Enterprises are looking for a technology solution to deliver product or services to its customers in the most predictable and cost-effective way without compromising on the overall quality goals and customer experience. Predictable outcome and costs are directly linked to how assets and operations are managed. The term “asset” refers to any “thing” that’s capable of generating data on its own using the sensors or with externally mounted instrumentation.
Across all Industries, whether it is manufacturing, energy, transportation or health care, there are assets that need to be managed smarter. These assets could be a robotic arm in manufacturing, a windmill in energy, a train in transportation or an MRI scanner in Healthcare. Availability and performance of these assets are not only just the need but also a business imperative.
Advances in the Information Technology world – Machine Learning, Analytics, Computing and Storage – has made a strong case for using the Operation Technology data to enable use cases such as remaining useful life, prevent equipment failure, reduce maintenance costs, degrading performance indicators and asset safety. There is the operational need for preventive actions to ensure uptime, contain further losses from degrading asset performance and cascading issues in downstream processes.
Machine learning algorithms use pre-existing data gathered from the assets and blend it with an environment and business data to create a theoretical model. There are several steps involved in this process of preparing the data to get the theoretical model which enables insights – OSEMN (read as awesome) which means obtain, scrub, explore, model and interpreting. Once the theoretical model is created, it needs to tested through unseen data to evaluate the model qualitatively and determine the accuracy (or precision) of the model before it is applied in the real use case. Precision is a really important aspect to remove any bias from the model and use it as a repeatable model.
Some of the commonly used algorithms in Predictive Analytics are:
Statistical modeling for estimating the relationship between variables. It is used in predicting output variable using cause-effect relationship with input variable
Predicting event based on the decision tree. This model is about the likelihood of a specific event.
Time Series Forecasting:
It comprises methods for analyzing time series data to predict future time event given the past history
Similar objects are grouped in clusters so that observations and finding can be associated mathematically
Association rule mining:
It’s application of “if and then” pattern for data occurring together
These algorithms are frequently applied in predictive analytics use cases and used in augmenting expert judgment and create a strong case against the biased manual decision making.
Predictive Analytics is the key to achieving higher operational efficiency and jump start your IoT journey towards data-driven outcomes!