Using AI to manage Time & Attendance
Author: Johan Steyn
Business enterprises are under constant pressure to get more out of fewer people. Managing time and attendance (T&A) in a smart and predictive way is key to future business success. OQLIS has shown that this topic can be controlled by utilising dashboards, and predictive data analysis, empowering business leaders to manage their teams more efficiently.
Maintaining an accurate and timely payroll through T&A management is essential to the performance of any business, as is monitoring staff presence and output. Because of its benefits in terms of precision, productivity, and security, the use of technology to manage T&A has become more widespread. Using these tools, businesses can increase productivity, decrease waste, and monitor and coordinate employee actions in novel ways.
I met with the co-founders of OQLIS, Shawn Winterburn and Andrew Bosma, to hear more about the platform they have created, and how it is different from other platforms in the market.
“Time and attendance is traditionally about when workers clock in and clock out. It is a binary transaction in terms of how the organisation operates.” Shawn tells me their platform enables business owners to have data points to proactively manage and improve employee performance.
It seems that T&A dashboards typically provide a backwards-looking, historical kind of view. I was wondering if the OQLIS platform offers a view into the future through predictive analytics.
Andrew told me that many of their clients are in the mining industry. “Automating T&A and having predictive abilities are key in this industry. From a health and safety perspective, it is imperative to know how many mine workers have gone down a shaft and how many have safely returned. We have created a visual layer overlaying the T&A data to track the movement of miners.” He also told me that the OQLIS platform, based on historical and real-time T&A data is used to predict demand and forecast resource allocation.
Staff churn and absenteeism is a problem for many organisations. Shawn elaborates that they use artificial intelligence (AI) and machine learning modules to predict such trends. “There are many factors that could change an employee’s behaviour. We can map these data points to predict the possibility of workers resigning.”
Having an accurate view of staff fatigue or potential mental problems is an important part of avoiding absenteeism. “Our platform can help to predict these issues in real-time as a tool for HR departments. It can be used, for instance, in creating better incentive schemes or leave-orientated initiatives. Especially for organisations with large staff numbers, it can be a wonderful way to proactively pinpoint those that are in need of help.”
Using T&A data responsibly
Andrew highlights the importance of using employee data in a responsible way. “AI is objective around the information it has to work with. We need to ensure that humans do not introduce bias in the data that will cause inaccurate predictions and especially that sensitive data like mental health issues are handled correctly.”
He tells me that their platform enables managers to have an objective view of their people. “It is better to use data and start making assumptions around the characteristics that are driving certain behaviours. One can definitely use AI to know which people you should be talking to within the organisation who are the most at risk.”
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Rated as one of the top 50 global voices on AI by Swiss Cognitive, Prof. Johan Steyn is on the faculty of Woxsen University, a research fellow with Stellenbosch University and the founder of AIforBusiness.net.