May 16, 2020

Creative thinking to address recruitment challenges in manufacturing

Jon Blaze is Head of Recruitme...
5 min
recruitment manufacturing
Jon Blaze is Head of Recruitment Operations at Jonathan Lee Recruitment. He qualified with a BEng in Manufacturing and Business Studies and had a succes...

Jon Blaze is Head of Recruitment Operations at Jonathan Lee Recruitment. He qualified with a BEng in Manufacturing and Business Studies and had a successful career in manufacturing engineering before transitioning into recruitment. Here, Jon looks at how recruitment around new technologies can keep businesses competitive.

How can we overcome an already significant skills shortage while advancements in new technologies continue to accelerate? It is a growing concern which cuts through in our daily conversations with manufacturers in the UK and beyond.

‘Technology is moving so fast that the labour market can’t keep up’ is the message that echoes right across the sector.

Simply put, there are not enough people in the marketplace with the requisite skills to plan, implement or manage the technologies required by individual manufacturers to give them the edge over their competitors.

Some forward-thinking companies have already independently invested or established links with innovation centres (e.g. Catapult, European Innovation and Development Centre, Warwick) to access training for their staff on new technologies - be it robotics, automation or artificial intelligence – but it is highly unlikely that this will be enough to keep pace with the needs of industry. While essential for the long-term, manufacturers can ill-afford to wait for these skills to become available - they need ready-made skills in their businesses today to meet the short-term demands the market is placing on them.

With current efforts falling short, it is important for business leaders to think creatively when addressing recruitment or skills challenges.

One of the exciting and innovative initiatives we have seen taking shape is manufacturers building small teams of highly-skilled individuals; different talents brought together from seemingly unrelated backgrounds, industries and disciplines that work as a unit, applying a more holistic, collaborative and effective approach to problem solving.

That way, rather than looking for one person with all of the required skills – who if they exist at all, are likely to be few and far between – individuals from very different sectors and backgrounds are sharing their knowledge to create a winning formula.

Bringing together people from non-manufacturing environments – digital scientists and programmers for instance – alongside skilled workers from traditional manufacturing engineering backgrounds may not seem a natural fit at first glance. However, it can produce a complementary set of skills to create great new ideas for improvements whilst maintaining a competitive advantage and improving performance.

We saw this recently when a graduate games designer joined one of our automotive clients. The candidate had completed a degree in Games Design but was struggling to find a job in his chosen field. With very minimal previous exposure to the automotive industry, he was hired by a company developing autonomous vehicles to render virtual environments for vehicle testing.

In another example, a traditional manufacturing company was experiencing significant delays in getting its product from design release phase into full production. Through their partnership with Future of British Manufacturing Initiative (FoBMI), the manufacturer learned of a new programme where second-year undergraduates – the workforce of the future - were offered to the business on a two-week placement, to act as a ‘digital catalyst’.

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The IT student that arrived at the factory brought a completely fresh set of eyes to the problem. He very quickly saw and understood the set-up of the production planning systems and was able to use technology to solve the issues almost instantly. Lead times fell significantly, all achieved as a result of a short two-week placement, after which the student returned to his studies.

Some manufacturers might wonder what an IT professional can bring to their processes, but the possibilities of ‘big data’ are endless. Imagine the results that could be achieved if the data to drive important decisions was at your fingertips?

Many manufacturers have continuously improved their processes over time; adopting advanced technology as well as utilising tried and tested methods of manufacture, often combined with lean and 6 sigma techniques. However, there is still, understandably, a hesitancy around bringing in the completely new skills that connectivity and big data require and from which automated factories can benefit dramatically.

Brexit, and the confusion around it, is not helping this with industry naturally warier of making bad decisions. Hopefully this is a short-term headache.

In the long-term, businesses and their leaders know they have to both develop the workforce they already have and hire new talent to drive innovation and change in order to achieve their objectives.

It’s a delicate balancing act and – just like a football club which needs a good youth development programme as well as a strong transfer policy to succeed - a combination of both development and structured acquisition is needed.

There is a growing fear that robots and automation pose an imminent threat to jobs in our society and, to an extent, the mainstream media is guilty of stoking this fire.

Change is inevitable and, while it is true that there is a risk to some lower skilled jobs, we are confident that technological innovation offers more opportunities than it takes away. Designers, application engineers, data analysts, programmers and automation engineers to name a few, will all be required in greater numbers to manage and optimise new technologies.

It’s imperative that the UK embraces and supports the development of skills in the creative spectrum sooner rather than later; the need for innovation and invention is only going to increase with time.

It is my belief that the more efficient we become in designing and manufacturing products, the better chance UK manufacturing has to compete in the global market, which will make life better for all of us. We must not be afraid to try new ideas and to bring people together to get the best out of technology.

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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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