May 16, 2020

How can software quality assurance address the skills gap in automotive industry?

skills gap
People and Skills
4 min
After years of decline, the UK automotive industry is undergoing a welcome resurgence.
After years of decline, the UK automotive industry is undergoing a welcome resurgence. UK car plants produced more than 1.5 million vehicles last year...

After years of decline, the UK automotive industry is undergoing a welcome resurgence. UK car plants produced more than 1.5 million vehicles last year, the highest number since 2007. By 2017 that number is expected to rise to more than two million vehicles per year, a new record according to The Society of Motor Manufacturers and Traders (SMMT)[1]. These numbers are impressive, but this has led to calls for the UK automotive industry to future proof their manufacturing processes to deal with the increase in production quantities and rising manufacturing complexities.

Whilst the growing automotive industry is a positive sign for the future of UK manufacturing, the shortage of skilled engineers is an ongoing challenge. The rising demand for UK-built cars is just one of the contributory factors though. Manufacturers are now working on the next generation of highly complex, technical ‘connected cars’ built upon vehicles which today already have more software code than any other form of transport, (including fly-by-wire fighter jets and space shuttles) more even than your home computer operating system.

Technology market intelligence company, ABI Research, predicts[2] that the number of connected cars with Internet of Things (IoT) type capabilities will hit 400 million units worldwide come 2030. Growing consumer demand for the connected car has led to a paradigm shift in consumer expectation. In a recent What Car? Motoring Panel survey[3], connectivity was deemed a more important purchasing factor than a car’s brand prestige, previous experience with the model, ability to personalise and its CO2 emissions.

As consumer expectations rise towards more sophisticated cars, so do the manufacturing complexities and thus the need for ever more skilled engineers. This has also led to the importance of implementing stringent quality assurance to guide the engineers hand, eliminate avoidable errors in the manufacturing process and ensure brand reputation is maintained.

There is a planned investment of £5.4 million that was announced last October by the Department for Business, Innovation and Skills (BIS) to help UK automotive suppliers keep pace with demand whilst attempting to close the growing skills gap. The investment includes £2.7 million from the Employer Ownership Fund automotive supply chain competition, designed to help employers in the industry design training projects that can address the 100,000 person skills shortage thought to be holding back further sustained growth in the automotive industry.

But, it will be several years before the fruits of this endeavour will ripen. The automotive industry cannot wait this long and gamble on its future. It needs to look at all aspects of its business now and identify how their current employees can be utilised more effectively towards engineering better products.

Technology is often the enabler of businesses progression but with some irony many of the enterprise software systems originally conceived to save manufacturers time and money have become large drains of intellectual resources. Companies often struggle to deploy ever more complex solutions, such Enterprise Resource Planning (ERP) or Product Lifecycle Management (PLM), running over budget and behind schedule. Furthermore, they often use some of the best and brightest engineers as so-called “subject matter experts” (SMEs) to both design and test such solutions in fear that nobody else will be good enough. However, in an industry already short of skills this only exacerbates the challenge when these individuals could add more value to the final product, not working within IT functions.

To address this, one starting point would be software quality assurance and its role in helping translate business processes into functioning, capable software. By working with a quality assurance specialist, they can ensure the increasingly complex software systems are delivered faster, more robustly and ultimately are more suited to developing the complex vehicles of the future. This not only allows a company’s skilled engineers to concentrate on product areas where they are more valuable but also ensures delivery of engineering software which gives them greater scope to achieve engineering excellence in the finished vehicle and keeping customers happy.

Moreover, by delivering better quality enterprise systems ensures when new engineering talent is introduced into these companies in the coming years they can be assured that the business will be ready to capitalise on this skills injection. Therefore, if the automotive industry is to stay on the road of reaching and sustaining the record-breaking production levels predicted by the SMMT, it must ensure that its business system infrastructure is ready to meet these demands, without placing further burden on an already overstretched workforce. Software quality assurance is one of the answers. Working smarter, not harder.

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May 11, 2021

5 Minutes With PwC on AI and Big Data in Manufacturing

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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