Tomorrow's Engineers Week: How UK manufacturers plan to overcome skills shortages
Engineers will design and create the innovations that will shape our world and young people certainly feel inspired by the world they imagine for the future. Talk to the UK engineering community and you get a different view. Any excitement about the future is often tempered with very serious concerns about the disparity between the number of engineers the industry needs and the numbers coming through the ranks.
With over 2.5 million estimated job openings in UK engineering companies by 2022 more needs to be done to inspire, inform and advise young people so we can build the next generation of engineers. If the UK is to meet the demand we will need to double the number of apprentices and graduates entering the industry. There is rising demand for science, technology, engineering and maths (STEM) graduates throughout the European Union. According to CEDEFOP, demand for STEM professionals and associate professionals is expected to rise by around 8 percent between now and 2025, which is higher than the 3 percent growth forecast for other occupations.
Our research shows that while three quarters of UK parents would recommend a career in engineering to their children almost half say they don’t know a lot about what engineers do. Nearly half (47 percent) of secondary school children would consider a career in engineering, with 29 percent of them girls. However, only a third (34 percent) say they know what to do next in order to become an engineer.
If we are to build the talent pipeline engineering so desperately needs, we need to do more to ensure schools and colleges have the information and resources they need to support the engineering ambitions of their students and that parents are well informed about the opportunities. More than half (56 percent) of the GCSE science, technology, engineering and maths teachers we surveyed have been asked for advice about engineering careers by their pupils in the last year. When almost one in five STEM teachers say they feel a career in engineering is undesirable you realise how important an issue this is.
The aim of Tomorrow’s Engineers Week in the UK is to change perceptions of engineering among young people, their parents and teachers and celebrate the everyday engineering heroes that design, create and innovate to improve our lives. The Week shines a spotlight on engineering and highlights the incredible range of career opportunities for people with engineering skills.
Smaller companies can struggle to compete with global conglomerates when it comes to recruitment, they need to work harder to secure new recruits and retain existing talent. However, skills shortages are neither restricted to SMEs nor a problem unique to the UK. Roughly 40 percent of establishments in Europe are having difficulty finding workers with the skills they require. Manpower Group’s Global Talent Shortage Survey 2014 shows that the top three shortages globally are skilled trades, engineers (second place for the third year running) and then technicians.
We need to widen and deepen the talent pool and to do that we need to encourage more young people to consider engineering careers. According to Eurostat youth unemployment across 28 EU member states stood at 22.9 percent in February 2014, more than double the overall unemployment rate of 10.6 percent. With the exception of Austria, Germany and Luxembourg, all member states have seen an increase in the number of young people not in employment, education or training since the peak of the economic crisis in 2008. With the right inspiration we may be able to give some of those young people an opportunity to find work in engineering, which is why we make available careers information that highlights the range of routes into the industry and the diverse opportunities available.
The Tomorrow’s Engineers programme aims to ensure that every child will understand the variety, excitement and opportunity presented by a career in engineering, with an equal number of girls and boys aspiring to become an engineer, so that UK employers of all sizes and in all sectors get the engineers they need. In working together to widen the talent pool - bringing people into the industry from all backgrounds and via all routes – we will create a more level playing field when it comes to recruitment.
During Tomorrow’s Engineers Week Shell announced a £1m+ investment in the Tomorrow’s Engineers programme, which last year, directly reached over 50,000 students in 1,200 UK schools. We call on engineering employers of every size and sector across the UK to join our national network. We need to join forces to double the number of engineering-related graduates and apprentices. We want them to give schools and colleges access to high-quality careers information and resources and to open their doors to show young people just how exciting a career as a 21st Century engineer can be.
5 Minutes With PwC on 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?
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.