The key to a successful predictive analytics deployment is its ability to predict outcomes quickly and accurately. Like all learning models, the accuracy of the result depends on the amount of effort, time, and data that has been invested in training the model.
However, the lead time for achieving a high correlation to outcomes means lost insights and opportunities. Achieving a short lead time to train the model will also mean the accuracy is higher, and the predicted outcomes’ reliability is better. Hence, manufacturing companies are always pursuing to accelerate the lead time to train a predictive model successfully.
The availability of sufficient (relevant) training data, suitable correlation analysis, and adequate training is critical in crashing the lead time. A correlation of 85% is a safe point to start running real-time data.
The training method also impacts the lead time. Most processes across industries aim to achieve the same outcome – improved efficiency through lesser waste or greater asset utilization.
Finding the right quality and quantity of data is a crucial step in training an ML model. Aspects of the model like fidelity, tolerance, and reliability largely depend on this stage. Too little data can result in a high level of approximation. It will also make data selection difficult as the rejection of anomalies and normalization, if required, will become difficult. On the other hand, large volumes of data can help build an accurate and reliable model. However, managing volumes of data will need sophisticated big data and deep learning skills. Moreover, with unstructured data like image or unconstrained text, developing labels or processing information becomes complex, resulting in longer training cycles.
Finding a right balance between the quantity and quality of data can help achieve a healthy correlation with a shorter lead time.
The purview of predictive analytics extends far beyond just maintenance activities. Shorter lead times to achieving a predictive model means more than just a quick transition from a predictive model to a preventive model. It results in the following:
These benefits, combined with the ability to minimize or avoid unplanned downtime, help reduce the overall manufacturing cost.
There is no shortage of choices when picking the right platform for implementing a predictive analytics strategy. Identifying and choosing the right platform is critical. Numerous platforms can train massive data sets before arriving at an optimal model with a healthy regression rate. But spending time on training and equipping a model can be expensive and often is a task with little return.
An ideal platform is one that manufacturers can quickly use to drive results rather than training and tuning. The actual benefit of predictive analytics is not infrastructure or tool setup, but rather the analytics-driven insights to achieve optimal efficiency, reduce unplanned downtime, and minimize wastage. Such models’ development and deployment can be left to capable technology partners who can accelerate the transition towards predictive analytics with shorter lead time.