The EU Commission is proposing a European approach to make the most out of the opportunities offered by artificial intelligence (AI), while addressing the new challenges AI brings. Building on European values, the Commission is proposing a three-pronged approach: increasing public and private investments; preparing for socio-economic changes brought about by AI; and ensuring an appropriate ethical and legal framework.
Boosting the EU’s technological and industrial capacity and AI uptake across the economy
What kind of challenges can AI address? What kind of AI projects will the EU fund?
AI helps us solve many societal challenges from helping doctors make faster and more accurate medical diagnoses to assisting farmers in using fewer pesticides for their crops. It also helps public administrations to provide tailor-made responses to citizens and to decrease the number of traffic accidents. AI can help fight climate change or anticipate cybersecurity threats. The Commission will fund projects to support the use of AI in many applications, from health to transport, and to digitise industry. EU funding will also support projects to improve the performance of AI technology (e.g. the quality of speech recognition).
The Commission will support fundamental research, and also help bring more innovations to the market through the European Innovation Council pilot. Additionally, the Commission will support Member States’ efforts to jointly establish AI research excellence centres across Europe. The goal is to encourage networking and collaboration between the centres, including the exchange of researchers and joint research projects.
The Commission will also support the uptake of AI across Europe, with a toolbox for potential users, focusing on small and medium-sized enterprises, non-tech companies and public administrations. The set of measures will include an EU ‘AI-on-demand platform’ giving advice and easy access to the latest algorithms and expertise; a network of AI-focused Digital Innovation Hubs facilitating testing and experimentation; and industrial data platforms offering high quality datasets. Several priorities have also been identified for the post-2020 multiannual financial framework (such as increased support in fields such as explainable AI to develop AI systems in a way which allows humans to understand the basis of their action or AI systems which need less data).
How will the European Fund for Strategic Investments (EFSI) help companies to adopt AI and when?
The European Fund for Strategic Investments will support the development and the uptake of AI, as part of the wider efforts to promote digitisation. The Commission – together with its strategic partner, the European Investment Bank Group – aims to mobilise more than €500 million in total investments in the period 2018-2020 across a range of key sectors. To this end, a thematic investment platform under the EFSI could be set up. In addition, the European Commission and the European Investment Fund have just launched VentureEU, a €2.1 billion Pan-European Venture Capital Fund-of-Funds programme, to boost investment in innovative start-up and scale-up companies across Europe.
What are Digital Innovation Hubs and how will they contribute to the use of AI?
Digital Innovation Hubs are local ecosystems that help companies in their vicinity (especially small and medium-sized enterprises) to take advantage of digital opportunities. They offer expertise on technologies, testing, skills, business models, finance, market intelligence and networking. For example, a small company that produces metal parts for the automotive industry could consult the regional hub and ask for advice on how to improve the manufacturing process with AI. Experts from the hub would then visit the factory, analyse the production process, consult with other AI experts in the network of hubs, make a proposal and then implement it. These activities would be partially financed with EU money.
Preparing for socioeconomic changes
What is the Blueprint for Sectoral cooperation on Skills? Which sectors are targeted?
Europeans should have every opportunity to acquire the skills and knowledge they need and to master new technology. National schemes are essential for providing such up-skilling and training. They can benefit from support by the European Structural and Investment Funds (supporting skills development with €27 billion over the period 2014-2020, out of which the European Social Fund invests €2.3 billion specifically in digital skills) and should also benefit from support from the private sector.
The Blueprint for Sectoral cooperation on Skills identifies skills needed and gaps in a sector and connects with partners that can help address those needs by developing a common European strategy and curricula development. Five sectors were chosen to pilot the Blueprint in 2017 (automotive, maritime technology, space/geo information, textile/leather/clothing/footwear and tourism) and six other sectors have been recently added (additive manufacturing, construction, maritime shipping, paper-based value chain, renewable energy and green technologies and steel industry) with EU funding support of close to €50 million.
What is the Digital Opportunity Traineeship in advanced digital skills for students and recent graduates? How will it support AI?
The Digital Opportunity traineeship initiative will provide cross-border traineeships for up to 6,000 students and recent graduates as of summer 2018. It will give students of all disciplines the opportunity to get hands-on digital experience within companies, in fields demanded where there is a skills gap, and strengthen their ICT skills, in fields such as AI.
