (UN General Assembly, 2015). Today, the scarcity of material or human resources and increasing environmental and social regulations mean that manufacturing companies must not only address individual aspects of sustainability, but they must also develop an overall strategy and concept for their implementation. This chapter examines how companies can implement this ambition within their own existing manufacturing processes.

As discussed in the previous two chapters, achieving the goal of responsible production requires a new, circular approach to product design (Chap. 5) that has far-reaching implications for sustainable value chains (Chap. 6). Before the next chapter (Chap. 8) discusses the technological disruptions that can drive the transition to climate-friendly mobility, this chapter looks at Sustainability in Manufacturing as a critical step in this transition journey. While the design of products and value networks is vital, it is through the manufacturing process itself that the involved companies can directly modify their material, energy, social, and environmental footprints.

The purpose of this chapter is to discuss the contributions, tools, and challenges of using sustainable manufacturing to advance the goal of responsible production. The chapter is divided into three parts. Section 7.2 begins with a brief overview of the origin and definition of sustainable manufacturing and then launches an explanation of the three dimensions of sustainability and their implications for manufacturing. The presentation of three use cases illustrates how sustainability is managed at the operational level. Finally, future research perspectives regarding energy use, manufacturing technologies, and circular processes are discussed. Section 7.3 presents the expert conversation between Prof. Oliver Zipse, Chairman of the Board of Management of Bayerische Motoren Werke (BMW) AG, and Prof. Dr.-Ing. Hanenkamp, Institute of Resource and Energy Efficient Production Machines at FAU Erlangen-Nürnberg. Section 7.4 shifts the focus to the sustainable factory of the future, and the chapter concludes in Sect. 7.5 with a short summary and a link to Chap. 8 on The Power of Technological Innovation.

7.2 The Three Dimensions of Sustainable Production

Even after almost three decades of research and practical implementation, no common definition exists for sustainable manufacturing (Moldavska & Welo, 2017). However, a consensus has been reached that sustainable manufacturing must cover the three dimensions of economic, ecological, and social aspects (Von Hauff & Jörg, 2017). Although the lack of an abstract definition may seem unimportant at first glance, researchers claim that its absence creates challenges when attempting to take sustainability concepts from theory to practice in the production environment and on the shop floor. Whether sustainable manufacturing is an environmental initiative, a systematic process, a paradigm, or a balance between the dimensions also remains in question. Since the 1990s, a variety of definitions have emerged, but these have served to create more confusion than clarification. The U.S. Department of Commerce defined sustainable manufacturing in 2008 as “the creation of manufactured products that use processes that minimize environmental impacts, conserve energy and natural resources, are safe for employees, communities, and consumers, and are economically sound” (cited in Haapala et al., 2013, p. 041013–2). Since then, research and practice have either referred directly to this definition or adopted similar terms.

The ecological dimension is directly impacted by manufacturing due to the use of (non)renewable resources and the release of emissions into the environment. While the use of renewable resources must not exceed the rate of regeneration, nonrenewable resources should only be used if the possibility of substituting them exists in the long term. From the point of view of an individual company, the economic dimension means reducing the life cycle costs of equipment and manufacturing costs. Finally, the social dimension addresses the needs of employees and society in the manufacturing environment and supply chain. It covers both the health and safety requirements within the production and targets equality among employees with diverse backgrounds while also addressing social aspects within the supply chain (human rights, working conditions, etc.). In the past, many companies prioritized economic and environmental aspects in their sustainability strategies; however, the upcoming demographic change to an aging population in developed countries, which limits the availability of human labor, is now forcing the manufacturing sector to put more emphasis on social aspects (Yuan et al., 2012). Finally, research has shown that the dimensions of sustainability are strongly interlinked, so the full potential of sustainable manufacturing can only be realized by consistently adopting a three-dimensional (3D) approach (Stark et al., 2014). Upcoming regulations, such as the European Sustainability Reporting Standards (ESRS), with their defined structure of reporting elements and key performance indicators (KPIs), can guide practitioners during implementation (European Financial Reporting Advisory Group, 2022). The combination of ecological, economic, and social aspects simultaneously increases a company’s competitiveness, as reflected in improved business performance for companies with a consistent three-dimensional approach to sustainable manufacturing.

Manufacturing companies have always striven to improve their operational performance and have developed appropriate principles and management systems, such as lean management, green manufacturing, or Six Sigma. These mature systems already contribute to sustainability in production; however, practices such as lean management alone are insufficient to address all sustainability aspects (Hartini & Ciptomulyonob, 2015). One reason is that the different types of waste only partially address sustainability aspects and do not necessarily focus on a life cycle perspective. Therefore, the challenge from an implementation point of view is to integrate different concepts and management systems, each with a specific focus and expertise, to provide overall sustainability to manufacturing.

The typical research objects tackled with regard to sustainable manufacturing include technologies, the product life cycle from a holistic perspective, value-added networks, and the global manufacturing impact. For each group of research objects, the three dimensions need to be addressed equally.

7.2.1 Practical Perspectives on Sustainable Manufacturing

The following section illustrates the successful implementation of sustainable manufacturing by comparing three use cases from BMW’s iFACTORY, each with an equal focus on each of the three dimensions but covering the different groups of research objects. With the iFACTORY, BMW addresses the three pillars—LEAN, GREEN, and DIGITAL—thereby setting the direction for the transformation of manufacturing expertise throughout the entire production network (see BMW AG, 2022). This means:

  • LEAN—efficient, high-precision, and flexible,

  • GREEN—Resource-optimized and circular

  • DIGITAL—A new level of data consistency through the efficient use of AI, data science, and virtualization

The first use case shows that incorporating innovative circular materials and systems helps to conserve resources and creates ergonomic benefits for associates. To conserve even more resources, the BMW Group has implemented various projects in packaging logistics. These aim to reduce carbon dioxide (CO2) emissions in cooperation with suppliers and to implement the principles of circular economy to the greatest extent possible. European plants are increasingly using recycled materials for packaging. In 2022, new contracts for reusable packaging in logistics specified almost double the quota of recycled material, increasing from approximately 20% to over 35%. CO2 emissions are also being reduced through the use of alternative sustainable materials, less single-use packaging, lightweight packaging, and reduced transport volumes. The BMW Group plans and monitors the effects of individual measures via a CO2 calculator for packaging.

