How to overcome the manufacturing skills-gap
The severe shortage of highly skilled manufacturing workers is an increasing issue as manufacturing output expands and many of today’s experienced machinists, engineers, CNC operators and test technicians are reaching retirement age.
Companies of all sizes need to become more proactive in addressing skills gaps and planning for the future. Industry leaders should partner with schools, government agencies, and nonprofit associations to keep the pool of skilled workers growing. But the process doesn’t end there. Manufacturers need to take aggressive action both inside and outside of their factories to foster a viable workforce for today and tomorrow.
Since many manufacturing jobs require only a high school education and on-the-job training, companies should build their visibility in high schools, promote manufacturing career opportunities and launch recruitment efforts. The education system overall must recognize the value of training students for these types of careers as well as understand how substantially these jobs can support the economy. Small manufacturers should partner with community colleges and vocational programs to assure that the pool of skilled candidates continues to grow.
Corporations should also collaborate more often with education partners and the government on programs that are geared towards developing specific skills. If effective public-private partnerships do not currently exist in their regions, manufacturers should commit to building them, applying the best practices of successful programs that exist elsewhere. Public agencies such as local governments should also heavily analyze the availability of specific manufacturing skills in their region. If the labor pool cannot sufficiently support both the current needs of existing production facilities, they are unlikely to support investments planned for the future. These communities need to support training programs and offer financial aid or loan forgiveness to people who enter college or vocational programs geared towards obtaining manufacturing skills.
Educational and supporting organizations need to be aware of the hiring needs of manufacturers in their communities and link the worker supply chain at colleges and vocational schools with those needs. Hybrid educational systems that teach technical skills as well as critical thinking and leadership are ideal, as all are valuable tools in manufacturing operations.
But recruitment alone doesn’t solve the problem, as there are still countless factory workers worldwide who lack the advanced skills and motivation to be truly valuable employees. And this isn’t entirely their fault. One common issue with manufacturers is that they aren’t investing in their current employees.
“I’ve been working as a temp for a manufacturing company for over two years now,” a California factory worker who declined to be named told me. “At the end of each year, they lay me off for 90 days and then bring me back as a temp again. That way, they can keep using me without having to pay for health benefits or give me any stock options.”
The company that he works for has reportedly been doing this to employees for years, and apparently they are not the only company to employ this system. After acquiring useful skills and industry knowledge, many factory workers tire of their temp status—and of the lack of access to benefits and job security—and leave the industry altogether. Although excellent manufacturing employees are created in the process, they aren’t properly retained. And when companies don’t incentivize them or even guarantee their return, these employees are bound to leave for other opportunities.
Investing in internal training programs that further factory workers’ education can add to their skill set and make them additionally valuable to the company. Extensive training will not only give them the expertise needed to complete the job, it will also ensure their viability as an employee and strengthen their position at the company. Capable employees lead to better production, and a talented workforce is necessary for a business to remain competitive.
Whether young manufacturing workers are new recruits or retained employees, they need guidance and mentorship. Matching younger talent with experienced employees in the matter of an apprenticeship model is a good way to ensure that skills are taught and retained even after initial training has been completed.
Millennials are a very different breed from the Boomers they will soon be replacing. It has been projected that by 2020, Millennials will make up nearly 50 percent of the U.S. workforce. Manufacturers need to focus on promoting their companies and the industry as an increasingly innovative environment, and Millennials need to be shown the role they can play in bringing the industry into the future. The technical skills that Millennials acquire simply through being raised with advanced technology can be an enormous asset in the factory environment and can give them a competitive edge in the job market.
By understanding the threat of the qualified labor shortage and investing to galvanize the current workforce and cultivate the next generation of professionals, the industry can ensure that a skills deficiency will not derail the global manufacturing resurgence.
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.