In addition to the Digital Opportunity traineeships, the Commission asked all Member States to develop national digital skills strategies by mid-2017 and to set up national coalitions to support their implementation. National Coalitions bring together ICT and ICT-intensive companies, education and training providers, education and employment ministries, public and private employment services, associations, non-profit organisations and social partners, who all develop measures to bring digital skills to all levels of society. Through the Digital Skills and Jobs Coalition the Commission will encourage business-education partnerships for AI.
The European Institute of Innovation & Technology also designs specific programmes at Master and PhD levels to address needs arising from the digital sector and digital transformation. The programmes combine in-depth technical skills with strong innovation and entrepreneurial components. They develop skills linked to data collection techniques, data analysis methods, computer science, electronic engineering, deep learning and image recognition. These are all skills needed in areas of AI applications such as self-driving cars and robotics and image/video identification with applications in security and safety.
Ensuring an appropriate ethical and legal framework
How is the Commission encouraging the transparency of algorithms?
Algorithms are behind more and more decisions that affect our everyday lives such as access to universities, getting a loan, or the selection of filtering of information; transparency is therefore crucial. In several areas, there are already EU rules for algorithmic decisions. Examples include automated decisions based on personal data (General Data Protection Regulation, GDPR) and for high-frequency trading on the stock-market (Markets in Financial Instruments Directive, MiFID II).
Algorithmic transparency will be a topic addressed in the AI ethics guidelines to be developed by the end of the year. The AI ethics guidelines will build on work from various relevant initiatives such as the Algorithmic Awareness Building Project which will address issues related to algorithmic transparency, accountability and fairness.
Algorithmic transparency is not about disclosure of source code as such. It can take different forms, depending on the situation, including meaningful explanation (as required in GDPR), or reporting to the competent authorities (as required in MiFID II).
What is the product liability directive? Why is guidance needed?
The EU has liability rules for defective products. The Product Liability Directive dates from 1985 and strikes a careful balance between protecting consumers and encouraging businesses to market innovative products. The Directive covers a broad range of products and possible scenarios.
In principle, if AI is integrated into a product and a defect can be proven in a product that caused material damage to a person, the producer will be liable to pay compensation.
The actual cause of events that lead to damage or incident is decisive for the attribution of liability. The Commission plans to issue an interpretative guidance clarifying concepts of the Directive in view of the new technologies, building on a first assessment on liability for emerging digital technologies published today.
How does the General Data Protection Regulation apply to AI?
The General Data Protection Regulation (GDPR) ensures a high standard of personal data protection, including the principles of data protection by design and by default. It has provisions on decision-making based solely on automated processing, including profiling (AI-based systems). In such cases, data subjects have the right to be provided with meaningful information about the logic involved in the decision.
The GDPR also gives individuals the right not to be subject solely to automated decision-making (except in certain situations) such as automatic refusal of an online credit application or e-recruiting practices without any human intervention. Such processing includes profiling that consists of any form of automated processing of personal data evaluating the personal aspects relating to a natural person (AI-based systems), in particular to analyse or predict aspects concerning the data subject’s performance at work, economic situation, health, personal preferences or interests, reliability or behaviour, location or movements, where it produces legal effects concerning him or her or similarly significantly affects him or her.
What will the ethics guidelines be about? What role will the AI Alliance play?
Draft AI ethics guidelines will be developed on the basis of the EU’s Charter of Fundamental Rights, following a large consultation of stakeholders within the AI Alliance. The draft guidelines will build on the statement published by the European Group of Ethics in Science and New Technologies. They will address issues such as the future of work, fairness, safety, social inclusion, algorithmic transparency, and more broadly, will examine the impact on fundamental rights, including privacy, dignity, consumer protection and non-discrimination.
Given the scale of the challenge associated with AI, the full participation of all actors including businesses, academics, consumer organisations, trade unions, policy makers and representatives of civil society is essential. This is why the Commission wants to bring together a broad community of stakeholders around AI-relevant questions under the European AI Alliance. The Alliance will be set up by July 2018, and AI ethics guidelines will be published by the end of the year.
