Asian plane makers brace for bumpy ride

Mitsubishi Aircraft Corporation's SpaceJet M90
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Updated 15 February 2020

Asian plane makers brace for bumpy ride

  • The Asia-Pacific region is the world’s biggest aviation market for commercial aircraft
  • Japanese and Chinese firms have embarked on programs to build their own planes

SINGAPORE: Asian plane makers have thrown huge sums at building jets but flagship projects have suffered repeated setbacks, and they face a tough time breaking into a market dominated by established players.

The Asia-Pacific region is the world’s biggest aviation market for commercial aircraft, and Japanese and Chinese firms have embarked on programs to build their own planes.

Asia’s two biggest economies are home to myriad companies making hi-tech goods, from cars to smartphones, which in many cases have succeeded in rivalling Western firms.

But when it comes to building planes — which requires huge investment, years of development, and rigorous safety standards — progress has been slow.

The companies at the forefront of the Asian drive, Japan’s Mitsubishi and Chinese state-owned manufacturer COMAC, have both seen their flagship projects delayed for years.

China “could be successful in 10-15 years but at this time, the odds are not really in their favor,” Shukor Yusof, founder of Malaysia-based aviation consultancy Endau Analytics, said.

“The international market is just too saturated with aircraft from the established manufacturers so there us very little space for new players.”

FASTFACT

$7.3bn

The SpaceJet M90 aircraft has cost an estimated 800 billion yen ($7.3 billion) to develop.

Asia’s biggest air show in Singapore this week was dominated by European plane maker Airbus, US manufacturer Boeing and a handful of smaller, mostly Western manufacturers.

Chinese manufacturers were forced to pull out because of a ban on travelers from China due to the coronavirus outbreak.

Mitsubishi Aircraft Corporation was showing off a mock-up of the interior of its SpaceJet, the first version of which was originally due for commercial rollout in 2013.

After repeated delays, Japanese carrier All Nippon Airways had finally been due to receive the first of the SpaceJet M90 aircraft in the middle of this year.

But the model suffered its sixth delay this month, with the first delivery now expected next year at the earliest. The setbacks, due mainly to technical glitches, have raised the development cost for the plane to an estimated 800 billion yen ($7.3 billion).

Steve Haro, vice president and head of global marketing and strategy at Mitsubishi Aircraft Corporation, said that more than 900 changes had made to the aircraft’s original design.

But he added that a milestone had been reached as the latest version was ready to be certified by regulators.

“We’re really at the place where we’re crossing the finish line of a long race,” he said.

The plane is for short, regional flights, and its main rival are aircraft made by Brazil’s Embraer, he said.

“We’re not interested in competing with Boeing on the large airplanes, or Airbus. We see ourselves meeting a vital market segment that has really been ignored too long,” added Haro.

Over 400 orders had already been received for the M90 from around the world, he said.

Meanwhile, state-owned Commercial Aircraft Corporation of China (COMAC)’s flagship jet has been delayed at least five years, and analysts believe it is likely to miss its 2021 schedule for the plane’s first delivery to a customer.

The single-aisle C919 is designed to compete with the Boeing 737 and Airbus A320, the favored workhorse of budget carriers. The manufacturer says there are 815 of the planes on order.


Man vs. machine in bid to beat virus

Updated 20 February 2020

Man vs. machine in bid to beat virus

  • Human and artificial intelligence are racing ahead to detect and control outbreaks of infectious disease

BOSTON: Did an artificial-intelligence system beat human doctors in warning the world of a severe coronavirus outbreak in China?

In a narrow sense, yes. But what the humans lacked in sheer speed, they more than made up in finesse.

Early warnings of disease outbreaks can help people and governments to save lives. In the final days of 2019, an AI system in Boston sent out the first global alert about a new viral outbreak in China. But it took human intelligence to recognize the significance of the outbreak and then awaken response from the public health community.

