Workforce demographic questions and technology answers

Posted on 8 May 2024 by The Manufacturer

Demographic trends might not be the first thing we think of when discussing the manufacturing workforce, but challenges around labour hiring and skills are part of a bigger population issue. Stephan Pottel, Manufacturing Practice Lead, Zebra Technologies, explains.

Most recently, a story hit global headlines telling us about a study in The Lancet projecting that 155 of 204 countries and territories worldwide, or 76%, will have fertility rates below population replacement levels by 2050. By 2100, that is expected to rise to 198, or 97%, say researchers. And people in Europe are also living longer and working longer, says Eurofound data. Industrial leaders and researchers are highlighting the possible impact on innovation, productivity, and the ability to fill skilled manufacturing and engineering roles.

Report data from one European manufacturing industry body showed that 75% of surveyed manufacturers are concerned about an ageing workforce. “Attracting and retaining a younger workforce is an ongoing problem…the average age of manufacturing staff has climbed and it is not uncommon for over-40s to now make up the majority of employees within the sector,” it said.

Manufacturers are facing a silver tsunami of an ageing workforce across the globe and the challenge of attracting and retaining newer, digital native workers who do not seem to find manufacturing jobs exciting. Business leaders are turning to digitisation and automation to fill labour gaps, train workers faster, and assist the workforce they currently have with better and easier to use tools.

Three strategic pillars helping manufacturers today

Better connected, visible, and optimised front-line worker solutions are critical in managing the challenge of ageing and retiring workers. These solutions can capture knowledge and experience into systems and procedures and for training AI systems, and help attract and empower newer workers to leverage insights, training and easy to use tools including low/no code tools that democratise AI for more workers.

In light of current demographic and labour scenarios, we see automation and AI in manufacturing powering three key strategic pillars today. The first is actionable visibility that generates real-time visibility into people, processes, assets and inventory, which can also feed data sets used to train AI models. One automotive manufacturer digitised and automated the track and traceability of vehicles in its plant, using a radio frequency identification (RFID) system. Results included a 50% improvement in resource allocation, a 10% reduction in human factor error, and an improved profitability metric of the process.

The second pillar is augmented and connected front-line workers – every worker needs to be connected, informed and data empowered so that insights can be delivered to the right person at the right time while also ingesting information about their workflows, making the overall factory floor and network of plants smarter. One automotive recycler and reseller processes an average of 70-80 vehicles daily, handles around 2,000 spare parts and maintains a stock of approximately 350,000 items. It equipped plant workers with rugged mobile computers featuring Wi-Fi and cellular connectivity and communication software for walkie-talkie and phone communication between employees. Front-line operators can receive an order with all the necessary product information directly, and their devices are connected to a machine learning platform for usage metrics and predictive maintenance.

The third is optimised quality – many repetitive tasks can be automated and augmented through a combination of AI and physical automation. These systems will automate and enhance many aspects of quality, anomaly detection, material transport, and track and trace workflows. Examples include the use of modern machine vision systems that automate and elevate quality and compliance inspection. One fresh food manufacturer implemented a machine vision robotic picking system that has

resulted in an estimated 75% reduction in overall costs compared to traditional camera and lighting options and efficient picking and placing without damaging the food or packaging. Another, a global agriculture machinery leader, is using machine vision with deep learning capabilities with a 99.5% accuracy rate, plus cost and time savings as visual inspection processes have been automated.

Jobs and tasks of the future  

When discussing AI, right now it’s not about simply creating and eliminating jobs, although many headlines may suggest that. And like the rise of the car, telephone, and internet, hosts of new jobs and industries are being created. What we see today are manufacturers equipping their engineers, technicians, programmers and data scientists with new, better automation and AI tools to do what they’re doing but faster, more efficiently and handing over certain tasks to AI-driven automation.

Workers with AI capabilities will set themselves apart in the near future, as they’ll have the knowledge and expertise manufacturers want in their plants. Breakthrough low/no code solutions encourage citizen developers to drive innovation within established guardrails. Workers can drive the next wave of productivity gains leveraging these next-gen AI tools available on robust hand-held devices, for example.

Some tools, like deep learning optical character recognition, are low/no code, meaning they’re ready out-of-the-box and don’t require specialist knowledge as it has been trained on thousands of images. Other tools operate more like readymade environments for programmers and data scientists to create solutions using the platform, tools and libraries provided.

AI and automation provide the ability for manufacturers and engineers to automate individual tasks, driving overall labour productivity increases and addressing labour shortages. In the next five years, 33% of decision-makers in the UK automotive industry and 29% in Germany want to automate over half of their visual inspection processes using machine vision, says a Zebra AI machine vision report.

Meanwhile, analysis by McKinsey suggests that nearly 30% of total labour hours could be automated over the course of the next decade, while bringing about an increase in labour demand and high levels of change in work activities in science, technology, engineering and math (STEM) professions. Ambitious manufacturing automation goals and increased demand for STEM jobs will be a struggle to meet and achieve without a skilled workforce, automation and AI supporting manufacturers along the way.

Learn more about automation and AI solutions for manufacturing workforces here.

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