A second example of innovative production processes with positive reductions in energy and water consumption is the so-called dry scrubber. In a major step toward greater sustainability, paint shops no longer wash away excess paint particles with wet scrubbing but instead are switching to a system of dry separation. In the spray booth, any overspray that does not land on the car body is now collected using limestone powder rather than water, thereby considerably reducing water consumption. Another major advantage is that, unlike wet scrubbing, dry separation can be carried out in up to 90% recirculated air. This means that only 10%, rather than 100%, of the air has to be brought up to the required temperature and humidity, thereby saving vast amounts of energy. The limestone powder also does not need to be processed and disposed of, unlike contaminated water. Instead, it can be returned to the material cycle—for use in the cement industry, for example.

The third use case pays in directly to all three dimensions of sustainable production. A 3D human simulation introduces a virtual model of a human into a virtual production environment. It uses a combination of connected planning data to simulate the complete production and assembly process in 3D. Through this, valuable information can be gathered by simple means, such as planned time analysis, ergonomics assessments, workplace optimization, and validation of planning. This enables optimization of process engineering, the conditions for production workers, and process maturity right at the start of production.

7.2.2 Research Perspectives on Sustainable Manufacturing

Sustainable manufacturing offers a broad spectrum of research opportunities. Due to the interdisciplinary character of sustainability studies, research on the social, economic, and ecological dimensions requires different research competencies. Because of this complexity, this section focuses primarily on the engineering perspectives involving energy, circular processes, and manufacturing technologies and strategies.

With regard to energy in the context of sustainable manufacturing, four main research perspectives can be identified. Improving energy efficiency has long been a major focus of research and practice in the past. In addition to energy efficiency (i.e., the relationship between the value created and the energy used; DIN, 2011), energy flexibility requires consideration in the future (Popp, 2020). Energy flexibility describes the ability of a factory or a process to adapt to a volatile energy supply with no negative effects on productivity, quality, or delivery service (VDI, 2020). Overall, 16 flexibility measures have been identified that can be assigned to the factory, production, or process levels. From a research perspective, manufacturing processes, operations management practices, and digitalization technologies all need to evolve to address both energy flexibility and efficiency.

The second perspective involves the substitution of fossil energy sources with renewable energy sources and technologies within a factory. Currently, a strong trend is evident toward the electrification of industrial processes (Wei et al., 2019). With the decreasing price level of solar panels and increasing battery storage capacity, the integration of volatile energy sources to operate industrial processes with a continuous demand is becoming both feasible and advantageous. Although industrial processes cover a wide range of temperatures, electric heating systems, high-temperature heat pumps, or solar thermal technologies can easily generate lower temperatures up to 140 °C.

The third perspective focuses on the systematic change observed across the entire energy supply chain for electricity, from generation to consumption. Decentralized energy generation using photovoltaic systems can now partially replace the traditional external energy supply generated by large power plants and transported over long distances. These approaches can help reduce costs and increase energy resilience.

Finally, production systems and factories based on direct current represent a major new area of research. These systems allow an easier integration of renewable energy sources, such as photovoltaics, while also eliminating the need for frequency inverters that lead to efficiency losses, such as harmonics, and enabling an easier recuperation of electrical energy (Sauer, 2020). This broad scope of the entire system of energy supply, transport, and consumption reveals tremendous improvement potential for energy efficiency, flexibility, and substitution.

With regard to circular processes, the second area of research in sustainable manufacturing places a strong emphasis on material flows and digitalization. The linear manufacturing approach of “take–make–use–dispose” not only exceeds the waste-carrying capacity of the earth, but has significantly increased the rate of resource extraction in the recent past. In the EU-28, the manufacturing sector generated 10.3% of all waste, making it the third largest contributor after construction and mining (Rashid et al., 2020). Decoupling resource consumption and waste generation from economic growth will require the application of circular manufacturing. The aim of conventional circular or closed-loop systems is to minimize energy and resource inputs, maximize the value generated, and reduce waste and emissions (Nasr & Thurston, 2006). Closing the loop between output and (re)input can be achieved through reuse, remanufacturing, or recycling. In many cases, this approach is limited because the present-day processes and products were not intentionally designed for closed-loop systems, and the effort to implement circularity exceeds the potential benefits.

According to Rashid et al. (2020) and in line with the circular economy definition of the Ellen MacArthur Foundation (2013), a circular manufacturing system is “a system that is designed intentionally for closing the loop of components or products, preferably in their original form, through multiple life cycles” (Rashid et al., 2020, p. 355). Circular manufacturing can operate at the macro-level (e.g., region and smart city), the meso-level (e.g., industrial parks and factory), or the micro-level (e.g., products and processes) (Urbinati et al., 2020). The micro-level is characterized by the shortest loops and thus has the greatest potential environmental benefits. Based on the original 3R concept (reduce, reuse, and recycle), the 6R framework for implementing circular manufacturing systems, which covers the entire product life cycle (reduce, reuse, recycle, recover, redesign, and remanufacture), represents the state of the art for research and practice (Jawahir & Bradley, 2016).

The first R (reduce) refers to the reduction of resource usage in the premanufacturing phase, the reduction of energy and material consumption in the manufacturing phase, and the minimization of emissions in the use phase. The second R (reuse) refers to the multiple life cycles of the original product or its components after each end of life (EOL). The third R (recycle) converts material that would normally be considered waste into new material and process input. To gather the product after the use phase, the fourth R (recover) has the task of recovering the products after their EOL. The fifth R (redesign) incorporates products or components from previous life cycles into the next design concept, while the final R (remanufacture) aims to restore used products to their original state. The 6R system combines traditional methods or tools, such as those for energy efficiency, with innovative remanufacturing processes and facilitates stepwise implementation (Brunoe et al., 2019).