What’s more, the mere mortals produced a similar alert only a half-hour behind the AI systems.

For now, AI-powered disease-alert systems can still resemble car alarms — easily triggered and sometimes ignored. A network of medical experts and sleuths must still do the hard work of sifting through rumors to piece together the fuller picture. It is difficult to say what future AI systems, powered by ever larger datasets on outbreaks, may be able to accomplish.

The first public alert outside China about the novel coronavirus came on Dec. 30 from the automated HealthMap system at Boston Children’s Hospital. At 11:12 p.m. local time, HealthMap sent an alert about unidentified pneumonia cases in the Chinese city of Wuhan. The system, which scans online news and social media reports, ranked the alert’s seriousness as only 3 out of 5. It took days for HealthMap researchers to recognize its importance.

Four hours before the HealthMap notice, New York epidemiologist Marjorie Pollack had already started working on her own public alert, spurred by a growing sense of dread after reading a personal email she received that evening.

“This is being passed around the internet here,” wrote her contact, who linked to a post on the Chinese social media forum Pincong. The post discussed a Wuhan health agency notice and read in part: “Unexplained pneumonia???”

Pollack, deputy editor of the volunteer-led Program for Monitoring Emerging Diseases, known as ProMed, quickly mobilized a team to look into it. ProMed’s more detailed report went out about 30 minutes after the terse HealthMap alert.

Early warning systems that scansocial media, online news articles and government reports for signs of infectious disease outbreaks help inform global agencies such as the World Health Organization — giving international experts a head start when local bureaucratic hurdles and language barriers might otherwise get in the way.

Some systems, including ProMed, rely on human expertise. Others are partly or completely automated.

“These tools can help hold feet to the fire for government agencies,” said John Brownstein, who runs the HealthMap system as chief innovation officer at Boston Children’s Hospital. “It forces people to be more open.”

The last 48 hours of 2019 were a critical time for understanding the new virus and its significance. Earlier on Dec. 30, Wuhan Central Hospital doctor Li Wenliang warned his former classmates about the virus in a social media group — a move that led local authorities to summon him for questioning several hours later.

Li, who died Feb. 7 after contracting the virus, told The New York Times that it would have been better if officials had disclosed information about the epidemic earlier. “There should be more openness and transparency,” he said.

ProMed reports are often incorporated into other outbreak warning systems. including those run by the World Health Organization, the Canadian government and the Toronto startup BlueDot. WHO also pools data from HealthMap and other sources.

Computer systems that scan online reports for information about disease outbreaks rely on natural language processing, the same branch of artificial intelligence that helps answer questions posed to a search engine or digital voice assistant.

But the algorithms can only be as effective as the data they are scouring, said Nita Madhav, CEO of San Francisco-based disease monitoring firm Metabiota, which first
notified its clients about the outbreak in early January.

Madhav said that inconsistency in how different agencies report medical data can stymie algorithms. The text-scanning programs extract keywords from online text, but may fumble when organizations variously report new virus cases, cumulative virus cases, or new cases in a given time interval. The potential for confusion means there is almost always still a person involved in reviewing the data.

“There’s still a bit of human in the loop,” Madhav said.

Andrew Beam, a Harvard University epidemiologist, said that scanning online reports for key words can help reveal trends, but the accuracy depends on the quality of the data. He also notes that these techniques are not so novel.

“There is an art to intelligently scraping web sites,” Beam said. “But it’s also Google’s core technology since the 1990s.”

Google itself started its own Flu Trends service to detect outbreaks in 2008 by looking for patterns in search queries about flu symptoms. Experts criticized it for overestimating flu prevalence. Google shut down the website in 2015 and handed its technology to nonprofit organizations such as HealthMap to use Google data to build their own models.

Google is now working with Brownstein’s team on a similar web-based approach for tracking the geographical spread of the tick-borne Lyme disease.

Scientists are also using big data to model possible routes of early disease transmission.