For the JIT model to work, the quality and supply of raw materials, the production of goods, and the customer demand for them must remain in alignment. If any one of the links in the chain breaks, stalls, or falls out of sync, the impact on the supply chains that crisscross the world can be felt immediately. For companies, unable to deliver on orders in a timely fashion, they risk losing not only efficiency gains but also brand credibility, market share, and revenue.
Now, companies are seeking new ways of managing their supply chains that offer greater flexibility and transparency. In the automotive sector, some companies including Nissan and JIT pioneer Toyota are increasing chip inventory levels, while others including Volkswagen and Tesla are trying to secure their own supplies of rare metals. But technologies, including Internet of Things (IoT), 5G, and business applications are also offering companies new ways to avoid disruption and respond to unforeseen circumstances.
Disruption and transformation
The transformation of the automotive supply chain is taking place in an increasingly-digitized world, beset with environmental concerns. As climate change concerns intensify, and governments across the world compel industries to switch to more environmentally-friendly practices, the automotive industry and its supply chain networks are undergoing a profound shift. Automotive manufacturers are moving away from internal combustion engines and large-scale manufacturing to zero-emission, carbon-neutral electric or autonomous vehicles with a focus on electric or hydrogen as energy sources. Autonomous vehicles, for example, are seen as “servers on wheels” that rely on batteries, wiring, laser technology, and programming rather than combustion engines. Tech giants such as Japan’s Sony and China’s Baidu have also announced plans for their own electric vehicles (EV), fueling an already heated race in the EV market.
According to the International Energy Agency, global sales of electric cars hit 6.6 million in 2021, making up 8.6% of all new car sales: more than double the market share from 2020, and up from a mere 0.01% in 2010. Business insights provider IHS Markit estimates the number of EV models in the US will increase 10 times over, from 26 in 2021 to 276 in 2030. At the same time, charging stations alone will need to increase from 850,000 in 2021 to nearly 12 million in 2030. To meet the increasing need for battery-powered vehicles, manufacturers must establish a new ecosystem of partners that supplies the parts and accessories required for the successful manufacturing and operating of these alternative vehicles. According to research from Transport Intelligence, “the supply chain for the entire powertrain will be transformed and the types of components, the logistics processes employed to move them, the markets of origin and destination as well as the tiered character of automotive supply chains will change.” This has enormous implications for how the automotive supply chain is ordered.
Meanwhile, everything in the automotive sector, from the automobiles themselves to entire factories, is becoming more connected, with the support of technologies such AI, IoT, 5G, and robotics. In recent months, Nissan has unveiled its “Intelligent Factory” initiative in its Tochigi plant in the north of Tokyo, which employs AI, IoT, and robotics to manufacture next-generation vehicles in a zero-emission environment. And Volkswagen has deployed a private 5G wireless network at its headquarter plant in Wolfsburg, Germany, to trial new smart factory use cases.
As manufacturing becomes more digitized, so too does consumer behavior. Automotive brands are rolling out direct-to-consumer sales models, enabling customers to complete more and more of the sales process through digital channels. While new players are taking an online-only approach to the sales model, incumbents are embracing digital initiatives in partnership with dealers where fulfillment, after sales, and services are still provided through a dealer. In 2020, 69% of dealers in the US added at least one digital step to their sales process. And 75% of dealers agreed that they would not be able to survive long term without moving more of the sales process online. Both models require greater visibility into the supply chain to ensure inventory and availability are accurate.
How manufacturers are responding
Ever more connected consumers, factories, automobiles, and supply chains generate a wealth of data. Gathering and analyzing this data can help enable manufacturers to reduce business risk and become more agile by identifying potential supply issues, increasing efficiencies, and giving customers more accurate timelines. Predictive analytics, for example, can help manufacturers answer the “What if?” questions and proactively reduce the impact of potential supply chain disruptions. Digital traceability enables companies to follow products and goods as they move along the value chain, providing them with exact information on the provenance of inputs, supplier sourcing practices, and conversion processes. “On the demand side, customers expect real-time visibility of when an automobile will be delivered to them, and the status of service, spare parts, and accessories,” says Mohammed Rafee Tarafdar, SVP and CTO, Infosys.
In a bid to harness data and develop greater visibility across the business, manufacturers are employing a variety of technology solutions including business applications—suites of software designed to support business functions. Paired with cloud services, the right business applications can give organizations greater access to cutting-edge technologies, which can then be managed at scale and address the need for visibility, analytics, and cybersecurity. As everything becomes more connected and more autonomous, “there is a need to have technology that can scale with demand. This is where cloud and business applications have very important roles to play,” says Tarafdar, who adds that manufacturers are embracing both private and public cloud to create hybrid clouds, with the support of private 5G networks.
The Download: DeepMind’s AI shortcomings, and China’s social media translation problem
Earlier this month, DeepMind presented a new “generalist” AI model called Gato. The model can play the video game Atari, caption images, chat, and stack blocks with a real robot arm, the Alphabet-owned AI lab announced. All in all, Gato can do hundreds of different tasks.
