Rolls Royce Joins the Drive to Reduce the Manufacturing Inspiration Gap
Much has been made of the so-called inspiration gap within manufacturing at present, but there are signs that some of the industry’s leading protagonists are promoting an environment of change.
In a recent Epicor study in the UK, more than 300 manufacturing executives put their company’s inspiration rating at only 5.7 out of 10, indicating the need for a change of mindset.
Only six percent of respondents in the survey rated themselves as highly inspired while nearly two-thirds signaled the need for the industry to adapt significantly if it is to achieve future success.
To get the inside track on what it is like trying to break into the industry, Manufacturing Digital spoke to Jessica Charter who has recently completed a one year placement at Rolls Royce as a Manufacturing Systems Engineer and believes, through her own intensive experiences with the manufacturing giant, that more can be done to encourage the next generation of skills into the sector.
Manufacturing Digital (MD): Could you firstly describe your early ambitions and your subsequent decision to enter the world of manufacturing?
Jessica Charter (JC): I’d always been interested in the concept of engineering, taking something and seeing what was possible in order to improve on it. The manufacturing sector seemed structured and process orientated which appealed to me, given my analytical educational background. Then when I finished my A-levels in math, physics, design and technology I opted for a year in industry and was lucky to get a good position at Rolls Royce.
MD: In what ways did your time at Rolls Royce confirm your interest in continuing this path into the industry?
JC The experience of working at Rolls Royce was amazing and opened my eyes up to the manufacturing industry and the opportunities it offers. It fuelled my interest in aerospace engineering and led me to carry out an extended project researching the fundamental principles of a Gas Turbine Engine. I took my interest a step further by attending a training course and examination and gained a Level 1 Award on Basic Holistic Gas Turbines.
MD: How well were you received by fellow workers as a young person entering the industry?
JC: I really loved working at Rolls Royce. Not only are the people there very friendly but as a young person and a female I was welcomed, taken seriously and treated with respect from more experienced members of the team – like I’d worked there for a few years, not just a pre-university student.
MD: Do you feel that common perceptions and images of getting into manufacturing were confirmed or slightly off from your early experience at a major company like Rolls Royce?
JC Honestly, I didn’t really know what to expect – but I quickly found that I loved the culture and values in this branch of the manufacturing industry. What I liked most of all was knowing that the day to day work I did resulted in continuous measureable improvement in the company’s manufacturing systems. Each project I worked on resulted in significant financial savings.
MD: A lot has been made of the so-called ‘inspiration gap’ currently occurring within manufacturing, so do you feel that you have been suitably ‘inspired’ and encouraged to enter this industry throughout the education process?
JC I would put my level of inspiration at nine out of ten. I think manufacturing is inspiring in that you can take a process and look to make it better. Once you’ve done that you can take and apply that knowledge to other situations, sharing it with the team around you so everyone can tangibly see what you have done that makes a difference to the business.
MD: What is the general feeling among your peers and people you have learnt alongside in regards to levels of inspiration to enter the industry at such a young age?
JC As a career option, manufacturing provides you with the opportunity to learn on the job and develop transferable skills. If you have passion you can go into any manufacturing role and succeed. However I do feel that the manufacturing sector has not been very well understood and many do not consider it as a career option simply because they are not exposed to its true potential.
MD: In the long term, what are your personal aspirations, and how do you feel that the industry and its ability to inspire young people will develop?
JC One day I’d like to lead and inspire more people into engineering and also the manufacturing industry. I would especially like to inspire more women into manufacturing as I believe that they have a great deal to add to the sector.
Aside from this I think the public image of the industry is actually far from the reality of it, which is one of the main problems in terms of inspiring young people. You don’t really know what it’s like to work in the sector until you’ve tried it and many people just think it’s about factories and machinery.
Actually it’s just as much about people working effectively together - as in the services industries. The difference in manufacturing is that you all contribute to a tangible finished product to be proud of at the end of the day.
MD: What do you feel needs to change to encourage more young people into manufacturing in the future?
JC There are definitely not as many young people in the industry as there should be. I would urge anyone to give it a try and have no preconceptions.
This lack of attracted talent and skills is probably down to the image that the industry portrays more than anything else, so a key development would be to work on this image – to let a true picture of the inspirational culture to shine through.
My experiences are only good and suggest that the sector is full of inspired people who can help to overcome this image problem. For me it was great to be part of a team of professionals all working towards a common goal.
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.