The future of AI in German manufacturing

Since the term Industry 4.0 was introduced at the 2011 Hanover Fair, Germany has been intensely focused on progress toward smart manufacturing through automation, data exchange and artificial intelligence (AI).

Yet German manufacturing still lags behind that of other developed countries in adopting Industry 4.0 technologies. After nearly a decade of consistent economic growth, many companies have ignored the need to digitize to stay competitive in a global economy. Dorothee Baer, the newly appointed minister of state for digital affairs, notes that this false sense of economic security has also hindered the government’s efforts to close the digital technology gap.

Meanwhile, a study conducted by Germany’s Federal Ministry for Economic Affairs and Energy revealed that a key obstacle for Industry 4.0 is a lack of transparency regarding the specific benefits of investing in Industry 4.0. Thus German manufacturing companies, particularly SMEs, have expressed a reluctance to invest in the face of uncertain ROI and long implementation times.

 

 

Because Germany’s export model is almost entirely based on traditional manufacturing, the industry’s complacency and reluctance could have significant economic impact. That’s why Angela Merkel and the German government have announced a bold new play to jumpstart investment in AI, a key enabler of Industry 4.0. Experts from the public and private sector, including members of the Actyx team and other members of the Federal Association of Artificial Intelligence, were invited to weigh in on the plan, which Merkel officially presented at the December 2018 Digital Summit in Nuremberg.

Key components of the plan

One key challenge for Germany is that most cutting-edge AI technology is consumer facing, dominated by companies like Amazon, Google and Alibaba. These companies are subject to fewer regulations regarding consumer data protection than German entities are. But the next wave of AI will be on the industrial side, rather than the consumer side, so data protection will be less important — data gleaned from machines is much less sensitive. The new challenge will be ensuring that German researchers and businesses have the data necessary for driving value through AI.

Merkel’s goal is to capitalize on the wealth of industrial data available to make Germany a world leader in AI and smart manufacturing. She has pledged that the government will invest €3 billion in AI by 2025. The plan represents an innovative and comprehensive approach to stimulating AI capabilities:

  • Cooperation between industry and academia: Data will be made available not only to businesses, but also to German researchers, developers and members of academia. Data access will be free until an organization has earned a certain profit. Meanwhile there will also be 100 new positions created for professors who specialize in AI.
  • Relaxed regulations: Current German regulations restrict AI technologies in ways that other nations’ laws do not. For example, self-driving cars cannot use self-learning onboard computers. These laws will be updated to better accommodate developments in AI.
  • Strategic relationships with other countries: Internationally, Germany will seek to expand informal relationships with other nations dedicated to expanding AI and smart manufacturing capabilities, specifically Canada, Japan and several countries in Africa.
  • Focus on AI-related employment and startups: The success of the initiative will be evaluated based on the number of AI-related jobs created and the number of AI startups that emerge from German universities.
  • Matching private investment: Experts predict that the government’s investment in AI will have a sort of “leverage effect” that spurs more private spending on AI, potentially doubling the value of the public dollars spent.

Merkel stressed that a primary intention of this initiative is to improve Industry 4.0 readiness of SMEs. “Since we’re a country that prides itself on saying that the Mittelstand is the backbone of our industrial value creation, we have to make sure that the frontrunners pull the others along,” she said. The Mittelstand includes tens of thousands of companies that often manufacture highly specialized products.

Potential impact on German manufacturing

Research has overwhelmingly indicated that Industry 4.0 will bring significant economic growth. In an April 2018 report, the Information Technology and Innovation Foundation cited the following statistics:

  • According to a report issued by the European Commission, Industry 4.0 will increase productivity by 20%; cut downtime by 50%; and increase total value added for manufacturing to a targeted 20% of all value by 2020.
  • A 2014 Fraunhofer study concluded that the application of Industry 4.0 could boost value in Germany’s agricultural, chemical, electrical, ICT and mechanical sectors by an additional €78 billion by 2025, a 15% increase.
  • A Boston Consulting Group study found that Industry 4.0 will add 1% per year to Germany’s GDP by 2025, creating 390,000 jobs and spurring $250 billion in manufacturing investment.

While these numbers are impressive at the macroeconomic level, it can be challenging for an SME to determine the benefit of investing in AI as an individual organization. Indeed, understanding the ROI of Industry 4.0 can be difficult given the lack of conventional historical data to inform investment decisions. But consider these use cases where AI can play a key role.

  • Anticipated value drivers for digital manufacturing technology implementationsBottleneck identification: The production floor is a complex system with high risk of bottlenecks at individual machines or workstations. AI-empowered technologies can analyze productivity patterns to predict bottlenecks, which allows managers to better prioritize their attention and continuous improvement efforts.
  • Predictive maintenance: Powered by advanced AI algorithms, IoT-equipped machines can collect and analyze data on their own performance, so maintenance can be conducted only when necessary, rather than on a set schedule. When predictive maintenance improves OEE of even one expensive asset, there’s often significant cost savings.
  • Quality control: The implementation of AI algorithms can improve quality control through the detection of variations or abnormalities that impact product quality. And post-production data can also be collected on those products, to aid decision-making for product development teams.
  • Demand prediction: AI can pull data from myriad sources to make more accurate forecasts of product demand than humans, by analyzing a greater volume of data. These predictions allow optimization of inventory, materials supply and staffing.

Germany’s new commitment to AI will undoubtedly stimulate change on factory floors throughout the country, particularly among SMEs. The key to capitalizing on the investment is a keen focus on relevant use cases with proven ROI.

 

Would you like to find out how you can successfully promote your Industry 4.0 ideas in your company? Take a look at our free webinar with CEO Oliver Stollmann.

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Topics: Digitization & IIoT, Industry 4.0

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