Since the infamous, Piper Alpha Disaster in July 1988, Employee competence assurance has been at the fore of energy production plants, with the Health, Safety and Environment (HSE) Management System, evolving into a regulation which demands that specific competences must be in place. Competence assurance and production processes play a pivotal role across a several industries, in the African energy sector. Competence is the fundamental control that is used in the industry to function in a safe and effective manner.
The quest to identify competence to optimise production methods has always been a complex and time-consuming procedure, often hindered by the limitations of traditional approaches. An expert has put forth Artificial Intelligence (AI) and automation as the panacea, having the potential to improve safety in complex processes.
According to the Electrical and Electronics Engineer, and Certified Maintenance and Reliability Professional, Babayeju Olusile said the integration of these technologies has the potential to revolutionise training and competence assurance practices, leading to improved performance, reduced risks, and increased operational efficiency in the energy sector in Africa.
“The integration of Machine Learning (ML) and AI in business intelligence has brought forth a plethora of trends and opportunities. Analysing large volumes of data, AI algorithms can identify patterns and trends that commonly correlate with successful employee performance. This enables organisations to develop more targeted training programs and personalise learning experiences for individual employees, ultimately improving their competence and efficiency,” he said.
Babayeju admitted that the reach of Artificial Intelligence and Machine Learning Models across the African continent is still limited, however, it’s potential to make in-depth analysis on employee competence using pre-existing data, make it a massive area of interest to the African energy industry.
He asserts that while the implementation of AI and ML in employee competence assurance is still in its early stages in the African energy portfolio, there is significant potential for growth and development.
“AI-powered predictive analytics can help identify potential areas of improvement for employees based on historical data and performance indicators ML techniques are also being employed to automate the assessment and evaluation of employees’ competence; this allows organisations to proactively address skill gaps, optimise training resources, and allocate personnel effectively,” he added.
Babayeju further noted that to ensure safe and efficient procedures across every sector, continuous evaluation of work staff should become a necessity, to constantly weigh their ability to deliver at an optimal level in the workplace.
“Regular assessment and evaluation of employee competence are critical. This may involve conducting written tests, practical exams, simulations, or observations of employee performance. These assessment methods should be designed to measure employees’ understanding, skills, and their ability to apply their knowledge in real-world situations,” Babayeju stated.
Babayeju expressed his optimism at the emerging trends of AI and ML in competence assurance processes within the African energy industry, while noting that the safe integration of these systems is poised to revolutionise competence assessment by fostering adaptability to evolving industry needs and enhancing predictive analytics for workforce planning.
“The future trajectory suggests that leveraging these advancements will empower the African energy industry to establish robust and context-specific competence assurance frameworks, testing requirement to measure employees’ proficiency, job competency requirements and specific collective competencies needed within teams to mitigate operating risks,” Babayeju added.
