Three reasons why manufacturing jobs are suited to millennials
The manufacturing industry has a considerable employment hole to fill due to a looming skills gap. This is a daily fact we are coping with and are well aware of.
One of the best ways to combat this issue is by putting more emphasis on what a great industry it is for young people to enter. Since the younger generation loves lists, here is a top three list of reasons to give millennials on why manufacturing is a great career choice.
Reason 1 – Well paying positions. According to the NTMA (National Tooling & Machining Association), those in a manufacturing-related job in America tend to make an average of $15,000 more per year than other job fields. This extra amount of money alone can pay for rent, a new car, or help to significantly pay off school or other related debts, while still having money left over each year. More money for vacations, or saving to get to retirement faster.
James Baker, who is a CNC Supervisor for Amarillo Gear in Texas, said he first got into machining because the pay would be beneficial for his growing family when he was younger. His appreciation for the field, however, has evolved past paychecks.
“It’s been 20 years in programming and 30 years in the machinist field,” James said, when asked about his career. “For me, it’s something I feel I’ll always do. It would take a lot for me to want to leave this field.”
The work is not only well paying, but rewarding. “The pay is good,” says Ben Molinar, who is an Operations Manager at GMI Group in Texas. “I get a lot of satisfaction knowing that I can turn a piece of raw materials into a finished product.”
Reason 2 – Flexible work environment with a changing technological and social landscape. Machinist jobs are well known to have a casual dress code, which is usually comprised of thick t-shirts, jeans and hoodies, due to the work environments they expose themselves to. There are also lots of young machinists working today who have tattoos, piercings and an overall unconventional look, which is completely fine with most manufacturing shop floor employers.
There is also the flexibility in being able to bring these skills to any manufacturing shop floor. “I can go anywhere in the world and work,” Ben from GMI said on the subject. “Because of my experience and background in machining, I have been able to work all around the globe.”
With the industry getting younger, it is also easier for people in this job field to not only find their niche community within the realm social media, but for employers to reach new talent via the platforms of Facebook, Twitter, Instagram, and beyond.
“Having a footprint online aside from just a website is crucial,” says Amanda Rosenblatt, who is the Marketing Coordinator at Shop Floor Automations and a social media specialist. “You have middle school kids, high school teens and college-aged young adults…attached to their devices and social media – we can reach them and show them this industry is a community.”
“More manufacturers realize that social media can help, not hinder, their marketing efforts,” says Marjorie Clayman, who is a director in B2B Client Services for Clayman & Associated LLC. “Like most things, though, social media is only as good as what your effort garners.”
Reason 3 – Less time in school after high school, and you can often learn the trade during high school! While there is a serious need of resources for STEM learning (science, tech, engineering and math) for youth these days, there are some resources that can be highlighted as great examples.
For any classroom environment, it is highly recommended that educators check out the video platform called Edge Factor, which has an abundance of resources to let young people discover what they would like about working in this industry. There is also the Cardinal Manufacturing program from the Eleva-Strum School District – it’s a real machine shop high school kids can work in, and that school district also has a very progressive Digital Learning Initiative to keep these kids up to pace with current technology.
The great news is that to get a job in the manufacturing field working at a machine, a college degree is not necessary. Most employers will look for certifications, or may even offer an apprenticeship, to get new talent through the door. To gain certifications, there are online colleges, community colleges, and even vendors who offer these valuable certification learning resources, as well as the program Workshops for Warriors for military veterans.
Because of the financial freedom that working in manufacturing provides, it also gives those who go into the field a chance to continue their learning throughout their adult lives. “I was able to go back to school,” says James at Amarillo Gear. “Now, I’m 52 years old and I’m still enrolled in continuing education for my career.”
What subjects are the best for young people to get started in, if they want to pursue a manufacturing career? “The machine shop is where you actually use math, trigonometry, and algebra,” James from Amarillo Gear said. “I can program, understand and axis machines, and live tool equipment. We have 35k programs online. It’s a big deal.”
By Amanda Rosenblatt at Shop Floor Automations
Follow @ManufacturingGL and @NellWalkerMG
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.