Predictive AI-based solutions provide actionable data-driven intelligence to drive real-world impact in industrial applications and increase profit margins by optimizing productivity, improving quality, and reducing costly unplanned downtime and waste.

Current practices for addressing asset health, such as reactive maintenance or preventative scheduled maintenance, fail to prevent unplanned downtime or result in unnecessary maintenance efforts and wasted resources. Leveraging machine learning and AI solutions, such as the ones offered by Predictronics, can address these limitations by providing anomaly detection, fault identification, and prediction of impending asset failures before they occur. This helps responsible personnel determine the optimal maintenance schedule to avoid costly shutdowns. These solutions can also improve product quality by pinpointing process deviations or other issues early in production to avoid scrap, rework, and inspections that are inefficient and less accurate.

The benefits of AI-driven solutions are clear; however, some preparation for these solutions must be made. Additional infrastructure is often needed to connect assets; acquire data; transmit, store, and process that data; and integrate with the chosen solution. Setting up this necessary IoT infrastructure in the early stages, as well as developing a digital transformation strategy, will ensure a quick and efficient processing when adopting these AI-driven solutions, optimizing value and accelerating ROI.

These prior investments can create challenges in terms of the initial cost of machine learning and AI implementation. The cost is especially impactful for small and medium-sized enterprises. Even though the infrastructure required for digitization is becoming increasingly accessible, the price tag associated with these additional needed technologies, such as software, sensors, computing resources, network equipment, and more, can be prohibitive. The cost of the actual software solution, which includes data science and development services, can also impede adoption. However, some mitigation of these software costs can be accomplished. Predictronics specifically uses a template-based approach to reduce the cost of implementation. This approach relies on our experience in predictive solutions for a set of common industrial assets and components. With these templates, we reduce the time needed to configure and deploy a solution for our customers. We also leverage our partner network to recommend affordable options for accompanying technologies and instrumentation.

Cultural barriers and skepticism towards AI-driven solutions can also be a challenge. These barriers can be due to a lack of understanding of the benefits of predictive solutions, a reliance on traditional approaches, or even the lack of an immediately definable ROI. A defined digital transformation strategy, and one that was constructed with input from all stakeholders, can go a long way towards eliminating or avoiding cultural barriers. Starting with a pilot project also helps prove the value of the solution before incrementally adding new assets and applications at the facility. Fostering a greater understanding of these solutions to all in the company is critical to successfully expanding and scaling that solution.

Steps for Preparing Your Business for Predictive Solutions

1. Shifting the culture within an organization can be one of the greatest challenges in achieving digital transformation and, specifically, in shifting current practices from traditional approaches to those that incorporate machine learning and AI for predictive solutions. Obtaining buy-in, from stakeholders and management to IT and personnel on the shop floor, is key.
2. Criticality analysis is essential in order to select the asset and application for which a predictive solution can best be applied. Businesses must choose a solution that provides the most value and generates the most impact.
3. After determining the target assets, the most critical and important failure modes for those assets should be identified, along with any relevant maintenance records for unplanned and planned downtime.
4. Determine what historical data has been collected, if any. Review what data could be available from the asset’s controller and the appropriateness of using it in a predictive solution.
5. Based on the available data and common failure modes, determine what, if any, additional sensors need to be added to the monitored assets.
6. Select a solution that is end-to-end and scalable, as well as one that can provide key information in an understandable and actionable format, ensuring the right actions are taken before costly issues can occur.

Check out part two of our blog miniseries where we take an in-depth dive into choosing the right predictive software for you.