May 16, 2020

Apple and BMW to form a joint partnership

Apple
BMW
BMW
Glen White
5 min
Apple and BMW to form a joint partnership
BMW and Apple may form a partnership after an exploratory visit by executives of the world's top maker of electronic gadgets to the headquarters of...

BMW and Apple may form a partnership after an exploratory visit by executives of the world's top maker of electronic gadgets to the headquarters of the world's biggest seller of premium cars.

Apple chief executive Tim Cook went to BMW's headquarters last year and senior Apple executives toured the carmaker's Leipzig factory to learn how it manufactures the i3 electric car.

The dialogue ended without conclusion because Apple appears to want to explore developing a passenger car on its own. BMW is also being cautious about sharing its manufacturing know-how because it wants to avoid becoming a mere supplier to a software or internet giant.

During the visit, Apple executives asked BMW board members detailed questions about tooling and production and BMW executives signalled readiness to license parts, one of the sources said. News of the Leipzig visit first emerged in Germany's Manager-Magazin last week.

“Apple executives were impressed with the fact that we abandoned traditional approaches to car making and started afresh. It chimed with the way they do things too,” a senior BMW source said.

The carmaker says there are currently no talks with Apple about jointly developing a passenger car and Apple declined to comment.

However, one of the sources said exploratory talks between senior managers may be revived at a later stage.

It is too early to say whether this will be a replay of Silicon Valley's Prometheus moment: The day in 1979 when Apple co-founder Steve Jobs visited Xerox's Palo Alto Research Center where the first mouse-driven graphical user interface and bit-mapped graphics were created, and walked out with crucial ideas to launch the Macintosh computer five years later.

BMW has realised next-generation vehicles cannot be built without more input from telecoms and software experts, and Apple has been studying how to make a self-driving electric car as it seeks new market opportunities beyond phones.

Since the visit, there has been a reshuffle at the top of BMW, with Harald Krueger, appointed BMW chief executive in May, in favour of establishing his own team and his plans for BMW by year end, before engaging in new projects.

A further complication was the departure of BMW's board member for development Herbert Diess, who played a leading role in initial discussions with Apple.

He defected to Volkswagen in December.

Diess, who declined to comment for this piece, oversaw the development of BMW's “i” vehicles which are built using light weight carbon fibre, using a radical approach to design and manufacturing.

Car technology has become a prime area of interest for Silicon Valley companies ranging from Google, which has built a prototype self-driving car, to electric car-maker Tesla.

Diess has said the German auto industry needs to undergo radical change because consumers are demanding more intelligent cars and anti-pollution rules mean the next generation vehicles will increasingly be low emission electric and hybrid variants.

In 2030, only two generations of new cars away in auto manufacturing time scales, only a third of vehicles will be powered by a conventional combustion engine alone, experts predict.

“It means that in two cycles we will shut down two thirds of our engine manufacturing,” Diess told a panel discussion in July last year, adding that the value chain for new electric cars is already shifting, with vehicle batteries made mainly in Asia.

“The second part is that the car will become intelligent, part of the Internet,” Diess continued.

“And the strong players in this area are in the United States, in the software development area. We will surely need to find alliances in this field.”

Germany has two years to prove that it can hold its own against new entrants when it comes to shaping the future of luxury vehicles, Diess said.

Carmakers including BMW have already developed next generation self-driving cars, vehicles which need permanent software updates in the form of high-definition maps allowing a car to recalculate a route if it learns about an accident ahead.

The technology is moving ahead faster than the legal and regulatory rules which would allow large-scale commercial availability.

Earlier this year, BMW's new R&D chief Klaus Froehlich said his company and Apple had much in common, including a focus on premium branding, an emphasis on evolving products and a sense of aesthetically pleasing design.

Asked, in general terms, whether a deeper collaboration beyond integration of products like the iPhone would make sense, Froehlich initially said BMW would not consider any deal that forces it to open up its core know-how to outsiders.