The Dark Ghosts of Technology
Last many decades, if accidently, we missed the boat on understanding equality, diversity and tolerance, nevertheless, how obediently and intentionally we worshiped the technology no matter how dark or destructive a shape it morphed into; slaved to ‘dark-technology’ our faith remained untarnished and faith fortified that it will lead us as a smarter and successful nation.
How wrong can we get, how long in the spell, will we ever find ourselves again?
The dumb and dumber state of affairs; extreme and out of control technology has taken human-performances on ‘real-value-creation’ as hostage, crypto-corruption has overtaken economies, shiny chandeliers now only cast giant shadows, tribalism nurturing populism and socio-economic-gibberish on social media narratives now as new intellectualism.
Only the mind is where critical thinking resides, not in some app.
The most obvious missing link, is theabandonment of own deeper thinking. By ignoring critical thinking, and comfortably accepting our own programming, labeled as ‘artificial intelligence’ forgetting in AI there is nothing artificial just our own ‘ignorance’ repackaged and branded. AI is not some runaway train; there is always a human-driver in the engine room, go check. When ‘mechanized-programming, sensationalized by Hollywood as ‘celestially-gifted-artificial-intelligence’ now corrupting global populace in assuming somehow we are in safe hands of some bionic era of robotized smartness. All designed and suited to sell undefined glittering crypto-economies under complex jargon with illusions of great progress. The shiny towers of glittering cities are already drowning in their own tent-cities.
A century ago, knowing how to use a pencil sharpener, stapler or a filing cabinet got us a job, today with 100+ miscellaneous, business or technology related items, little or nothing considered as big value-added gainers. Nevertheless, Covidians, the survivors of the covid-19 cruelties now like regimented disciples all lining up at the gates. There never ever was such a universal gateway to a common frontier or such massive assembly of the largest mindshare in human history.
Some of the harsh lessons acquired while gasping during the pandemic were to isolate techno-logy with brain-ology. Humankind needs humankind solutions, where progress is measured based on common goods. Humans will never be bulldozers but will move mountains. Without mind, we become just broken bodies, in desperate search for viagra-sunrises, cannabis-high-afternoons and opioid-sunsets dreaming of helicopter-monies.
Needed more is the mental-infrastructuring to cope with platform economies of global-age and not necessarily cemented-infrastructuring to manage railway crossings. The new world already left the station a while ago. Chase the brain, not the train. How will all this new thinking affect the global populace and upcoming of 100 new National Elections, scheduled over the next 500 days? The world of Covidians is in one boat; the commonality of problems bringing them closer on key issues.
Newspapers across the world dying; finally, world-maps becoming mandatory readings of the day
Smart leadership must develop smart economies to create the real ‘need’ of the human mind and not just jobs, later rejected only as obsolete against robotization. Across the world, damaged economies are visible. Lack of pragmatic support to small medium businesses, micro-mega exports, mini-micro-manufacturing, upskilling, and reskilling of national citizenry are all clear measurements pointing as national failures. Unlimited rainfall of money will not save us, but the respectable national occupationalism will. Study ‘population-rich-nations’ and new entrapments of ‘knowledge-rich-nations’ on Google and also join Expothon Worldwide on ‘global debate series’ on such topics.
Emergency meetings required; before relief funding expires, get ready with the fastest methodologies to create national occupationalism, at any costs, or prepare for fast waves of populism surrounded by almost broken systems. Bold nations need smart play; national debates and discussions on common sense ideas to create local grassroots prosperity and national mobilization of hidden talents of the citizenry to stand up to the global standard of competitive productivity of national goods and services.
The rest is easy
China and AI needs in the security field
On the afternoon of December 11, 2020, the Political Bureau of the Central Committee of the Communist Party of China (CPC) held the 26th Collective Study Session devoted to national security. On that occasion, the General Secretary of the CPC Central Committee, Xi Jinping, stressed that the national security work was very important in the Party’s management of State affairs, as well as in ensuring that the country was prosperous and people lived in peace.
In view of strengthening national security, China needs to adhere to the general concept of national security; to seize and make good use of an important and propitious period at strategic level for the country’s development; to integrate national security into all aspects of the CPC and State’s activity and consider it in planning economic and social development. In other words, it needs to builda security model in view of promoting international security and world peace and offering strong guarantees for the construction of a modern socialist country.
