1、Smartening up with Artificial Intelligence (AI) - What s in it for Germany and its Industrial Sector?3 Preface Artificial intelligence (AI) is finally bringing a multitude of capabilities to machines which were long thought to belong exclusively to the human realm: processing natural language or vis
2、ual information, recognizing patterns, and decision making. While AI undoubtedly holds great economic potential for the whole world, in this report we explain how and where AI will likely affect the German industrial sector by exploring several questions: Which subindustries are most strongly affect
3、ed by the automation potential of AI? What are the most promising use cases? What are pragmatic recommendations for managers of industrial players planning to harness the power of AI? We describe several use cases in which we highlight the impact of AI and aim to quantify it. These use cases were ca
4、refully selected based on their economic potential and their ability to demonstrate the benefits of AI in practice. We do not claim that AI despite its enormous potential is the silver bullet for every business problem. We realize that AI is very often the enabler for performance improvements whose
5、actual realization requires changing business processes. It is a rapidly evolving field. Thus, the present report needs to be understood as a peek into the future based on the current state of the art. With these caveats we are confident that this report will provide managers in the German industria
6、l sector with valuable guidance on how they can benefit from AI.45 Acknowledgements This study was conducted by McKinsey & Company, Inc. We wish to express our appre- ciation and gratitude to UnternehmerTUMs 1artificial intelligence application unit for their support and valuable contributions. The
7、authors would especially like to thank: Andreas Liebl, Partner New Venture Creation, UnternehmerTUM Alexander Waldmann, Visionary Lead AI, UnternehmerTUM 1 UnternehmerTUM, founded in 2002, is one of the leading centers for entrepreneurship and business creation in Europe.67 Contents Executive summar
8、y 8 1. AI is ready to scale 10 2. AI will increase productivity and transform the German economy .14 3. Players in the industrial sector should consider eight use cases of AI to achieve the next level of performance .183.1. Product and service improvement use case . 223.2. Manufacturing operations u
9、se cases . 243.3. Business process use cases 32 4. Players in the industrial sector should follow five pragmatic recommendations for enabling AI-based performance improvements . 38 Outlook: Get started early with the journey towards a fully AI-enabled organization . 44 Appendix: Nomenclature and ter
10、minology of AI . 45 Important notice .478 Executive summary Self-learning machines are the essence of artificial intelligence (AI). While concepts already date back more than 50 years, only recently have technological advances enabled suc- cessful implementation at industrial scale. According to the
11、 McKinsey Global Institute (MGI), at least 30% of activities in 62% of German occupations can be automated, which is at a similar level as the US 2 . Freed-up capacity can and needs to be put to new use in value-adding activities to support the health of Germanys economy. AI has proven to be the cor
12、e enabler of this automation based on advances in such fields as natural language processing or visual object recognition. Highly developed economies, like Germany, with a high GDP per capita and challenges such as a quickly aging population will increasingly need to rely on automation based on AI t
13、o achieve its GDP targets. About one-third of Germanys GDP aspiration for 2030 depends on productivity gains. Automation fueled by AI is one of the most significant sources of productivity. By becoming one of the earliest adopters of AI, Germany could even exceed its 2030 GDP target by 4% 3 . Howeve
14、r, if the country adopts AI more slowly and productivity is not increased by any other means it could lag behind its 2030 GDP target by up to one-third. AI is expected to lift performance across all industries and especially in those with a high share of predictable tasks such as Germanys industrial
15、 sector. AI-enabled work could raise productivity in Germany by 0.8 to 1.4% annually. We selected eight use cases covering three essential business areas, (products and services, manufacturing operations, and business processes) to highlight AIs great potential in the industrial sector. Products and
16、 services: Highly autonomous vehicles are expected to make up 10 to 15% of global car sales in 2030 with expected two-digit annual growth rates by 2040. The efficient, reliable, and integrated data processing that these cars require can only be realized with AI. Manufacturing operations: Predictive
17、maintenance enhanced by AI allows for better prediction and avoidance of machine failure by combining data from advanced Internet of Things (IoT) sensors and maintenance logs as well as external sources. Asset productivity increases of up to 20% are possible, and overall maintenance costs may be red
