May 16, 2020

Why Change-Hungry Young Generations are the Key to Unlocking Growth

Sabby Gill
5 min
Guide to recruiting young workers
The struggle to recruit skilled workers is a recurring concern for manufacturing businesses across the world. An EEF report recently revealed that three...

The struggle to recruit skilled workers is a recurring concern for manufacturing businesses across the world. An EEF report recently revealed that three-quarters of manufacturers have struggled to recruit skilled workers in the last three years.

This industry analysis suggests that manufacturers are dealing with a lack of both quantity and quality when it comes to applicants, with not enough people applying for roles and job-seekers lacking the technical skills required to fill available vacancies. The report cautions that manufacturers’ plans to drive productivity improvements and to capitalise on Industry 4.0 could be derailed and with demand for skills set to rocket, the situation is about to spiral further.

Yet, with new generations entering the workforce perhaps there’s light at the end of the tunnel. These younger generations bring with them a fresh outlook and a desire for personal growth that, if harnessed properly, could be the injection that businesses need to succeed. However, attracting this new talent is not always easy.

Changing workplaces

Younger generations are entering the workforce at a time of technological change and economic disruption. These forces are shaping the choices they make and the experiences they seek when applying for jobs. What’s more, the career choices this generation makes now, will in turn eventually shape the global economy.

Organisations face a unique set of challenges when it comes to integrating new generations into the workforce, and balancing their needs and expectations, along with those of existing workers. For example, the so-called Baby Boomer generation, which comprises of workers born before the sixties, has very different ways of working compared to Generation X, currently aged 36-55. Studies, such as those by The Gerontologist and The Open University have characterised Baby Boomers as motivated by position, perks and prestige, making them believe that Generation X and younger generations should, like them, conform to a culture of overwork.

Some members of these generations may be soon to retire, but they will nonetheless need to work alongside much younger generations first—including Millennials, who are currently under the age of 30 and members of Generation Z, who were born in the late nineties. These groups, in contrast to their older colleagues, grew up in an environment where diversity and the need to balance multiple tasks at once was the norm. Thus, they seek flexibility, team work, and the acceptance of their ideas.

What’s more, as these younger generations come up through the ranks, they are rapidly becoming the most dominant population in today’s workplace, bringing with them innovative ways of working, fresh ideas and challenges for their older colleagues.

Indeed, a clash in attitudes and priorities has resulted in many commentators giving younger generations a bad rap. Only recently, the CBI published a report which showed a third of companies are concerned about young people’s attitude to work, believing that many graduates and school leavers lack the mindset and skills required to thrive in the current workplace.

But are we giving young people a hard time when we should perhaps be looking at the assets they bring to a company instead?

Renewed focus on growth

As part of its global growth survey, Epicor set about trying to better understand how attitudes to business growth vary amongst different generations. Millennials and Generation Z, referred to as Gen ZY in the research, want to know that work is a place of growth and development, where they can find their purpose and be passionate about what they do.

The survey found that these generations are more “excited” about the growth experienced in their company than Baby Boomers (29% versus 27%), and that they are also a lot more optimistic about growth trends. Three quarters (72%) believed their business’s growth prospects had improved in the last 12 months, whereas just 50 per cent of Baby Boomers felt the same.

This optimism of youth can be harnessed by organisations looking to drive growth. For example, we are seeing the proliferation of creative office design, short term office leases and co-working spaces across regions around the world, as well as increases in flexibility in work schedules, work attire and human resource policies at both large and small companies.

The use of technology is also a factor, as having access to the latest tech goes a long way towards attracting the best talent, boosting morale and driving operational efficiencies. Many of these ideas and trends are refreshing and useful to growing innovative companies.

New generational challenges

Generation ZY is likely to have to confront obstacles to prosperity that their parents didn’t face. They are better educated than previous generations—but in today’s world, many, if not most, will be forced to retool and switch careers several times during their working life.

IT advancement is in many ways a double-edged sword for young people. The digital economy has enabled the creation of millions of new jobs, but artificial intelligence and robotics may soon displace many traditional manufacturing roles.

However, the good news is that many younger people are aware of these challenges and often look to the application of the latest technologies to overcome their problems. The Epicor survey found that younger generations are more likely to embrace new innovative technologies. Over half (52%) see better technology as a solution to current stresses and challenges, compared to 45 per cent of Gen X, and 38 per cent Baby Boomers.

But beware of the ‘one size fits all’ mindset, because not every employee will fit the description of their generation.  Mark Zuckerberg for example, who was born in 1984, is arguably too old to be classed as a Millennial but is nonetheless renowned for his capacity for being on the cutting edge of social technologies. Many Gen X and Baby Boomer employees too, are also technologically forward-thinking and willing to adapt to today’s digital world.

As the generation of digital natives makes waves in the work environment, organisations that play to these young people’s strengths will ultimately gain thriving and motivated workers, allowing them to grow in the long term.

This involves engaging with young workers on new, social and truly interactive platforms, such as using social enterprise resource planning (ERP) tools to collaborate on projects, or by using gamification methods to motivate teams and drive results. Also important is the need to utilise the latest technology for training, for efficiency, and for automating manual tasks that might otherwise leave younger generations bored. This is the future workplace. Is your business ready?


Sabby Gill is the Executive Vice President of International at ‎Epicor Software.

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May 11, 2021

5 Minutes With PwC on AI and Big Data in Manufacturing

Georgia Wilson
6 min
PwC | Smart Manufacturing | Artificial Intelligence (AI) | Big Data | Analytics | Technology | Digital Factory | Connected Factory | Digital Transfromation
Manufacturing Global speaks to Kaveh Vessali, PwC Middle East Partner (Digital, Data & AI) on the application of 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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