In this regard, a new cycle of AI-driven technological revolution and industrial transformation is on the rise in the Middle Empire. Driven by new theories and technologies such as the Internet, mobile phone services, big data, supercomputing, sensor networks and brain science, AI offers new capabilities and functionalities such as cross-sectoral integration, human-machine collaboration, open intelligence and autonomous control. Economic development, social progress, global governance and other aspects have a major and far-reaching impact.
In recent years, China has deepened the AI significance and development prospects in many important fields. Accelerating the development of a new AI generation is an important strategic starting point for rising up to the challenge of global technological competition.
What is the current state of AI development in China? How are the current development trends? How will the safe, orderly and healthy development of the industry be oriented and led in the future?
The current gap between AI development and the international advanced level is not very wide, but the quality of enterprises must be “matched” with their quantity. For this reason, efforts are being made to expand application scenarios, by enhancing data and algorithm security.
The concept of third-generation AI is already advancing and progressing and there are hopes of solving the security problem through technical means other than policies and regulations-i.e. other than mere talk.
AI is a driving force for the new stages of technological revolution and industrial transformation. Accelerating the development of a new AI generation is a strategic issue for China to seize new opportunities in the organisation of industrial transformation.
It is commonly argued that AI has gone through two generations so far. AI1 is based on knowledge, also known as “symbolism”, while AI2 is based on data, big data, and their “deep learning”.
AI began to be developed in the 1950s with the famous Test of Alan Turing (1912-54), and in 1978 the first studies on AI started in China. In AI1, however, its progress was relatively small. The real progress has mainly been made over the last 20 years – hence AI2.
AI is known for the traditional information industry, typically Internet companies. This has acquired and accumulated a large number of users in the development process, and has then established corresponding patterns or profiles based on these acquisitions, i.e. the so-called “knowledge graph of user preferences”. Taking the delivery of some products as an example, tens or even hundreds of millions of data consisting of users’ and dealers’ positions, as well as information about the location of potential buyers, are incorporated into a database and then matched and optimised through AI algorithms: all this obviously enhances the efficacy of trade and the speed of delivery.
By upgrading traditional industries in this way, great benefits have been achieved. China is leading the way and is in the forefront in this respect: facial recognition, smart speakers, intelligent customer service, etc. In recent years, not only has an increasing number of companies started to apply AI, but AI itself has also become one of the professional directions about which candidates in university entrance exams are worried.
According to statistics, there are 40 AI companies in the world with a turnover of over one billion dollars, 20 of them in the United States and as many as 15 in China. In quantitative terms, China is firmly ranking second. It should be noted, however, that although these companies have high ratings, their profitability is still limited and most of them may even be loss-making.
The core AI sector should be independent of the information industry, but should increasingly open up to transport, medicine, urban fabric and industries led independently by AI technology. These sectors are already being developed in China.
China accounts for over a third of the world’s AI start-ups. And although the quantity is high, the quality still needs to be improved. First of all, the application scenarios are limited. Besides facial recognition, security, etc., other fields are not easy to use and are exposed to risks such as 1) data insecurity and 2) algorithm insecurity. These two aspects are currently the main factors limiting the development of the AI industry, which is in danger of being prey to hackers of known origin.
With regard to data insecurity, we know that the effect of AI applications depends to a large extent on data quality, which entails security problems such as the loss of privacy (i.e. State security). If the problem of privacy protection is not solved, the AI industry cannot develop in a healthy way, as it would be working for ‘unknown’ third parties.
When we log into a webpage and we are told that the most important thing for them is the surfers’ privacy, this is a lie as even teenage hackers know programs to violate it: at least China tells us about the laughableness of such politically correct statements.
The second important issue is the algorithm insecurity. The so-called insecure algorithm is a model that is used under specific conditions and will not work if the conditions are different. This is also called unrobustness, i.e. the algorithm vulnerability to the test environment.
Taking autonomous driving as an example, it is impossible to consider all scenarios during AI training and to deal with new emergencies when unexpected events occur. At the same time, this vulnerability also makes AI systems permeable to attacks, deception and frauds.
