May 16, 2020

Bridging the manufacturing industry’s skills gap

skills
engineers
training
John Kirven
4 min
Shortage of skilled manufacturing workers
It’s no secret the manufacturing industry is in the midst of a talent drought; an estimated 186,000 new engineers will be needed each year until 2024...

It’s no secret the manufacturing industry is in the midst of a talent drought; an estimated 186,000 new engineers will be needed each year until 2024, yet there’s a shortfall of 20,000 graduates annually. The challenge only looks set to intensify, with the UK government considering slashing the costs of humanity degrees to leave engineering courses as the more expensive option.

To maintain profitability, manufacturers must meet multiple objectives — such as creating new service revenue streams, boosting productivity, and developing better relationships with end users. But doing so is near impossible without the right people fuelling progress. And with manufacturing driving 26% of UK GDP, this puts the entire economy at risk.

The question is: what can be done to fill the growing void?

Bridging the skills gap isn’t just about attracting new entrants. Retaining existing staff is also key to effectively navigating the fourth industrial revolution as it transforms the way manufacturing operates. Consequently, it’s vital for businesses to develop talent strategies that build skills in emerging areas – such as AI and robotics – while holding on to traditional expertise.

With that in mind, let’s explore what’s causing the skills deficit, and how it can be addressed.

Barriers to training

According to a report published by the Department for Education, there is a shortage of specialist teachers in all school subjects that lead to further study in engineering. In addition, the Department for Business, Energy & Industrial Strategy and the Department for Education have found that the high cost of delivering up-to-date engineering training in further and higher education is also contributing to the lack of new talent entering the profession. As a result, talent stores are running low; with 85% of manufacturers stating they have skilled jobs that need to be filled. Meanwhile concerns about what will happen when the current aging workforce reaches retirement age are on the rise.

The importance of education

Universities hold the key to unlocking the skills needed by employers across a range of industries, sectors, and professions, and play a crucial part in increasing the number of applicants entering manufacturing. But the introduction of annual tuition fees of up to £9,000 to study a STEM course has caused applications to fall by 7.7% in England, according to UCAS, pushing more students away from an industry in high demand of new recruits. In addition, recent news cites the proposed decrease of fees in humanity degrees, which will only further alienate STEM qualifications to under graduates.

Despite these challenges, interest in manufacturing as a career can begin even earlier than university. Manufacturers must do more to partner with local schools and participate in careers advice events to drive awareness with young people. And for those school-leavers who don’t wish to contemplate a degree, there is more focus than ever on apprenticeships and the planned introduction of T-Levels – a vocational equivalent to A-Levels – that includes a three-month industry placement.

Modernising manufacturing

There is still a widely held and outdated image of manufacturing as an industry that it is low paying, unstable, and lacking in creativity. Manufacturers must lead the way in countering this misconception by creating a strong brand identity and an attractive place to work, thereby enticing more young talent and retaining existing workers. To achieve this they should communicate the vast opportunities the industry has to offer in terms of roles and innovation, in particular highlighting new doors opened by technological advancements in AI, automation, and the Internet of Things. They must take care to emphasise the fact that such developments create new roles, like user-interface designers or robot coordinators, instead of triggering job losses.

If manufacturing is to become future proof and continue to drive the UK economy, change is essential. It is crucial to increase the flow of incoming talent by connecting with potential industry workers at a younger age and reducing the cost of learning for relevant subjects. But it’s also important for the industry to keep modernising its practices, image, and recruitment approach in line with ever-evolving technology that is reshaping the future of manufacturing, and its workforce.

John Kirven is the Senior Value Proposition Consultant at Canon

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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 PwC on the application of artificial intelligence (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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