18、uced by up to 10%. Collaborative and context-aware robots will improve production throughput based on AI- enabled human-machine interaction in labor-intensive settings. Thereby, productivity increa- ses of up to 20% are feasible for certain tasks even when tasks are not fully automatable. Yield enha
19、ncement in manufacturing powered by AI will result in decreased scrap rates and testing costs by linking thousands of variables across machinery groups and sub- processes. E.g., in the semiconductor industry, the use of AI can lead to a reduction in yield detraction by up to 30%. 2 See MGI “A future
20、 that works,” January 2017. 3 Assumption: Displaced labor is redeployed into productive uses.9 Automated quality testing can be realized using AI. By employing advanced image recognition techniques for visual inspection and fault detection, productivity increases of up to 50% are possible. Specifica
21、lly, AI-based visual inspection based on image recognition may increase defect detection rates by up to 90% as compared to human inspection. Business processes: AI-enhanced supply chain management greatly improves forecasting accuracy while simultaneously increasing granularity and optimizing stock
22、replenishment. Reductions between 20 and 50% in forecasting errors are feasible. Lost sales due to products not being available can be reduced by up to 65% and inventory reductions of 20 to 50% are achievable. The application of machine learning to enable high-performance R&D projects has large pote
23、ntial. R&D cost reductions of 10 to 15% and time-to-market improvements of up to 10% are expected. Business support function automation will ensure improvements in both process quality and efficiency. Automation rates of 30% are possible across functions. For the specific example of IT service desks
24、, automation rates of 90% are expected. Our findings concerning AI as well as our observations of the most successful players in both the industrial and adjacent sectors reveal five effective recommendations that address the challenges of AI and help get firms in the industrial sector started on the
25、ir AI journey: Get a grasp of what AI can do, prioritize use cases, and dont lose sight of the economics without a business case no innovation survives. Develop core analytical capabilities internally but also leverage third-party resources trained people are scarce. Store granular data where possib
26、le and make flat or unstructured data usable it is the fuel for creating value. Leverage domain knowledge to boost the AI engine specialized know-how is an enabler to capture AIs full potential. Make small and fast steps through pilots, testing, and simulations the AI transformation does not require
27、 large up-front investments, but agility is a prerequisite for success. Beyond deciding where and how to best employ AI, an organizational culture open to the collaboration of humans and machines is crucial for getting the most out of AI. Trust is among the key mindsets and attitudes of successful h
28、uman-machine collaboration. Initially, cultural resistance may be strong because the relationship between the inner workings of an artificially intelligent machine and the results it produces can be rather obscure. In a sense, it is no longer the algorithm but mainly the data used to train it that l
29、eads to a certain result. Humans will need some time to adjust to this shift. Getting started early not only helps produce results quickly but also helps speed up an organizations journey toward embracing the full potential of AI.10 10 AI is ready to scale 1.11 The essence of intelligence is learnin
30、g. Just as humans learn how to communicate, identify visual patterns, or drive a car, machines can similarly be trained to perform such tasks based on powerful learning algorithms. A common method of training machines consists of pro- viding them with labeled data, e.g., photographs of cats combined
31、 with the word “cat” as a label. Such machines are then said to possess AI 4if they can given their training ascribe the correct label to a previously unknown data set with sufficient accuracy. Following the previous example, a machine would then be able to correctly identify a cat in an unfamiliar
32、photograph. Typical applications of AI include autonomous driving, computer vision, decision making, or natural language processing. AI holds the benefit of being adaptable to very heteroge- neous contexts just like humans. Well-trained AI is capable of performing certain tasks at the same skill lev
33、el as humans but with the additional advantages of high scalability and no need for pauses. AI can discover patterns in the data that are too complex for human experts to recognize. In some specific applications such as computer vision, AI has already achieved performance levels surpassing that of h
