Aug 4, 2020

BMW Group implementing Artificial Intelligence 2020

Manufacturing
Artificial intelligence
BMW Group
Automotive industry
Emily Cook
3 min
BMW group creates systems using artificial intelligence for manufacturing cars and automobiles using robotics to face logistic challenges in factory
BMW leads the way for automotive manufacturers to implement artificial intelligence into it's production processes...

BMW utilises artificial intelligence to perform monotonous specific tasks and as quality control in their manufacturing processes on their sports cars. In 2018 alone, BMW produced nearly 2.5 million cars. They offer a high level of customisability meaning that approximately 99% of customer orders are unique. This means that the BMW Group Factory Logistics team handles 30 million raw parts daily, shipped from 4500 suppliers in 31 different countries.  

Reducing Monotonous Human Tasks

In a 2019 Press Release, BMW shared its algorithms from artificial intelligence in production on an open-source platform. By making them public, software developers around the world view, edit and improve the source code. For quality control purposes, BMW tracks all incoming user suggestions before putting them into production or shared.

The AI intelligence model that they shared is it’s ‘innovative digital image tagging software’. An AI system that relieves workers of monotonous tasks such as checking whether the warning light has been correctly positioned on the car’s boot. This task is now performed by a camera and self-learning software that relays the camera's live information and compares it to hundreds of stored images in milliseconds. 

Quality Control AI System

Another intelligent system that is used in the manufacturing of cars is one to increase paint shop quality. Despite BMW’s state-of-the-art filtration technology, dust particles in paint are still a possibility depending on the ambient air which could significantly impact the painted surface.

As of May 2020, every car must partake in an automatic surface inspection in the paint shop. This consists of gathering data to be used as part of a high-level data base for dust particle analysis. The AI systems evaluate live data from dust particle sensors in paint booths and dryers with the help of this database. “Smart data analytics and AI serve as key decision-making aids for our team when it comes to developing process improvements.” says Albin Dirndorfer, Senior Vice President Painted Body, Finish and Surface at the BMW Group. 

BMW and NVIDIA

NVIDIA is known for its production of high quality graphics units intended for gaming and professional markets. It also manufactures system on a chip units for the mobile computing and automotive markets. However, on May 14th 2020 at NVIDIA’s GTC Conference, it announced that BMW had chosen to partner with NVIDIA Isaac robots platform to improve their automotive factories.  

This partnership is based around the logistical challenges created by BMW’s customisability on it’s vehicles. From this, NVIDIA assisted in manufacturing 5 AI-enabled robots designed to improve workflow; Smart Transport Robot, SplitBot, PickBot, PlaceBot and SortBot. The main task of these robots is to sort, move and organise boxes and pallets. The robots are trained using real and simulated data using NVIDIA’s GPU. The data is then used on NVIDIA DGX system, a fully optimised an integrated system that NVIDIA claims to be "world's fastest workstation for leading-edge AI development". 

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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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