How Manufacturing Companies Can Use Skills Data to Upskill the Workforce

Digital tools, smart factories, and automation are extensively transforming manufacturing roles. The World Economic Forum report predicts that automation may replace almost 85 million jobs globally by 2025. Yet, it will also help create 97 million new ones.

In the meantime, a Deloitte study has stated that the U.S. could deal with a shortage of skilled manufacturing jobs of up to 2.1 million by 2030, costing $1 trillion. Amidst fast change, traditional instructor-led training often turns inadequate.

Generic, static programs cannot keep up with the real-time demands of the latest industries. On the other hand, skills data helps manufacturers understand precisely which skills are lacking in their workers, enabling real-time development for upskilling.

In this post, let’s look at how manufacturing companies can use skills data to upskill their workforce.

The Shift: From Job Roles to Skills

The manufacturing industry is experiencing a significant evolution. This shift now enables the top manufacturers to focus on particular skill clusters such as "predictive analytics," "PLC programming," or "robotics maintenance," instead of generic job titles such as a "quality inspector," or a "machine operator."

The change is due to the need for operational accuracy, the introduction of AI and machine learning, and the rising complexity.

Here is why the change is essential:

• Agility: Skill-based models allow manufacturers to redeploy workers in alignment with real-time production commitments quickly.
• Innovation: Skilled workers in machine learning, data analysis, and automation can easily optimize smart factories.
• Retention: Clarification of skills leads to more evident progression, enhancing employee engagement and loyalty.
• Safety: Specializing skills training helps workers understand complex systems better, decreasing accidents.

By relating jobs to granular skill sets, manufacturers can ensure the future relevance of their workforces and keep pace with the dynamically changing environment.

What is Skills Data and How Can Manufacturers Use It?

Skills data gathers role-specific, validated insights into employees' soft, digital, and technical capabilities, built from performance data, project delivery, manager input, LMS activity, and assessments. 

Instead of depending on basic job descriptions, it creates a dynamic skills inventory that organizations can continuously update. 

Manufacturers can capitalize on this data in various ways:

• AI-Powered Enrichment: Platforms such as iMocha offer advanced AI to standardize, tag, and map employee skills against established proficiency levels and skills taxonomy. 
• Insights from Diverse Sources: Skills profiles get updated from several combined signals such as manager feedback, learning data, performance metrics, and skills assessments, ensuring accuracy. 
• Role-Skill Alignment: Each job profile is linked to granular skill clusters and capabilities, making it seamless to spot skill gaps and build training pathways. 

Skills intelligence platforms, and AI turn raw data into actionable intelligence. They convert fragments into unified skills graphs, enabling manufacturers to upskill their teams accurately and swiftly.

Check out iMocha Skills Data Enrichment for a more integrated experience, which offers dynamic skill discovery, proficiency mapping, and more. 

5 Practical Ways Skills Data Drives Upskilling

Skills data is not just about finding information on a dashboard. Instead, it is a roadmap that helps with workforce transformation. The following are some ways manufacturers can use skills data for efficient and quick upskilling:

1. Targeted Learning Paths

Skills data gives manufacturers the perspective needed to customize employee development by identifying specific skill sets. Rather than creating a generic training program, the team gets training content that matches an employee's current skill level and future job needs.

This ensures funds spent on training are better used, making employees feel good about learning that applies directly to their work. The result is quicker time-to-production, better performance, and faster upskilling.

2. Cross-Training & Multi Skilling

Skills data can help identify employees who possess complementary skills or partial capabilities, making them an ideal choice for cross-training. It enhances shop floor flexibility, supports job rotation, and builds a workforce capable of moving across various positions as demand shifts.

Multiskilled employees reduce reliance on specific roles, assisting manufacturers in becoming agile and decreasing bottlenecks to keep up production even during workforce disruptions.

3. Tech Adoption Readiness

Introducing digital twins, ERP, and automation systems means the staff must be prepared to adopt technologies quickly. Skills data examines the workforce's readiness by highlighting those with foundational tech skills and those needing further training.

This helps L&D teams prepare the staff for the rollout. This results in fewer errors during implementation, decreased change resistance, and effortless tech adoption.

4. Compliance & Safety Certifications

Manufacturers must ensure that employees meet standards and requirements in a hazardous environment. Skills data tracks upcoming expirations, highlights gaps, and monitors certifications like ISO standards, hazardous material handling, and machine safety.

Only certified workers can operate high-risk equipment, decreasing compliance infringement risks and potential accidents. Automating the process will cut down administrative time and guarantee safety.

5. Succession Planning

Skill data lets you look at the growth and development of an individual concerning potential succession candidates, resulting in pre-emptive succession planning. As the skill development is tracked over time, they can recognize high-performing employees capable of being promoted into leadership positions.

Based on real, observed skill progression, managers must avoid the traditional method of selecting candidates. This further feeds into the employee's motivation as they see career prospects, increasing retention and engagement on the floor.

Best Practices to Get Started

Here are some best practices that manufacturers can adopt to ensure effortless upskilling through skills data:

• Build or Adopt a Manufacturing-specific Skills Taxonomy: Create or implement a skills taxonomy for manufacturing, including soft, digital, and technical skills relevant to the operation. This would allow a common language for defining, tracking, and developing skills in the workforce.
• Start with Critical Lines or Plants to Pilot: Implement skills data initiatives in high-impact areas, such as lines with bottlenecks, or plants undergoing technological upgrades. Such a focused beginning allows fast wins, measurable ROI, and easy scalability.
• Validate Skills Through Multiple Channels: Use AI-based skills analysis, formal testing, peer reviews, and manager feedback. A cross-validated approach will ensure skills are defined correctly.
• Align L&D, Operations, and HR on Goals and Metrics: Agree with all key stakeholders on what constitutes success measurements, such as certification completion rates, reduced downtime, or improved productivity, so that the skill initiatives directly contribute towards business outcomes.

Conclusion

Though manufacturing is being reshaped by digital tools, smart factories, and automation, skills data is becoming a compass organizations can use to build a future-ready workforce.

Manufacturers can upskill by mapping current skills, discovering gaps, and aligning real-time needs with training. Begin with an extensive skills gap audit, and it will reveal the areas to focus on.

FAQs

How is skills data different from job performance data in manufacturing?

Skills data is related to skills and potential, whereas job performance refers to the results of past activities. Skills data helps upskill for the future, while performance data looks at the results and performance-based metrics that occurred in the past.

What types of skills are most commonly mapped in manufacturing?

The most commonly mapped skills in a manufacturing environment include technical skills such as PLC programming, robotics maintenance, and quality control; digital skills, mainly data analytics; and soft skills like problem-solving, teamwork, and adaptability.

Which industries use skills intelligence the most?

Skills intelligence is found among IT, ITES, banks, and manufacturing industries that must continually map skills considering the ever-changing technologies and compliance requirements for agility, innovation, and workforce readiness.