Machine Learning: Can AI change Oil and Gas?

artificial-intelligence

There is a lot of buzz around Machine Learning (the most implemented type of Artificial Intelligence) these days and its applications. The RigBasket technology team thought of offering a quick explanation of what it means and how it could help your business.

To begin, let’s answer whether machine learning really works.

In order for us to understand how Machine Learning can help solve inventory management problems, we first need to understand what it is and how it works. To most people, Machine Learning is a black box where you throw something in, some advanced calculations are made and the computer predicts the outcome or the future.

However, it is not that simple. Machine Learning has some deep underlying statistical roots and no model will every be 100 % accurate when predicting outcomes.

Is that a problem? Well the real question is are you ever 100% certain of any outcome?

In the world of statistics we use what are called confidence intervals to highlight the level of confidence we have in a particular outcome. In businesses, most of our decisions are based on a level of confidence that most executives derive from experience. No one is ever 100% sure regarding any outcome. If that were the case, then we could make the world a perfect place. Unfortunately, statistical anomalies are a part of our day to day lives whether we like them or not.

The next question you probably have is how exactly does it work?

In the most simple terms, Machine Learning takes a data set and using some algorithm to predict or forecast based on historical data, what the outcome will be in the future. Now the algorithm used might not always be the most appropriate. That’s why the first part of Machine Learning focuses on testing the data set against the algorithm to see how well it can predict some historical data. The test would be conducted on multiple portions of the test data set to determine the feasibility of the algorithm being used. Based on the success of the tests the algorithm is given a score to determine how well it can predict the data. The higher the score the better the algorithm will predict the model.

Now a point worth mentioning is that just because the algorithm fits the data set well today doesn’t mean it’ll make good predictions tomorrow.

In fact as more and more data is acquired the algorithm’s predictions might start getting worse. That’s why the algorithm would have to be tested periodically to make sure the correct one is being used for the application.

Why do we care about this at RigBasket?

Inventory management is a problem that has relied heavily on historical data and intuition. In industries with demand uncertainties, organizations often overstock or run out of parts as they fail to understand the correlation between external factors and demand. Where Machine Learning can help organizations is in understanding the relationship between different factors and how that will cause demand for their products to change. When an appropriate model is selected, it will provide the organization a recommendation regarding the amount of inventory to carry based on historical data and correlations with different external factors. So in a way if oil prices fall below $50/barrel, Machine Learning can help oil and gas organizations predict what level of inventory to carry the following year based on the oil price trend. It can also help automotive industries understand how demand for cars might go up and how many order to expect for the upcoming year.

Of course these models could be quite complex based on the number of factors that can affect the outcome however, once the appropriate model is chosen, the predictions made will be fairly close to the real outcome. As mentioned before, it is important to understand that the model might have to be updated periodically to make sure it is still making accurate predictions. It might even have to be completely changed. Say if tomorrow alternate energy became common across all households, the oil price drop or rise would affect oil and gas organizations demand a lot different to today.

Conclusion

While machine learning certainly doesn’t solve all our problems, it can be extremely useful in executive decision making. Using pure statistical data organizations can make more accurate predictions on demand and hence inventory. No longer would organizations have to use fuzzy forecasts to make demand predictions, correlation models will significantly help them understand the inventory requirements.

RigBasket’s mission is to make the lives of professionals easy and Machine Learning allows us to take major strides in that direction.

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