“We do not collaborate to open our eco systems but we find ways, because we respect each other,” Froehlich told Reuters.

BMW will keep in mind the needs of the customer, and what the company's core strengths are, when it considers the merits of entering any strategic collaboration, Froehlich added.

Peter Schwarzenbauer, BMW's management board member in charge of the Mini brand as well as digital services declined to comment on possible talks with Apple in an interview earlier this year.

But he said: “Two worlds are colliding here. Our world, focused on hardware and our experience in making complex products, and the world of information technology which is intruding more and more into our life.”

The winners will be those companies that understand how to build intelligent hardware, he said, adding it made sense for carmakers and tech firms to cooperate more closely.

“We need to get away from the idea that it will be either us or them ... We cannot offer clients the perfect experience without help from one of these technology companies,” Schwarzenbauer said.

That dialogue is well underway, he stressed.

With $202.8 billion in cash, Apple has the resources to enter the automotive market on its own, said Eric Noble, president of the Car Lab, a consulting firm in Orange, California.

The tech giant would have an edge on the dashboard, its CarPlay infotainment system connecting iPhones to cars, but would be at square one with the rest of the car, Noble said.

If Apple decided to sell a car it could make sense to find a partner to help with industrial scale production, retail and repair, since demand for such a vehicle could be high.

There are no estimates for potential Apple car sales but the brand and its products command a loyal following.

So if only one percent of Apple's annual iPhone customers decided to order a car, it would need to make 1.69 million vehicles. That's more than the 434 311 vehicles Jaguar and Land Rover produced last year. Even BMW Group, which made just over two million cars last year, would struggle to free up capacity.

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May 11, 2021

5 Minutes With PwC on AI and Big Data in Manufacturing

SmartManufacturing
ArtificialIntelligence
bigdata
Technology
Georgia Wilson
6 min
PwC | Smart Manufacturing | Artificial Intelligence (AI) | Big Data | Analytics | Technology | Digital Factory | Connected Factory | Digital Transfromation
Manufacturing Global speaks to Kaveh Vessali, PwC Middle East Partner (Digital, Data & AI) on the application of AI and Big Data in Manufacturing

Please could you define what artificial intelligence is, and what Big Data is?

AI is the ability of a machine to perceive its environment and perform tasks that normally require human intelligence, and it’s a whole field of different technologies, techniques and applications. 

Big data is a set of tools and capabilities for working with, for processing, extremely large sets of data. 

How does AI and Big Data work together?

Big data is just one of the enablers of AI, though as we see increasing volumes of data, it’s one of the most important 

How can this be applied to a manufacturing setting?

Broadly speaking, there are many benefits of AI, and the use of data, which include reducing costs, minimising human error, and increasing productivity and efficiency. The important thing to consider is any setting - for the use of any technology - is what is the problem you are trying to solve? Be it merely automating repetitive tasks or to reinventing the nature of work in factories by having humans and machines collaborate in order to make better and faster decisions.  

Why should manufacturers use AI and Big Data when adopting smart manufacturing capabilities, what is the value for manufacturers?

One view is, again, the economic benefits of AI, which come in manufacturing as a result of: 

1. Productivity gains from automating processes and augmenting the work of existing labour forces with various applications of AI technologies. 

2. Increased consumer demand due to the increased ability to personalise and tailor manufactured products, along with higher-quality digital and AI-enhanced products and services. 

Manufacturing (and construction industries) are by nature capital intensive, and in our 2018 report, “The potential impact of AI in the Middle East,” we estimated that the adoption of AI applications could increase the sectors’ contribution to GDP gains by more than 12.4% by 2030. 

How can AI and Big Data help manufacturers to evolve in the Industry 4.0 revolution? What about those already looking at Industry 5.0?

It’s really about the investment you make now, in order to futureproof your business. 