Although circular manufacturing offers tremendous potential for sustainability, its implementation is often hindered by heterogeneous barriers. Because different stakeholders are involved, typically including at least suppliers, the manufacturer, users, and remanufacturing experts, the sharing of data and information is a major challenge. Digital twins of material flows can be used to provide and manage complex and heterogeneous data in discrete manufacturing between them (Acerbi et al., 2022). As an alternative to hierarchical data models, blockchain technology has been implemented to share data among different stakeholders (Govindan, 2022). In doing so, these data models describe the relationships between processes and material flows, reveal optimization potential for circular manufacturing, and deliver consistent and trustworthy data. Thus, in addition to the 6R methodology, the sharing of data and information is considered a prerequisite for implementing circular manufacturing.

Finally, with regard to sustainability in operations, manufacturing technologies and strategies represent a third area of research. On the one hand, innovative processes, such as additive manufacturing (AM) or digitalization technologies, have a strong impact on well-established process chains. On the other hand, further development is required to bring innovative technologies to similar quality levels and process capabilities or to scale them up for manufacturing in batch sizes of single products and high-volume production. On the technological side, additive manufacturing (AM) is a primary area of research. For production scenarios with high complexity and low volumes, AM has already demonstrated competitiveness compared with subtractive or formative technologies (Pereira et al., 2019). Due to the reduction in resource consumption and waste generation, AM has a strong positive impact on sustainability. The main challenge for future AM processes and machines is their integration into complete supply chains that meet the requirements of high complexity and large volumes. Other technological challenges arise during the production of electric cars, particularly battery production, or the production of components for hydrogen applications. Both of these examples require innovative, isolated process steps, as well as completely new entire production systems and machines; consequently, low quality levels with high fluctuations are a major concern and have a negative impact on overall equipment effectiveness (OEE) (Schnell & Reinhart, 2016). Finally, process chains for innovative applications or AM will not replace traditional technologies. Further potential for improvement lies in the adoption of hybrid manufacturing approaches, such as configuring the most suitable manufacturing technology for a best practice process chain or even combining technologies with the machine tool (Merklein et al., 2016).

Digitalization and the use of artificial intelligence offer future research perspectives regarding sustainability. At present, Industry 4.0 approaches have been used primarily to address the environmental dimension, but researchers have already outlined research agendas to address the social and economic dimensions in a holistic approach (Machado et al., 2020; Stock & Seliger, 2016). Digitalization techniques, such as the Internet-of-things (IoT) or cloud manufacturing, represent technological tools that must be adopted to pursue sustainability objectives. Artificial intelligence (AI) can be used to manage the complexity of sustainability-related data (e.g., with big data analytics approaches). In any case, digitalization and AI require access to reliable data at the process level.

Manufacturing strategies are an additional area of research. Due to the cross-dimensional nature of sustainability, its strategy must be strongly linked to functional strategies, such as product or process development. Sustainable manufacturing involves technological aspects as well as methods and tools; therefore, a challenge for future research is to integrate well-established management processes, such as quality and supply chain management, and production systems, such as lean management, with sustainability approaches. Replacing existing processes and tools is not recommended; rather, these should be further developed by considering sustainability aspects (Pampanelli et al., 2014).

In summary, various aspects of future research on energy, circular processes, and manufacturing technologies have been highlighted, without claiming to be exhaustive. An important point to note is that intrinsically motivated employees drive the transformation to sustainable manufacturing. They use valid and real-time data in their decision-making to achieve specific and individual sustainability goals. Therefore, in addition to the technical and organizational challenges described above, a suitable qualification concept is of particular importance. To achieve broad acceptance for the implementation of sustainable manufacturing, specific training content and programs with theoretical and practical content must be developed for all hierarchical levels within a company.

7.3 Expert Conversation on Sustainability in Production

What Is Important in Managing Change Toward Sustainability?

  • Hanenkamp: Change management is an integral part of any successful business. Sustainability brings with it a whole new set of challenges and thus changes. How would you describe the strategic approach to managing change toward sustainability? How do you manage conflicts related to sustainability?

  • Zipse: Change management is necessary, especially in a high-investment industry. Behind us is a big factory. That is a big investment, an investment of about 2 billion euros. There are certainly good arguments not to change anything about that. So, we need a method to develop a corporate strategy that also takes into account external inputs and answers the question: Is the status quo—including the innovation structure, customer behavior, and cost structure—sustainable in the future? It is necessary to question this status quo at any time. If the status quo of your methods and processes, as well as of your corporate culture, is not good enough, you must change. At BMW, we coined a term to describe our desired culture. We call it “Be more BMW.” Everyone at BMW knows what BMW should be: entrepreneurial, highly innovative, and building the best cars in the world—the ultimate driving machines. At the same time, however, this term stands for a sustainable and profit-oriented strategy. There are diverse requirements, but everyone at BMW knows that this is a solvable equation.

  • Hanenkamp: Sounds like a continuous journey.

  • Zipse: It really is. To achieve that, you have to change every day. You have to look for better opportunities every minute, and you have to disregard the status quo if it is not good enough for the future of the company. In production, we all know the old principle of Kaizen (continuous improvement), where all employees consciously question their own activities again and again and constantly improve the way they work. Change management is about looking not only for the big, visible steps, but also for the small, everyday improvements. If you reduce your energy consumption by 30% this week, why not add another percent or two the next week? And it never stops. Change never stops, and there is never a best possible process. Manufacturing is made up of thousands of processes, so it is extremely important that this optimization process never stops. It is a cultural issue but, of course, it is also a technical issue.