The problem of security in AI does not lie in politicians’ empty speeches and words, but needs to be solved from a technical viewpoint. This distinction is at the basis of AI3.
It has a development path that combines the first generation knowledge-based AI and the second generation data-driven AI. It uses the four elements – knowledge, data, algorithms and computing power – to establish a new theory and interpretable and robust methods for a safe, credible and reliable technology.
At the moment, the AI2 characterised by deep learning is still in a phase of growth and hence the question arises whether the industry can accept the concept of AI3 development.
As seen above, AI has been developing for over 70 years and now it seems to be a “prologue’.
Currently most people are not able to accept the concept of AI3 because everybody was hoping for further advances and steps forward in AI2. Everybody felt that AI could continue to develop by relying on learning and not on processing. The first steps of AI3 in China took place in early 2015 and in 2018.
The AI3 has to solve security problems from a technical viewpoint. Specifically, the approach consists in combining knowledge and data. Some related research has been carried out in China over the past four or five years and the results have also been applied at industrial level. The RealSecure data security platform and the RealSafe algorithm security platform are direct evidence of these successes.
What needs to be emphasised is that these activities can only solve particular security problems in specific circumstances. In other words, the problem of AI security has not yet found a fundamental solution, and it is likely to become a long-lasting topic without a definitive solution since – just to use a metaphor – once the lock is found, there is always an expert burglar. In the future, the field of AI security will be in a state of ongoing confrontation between external offence and internal defence – hence algorithms must be updated constantly and continuously.
The progression of AI3 will be a natural long-term process. Fortunately, however, there is an important AI characteristic – i.e. that every result put on the table always has great application value. This is also one of the important reasons why all countries attach great importance to AI development, as their national interest and real independence are at stake.
With changes taking place around the world and a global economy in deep recession due to Covid-19, the upcoming 14th Five-Year Plan (2021-25) of the People’s Republic of China will be the roadmap for achieving the country’s development goals in the midst of global turmoil.
As AI is included in the aforementioned plan, its development shall also tackle many “security bottlenecks”. Firstly, there is a wide gap in the innovation and application of AI in the field of network security, and many scenarios are still at the stage of academic exploration and research.
Secondly, AI itself lacks a systematic security assessment and there are severe risks in all software and hardware aspects. Furthermore, the research and innovation environment on AI security is not yet at its peak and the relevant Chinese domestic industry not yet at the top position, seeking more experience.
Since 2017, in response to the AI3 Development Plan issued by the State Council, 15 Ministries and Commissions including the Ministry of Science and Technology, the Development and Reform Commission, etc. have jointly established an innovation platform. This platform is made up of leading companies in the industry, focusing on open innovation in the AI segment.
At present, thanks to this platform, many achievements have been made in the field of security. As first team in the world to conduct research on AI infrastructure from a system implementation perspective, over 100 vulnerabilities have been found in the main machine learning frameworks and dependent components in China.
The number of vulnerabilities make Chinese researchers rank first in the world. At the same time, a future innovation plan -developed and released to open tens of billions of security big data – is being studied to promote the solution to those problems that need continuous updates.
The government’s working report promotes academic cooperation and pushes industry and universities to conduct innovative research into three aspects: a) AI algorithm security comparison; 2) AI infrastructure security detection; 3) AI applications in key cyberspace security scenarios.
By means of state-of-the-art theoretical and basic research, we also need to provide technical reserves for the construction of basic AI hardware and open source software platforms (i.e. programmes that are not protected by copyright and can be freely modified by users) and AI security detection platforms, so as to reduce the risks inherent in AI security technology and ensure the healthy development of AI itself.
With specific reference to security, on March 23 it was announced that the Chinese and Russian Foreign Ministers had signed a joint statement on various current global governance issues.
The statement stresses that the continued spread of the Covid-19 pandemic has accelerated the evolution of the international scene, has caused a further imbalance in the global governance system and has affected the process of economic development while new global threats and challenges have emerged one after another and the world has entered a period of turbulent changes. The statement appeals to the international community to put aside differences, build consensus, strengthen coordination, preserve world peace and geostrategic stability, as well as promote the building of a more equitable, democratic and rational multipolar international order.