We typically see two broad strategies or approaches to the adoption of AI. There are things that we can do immediately, without any recourse to Big Data - which is to adopt technologies we describe as Sensing, those involving computer vision, for example. There are plenty of use cases where these can be used immediately in manufacturing, such as for automatic fault detection. However, there is a longer term play which requires investing in data - getting the right collection mechanisms in place, storage, data governance, Big Data capabilities etc - in order to develop increasingly valuable machine learning driven AI use cases. This is absolutely necessary for long term adoption success. 

What is the best strategy for organisations looking to realise the value of AI and Big Data in manufacturing?  

AI and Big Data are only one part of a successful smart factory. The organisations that lead on AI adoption are those who have already made the most progress in digitising core business processes. In order get ahead in using AI solutions at scale, there are a number of technology investments and organisational decisions to be made, including: 

1. Digitising processes ultimately leads to improved ability to generate data, and in the manufacturing setting - with many 100s of sensors generating 1000s of measurements in real time, the result is Big Data. Data is key to building AI so reliable and accurate data acquisition, management and governance are key. The production line and factories play a critical and direct role in the data-acquisition process. 

2. AI strategy, both long and short term, begins with the use cases, the business applications. Manufacturers need to ask where they want to use AI and gather these use cases together and prioritising projects based on a balance of expected impact and complexity of implementation. 

Of course, in addition to technology and business processes, people are at the heart of any successful technology adoption. AI teams need to be composed not only of data scientists, also data engineers and solution architects to enable their work, data stewards to ensure accuracy, and increasingly so call “Analytics/AI translators” who are able to communicate with business leaders and technology experts. Culture is also key, and manufacturers need to enable a data and AI-driven culture, building trust in data and algorithms by educating their workforce about AI and its capabilities, how best to extract value. It’s not just the positive of course, but also the risks and limitations, as these when encountered without expectations having been set, can significantly impact willingness to invest. 

What are the challenges when it comes to adopting AI and Big Data in manufacturing?

PwC research has shown that one of the major challenges to implementing AI is uncertainty around return on investment (ROI). As I said, there is significant investment required for a long term data and AI strategy to be successful, and expectations around the time to see tangible returns must be set realistically. 

Many companies also struggle with the data side: collecting and supplying the data that an AI system needs to operate, and ensuring that it is accurate. Again, this speaks to the bigger investments required in digitisation. 

Some of the main challenges for manufacturing companies with implementing AI at a scale from our research include:  

  • 40% → Technologies not mature  
  • 40% → Workforce lacks skills to implement and manage AI  
  • 36% → Uncertain of return on investment  
  • 33% → Data is not mature yet 
  • 32% → lack of transparency and trust  
  • 24% → Work councils and labour unions  
  • 22% → Regulatory hurdles in home & important markets  

One element highlighted here, particularly around lack of trust, and labour unions, is that AI is typically misrepresented in the media as “replacing” workers, and taking jobs. Yes, there are efficiency gains to be made from automation, as there have been since the first industrial revolution. But we believe that Data and AI are at their most valuable when they are used to augment workers, enhancing their abilities and the products being manufactured. 

Another challenge we’re starting to see emerge is cyberattacks increasingly targeting interconnected equipment and machinery in smart factories. PwC recently hosted a webcast, in cooperation with the National Association of Manufacturers in the US and Microsoft to discuss this. 

What are the current trends in AI and Big Data in manufacturing?  

  • We see companies putting slightly more focus on adding AI solutions to core production processes such as the engineering, and assembly and quality testing 
  • Safety is of significant importance, with techniques adopted in protocol adherence capabilities (for example maintaining safe distance from specific machinery) being adopted in more every day scenarios for COVID-19 protocol adherence 
  • There is considerable interest in predictive maintenance for large machinery involved in manufacturing processes, and also supply-chain optimisation

What do you see happening in the AI and Big Data industry in manufacturing in the next 12-18 months? 

Honestly, I think we’ll see a continuance of where we’ve already been going for the last 12- 18 months. AI and data are already being used in manufacturing but this use doesn’t get as much attention in the media as, say, healthcare, but the success stories are there, and they will continue as operations continue their digital journeys. 

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