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.

The checklist of actions required for a faster TAT of training a predictive model is nothing new. It is as old as predictive modeling itself. However, business dynamics and project milestone pressures often can result in ignoring the basics. Ironically, this leads to longer lead times, making overall schedule adherence more of a challenge. The following is a critical checklist that can help ensure a faster training of a predictive model using an optimal data set.

Defining a clear objective is a critical step in the process of building a predictive analysis model. A well-defined problem statement can define the data required for testing, which can help save time in the successive stages. A vague or constantly fluctuating problem statement, on the other hand, can blow things out of proportion by leading to gathering irrelevant data. For instance, if the problem to solve is wastage due to in-line geometric deviations, factors not influencing geometric deviations will add complexity and training duration to the model. This way, the predictive analytics will be focused on the problem it is intended to address and can be trained with the right data. The nature of the problem will influence other aspects like the training model, accuracy or tolerance, data structure, and need for normalization.

Once the problem statement has been defined, the subsequent step involves picking out appropriate data points. The benefit of a well-defined problem statement can be harvested in this stage. As discussed earlier, the data fields that must be included and those that should not clog the model will define the TAT for training the model.

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 source of the data is as important as the data itself. It plays an essential role in influencing the fidelity and frequency of the data. If the objective is to develop a predictive model for a particular objective in diverse environments, then the data source should also be diverse. This ensures a deliberately random data set. However, if the scope is very particular and is relevant only to a constrained environment, it is sufficient to choose a source that gives similar data. Respect for privacy when the data set contains information that makes any individual identifiable is critical. The use of such data is governed by different regulatory guidelines and laws in different countries.

Normally, three types of data are available for modeling: demographic, behavioral, and psychographic. While these diverse types of data have to be standardized, structured, and normalized, care must be taken to ensure that the process does not lessen its intelligence. Although this can be one of the most time-consuming stages in the process, a combination of technology with relevant data can help quickly arrive at the optimum level of preparedness.

Identifying the right ML algorithm can go a long way in achieving a shorter lead time. Selecting the right algorithm will largely depend on the problem the model aims to solve. For instance, if the model has to predict a binary outcome – either a “yes” or a “no,” then a binary classification model would suffice. However, for predictions to questions like “how many units of a particular product will be rejected as waste?”, the regression model can be more appropriate. These are examples of supervised learning models. Similarly, unsupervised models based on cluster analysis and association study can be used to train models that group or categorize outcomes.

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:

- Minimizes the amount of data needed to implement
- Accelerates turnaround time while minimizing waste
- Enables manufacturers to align activities with demand forecasts
- Improves and supports decision making
- Enhances personnel and equipment safety across the factory floor

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.