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Can China electrify all new passenger cars by 2030?

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China’s electric vehicle industry is entering a new phase of accelerating development, President Xi Jinping wrote in a congratulatory message to participants of a new energy vehicle conference in early July. In 2018, China sold almost as many electric vehicles as the rest of the world combined. At the same event, the chairman of Chinese electric vehicle giant BYD upped the ante, challenging China to electrify all passenger vehicles by 2030.

New energy vehicle sales are booming, but they still only amounted to 2.5% of car sales in China in 2018. Could all sales feasibly be electric within the next decade?

A recent report from the Innovation Centre for Energy and Transportation (iCET) made the first public proposal of a timeline for the phaseout of petrol and diesel vehicles across China. According to the Beijing-based thinktank, 2030 is premature, but an entire phaseout could be possible by 2040. However, the report also highlights significant uncertainties ahead, including whether consumer appetite for electric vehicles will wane when government subsidies are cut.

Why phase out traditional vehicles?

Starting in 2016, regions and countries around the world began proposing an end to driving as we know it. China’s vice minister of industry and information technology made waves when he announced in 2017 that China, the world’s largest car market for the past decade, was researching a phaseout of petrol and diesel vehicles.

The news followed a steady drumbeat of policies supporting the growth of China’s new energy vehicle industry in recent years. From generous government subsidies to driving restriction exceptions in China’s congested cities, the government has been coaxing the industry along.   

China has much to gain from phasing out all petrol and diesel vehicles. For one, the country relies on imports to meet 70% of its crude oil demand, 42% of which is consumed by vehicles. Petrol and diesel cars also have a major impact on public health. They are among the main perpetrators of air pollution in many of China’s cities. As car ownership has climbed, increasing oil use has also contributed to China’s rising greenhouse gas emissions.

With solar panels and wind turbines, China used subsidies to build companies that now dominate the industries worldwide. The burgeoning electric vehicle market presents a similar opportunity.

Is a phaseout possible?

Hainan, the island province in China’s south, has emerged as a green pioneer in recent years. In a plan released in March this year, it became the first region in China to set an official date for the phaseout of petrol and diesel vehicles.

Hainan has its sights set on 2030, but the rest of the country is unlikely to meet that deadline according to iCET’s report. The group built a model based on China’s automobile industry trends, national policies and oil consumption under a scenario of limiting global warming to under 2C, and proposed a phaseout timetable accordingly. The timetable states that smaller petrol and diesel passenger vehicles will be phased out between 2020 and 2040. Larger “commercial vehicles”, such as buses and trucks, will follow, so that all petrol and diesel vehicles are phased out by 2050.  

The study proposes an incremental phaseout based on the type of vehicle and region. The largest cities that already have strong electric vehicle markets are prioritised along with cities suffering the most from pollution, while relatively underdeveloped regions are given more time to make the transition. Taking the lead will be government-owned vehicle fleets, followed by private vehicles, which will allow some time for costs to come down further for alternative vehicle technologies. The majority of passenger vehicles will be replaced by new energy vehicles and non plug-in hybrids (like the Toyota Prius) according to the study.

Taiyuan, an industrial city in west China, has already demonstrated this model by electrifying its taxi fleet. Shenzhen followed suit this year. However, Li Wanli, formerly of the Ministry of Industry and Information Technology, commented at the report launch: “I personally think the proposed timetable is too early and tight for privately owned vehicles.”

He also cautioned that the study’s suggested approach may pose problems. Citing fuel efficiency standards being rolled out regionally right now, he said the piecemeal approach has caused headaches for manufacturers and is a case to learn from. 

Potential speedbumps

Although the study’s timetable aligns with current policies and projections, the authors elaborate that several uncertainties could influence China’s path. The electric vehicle industry is in the midst of a major transition. Subsidies have long been boosting sales, accounting for 20-35% of the take-home sale price for manufacturers in 2016. Now, the government has decided to wean the industry off the handouts, likely entirely by 2020.

This shift could dampen consumer appetite. Projections show that electric vehicles could reach price parity with petrol and diesel vehicles by 2030, but for now they will likely remain out of reach for many Chinese buyers without government support. The Tesla Model 3, for instance, is being advertised as a vehicle for the mass market. But its price tag is still about US$15,000 above the average car in China.

Whether enough alternative cars can be produced is also moot. Production of new energy vehicles is slightly above sales in China, but even at over one million sales in 2018, it is dwarfed by the market for conventional vehicle. To encourage production, this year China is introducing a national production policy for large manufacturers. The system is slightly more complex than a pure quota, but it essentially requires automakers to meet production targets for 2019 and 2020 or buy credits from overperforming companies. The policy is expected to double new energy vehicles’ share of sales, according to Bloomberg New Energy Finance, but no quota has been set for after 2020.

Whether infrastructure can keep up with the phaseout is also a looming question. Building out enough charging stations to supply a rapidly expanding electric vehicle fleet is a government priority, and an unprecedented challenge. The power grid may also struggle to keep up with charging if demand is not timed intelligently. A Natural Resources Defense Council (NRDC) study found that peak load on the grid could increase 58% by 2030.

Environmental pros and cons

The iCET study finds that greenhouse gases and air pollution would be reduced significantly if their timetable is followed. A study by the China Automobile Technology Research Centre found that phasing out petrol and diesel vehicles would lead to a 41% drop in nitrogen oxide and a 35% drop in particulate matter emissions in 2050, compared to a 2017 baseline. Based on the iCET study, end-user greenhouse gas emissions would fall 51% in 2040 and 77% in 2050 while lifecycle emissions (including from electricity generation) would fall 55% in 2050.

However, electric vehicles are not without their own environmental hazards. Battery supply in particular has raised red flags. Currently, battery recycling remains very low due to there being diverse battery types and an unwillingness from recyclers to take responsibility for safety risks. The iCET study warns that if a better recycling system is not established, lithium, cobalt and manganese in the batteries could cause significant damage to public health and the environment. Dealing with this blockage in the electric vehicle lifecycle could slow down the rollout, the authors argue.

Setting a date

The government has set a number of long-term targets for new energy vehicle production. The most ambitious is for them to account for 40% of car sales by 2030. Will China ratchet up the pace by setting a phaseout target on top of that?

Hainan has already fired the starting gun. However, its vehicle market is relatively small (the province has about one sixth as many cars as Beijing) so it will not be as significant an undertaking there. A Caixin article suggests that Beijing might be a good candidate to follow Hainan’s example as it has led in the establishment of other new energy vehicle policies in the past.

At the report release, Wang Baixia, one of the drafters of Hainan’s phaseout plan, said having a target would send a strong signal: “A timetable is still needed, for the government and companies, everyone needs such a timetable (…) this long-term expectation is very important.” 

The government is working on a 15-year new energy vehicle development plan, which may provide further clarity on its phaseout plans.

From our partner chinadialogue.net

Lili Pike is a researcher for chinadialogue and the executive producer of the Beijing Energy Network's podcast, Environment China.

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The Dark Ghosts of Technology

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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.

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China and AI needs in the security field

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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.

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Factories of the Future Find Growth and Sustainability Through Digitalization

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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:

Asia

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%.

Europe

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.

Middle East

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%.

North America

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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