  • Hanenkamp: I understand that continuous improvement is an integral part of a successful company and is also essential for sustainability. We have many processes in place for continuous improvement: quality management systems, lean management culture, etc. There are overlaps, for sure. How do you plan to implement sustainability management in the future? Will there be a standard, a separate sustainability toolbox, with all the sustainability methods? Or will we find a way to integrate these aspects into other management processes?

  • Zipse: Integration is key. You cannot say, “On this side of the room, we do sustainability. On the other side of the room, we leave it as it was.” It is an integrated approach. In production, especially, sustainability comes in two steps. The first and the best thing is that you do not use resources at all. You simply minimize the use of resources in the sense of resource efficiency. Use less light, less energy, and less material, the traditional Kaizen way. This becomes critical because the energy that is not used is the best thing for sustainability. The second step is technical and deals with the question: What kind of new processes can you implement to help you achieve your sustainability goals? What is the role of the digital arena in improving your processes? What kinds of new technologies can you use to be more sustainable? So, in manufacturing, we have two frameworks to be sustainable: resource reduction and technological advancement.

    After Kaizen and continuous improvement, what do you see in academia as the next step in optimization? Do you see anything that will dominate the next 20 years of production? Specifically, sustainable manufacturing?

  • Hanenkamp: First, we need to integrate sustainability aspects into our existing processes and culture. Second, we need to open up to sustainability, as well as to digitalization, and improve our ability to create a digital twin of all production processes and steps. But many open questions remain. We have to figure out how to do this systematically: how to collect data, structure it, make it accessible over time, and maintain it properly. This is our task for the future: to integrate the knowledge and experience that we have from several decades since the early days of Kaizen culture and quality management systems and mix it with digital opportunities. We need to address our processes, first and foremost, without forgetting the corporate culture and mindset of our people.

Is Recuperation a Promising Technology for Sustainable Energy Production Systems?

  • Zipse: In our factories, we are used to running all our machinery and tools on alternating current (AC). The iX runs on direct current (DC), which is why we can recuperate. When the car brakes, we recuperate the kinetic energy of the car. If you look at a factory, everything is moving, and, of course, everything needs to be accelerated and decelerated. If we had a direct current plant, we could use all that recuperating energy and put it back into the system. We’ve identified this as an important area of research, and we are very close to some applications.

    Is this something that could be a next step in a sustainable, energy-efficient production system?

  • Hanenkamp: Yes, for sure. This is a very important aspect. There are many other aspects that you can integrate, such as bidirectional loading. What we have to understand is that direct current is more efficient in terms of transferring energy from supply to demand because of harmonics losses. There is tremendous potential in avoiding these losses. The benefit is that the production machines do not necessarily have to change, but we have to reconfigure, redesign the energy supply structure within the plants, and integrate DC principles, and then, the potential is huge.

  • Zipse: We are thinking along similar lines. It is about questioning how we have thought about energy in the past.

  • Hanenkamp: Absolutely. From an energy point of view, our whole mindset has to change. In the past, we looked at energy as an unlimited resource; therefore, we did not think much about it. But now, if we look beyond the direct current that could come from renewable energy systems, we see that in many manufacturing plants we have more distributed energy generation systems—thermal block-type power plants, renewable energy systems, etc.—which means that our supply varies over time. We also need to integrate storage systems. In the past, we spent a lot of time and effort trying to find a single stable operating point for the plant. Today, the challenge is to find several of them, because we have to constantly adapt to this fluctuating supply. This is a great opportunity for the future.

What Potential Does System Coupling Hold?

  • Zipse: You mentioned the topic of system coupling, which is critical for a plant like this, as we have a lot of energy subsystems. Often, the output of one energy system can be the input of another one. For example, the heat we generate in a power-heat coupling can be used in our paint shop. Combining these different systems has an enormous effect. Another example is one we introduced more than 10 years ago: A new paint we introduced made the so-called wet process obsolete. Just by eliminating this one process, we were able to reduce energy consumption by 30%. This phenomenon of looking into product and process design together in terms of sustainability is very common today. Look at the bionic design systems using additive manufacturing technologies. We have brought the cost down—they are still too expensive to be scalable—but every year we take another step. Then, you have product design, weight reduction, and resource efficiency, all in one. If you look at product design and production design pulling together, there is still undiscovered potential.

  • Hanenkamp: Talking about system coupling, I completely agree. There is huge potential that we can tap into. Production facilities and the technical building infrastructure are often not really coordinated. But it can be done. The technical building infrastructure sometimes consumes up to 50% of the energy of a plant. It runs completely independently of what happens on the shop floor. So why must we turn on the heat 5 min before the shift ends? It does not make sense. Sometimes these processes just have very simple controls like minimum/maximum temperatures. If we could find a way to have something like a projection and see what is coming up, then we could easily adjust the control parameters and not have to turn the heaters off 5 min before the end of the shift. It saves a lot of energy, and it is very easy to do. Today, we can access control parameters via standardized interfaces, but we have to model our process and our production and do a projection. From there, we can access this potential. There is no need to couple production systems at the machine level—a lot of that has already been done. However, the bigger potential is the coupling of the technical building infrastructure with the shop floor.

What Role Does AI Play?

  • Zipse: What is the potential for the use and implementation of AI in production?

  • Hanenkamp: There is potential, but it is not an easy thing to implement. The challenge we have to solve is not just to implement islands here and there. AI systems already exist today. We can think of vision systems for quality assurance, for example. We have been doing that for 20 years for specific applications. But the bigger potential is to take a common data perspective to see correlations between two different processes.

    Before you can talk about AI, you have to talk about digitalization: You have to have data. It is not just the basis of AI applications, but it is needed to make any kind of fact-based decisions. The challenge that comes with data is that once you invest in data collection and data gathering, you have to do it efficiently. It does not make sense just to collect data, put it in a box, and then figure out what to do with it. You have to allocate it to your specific use cases and what you want to accomplish with it. Otherwise, you overengineer the data. You simply collect it, and you have to manage and store it for a long time. That takes time, money, and energy.

