Adopting new and emerging technologies is top of mind for many manufacturers looking to enhance front-line worker safety and efficiency. To achieve this goal, some organizations are turning to skills management software to catalog and organize the skills and capabilities of individual workers.
In the past, standalone skills management systems were sufficient because turnover was infrequent and often times line supervisors knew the capabilities of everyone on their team. This greatly increased the likelihood that training, reskilling and upskilling could happen either in a one-size-fits-all approach or through a purely subjective or anecdotal process.
However, today, a different situation exists.
There is a significant labor shortage in manufacturing, and it’s projected to continue. According to the National Association of Manufacturers, without making changes to the skills composition of the workforce, manufacturers could leave up to 2.1 million jobs unfilled between 2020 and 2030.
A different environment and workforce equal different needs, including more training, better onboarding and personalized work instructions to help novice workers become proficient faster. As a result, manufacturers must seriously consider adapting their hiring, onboarding, workflow learning and training processes to support a future workforce in manufacturing.
Using a Skills-based Approach To Training and On-the-Job Guidance
According to a recent survey by McKinsey, companies reported that tracking and validating skills and competencies was their top talent challenge.
When executed correctly, skills tracking can improve safety, productivity and worker performance by helping match the right people with the right tasks. Needs analyses and skills assessments are commonly used methods to evaluate training needs, but the increase in workforce variability, absenteeism and turnover call for a nuanced solution.
Manufacturers are increasingly turning to artificial intelligence (AI)-based software solutions to help digitally track and manage skills and connect them with work execution.
Learning management systems (LMSs) or standalone skills tracking software solutions that attempt to automate skills tracking fall short of meeting the needs of today’s manufacturers because they do not connect the “skills that workers know” with the “skills that are required of them.”
An integrated, closed-loop skills management system that combines skills tracking capabilities with AI-based technology and on-the-job digital guidance is critical to successfully navigating this era of high workforce turnover and absenteeism.
Personalized performance support and guidance can offer a myriad of benefits, including help with compliance and safety, ensuring quality and reducing defects and waste, increasing onboarding time and improving training effectiveness.
More specifically, real-time workforce intelligence provides insights into training, guidance and performance support needs and offers actionable insights into work process improvements.
Rethinking Legacy Training and Skills Management Systems
Every worker learns and approaches problems differently. So why not use the technology that recognizes and adapts to those differences to your advantage?
This combination of smart digital technology can also leverage your existing training materials, such as instructional videos, written instructions or access to remote experts to deliver personalized guidance for the worker to perform at their best. In addition, these tools intelligently work together to help you assign workers to procedures based on required skill levels.
AI-based analytics can help organizations better understand their workforce and make informed workforce development decisions. Front-line work is generally data sparse with most improvement activities based on systematic, time-in-motion studies that must catch up with the rapidly changing manufacturing workforce. AI-based systems can individualize information about workers based on previous training and data-driven performance insights.
Imagine if we can teach someone in the context of doing their work. We could see an increase in productivity as they constantly evolve their learnings. With these tools, managers learn about workers’ existing skills, build a rationale for specific roles, resources and certification support, and make clear recommendations based on demands.
The variability of the workforce, both skilled and novice, proves that there’s not a one-size-fits-all approach to troubleshooting and performance support.
Workers connected and empowered with digital technology can discover and nurture diverse skills based on their unique competencies and experience. As a result, they can earn greater responsibility and independence, which increases confidence and job satisfaction — ultimately improving employee retention and slowing the revolving door of continual recruiting and training.