In view of ensuring all this, the independence enshrined by international law is obviously not enough, nor is the possession of nuclear deterrent. What is needed, instead, is the country’s absolute control of information security, which in turn orients and directs the weapon systems, the remote control of which is the greedy prey to the usual suspects.
Factories of the Future Find Growth and Sustainability Through Digitalization
The World Economic Forum announced today the addition of 15 new sites to its Global Lighthouse Network, a community of world-leading manufacturers using Fourth Industrial Revolution technologies to enable bottom-line growth. Despite the COVID-19 pandemic’s unprecedented disruption, 93% achieved an increase in product output and found new revenue streams.
Notably, these leading innovators created new revenue streams while driving environmental sustainability – 53% are seeing measurable and marked environmental sustainability benefits. Some have seen almost a total reduction in CO2 emissions, double-digit increases in efficiency and reduction in material use. The new report, Reimagining Operations for Growth, outlines how manufacturers accomplished these results. Their CEOs will provide more insights at the Lighthouses Live event, featuring keynote speaker Satya Nadella, CEO of Microsoft and Alex Gorsky, chairman and CEO of Johnson & Johnson on 17 March at 14.00 CET. See below for a full list of the new Lighthouses and their achievements.
The Lighthouse Network and its 69 sites are a platform to develop, replicate and scale innovations, creating opportunities for cross-company learning and collaboration, while setting new benchmarks for the global manufacturing community.
While 74% of companies remained stuck in pilot purgatory in 2020, research based on learnings from the network reveals that scalable Fourth Industrial Revolution technologies are key to long-term growth. By fully embracing agile ways of working, these manufacturers have been able to respond to disruption and ongoing shifts in supply and demand along their production network and value chains. They also prioritized workforce development – reskilling and upskilling employees for advanced manufacturing jobs – at the same pace and scale.
The new Lighthouses:
Bosch (Suzhou, China):As a role model of manufacturing excellence within the group, Bosch Suzhou deployed a digital transformation strategy in manufacturing and logistics, reducing manufacturing costs by 15% while improving quality by 10%.
Foxconn (Chengdu, China): Confronted with fast-growing demand and labour skill scarcity, Foxconn Chengdu adopted mixed reality, artificial intelligence (AI) and internet of things (IoT) technologies to increase labour efficiency by 200% and improve overall equipment effectiveness by 17%.
HP Inc. (Singapore): Facing an increase in product complexity and labour shortages leading to quality and cost challenges, along with a move at the country level to focus on higher-value manufacturing, HP Singapore embarked on its Fourth Industrial Revolution journey to transform its factory from being manual, labour intensive and reactive to being highly digitized, automated and driven by AI, improving its manufacturing costs by 20%, and its productivity and quality by 70%.
Midea (Shunde, China): To expand its e-commerce presence and overseas market share, Midea invested in digital procurement, flexible automation, digital quality, smart logistics and digital sales to improve product cost by 6%, order lead times by 56% and CO2 emissions by 9.6%.
ReNew Power (Hubli, India): Facing exponential asset growth and rising competitiveness from new entrants, ReNew Power, India’s largest renewables company, developed Fourth Industrial Revolution technologies, such as proprietary advanced analytics and machine learning solutions, to increase the yield of its wind and solar assets by 2.2%, reduce downtime by 31% without incurring any additional capital expenditure, and improve employee productivity by 31%.
Tata Steel (Jamshedpur, India): Facing operational KPI stagnation and an impending loss of captive raw material advantage, Tata Steel Jamshedpur’s 110-year-old plant with deeply rooted cultural and technology legacies deployed multiple Fourth Industrial Revolution technologies, such as machine learning and advanced analytics in procurement to save 4% on raw material costs, and prescriptive analytics in production and logistics planning to reduce the cost of serving customers by 21%.
Tsingtao Brewery (Qingdao, China): Facing growing consumer expectations for personalized, differentiated and diverse beers, Tsingtao Brewery rethought its use of smart digital technologies along its value chain to enable its 118-year-old factory to meet consumer needs, reducing customized order and new product development lead times by 50%. As a result, it increased its share of customized beers to 33% and revenue by 14%.
Wistron (Kunshan, China): In response to high-mix and low-volume business challenges, Wistron leveraged AI, IoT and flexible automation technologies to improve labour, asset and energy productivity, not only in production and logistics but also in supplier management, improving manufacturing costs by 26% while reducing energy consumption by 49%.