    Based on data, we can build AI applications, using data for training. A wide range of AI systems are available—but we need to gain experience regarding which system to use in which application. We have to get over the perception of AI systems as a black-box thing that we do not understand: We throw data in and get data out. We need to have more experience in how to parameterize a neural network system and apply appropriate systems to different use cases.

  • Zipse: This is a fascinating field of research—and it also reaps the secondary effects of AI.

  • Hanenkamp: Secondary effects? Can you explain?

  • Zipse: In the past, we needed perfect lighting for pattern recognition on car surfaces. Pattern recognition was always done with a liquid crystal display (LCD) camera. We would look at it exactly, and if a pattern was not exactly like the perfect condition, we would recognize a quality defect. But with AI, you can train imperfect lighting conditions. In a factory, lighting might differ during the day, during the night, and so on, and AI gives you a lot more flexibility, even during the darker parts of the day, by applying pattern recognition. This means that all the lighting that was extremely energy-intensive could also be spared and energy saved.

    That is the secondary effect of using AI. We should think not only about the application in a specific algorithm or a neural network, but also about the secondary effects. AI allows you to be imperfect and much more flexible, which is really exciting potential, especially in the production environment.

How Difficult Is It to Get Buy-In for Change? What Role Do Cooperation and Transparency Play in This Process?

  • Hanenkamp: What is your experience with the acceptance of sustainability-based changes? When you look at your workforce, are they all open to thinking about sustainability issues? Do some of them see it as a threat?

  • Zipse: The really amazing thing is that our team at BMW and our employees are very willing to contribute. I have never seen it as a threat. We are looking for new ways to make the company more effective and ultimately more successful, day by day. Sustainability, resource reduction, and improving the quality of our products every day are combined and aligned, not differentiating goals. They are on the same sheet of paper. As society changes, all our employees want to help make processes and products more sustainable and use less energy, because this has become mainstream thinking in our society. If we did not demand a highly sustainable working attitude, our team would be disappointed. In our sustainability strategy, we have the full support of our employees. They like to contribute. Of course, this is also a cultural thing. We want to win this game. The greenest electric car has to come from BMW. That may be easy to say as a goal, but it is not easy to achieve. When we have our integrated report, it will measure who has the lowest resource footprint: energy consumption, CO2, or all kinds of emissions. We also count on human resources: the tons of labor extracted. All kinds of KPIs you can think of. Then, this will become a field of competition, and BMW is a competitive company.

  • Hanenkamp: I completely agree. If we go beyond our own organization and look at the supply chain, a car company has a low internal value-add. The majority of the value-add happens upstream in the supply chain. Now, we want our suppliers to contribute to CO2 reduction as well. Imagine that your in-house processes are already sustainable and set in place. How will you work together with your suppliers to further reduce your carbon footprint? What will this continuous improvement look like outside of your own organization?

  • Zipse: We have three effects. If you look at the normal production route of an electric car, its CO2 footprint is higher than that of a combustion-engine car, assuming that 50% of our energy here in Germany is not renewable. This is not sustainable, but we are improving that every day. However, it means that our supply chain has to contribute better results in the production cycle to reduce that footprint. If we take the status quo and ramp up our electric mobility strategy, we will actually increase the carbon footprint in the supply chain. We would be doing exactly the opposite of what we actually want to achieve.

  • Hanenkamp: So what is the way out?

  • Zipse: The only way to make sure your suppliers’ production is sustainable is to work closely together. For example, if you want your battery supplier to use green energy for cell production, you need to agree on that in your contracts with them. The next step is to have shared transparency on the footprint. That is why we founded the automotive alliance Catena-X, together with SAP, Bosch, ZF, Siemens, Telekom, and so on, to get digital transparency across many companies. This network is growing fast and has strong support from the German government. It is the Internet of companies. We have Industry 4.0 in our own factories as the status quo, and the next step is the Internet of companies, Industry 5.0. A new era where we can document complete supply chains in terms of CO2 footprint, quality issues, compliance with emission standards, and so on.

  • Hanenkamp: I completely agree. You cannot improve if you do not have transparency on the baseline. Many suppliers I have talked to are now challenged by this increased transparency, but there is no alternative. To improve, we want to know where to start. We need to collect data efficiently, highlighting waste, losses, and emissions throughout the year. We also have to break it down into smaller reference units, not yearly, monthly, or weekly, but on a daily level, down to a single piece, so we can see deviations over time. We can then see unstable processes precisely and can act on them specifically. This also has a tremendous impact on overall CO2 emissions. Transparency is first. This is where digital technologies are going to help us gather data, allocate it correctly, and then use it for improvement.

  • Zipse: It is interesting: The steep drop in the price of sensors—temperature, pressure, and all kinds of IoT sensors—and at the same time the advent of big industrial clouds that are not very expensive and legislation that requires transparency. Together, these three things have an enormous impact because, all of a sudden, you have the tools in your hand to provide transparency. It is no longer a technical issue. You can measure almost any physical state in the supply chain, in a factory, or even in a car. Then, it becomes a matter of collaboration: Who is willing to share that data? We have started to bring these three things together. We do a lot of contracting, but even better than contracting is cooperation.

  • Hanenkamp: I totally agree. In terms of data collection and sensors, it is much easier than it was a few years ago. The other thing, from a mindset point of view, is: If I am going to capture data, do I have to collect it forever? All the time? Or does not it make sense to capture it temporarily? If you look at a tooling machine that has to manage all kinds of sensors to measure vibrations and temperature, flow rates, and things like that, it costs 40% more than a standard machine. So, that is something you would not want to do everywhere. Measuring vibration, for example, is only important if you have some specific processes. One solution—if we have the sensor and data collecting technology—would be to use it spot-wise and move it from one machine to the next, to be more flexible at a lower cost, yet still get the same information. The other thing is that sometimes we tend to buy a machine that already has all the sensor technology from the supplier. Why do not we do a part of it ourselves? It is not that complicated to put a sensor here and there, but it is still enough to see deviations.