But while Gato is undeniably fascinating, in the week since its release some researchers have got a bit carried away.
One of DeepMind’s top researchers and a coauthor of the Gato paper, Nando de Freitas, couldn’t contain his excitement. “The game is over!” he tweeted, suggesting that there is now a clear path from Gato to artificial general intelligence, or ‘AGI’, a vague concept of human or superhuman-level AI. The way to build AGI, he claimed, is mostly a question of scale: making models such as Gato bigger and better.
Unsurprisingly, de Freitas’s announcement triggered breathless press coverage that Deepmind is “on the verge” of human-level artificial intelligence. This is not the first time hype has outstripped reality. Other exciting new AI models, such as OpenAI’s text generator GPT-3 and image generator DALL-E, have generated similar grand claims.
For many in the field, this kind of feverish discourse overshadows other important research areas in AI. Read the full story.
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Volunteers are translating Chinese social media posts into English
Even though the posts have passed China’s internet censorship regime, Beijing is unhappy. (The Atlantic $)
+ WeChat wants people to use its video platform. So they did, for digital protests. (TR)
2 Ukraine’s startup community is resuming business as usual
Many workers are juggling their day jobs with after-hours war effort volunteering. (WP $)
+ Russian-speaking tech bosses living in the US are cutting ties with pro-war workers. (NYT $)
+ YouTube has taken down more than 9,000 channels linked to the war. (The Guardian)
3 The Buffalo shooting highlighted the failings of tech’s anti-terrorism accord
Critics say platforms haven’t done enough to tackle the root causes of extremism. (WSJ $)
+ America has experienced more than 3,500 mass shootings since Sandy Hook. (WP $)
4 Crypto appears to have an insider trading problem
Just like the banking system its supporters rail against. (WSJ $)
+ Christine Lagarde thinks crypto is worth “nothing.” (Bloomberg $)
+ Crypto is weathering a bitter storm. Some still hold on for dear life. (TR)
+ The crypto industry has lost around $1.5 trillion since November. (The Atlantic $)
+ Stablecoin Tether has paid out $10 billion in withdrawals since the crash started. (The Guardian)
5 The nuclear fusion industry is in turmoil
It isn’t even up and running yet, but fuel supplies are already running low. (Wired $)
+ A hole in the ground could be the future of fusion power. (TR)
+ The US midwest could be facing power grid failure this summer. (Motherboard)
6 Big Tech isn’t worried about the economic downturn
Even if it drops some of its market valuation along the way. (NYT $)
+ But lawmakers are determined to rein them in with antitrust legislation. (Recode)
+ Their carbon emissions are spiraling out of control, too. (New Yorker $)
7 The US military wants to build a flying ship
The Liberty Lifer X-plane would be independent of fixed airfields and ports. (IEEE Spectrum)
8 We need to change how we recycle plastic
The good news is that the technology to overhaul it exists—it just needs refining. (Wired $)
+ A French company is using enzymes to recycle one of the most common single-use plastics. (TR)
9 Why you should treat using your phone like drinking wine
Striking that delicate balance from stopping the positive tipping into negative. (The Guardian $)
10 Inside the wholesome world of internet knitting 🧶
Its favorite knitter’s creations have gained a cult following. (Input)
+ How a ban on pro-Trump patterns unraveled the online knitting world. (TR)
Quote of the day
“I like the instant gratification of making the internet better.”
—Jason Moore, who is credited with creating more than 50,000 Wikipedia pages, tells CNN about his motivations for taking on the unpaid work.
The hype around DeepMind’s new AI model misses what’s actually cool about it
“Nature is trying to tell us something here, which is, this doesn’t really work, but the field is so believing its own press clippings, that it just can’t see that,” he adds.
Even de Freitas’s DeepMind colleagues, Jackie Kay and Scott Reed, who worked with him on Gato, were more circumspect when I asked them directly about his claims. When asked about whether Gato was heading towards AGI, they wouldn’t be drawn. “I don’t actually think it’s really feasible to make predictions with these kinds of things. I try to avoid that. It’s like predicting the stock market,” said Kay.
Reed said the question was a difficult one. “I think most machine learning people will studiously avoid answering. Very hard to predict, but, you know, hopefully we get there someday.”
In a way, the fact that DeepMind called Gato a “generalist” might have made it a victim of the AI sector’s excessive hype around AGI. The AI systems of today are called “narrow” AI, meaning they can only do a specific, restricted set of tasks such as generate text.
Some technologists, including at Deepmind, think that one day humans will develop “broader” AI systems that will be able to function as well or even better than humans. Some call this artificial “general” intelligence. Others say it is like “belief in magic.“ Many top researchers, such as Meta’s chief AI scientist Yann LeCun question whether it is even possible at all.
Gato is a “generalist” in the sense that it can do many different things at the same time. But that is a world apart from a “general” AI that can meaningfully adapt to new tasks that are different from what the model was trained on, says MIT’s Andreas. “We’re still quite far from being able to do that.”
