Machine learning has proved its worth in businesses of all types. The convenience of data and predictive analysis is unmatched and it does not take much time for business owners to fall in love with machine learning. While ML has established itself as a trustworthy sector in some sectors, it is comparatively new in others.
The benefits of machine learning are the same but it works slightly differently in every sector because of the type of data (input) and the type of expectation (output). This variation is what determines the success of ML application in different sector. The environmental, health, safety, and quality (EHSQ) sector is considerably a large division which is diverse in its own way with some common points.
A Comparison with the IT Industry
The question that arises is whether machine learning will be as useful in EHSQ as it has been in IT sector. IT sector and machine learning can be considered two sides of the same coin where implementing ML was easy as the industry was comparatively new and the approaches to work were not deep rooted. Another reason for success was the use of computers which allowed easy implementation and better ways of reaping benefit.
The EHSQ sector has been relying on traditional approaches to work for a very long time. The first challenge of implementing machine learning will be upgrade employees and infrastructure in order to get started. The other challenges will be to transition and establish trust which will happen gradually. The problem here is that no one really wants to wait to assess the success of a solution as everyone want quick results and thus, companies employing machine learning might not be patient enough.
An Overview of the Sector
Machine learning can be extremely useful and successful in the EHSQ sector. The dominant reason is that the sector is large and thus data from different sources make a large pool which is useless for human observation but extremely functional when processed through machine learning. Irrespective of how capable an employee is, he or she cannot glance through as much data as an ML system can and this acts as bonus.
The EHSQ sector can be broadly classified under the following:
- Risk Management
- Accident Management and Investigation
- Environment Management
- Compliance and Auditing
If you have a basic understanding of machine learning, a brief look at these categories will assure you that machine learning can help in every aspect and we will discuss that with examples.
The first aspect that is risk management involves analyzing the infrastructure, workforce, compliance, environmental concerns, and a lot of other involved factors to decipher the risk. Without a machine learning solution, the chances of right risk assessment are very less because a lot of things need to be assessed. A machine learning solution or to say an EHSQ management solution can provide the right platform to acquire and analyze data that will provide valuable insights.
The second categorization is accident management and investigation. This involves studying the causes of previous accidents, understanding the reasons that can cause accidents, ensuring that preventive measures are up-to-date, etc. With machine learning, you can easily use algorithms to get analysis on measures that can be used to prevent accidents and also investigate causes for unfortunate accidents.
The third point pertains to environment management. For effective management, you would want to use templates or other mediums for securing information, storing it, and even retrieving it for variety of purposes. Machine learning can help you manage things easily which in turn can increase productivity of the employees and also increase accuracy in the work that they are doing.
The last aspect is HSE compliance and auditing. Machine learning can help you comply to regulatory bodies’ protocols by continuously reminding you and also checking the list against what you are complying to and what you are not complying to.
With the benefits that machine learning has to offer, it would be interesting to see how it paves way for itself in the EHSQ sector. If you have some insights to offer, please share your knowledge in the comments section.
Author Bio: Author of this article works for a software application development company, providing custom software solutions.