  • Zipse: I’m glad that you mentioned that. Maintenance is mainly about existing machinery. An existing press shop, for example, lasts 40–50 years. Of course, you have the existing technology, but you can always reequip it with additional sensors. This is actually mainstream: It is not about buying new machinery; it is about digitizing existing machines with sensors. Then, you can improve your maintenance cycles, do preventive maintenance, and see huge effects. These are truly exciting times.

What Role Do Smart Cities Play?

  • Hanenkamp: Allow me to leave production and jump to smart cities. As I understand it, there is no common definition, but from my understanding, smart cities balance the economic aspects of companies, the people who live there, and other aspects. We balance ecological, economic, and social aspects, as well as digitalization and system coupling. These things we have already discussed as key enablers. If we look ahead, how much will smart cities change the position of manufacturing companies in terms of their locations, how they operate, and the availability of labor, for example?

  • Zipse: A smart city must be intelligent, as the name implies. It has to reflect the reality of its inhabitants’ lives, and of course, it has to be willing to invest, you know. We’re sitting here right next to our Munich factory, which is right in the middle of the city. I am a firm believer that there is no contradiction between industrial work and city life. It is possible. A modern city is a synergy of industrial and residential life. It is not the industry that is disappearing from the city. On the contrary, we have this factory here in Munich, and it is very much integrated into its community here, providing jobs and how people get here. We spend a lot on people, on public transport, on company bicycles, and so on.

    This is my idea of a smart city: It must be intelligent in terms of providing the right kind of transportation for people, from bikes to cars to buses and public transport. It is a combination of all of that, and it has to have a government structure that is willing and ready to invest. This is because mobility, in particular, depends very much on where the intelligence of the individual mobility lies: Is it in the car, or is it in the city itself? In different parts of the world, really smart cities are developing in which all the intelligence is put into the infrastructure of the city. Then, it does not have to be in the car. Smart cities are about combining individual mobility needs, the intelligence of the city’s infrastructure city, and the reality of the people who live there.

7.4 The Sustainable Factory of the Future

Increasing resource efficiency and implementing sustainability are key challenges for industry in the future. In 2010, industrial production was responsible for more than 30% of global greenhouse gas emissions, which is only one environmental factor. Companies are therefore called upon to make a significant contribution to reducing their environmental impact. The vision of sustainable production goes beyond the isolated ecological dimension and takes into account social responsibility, competitiveness, and environmental protection. Furthermore, positive interactions between the three dimensions lead to additional benefits for all stakeholders (Stark et al., 2014). The implementation of sustainable production is currently driven by both economic incentives and regulatory requirements. Successes have already been achieved, such as increased energy efficiency and the positive effects of introducing sustainability management systems.

On the one hand, the challenge is that no universal blueprint has been drawn for the transformation to sustainable manufacturing, so the journey must be planned, implemented, and tracked individually. On the other hand, the majority of manufacturing companies rely on experience to manage complex changes, such as the transformation toward a lean company or to implement Industry 4.0 principles and technologies. Despite these challenges, new technologies, such as artificial intelligence, or accepted standards, such as the life cycle assessment (LCA) methodology, can be applied to improve environmental impacts. In most cases, the transformation must follow a brownfield approach; that is, the existing equipment and infrastructure have to be upgraded and integrated into the new production system in combination with new production processes, such as additive manufacturing. In summary, this change is a complex transformation, the key principles of which are discussed in this article from an operations point of view. First, sustainable process and factory planning, as well as operations management, will be presented. Second, the contribution of digitalization and artificial intelligence in an industrial context is considered. Third, the impact of sustainable manufacturing standards and methods will be highlighted. Finally, the coupling of direct and indirect manufacturing systems and the integration of urban production in smart cities will be discussed.

7.4.1 Sustainable Manufacturing Processes along the Life Cycle

Research and practice agree that sustainability aspects can only be addressed if improvements consider all phases of the product life cycle (i.e., the development phase, the manufacturing phase, the use phase, and the end-of-life phase). Interactions between the phases need to be considered (Liu et al., 2019). In the following section, challenges and opportunities along the design and manufacturing life cycle phases that impact sustainability will be discussed.

The design of a manufacturing system is critical because changes in later stages can only be implemented with a substantial effort. Moreover, decisions have to be made under uncertainty and undefined boundary conditions. The dimensioning of a production system, and especially its capacity, is closely related to its sustainability impact and must therefore be derived using a systematic process. For example, if the technological and production capacity after ramp-up significantly exceeds the current process demand, this effect leads to inefficient operating points in the manufacturing phase. In addition to sizing, the specification of the production equipment in terms of its process steps and manufacturing technology is of great importance. Value-adding steps must be optimized, while non-value-adding steps and process waste must be minimized. If we are to manage the sizing uncertainty and define optimal processes, we must have reliable process data. However, collecting process data based on physical testing and design of experiments (DoE) is not only time-consuming; it is also often impossible to obtain because the manufacturing equipment is not yet available at this early stage. As digital twins and simulation technologies provide virtual representations of systems along the life cycle, they can also be used to model the dynamic behavior of the production system in terms of sustainability (Negria et al., 2017). Input factors, such as raw materials, consumables, and energy, and output factors, such as productivity or waste streams, can be determined based on varying operating conditions without performing physical tests. To minimize the implementation effort and to achieve high accuracy of digital twin modeling, most practitioners and researchers follow a systems engineering approach (i.e., the production system is broken down into smaller units for which reliable digital twins are developed based on existing data; Computer-Aided Design [CAD], Product Lifecycle Management [PLM], etc.). With increasing maturity and given the physical availability of manufacturing equipment and process design, congruence between digital twins and physical systems must be achieved. Finally, the increasing application of digital twins not only increases the efficiency in advanced product quality processes (APQP) within a single company, but they can also be used to model interdependencies related to sustainability at the interorganizational level.

