The industrial automation sector is at a turning point. Digital technologies and automation use-cases have heavily disrupted the traditional job roles and skillsets across the industrial automation sector.
For example, consider the role of a Testing Engineer, the cornerstone in any industrial value chain. With the rise of Robotic Process Automation, Digital Twin, IoT, & Telematics, this role is increasingly under threat of disruption.
Industry analysts estimate over 35% of jobs in the industrial automation at immediate threat of disruption. If the automation trend continues, which it is very well expected to, by 2030, another 30% of jobs across industrial value chain will be experiencing turbulent technology shifts rendering a significant workforce with obsolete talents & skills.
Re/Upskilling Powered by LMS: Benefits & Use Cases
To mitigate the effects of disruption, enterprises across sectors are resorting to re/upskilling their workforce with varying degrees of success.
In the industrial automation sector, too, Draup’s analysts have noticed this trend in action. But what stands out more in the COVID-era, is the resurgent use of LMS-based reskilling strategies.
Of the close to USD 300 million spent on reskilling initiatives across top enterprises, LMS-based modules are increasingly gaining a significant portion.
A few key benefits reported of leveraging LMS in the re/upskilling journey include:
- Less in-class training time which means more productivity at work
- HR can track who has completed which training through progress reports
- Performance can be mapped with training reports to generate insights on skill development
- LMS ensures consistent and customized training for different groups in the organization
- E-learning platforms enable employees to take training sessions anywhere and anytime
So this should mean that the enterprise learning problem has been solved, right?
Unfortunately, surveys by talent management pundits have repeatedly highlighted the failure of LMS to work as advertised. Some consultants have even gone so far as to mark LMS-based methodologies as wholly inefficient or low ROI.
This low rate of success has enterprises pondering over their re/upskilling strategies.
Despite thousands and sometimes millions of dollars poured into the latest, cutting-edge learning management systems(LMS), the results are nowhere near where they should be. Gamified talent management protocols, too, seem to have failed.
This is exactly the problem statement we explore in this whitepaper.
We have identified that progress in implementing successful career transitions are stalling because enterprises fail miserable with their LMS deployment model.
A Finetuned LMS deployment model powered by Requirements Analysis
Draup realized that most LMS implementation strategies followed by enterprises were setting themselves up for failure. The biggest roadblock was that companies were struggling to identify the highly disrupted job roles and map them with the relevant new-age skills to design end-to-end learning journeys.
In other words, they were not capable to performing requirements analysis at the role level.
Identifying these requirements include answering questions such as:
- Who are getting disrupted due to enterprise digitalization?
- How to train job-roles under disruption to address new-age skill gap?
- How to map the roles under disruption with the in-demand skillsets?
- What are the relevant learning modules to integrate in LMS?
- How to build custom career paths in LMS?
Identifying the job roles for training and identifying their skills gaps is one of the key components for mapping out the organization’s requirements and successfully implementing LMS.
Requirements Analysis Framework in Action
With the problem statement in hand, Draup analysts set out to solve the LMS puzzle in the industrial automation sector.
We mapped the use-cases with relevant Industrial Automation value chain workloads to identify the several job roles across Industrial Automation value chain that are getting disrupted. This step of job roles identification is the first in a four-step strategy that will help talent managers significantly cut down on re/upskilling costs and speed up the entire process.
The role that we narrowed down for this analysis is that of the Quality Assurance Engineer in the ‘Quality Assurance’ value chain. With the rise of AI-based cognitive automation & test procedure automation, this role is increasingly under threat of disruption.
The next step is Peer Benchmarking, where we identify new-age, in-demand, digital roles by comparing the organization’s talent with that of its peers.
For industrial automation, this includes analyzing high-demand job clusters in R&D, Manufacturing, Software, Supply chain, Automation, & Cybersecurity across peer companies.
Once a suitable mapping has been done to identify job roles in these clusters, analysts can then use the Reskilling Propensity Index to chart out the ideal end job role from our starting point of Quality Assurance Engineer.
Reskilling Propensity Index measures the feasibility of career transition from one role to another role. The index is built by analyzing some key attributes that include: Technical skills, Functional Skills, Soft skills, Historic Career transitions and Compensation.
In our example of Quality Assurance Engineer, our analysis reveals that the job role with the highest RPI is that of Automation Control Engineer (score of 6.8).
Now that we’ve identified the starting point and the desired final job role, it’s time to perform an exhaustive skills gap analysis and further flesh out a strategy to bridge this gap in the most efficient way possible.
The diagram below explains this comprehensive strategy.
Reskill To Stay Relevant
The above framework is an action map that can be replicated across industries. Here, we have developed a similar model with resounding success rates for the BFS industry.
Evidently, implementing LMS to extract maximum value is easier said than done. The reason that over 30% of organizations feel discontent with LMS is due to their improper implementation strategy.
By leveraging Draup for Talent’s exhaustive toolkit, talent managers can identify in-demand skills, zero-in on job roles that are at threat of disruption, map out a clear reskilling journey, all the while keeping diversity & inclusivity in check.