Henkel (Montornès, Spain): To drive further improvements in productivity and boost the company’s sustainability, Henkel built on its digital backbone to scale Fourth Industrial Revolution technologies linking its cyber and physical systems across the Montornès plant, reducing costs by 15% and accelerating its time to market by 30% while improving its carbon footprint by 10%.
Johnson & Johnson Consumer Health (Helsingborg, Sweden): In a highly regulated healthcare and fast-moving consumer goods environment, J&J Consumer Health addressed customer needs through increased agility using digital twins, robotics and high-tech tracking and tracing to enable 7% product volume growth, with 25% accelerated time to market and 20% cost of goods sold reduction. It made further investments in connecting green tech through Fourth Industrial Revolution technologies to become Johnson & Johnson’s first ever CO2-neutral facility.
Procter & Gamble (Amiens, France): P&G Amiens, a plant with a steady history of transforming operations to manufacture new products, embraced Fourth Industrial Revolution technologies to accommodate a consistent volume increase of 30% over three years through digital twin technology as well as digital operations management and warehouse optimization. This led to 6% lower inventory levels, a 10% improvement in overall equipment effectiveness and a 40% reduction in scrap waste.
Siemens (Amberg, Germany): To achieve its productivity goals, this site implemented a structured lean digital factory approach, deploying smart robotics, AI-powered process controls and predictive maintenance algorithms to achieve 140% factory output at double product complexity without an increase in electricity or a change in resources.
STAR Refinery (Izmir, Turkey): To maintain a competitive edge within the European refinery industry, Izmir STAR Refinery was designed and built to be “the technologically most advanced refinery in the world”. Leveraging more than $70 million investments in advanced technologies (e.g., asset digital performance management, digital twin, machine learning) and organizational capabilities, STAR was able to increase diesel and jet yield by 10% while reducing maintenance costs by 20%.
Ericsson (Lewisville, USA):Faced with increasing demand for 5G radios, Ericsson built a US-based, 5G-enabled digital native factory to stay close to its customers. Leveraging agile ways of working and a robust IIoT architecture, the team was able to deploy 25 use cases in 12 months. As a result, it increased output per employee by 120%, reduced lead time by 75% and reduced inventory by 50%.
Procter & Gamble (Lima, USA): A shift in consumer trends meant more complex packaging and an increased number of products that had to be outsourced. To reverse the tide, P&G Lima invested in supply chain flexibility, leveraging digital twins, advanced analytics and robotic automation. This resulted in an acceleration of speed to market for new products by a factor of 10, an increase in labour productivity by 5% year on year, and plant performance that was two times better than competitors in avoiding stock-outs during the year.
“This is a time of unparalleled industry transformation. The future belongs to those companies willing to embrace disruption and capture new opportunities. Today’s disruptions, despite their challenges, are a powerful invitation to re-envision growth. The lighthouses are illuminating the future of manufacturing and the future of the industry,” said Francisco Betti, Head of Shaping the Future of Advanced Manufacturing and Production, World Economic Forum.
Enno de Boer, Partner, McKinsey & Company, and Global Lead, Manufacturing, said: “The 69 Lighthouse manufacturers open a window into the future of operations. Though no industry is immune from digital transformation, four sectors are resetting benchmarks – Advanced Industries, Consumer Packaged Goods, Pharmaceutical and Medical products, and Heavy Industries. We are seeing a paradigm shift emerge, from reducing cost to more focus on enabling growth and environmental sustainability. The Lighthouses are proving that unlocking smart capacity through digital technologies is more effective than spending on capital infrastructure.”
The goal of the Global Lighthouse Network is to share and learn from best practices, support new partnerships and help other manufacturers deploy technology, adopt sustainable solutions and transform their workforces at pace and scale. The extended network of “Manufacturing Lighthouses” will be officially recognized at Lighthouse Live: Reimagining Operations for Growth at 14.00 CET/09.00 EST 17 March.
Together with a diverse group of experts and innovators, the meeting aims to initiate, accelerate and scale-up entrepreneurial solutions to tackle climate change and advance sustainable development.
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