Making models bigger will also not address the issue that models don’t have “lifelong learning”, meaning they can be taught things once and they will understand all of the implications and use it to inform all of the other decisions that they are going to make, he says.
The hype around tools like Gato is harmful for the general development of AI, argues Emmanuel Kahembwe, an AI/robotics researcher and part of the Black in AI organization co-founded by Timnit Gebru. “There are many interesting topics that are left to the side, that are underfunded, that deserve more attention, but that’s not what the big tech companies and the bulk of researchers in such tech companies are interested in,” he says.
Tech companies ought to take a step back and take stock of why they are building what they are building, says Vilas Dhar, president of the Patrick J. McGovern Foundation, a charity that funds AI projects “for good.”
“AGI speaks to something deeply human—the idea that we can become more than we are, by building tools that propel us to greatness,” he says. “And that’s really nice, except it also is a way to distract us from the fact that we have real problems that face us today that we should be trying to address using AI.”
Equipment management and sustainability
One area that Castrip has been working on for the last two years is increasing the use of machine intelligence to increase process efficiency in the yield. “This is quite affected by the skill of the operator, which sets the points for automation, so we are using reinforcement learning-based neural networks to increase the precision of that setting to create a self-driving casting machine. This is certainly going to create more energy-efficiency gains—nothing like the earlier big-step changes, but they’re still measurable.”
Reuse, recycle, remanufacture: design for circular manufacturing
Growth in the use of digital technologies to automate machinery and monitor and analyze manufacturing processes—a suite of capabilities commonly referred to as Industry 4.0—is primarily driven by needs to increase efficiency and reduce waste. Firms are extending the productive capabilities of tools and machinery in manufacturing processes through the use of monitoring and management technologies that can assess performance and proactively predict optimum repair and refurbishment cycles. Such operational strategy, known as condition-based maintenance, can extend the lifespan of manufacturing assets and reduce failure and downtime, all of which not only creates greater operational efficiency, but also directly improves energy-efficiency and optimizes material usage, which helps decrease a production facility’s carbon footprint.
The use of such tools can also set a firm on the first steps of a journey toward a business defined by “circular economy” principles, whereby a firm not only produces goods in a carbon-neutral fashion, but relies on refurbished or recycled inputs to manufacture them. Circularity is a progressive journey of many steps. Each step requires a viable long-term business plan for managing materials and energy in the short term, and “design-for-sustainability” manufacturing in the future.
IoT monitoring and measurement sensors deployed on manufacturing assets, and in production and assembly lines, represent a critical element of a firm’s efforts to implement circularity. Through condition-based maintenance initiatives, a company is able to reduce its energy expenditure and increase the lifespan and efficiency of its machinery and other production assets. “Performance and condition data gathered by IoT sensors and analyzed by management systems provides a ‘next level’ of real-time, factory-floor insight, which allows much greater precision in maintenance assessments and condition-refurbishment schedules,” notes Pierre Sagrafena, circularity program leader at Schneider Electric’s energy management business.
Global food manufacturer Nestle is undergoing digital transformation through its Connected Worker initiative, which focuses on improving operations by increasing paperless information flow to facilitate better decision-making. José Luis Buela Salazar, Nestle’s eurozone maintenance manager, oversees an effort to increase process-control capabilities and maintenance performance for the company’s 120 factories in Europe.
“Condition monitoring is a long journey,” he says. “We used to rely on a lengthy ‘Level One’ process: knowledge experts on the shop floor reviewing performance and writing reports to establish alarm system settings and maintenance schedules. We are now coming onto a ‘4.0’ process, where data sensors are online and our maintenance scheduling processes are predictive, using artificial intelligence to predict failures based on historical data that is gathered from hundreds of sensors often on an hourly basis.” About 80% of Nestle’s global facilities use advanced condition and process-parameter monitoring, which Buela Salazar estimates has cut maintenance costs by 5% and raised equipment performance by 5% to 7%.
Buela Salazar says much of this improvement is due to an increasingly dense array of IoT-based sensors (each factory has between 150 and 300), “which collect more and more reliable data, allowing us to detect even slight deteriorations at early stages, giving us more time to react, and reducing our need for external maintenance solutions.” Currently, Buela Salazar explains, the carbon-reduction benefits of condition-based maintenance are implicit, but this is fast changing.
“We have a major energy-intensive equipment initiative to install IoT sensors for all such machines in 500 facilities globally to monitor water, gas, and energy consumption for each, and make correlations with its respective process performance data,” he says. This will help Nestle lower manufacturing energy consumption by 5% in 2023. In the future, such correlation analysis will help Nestle conduct “big data analysis to carbon-optimize production-line configurations at an integrated level” by combining insights on material usage measurements, energy efficiency of machines, rotation schedules for motors and gearboxes, and as many as 100 other parameters in a complex food-production facility, adds Buela Salazar. “Integrating all this data with IoT and machine learning will allow us to see what we have not been able to see to date.”