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How will machine learning change the face of EHSQ?

(Image credit: Image source: Shutterstock/Sarah Holmlund)

Industries far and wide are realising the importance of machine learning and analytics, with giants including Google and Amazon taking the world by storm with ground-breaking new interfaces such as Google’s DeepMind.

Even though they may know what it is, many people will experience some form of machine learning every day. Whether it’s Netflix suggesting what show to watch next, or Spotify choosing your next playlist, machine learning is playing an important role in the way we live our lives.

But while the aforementioned examples illustrate how machine learning is playing a pivotal way in the way we interact as consumers, companies are also now seeing the how processes can be transformed. For example, Google’s open source machine learning software platform, TensorFlow, is playing a vital role in automating management of customer services.

As machine learning technology offerings become more advanced, companies will have to adapt faster than ever to cater towards this emerging paradigm. The Environmental, Health, Safety and Quality (EHSQ) sector is no exception.

Of course, better training, processes and controls lead to a better overall health and safety strategy. But there are ways in which technology, and the way it leverages data, can help companies and employees where human efforts aren’t sufficient.

Clearly, there are some aspects of occupational health and safety falling short. 317 million accidents occur in the workplace annually, and 137 workers in the UK have died this year alone at work. This is why disruptive tech like AI and machine learning is needed in industries like this, so that companies can better protect their employees and customers.

Because EHSQ encompasses a vast array of different sectors and industries, the applications for predictive analytics and machine learning are widespread. We’re already seeing the technology being implemented into current business models, especially within industries that have a statistical backbone, such as Industrial Hygiene for example.

Personnel within this sector are already using predictive models like Bayesian Decision Analysis, which helps industrial hygienists incorporate professional judgement into the statistical analysis and interpretation of EHS data. However, this isn’t quite at the level that uses machine learning in the way that Google’s DeepMind does. This form of predictive analytics is quantitative, but it isn’t predictive in its most complete manifestation. But watch out, the smart machines are coming!

And they’re coming across every industry, but particularly in EHSQ, where the main business goals lie in reducing risk, aiding the environment and preventing loss of life. Undoubtedly, there is huge value in the implementation of machine learning, which will propel the industry into the new paradigm of total organisational risk awareness, enabling companies to seamlessly monitor and manage data in real-time.

Moving beyond 'observation-based' processes

Initially at least, this will manifest itself in the key area of decision-making processes being performed by EHSQ professionals. Take a construction safety manager for example, who’s in charge of 100+ builders on-site. Instead of taking a great deal of time to make decisions based on data collected by the individual, they can implement predictive analytics to assess risks for them.

This means personnel will be able to move beyond ‘observation-based’ processes, giving them greater scope to make more informed decisions revolving around practices that put workers at risk.

With the aid of predictive analytics, safety professionals will be able to easily isolate variables to identify which options would result in better health and safety practices. This is known in the industry as machine-augmented decision making, and when we see it implemented in a widespread capacity, we expect to see a reduction in incidents and injuries.

The hype around AI and machine learning is real, but there is an elephant in the room that’s proving hard to tame amongst commentators in the sector - jobs. In the UK alone, it’s predicted that new technologies like AI and machine learning will put 35 per cent of jobs at risk. But is this an accurate forecast? Will multiple jobs in the EHSQ space really be lost due to machine learning adoption and practices?

We don’t think so. Rather, we believe that automating work processes will simply mean each member of a team will be more effective at their job. At present, EHSQ supervisors spend a lot of time doing manual tasks. A few years down the line they’ll be spending less time on these administrative duties and instead carrying out more important assignments such as observing workers and refining policies. In other words, they’ll have more time to ‘log’ instead of ‘lag’.

Furthermore, the technology offerings will be providing more complex data than a single person can process. Thanks to machine-augmented decision making, an EHSQ supervisor is far more aware of what is going on around them. This won’t diminish the size of a team; rather it will expand its scope.

‘Deep learning’, machine learning’s ‘cousin’, is another branch of artificial intelligence that may well will see huge evolution in the near future. It could be used to make sense of large volumes of data almost instantaneously, and with the ability to identify images and recognise speech, this technology will likely be another huge leap in the way we conduct business.

Machine learning is a game changer - that much is certain. It is and will continue to transform the way EHSQ professionals conduct their day-to-day work, while providing streamlined work processes, better flexibility and more effective business models that are set to transform safety and quality control in the workplace.

It’s not a question of ‘if’, but a question of ‘when’. In five years’ time, we believe we will see predictive modelling across all EHSQ practices, allowing users to draw on software powered by machine learning to fundamentality improve decision making and data analysis.

Pam Bobbitt, Director of Channels and Product Marketing, Cority
Image source: Shutterstock/Sarah Holmlund

Pam Bobbitt
With a background in chemistry, Pam Bobbitt is Director of Channels and Product Marketing at Cority where she is in charge of the expansion of Cority's partner program.