During the ramp-up and in series production, the focus must be on efficiency. One key metric is overall equipment effectiveness (OEE), with its three components: loss of availability, loss of performance, and loss of quality (Focke & Steinbeck, 2018). Since losses of availability include all downtime of the manufacturing system, the indicator shows the percentage of a period that the system is in stable operation. Sustainability is negatively impacted because a high level of availability losses requires additional capacity reserves to meet the total demand, with a negative impact on space, and frequent interruptions to operations result in ramp-up losses of energy, personnel, and raw materials. The second component of OEE is performance loss, which describes whether the production system is at its optimal operating point regarding energy consumption, material input and output, and personnel. Temporary or permanent deviations require additional production capacity. Finally, quality loss is the amount of scrap and rework that occurs in the process chain. Poor quality levels have a direct impact on sustainability because the initial raw material is not processed into finished goods. Emissions, material consumption, etc. from the raw material generation phase have already been incurred but cannot be used to create products or added value. To incorporate sustainability aspects, a stronger focus is needed on inputs and outputs that have not been considered before, such as emissions and waste streams. These extensions to existing key performance indicators will provide reliable and consistent data for effective sustainability decision-making. Practice and research reflect that modern shop floor management systems follow this approach and include sustainability metrics. Based on this information, anomalies from the defined operating points can be identified, and appropriate countermeasures can be initiated on the shop floor (Cerdas et al., 2017).

For the production system design and manufacturing phases, efficiency is the central objective. Industrial production processes transform material, energy, and other inputs into finished goods, delivering added value as well as by-products, such as waste streams and energy losses. To minimize these by-products, circular production processes must be developed and installed (Gupta et al., 2021). The concept of the ultra-efficient factory relies on reuse of all types of waste and energy in two main energy and material recycling loops. In the first loop, wasted energy and materials are fed directly back into the manufacturing phase. In the second loop, the product is returned to the supply chain at the end of its useful life. This minimizes downcycling of material (i.e., the use of material for lower performance applications). This means that sustainability is based on efficient product generation processes and on efficient recycling and remanufacturing concepts that must be designed into the manufacturing design phase.

7.4.2 Digitalization, Artificial Intelligence, and IoT

Digitization, Industry 4.0/IoT, and artificial intelligence have significant potential to allow manufacturing companies to implement sustainability (Stock & Seliger, 2016). However, an important point to consider is that digitization is not an end in itself, and its implementation requires a systematic approach. A generic model is the manufacturing analytics approach with four levels: (1) visibility, (2) transparency, (3) forecasting ability, and (4) prescription. This approach has been developed for the systematic implementation of digitization technologies (Meister et al., 2019). It ranges from lower levels of digitalization, such as simple data collection, to the modeling of complex system behavior using artificial intelligence. Although it is not primarily intended for the implementation of sustainability, it represents a systematic approach to the acquisition, handling, and management of data for specific objectives. When applied to sustainability issues, this analytics approach can be used to better understand correlations, optimize processes, and anticipate and prevent negative impacts on the three dimensions of sustainability.

The objective of first-level visibility is to capture data from the shop floor. Accessing shop floor data is a hurdle because it either has to be accessed through a wide variety of different protocols (OPC Unified Architecture [OPC UA], MTConnect, EuroMap77, etc.) or is not accessible at all. Since sustainability data are not always part of existing protocols, retrofitting existing machines with Internet-of-things–compatible sensors is often necessary. The result is that the aggregated data at each time step are stored on a common, often cloud-based platform. The objective of transparency (2) is to systematically identify the root causes of specific problems and deviations, such as the increased use of energy or material consumption. Individual and specific KPIs, for example, for different functional units, can be extracted. The forecasting ability (3) enables us to make projections of trends in the future and to proactively manage deviations. This ability can be used, for example, for the demand-side management of production equipment and the ramp-down of lower priority processes and machines in the event of energy shortages or price increases. At the prescription level, courses of action are being proposed.

As described above, the analytics approach and the application of artificial intelligence methods and tools are highly interdependent. AI algorithms require consistent data on a continuous basis, which is provided by the four-stage model (Weber et al., 2019). When this condition is met, AI methods can first be applied to reduce the complexity of data lakes. For example, principal component analysis can be used to identify the primary drivers of sustainability improvement actions. In addition, black-box AI systems, such as neural networks, can be applied to speed up simulation runs of energy consumption under different or uncertain conditions.

7.4.3 Application of Sustainability Standards

The development of methods and standards for sustainable production has long been a focus of research and practice. The goal is to make visible the relationships between production, consumption, and disposal and to assess the impacts of economic activities. Life cycle assessment (LCA) has emerged as the most important and accepted method from a technical perspective (Hagen et al., 2020). It is embedded in the ISO 14000 series of environmental standards that address environmental management issues associated with production processes and services. Common to all sustainability standards is the breaking down of industrial value streams into process modules for which mass flows (raw materials and fuel inputs, products, by-products, and waste), energy inputs, and emissions to water, air, and soil are analyzed. While the data of Scopes 1 and 2 of DIN EN ISO 14064 can be collected internally, cooperation with suppliers is required to collect data for Scope 3 raw and operating material inputs. Scope 3 CO2 emissions are particularly relevant, as they can account for up to 50% of the total footprint (Gross & Hanenkamp, 2021). In practice, suppliers are under increasing pressure from their customers to provide data on CO2 emissions.

