How AI is bridging the skills gap in manufacturing
Dan Wilson Dan Wilson
October 16 6 min

How AI is bridging the skills gap in manufacturing

Three concrete applications that manufacturers can implement today

It’s not an easy time to be in the manufacturing business: rising customer expectations for choice, quality and fast delivery are upending supply chains, which must now adapt in real time to meet rapidly changing customer demands⁠—and manufacturers need to keep up. In parallel, factories are increasingly connected through Industry 4.0 and Industrial Internet of Things (IIoT) solutions such as robotics, remote sensors and digital control systems, which produce seemingly unending new streams of data to be processed and interpreted. But perhaps no challenge is more pressing than the skilled labour shortage: as the baby boomer workforce retires, it’s getting more and more difficult for manufacturers to find experienced workers to replace them.

The labour shortage in manufacturing isn’t a new phenomenon. For years, manufacturers have been reporting difficulty filling open positions. This stems from a complex combination of different economic and demographic factors, not only an aging workforce, but also the rise of the gig economy, personal mobility, changing social trends and ups and downs across national and regional economies. But if the forecasts are correct, filling open positions is only going to get more challenging: by 2025, some 2 million manufacturing jobs are likely to be sitting vacant just in the U.S. alone, according to a report from the Manufacturing Institute and Deloitte.

The talent shortfall is especially dire in manufacturing because manufacturing processes rely on a combination of technical skill and craftsmanship that is difficult to teach, learn and transfer to new products and processes. Furthermore, the ability to share knowledge across skill and experience levels and functional silos in order to solve problems becomes more difficult as turnover increases. It’s clear that manufacturing companies need to find solutions that help enhance the capabilities of newer, less experienced workers and increase efficiency in training and onboarding in order to ensure that these ‘newbies’ have the support and knowledge required to maintain quality and target production output.

New technologies powered by artificial intelligence (AI) make these new solutions possible. A number of different core AI components, including computer vision systems, natural language processing, time series models and operations research models have recently seen drastic improvements with the development of deep learning and are ready to be deployed alone or in innovative combinations. Below we briefly introduce three different AI solutions that manufacturers can implement now to help mitigate the effects of the skilled labour shortage.

Give employees the information they need, quickly and in context

When new workers are starting in their roles, they will need to search for a large amount of information critical to doing their jobs correctly and efficiently. Quality analysts might search for information related to previous incidents similar to the one they are trying to solve, for example, or maintenance technicians may search for knowledge on a particularly vexing repair case. This data is often scattered across various systems and formats, including documents, spreadsheets and, very commonly, the “comments” field in a semi-structured system of record. AI can help employees access this information, and make sense of it, faster.

AI-powered knowledge management tools go beyond traditional “filter and search” capabilities. They ingest data from a wide range of different documents, database records and other combinations of structured and unstructured data, giving employees access to a massive amount of information. When the system is queried, it returns search results, sourced from a variety of systems, that are contextually relevant, allowing quality analysts and compliance specialists—both senior and junior alike—to accelerate the resolution of quality management cases and more accurately identify potential root causes. As analysts use the system, they provide feedback about which recommendations were most effective, and AI uses this information to learn and improve over time, capturing institutional knowledge that is at risk of being lost.

Let AI take over the repetitive, manual paperwork

Despite the increased automation that’s been seen across manufacturing processes, there are still many repetitive, low-value tasks in manufacturing that are largely manual. These take place across the enterprise, but perhaps especially in the back office. AI-powered back-office automation tools create valuable new opportunities for manufacturers to free up employees and redeploy them to other critical skilled areas of the business.

AI makes it possible to scan, interpret and process key elements of documents related to invoice reconciliation processes, bills of lading and other operational activities, saving time, increasing productivity and streamlining processes. As the AI solution takes on the task of reconciling invoices related to shipping documents and contracts, for example, the accounts payable team is relieved of a large amount of previously required manual document-checking work. In turn, the supplier management and treasury functions benefit from improved efficiency and accuracy, with the added ability to focus more on strategic elements of optimizing supplier relations and managing the cash cycle.

Automate more complex inspection tasks

Anomaly detection is also an area where AI and computer vision have the potential to add significant value. Visual inspection and anomaly detection processes often require a high level of experience and craftsmanship when performed by a human. The nature of these repetitive tasks, coupled with the high level of required skill, leads to challenges in staffing these positions.

Manufacturing businesses have increasingly turned to automated visual inspection systems to complement or replace manual visual inspection. However, these systems have fallen short where the inspection is more subjective and requires significant human experience for correct interpretation of quality. Now, advances in computer vision combined with explainability are driving AI inspection solutions that can be confidently adopted in a factory setting. This development has the potential to more broadly reduce dependence on humans for repetitive, manual inspection processes, freeing up quality departments to focus more on root cause analysis and continuous improvement.

Get started with a specific use case

Each of these solutions offers a concrete opportunity to help manufacturers mitigate the effects of a skilled labour shortage. While these opportunities are exciting and tangible, organizing your entire business to effectively tackle this issue is a major challenge in itself. But that’s the beauty of AI: it’s possible to start small and lean, with a specific use case, and scale from there. The shortfall of experienced workers in manufacturing isn’t going away. But businesses that succeed in deploying AI will be well-positioned to bridge the skills gap, optimizing production responsiveness and creating significant business value.

In our next blog post, we will look at some of the other global trends facing manufacturers and explore how AI can help address these challenges. Is there any challenge that interests you the most? Let us know!

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