34、umans (e.g., in skin cancer diagnostics). The idea of AI dates back to the 1950s when AI successes were largely limited to the sci- entific field. In the last years, established IT giants like Google, IBM, and nVidia fueled by the abundance of data, algorithmic advances, and the usage of high-perfor
35、mance hard- ware for parallel processing have begun bridging the gap between science and busi- ness applications. Nowadays, adoption of AI has become increasingly easier due to freely available algorithms and libraries, relatively inexpensive cloud-based computing power, and the proliferation of sen
36、sors generating data. Hence, not only established firms but also start-ups play a significant role in bringing AI to life. Start-ups with AI-savvy founders are capable of developing AI-based products in less than three months. In the industrial sector, AI application is supported by the increasing a
37、doption of devices and sensors connected through the Internet of Things (IoT). Production machines, vehi- cles, or devices carried by human workers generate enormous amounts of data. AI ena- bles the use of such data for highly value-adding tasks such as predictive maintenance or performance optimiz
38、ation at unprecedented levels of accuracy. Hence, the combination of IoT and AI is expected to kick off the next wave of performance improvements, espe- cially in the industrial sector. Given its growing accessibility, broadening applications, and specific relevance to the industrial sector, it come
39、s as no surprise that AI is a hot topic for leading researchers, inves- tors, think tanks, and companies. It is hard to open a newspaper without coming across an article on AI. As per a Tracxn 5analysis, start-ups dealing with AI-related topics have raised around USD 6 billion in funding in 2016 alo
40、ne. 4 The process described here refers to supervised learning, a type of machine learning. See Text Box 1 on the differentiation between AI and machine learning. Within AI, there is the distinction between strong AI and weak AI. Strong AI or true AI is often defined by using the Turing Test. Accord
41、ing to the Turing Test a machine possesses AI if it can provide a human with written responses to a set of questions so that the human cannot tell whether answers were given by a machine or another human being. In this report we follow a broader definition of AI that includes machines capable of lea
42、rning that would not pass the Turing Test (“weak AI”). 5 Venture capital investment tracking company.12 The global market for AI-based services, software, and hardware is expected to grow at an astonishing annual rate of 15 to 25% and reach USD 130 billion by 2025. Machine learning is expected to be
43、 the dominant methodology (see Text Box 1). In summary, AI is ready to scale across industries and it is has already begun to do so. In this publication, we: Outline the influence that AI will have on the German economy Dive into business applications along eight specific use cases, with a special f
44、ocus on the industrial sector 6 Describe five pragmatic recommendations that CEOs should consider in the upcoming months 6 Our particular focus is on aerospace, automotive OEMs and commercial vehicles, automotive suppliers, industrial equipment, and the semiconductor industry. Text Box 1: the nomenc
45、lature of artificial intelligence Artificial intelligence is a buzzword these days and, hence, subject to multiple interpretations. For the purpose of establishing a common understanding, we have defined various AI terms as they are used in this report. For additional information see also the append
46、ix. Artificial intelligence (AI) is intelligence exhibited by machines, with machines mimicking functions typically associated with human cognition. AI functions include all aspects of perception, learning, knowledge representation, reasoning, planning, and decision making. The ability of these func
47、tions to adapt to new contexts, i.e., situations that an AI system was not previously trained to deal with, is one aspect that differentiates strong AI from weak AI. In this report, we will not make the distinction between weak and strong AI for the sake of simplicity and due to our focus on the bus
48、iness context. Machine learning (ML) describes automated learning of implicit properties or underlying rules of data. It is a major component for implementing AI since its output is used as the basis for independent recommendations, decisions, and feedback mechanisms. Machine learning is an approach
49、 to creating AI. As most AI systems today are based on ML, both terms are often used interchangeably particularly in the business context. Machine learning uses training, i.e., a learning and refinement process, to modify a model of the world. The objective of training is to optimize an algorithms perfor- mance on a specific task so that the machine gains a new capability. Typically,13 large amounts of data are involved. The process of making use of this new capa- bility is called inference. The trained machine-l