Environmental impact categories are assigned to the life cycle inventory analysis, and their quantification allows us to focus on prioritized environmental impacts. In practice, the application of sustainability standards with precise data requires a high level of technical effort due to its complexity, as well as extensive methodological knowledge and expertise. As a result, assessments are often conducted on a project-by-project basis and are static in nature, making them unsuitable for the operational optimization of production processes. A dynamization of the LCA (i.e., continuous generation with real-time data) can be used to derive precise measures for the operational optimization of the production processes on the shop floor (Cerdas et al., 2017). Finally, to reduce the burden on all stakeholders in the supply chain, the exchange of sustainability-related data based on trust and using reference data models is required.

7.4.4 System Coupling, Urban Production, and Smart Cities

The need for more efficient and sustainable operations necessitates that we do not develop and optimize production systems independently, but rather consider them as interconnected entities. In the circular concept, energy, material, and waste streams from one process must be considered for secondary use in other processes. This can only be achieved by coupling different entities of the manufacturing system. The concept of system coupling allows the physical flow of materials between subsystems, the recuperation and use of wasted energy, such as electricity or heat, and the exchange of information, such as future demand or the current status. The peripheral components within production systems, such as cooling devices, are typically operated using simple control strategies with few set points; consequently, energy demand peaks cannot be avoided. Heating, ventilation, and air-conditioning systems (HVAC) rely on more complex control strategies, but their control parameters are not adapted to upcoming heating or ventilation demands. The prerequisite for system coupling within the factory is the exchange of data between manufacturing systems and technical building equipment. This allows for the identification of optimal operating points that lead to the adjusted control parameters of the subsystems.

Beyond the internal system coupling within the factory boundaries, the manufacturing site also interacts with the local urban environment. Historically, manufacturing and urban spaces have coexisted, and negative impacts have led to the location of factories on the outskirts of cities. Urbanization, as a megatrend, forces the development of new concepts, such as urban manufacturing or the integration of manufacturing in smart cities (Matt et al., 2020). By definition, an urban factory is not only a factory that is simply physically located in an urban environment; it is one that strongly interacts with other urban entities regarding information, material, and energy flows and that relies on the local market and suppliers (Ijassi et al., 2022). In this way, urban production can contribute to the sustainable development goals (SDG) of affordable and clean energy (SDG 7), decent work and growth (SDG 8), industry innovation and infrastructure (SDG 9), sustainable cities (SDG 11), and responsible consumption and production (SDG 12) (Juraschek et al., 2018). Thus, negative impacts, such as emissions, arise, but positive contributions, such as the availability of jobs in urban production scenarios, also occur. Given the global trend of urbanization, smart cities will also play a central role in sustainability. Although the concept of a smart city has no common definition in research and practice, it has a broader scope than manufacturing (Suvarna et al., 2020). It encompasses all entities within the city (including buildings, transportation, energy grids, health care, manufacturing, and commercial services) that need to be connected. This also means that material, information, energy, and people flows need to be considered and optimized in the context of the city ecosystem.

7.4.5 Summary and Outlook

Four different principles of the sustainable factory of the future were discussed in this chapter. First, the planning and operation of manufacturing processes with respect to sustainability were shown. While, in the planning phase, the dimensioning and specification of the production system are crucial, whereas, in the manufacturing phase, the focus has to be on efficiency and abnormality management. In the future, this will require, for example, bringing sustainability aspects to the daily shop floor management level. Second, the availability of real-time process and manufacturing data is critical. Sustainability-related decisions are often highly complex and require a systematic approach to collecting, processing, and proactively applying manufacturing data. Third, the factory of the future can be assessed for sustainability based on accepted standards. Compared with today’s static nature of assessments, the assessments will need to be performed more frequently with minimal effort. Finally, the sustainable factory of the future is characterized by system coupling at multiple levels. Within the factory, production systems, peripheral components, and HVAC systems are physically and digitally connected and operated with global optima in mind. Beyond the physical boundaries of the factory, the exchange of material, information, and energy with the urban space must be considered to have a positive impact on sustainability. Given these directions, research and practice are challenged to develop methods and tools for implementing sustainable factories.

7.5 Conclusion

Sustainability principles are widely accepted, but implementing them in manufacturing is a challenge. The three dimensions of sustainability—the social, ecological, and economic aspects—must be equally considered, as innovation and research are essential for sustainability in operations. Successful companies have a clear vision of sustainable manufacturing processes, high digitalization, and the use of artificial intelligence.

So, how can sustainability be integrated into manufacturing?—We would like to highlight five takeaways from this chapter that invite further discussion:

  1. 1.

    The path toward sustainable production is a continuous process and not a single and isolated project. Companies have to use their experience, methods, and tools of continuous improvement from quality or lean management to plan and implement sustainability measures following long-term objectives.

  2. 2.

    Sustainability depends on the availability of and access to data from various sources, such as production machines, information technology (IT) systems, or manual processes. To achieve greater transparency, IoT sensors and industrial clouds can be used to store and analyze the underlying sustainability-relevant data.

  3. 3.

    Improvement processes, such as Kaizen, have to be extended to develop and optimize production processes physically and through the use of digital twins. This approach allows the use and implementation of artificial intelligence for sustainability aspects.

  4. 4.

    While efficiency improvement measures must be implemented in the short term and at existing manufacturing sites, new investments in equipment and infrastructure must include sustainability aspects as important selection criteria.

  5. 5.

    Recuperation of the energy of production processes contributes to further efficiency improvements. This requires linking energy sinks and sources in the process chain. Similarly, circular processes for material flows should play a major role in industrial engineering.

On the Road to Net Zero, sustainable manufacturing provides companies with the most direct lever to drive decarbonization and other sustainability objectives in industrial value creation. The ability to innovate manufacturing, however, is also crucial for introducing new technologies in the marketplace. In the automotive industry, disruptive technological transformation is needed to replace fossil-fuel combustion engines with drive-train technologies based on renewable energy. For this reason, Chap. 8 now looks at The Power of Technological Innovation.