Launched in 2024, guardione® pdx is becoming one of onepredict’s core solutions.

While previous guardione® solutions focused on managing individual assets, guardione® pdx stands out by enabling integrated management at the factory and process level. By consolidating scattered data, it allows for more efficient resource management and faster business insights. We spoke with Jaekyung Shin, the Project Manager in charge of guardione® pdx at onepredict, who knows the platform better than anyone.

Q. Could you briefly introduce Jaekyung Shin, who plays a key role as the PM of guardione® pdx?

Hello, I’m Jaekyung Shin, leading the planning and development of guardione® pdx, an AI-based digital asset management platform. I was drawn to the data-driven approach to equipment management and maintenance optimization, which led me to this role. Working closely with our clients, I’m dedicated to developing innovative solutions that solve real-world challenges.

Q. Could you briefly explain what guardione® pdx is for those unfamiliar with it?

guardione® pdx is an AI-powered industrial asset management platform designed for use in manufacturing, power plants, refineries, and chemical facilities.

It’s a next-generation solution that integrates CMMS (Computerized Maintenance Management System) and APM (Asset Performance Management). We developed it for organizations aiming to prevent equipment failures, reduce maintenance costs, and improve both equipment uptime and product quality.
Key features include: ✅ Real-time asset condition monitoring ✅ Predictive maintenance (failure prediction) ✅ Optimized maintenance scheduling.

Q. What led to the development of guardione® pdx, and what is the ultimate goal it aims to achieve?

guardione® pdx was developed to address real pain points in the industrial field. While many companies have adopted APM (Asset Performance Management) systems, we've seen cases where these systems are left unused or become less effective as on-site environments evolve. Even when predictive maintenance results are generated, they often aren’t followed by appropriate maintenance actions.

To address these challenges, guardione® pdx was created. By combining APM with CMMS, it ensures that insights lead directly to maintenance actions.
It also leverages AI to easily generate work orders, and it continues to learn and evolve even as on-site conditions change.
Our ultimate goal is not just analyzing data, but transforming it into operational efficiency and real value in the field.

Q. What aspects did you focus on the most when planning guardione® pdx to achieve these goals?

There were three key areas we focused on when planning guardione® pdx.

First, anomaly detection based on TechCell-derived characteristics. We developed domain-specific indicators that combine domain knowledge and AI technology to precisely analyze equipment data. This allows us to detect even the slightest anomalies and predict operational risks in advance.

Second, classification of operating modes using PLC data. To reflect the various operational conditions of equipment, we designed a function that classifies operating modes based on PLC data. This helps us accurately understand the environment in which the client’s equipment is running and enables us to recommend optimal operation and maintenance strategies.

Lastly, alert-based maintenance integration. We built a system where anomalies trigger real-time alerts that are directly linked to maintenance work orders. This ensures that analytical insights naturally lead to actionable tasks, ultimately maximizing operational efficiency. These three elements were carefully designed to overcome the limitations of existing solutions and to deliver real value on the ground.

Q. What aspects did you focus on the most when planning guardione® pdx to achieve these goals?

A battery manufacturer adopted guardione® pdx to resolve equipment management and maintenance issues in a specific process. The company was facing multiple challenges, such as process downs (PD) caused by equipment degradation, failure to verify effectiveness after maintenance, and a reactive approach to repairs—only taking action after breakdowns occurred.

With guardione® pdx, the company applied AI-based predictive maintenance and TechCell indicator analysis to proactively assess equipment conditions, prevent failures, and validate maintenance effectiveness. As a result, 132 successful maintenance actions were recorded in one year, and downtime per line was reduced by 2 hours, leading to annual cost savings of approximately KRW 9.4 billion.

Q. It sounds like a very promising solution. What are the future upgrade plans for guardione® pdx?

Going forward, guardione® pdx will focus on enhancing its CMMS capabilities and offering a more customized environment tailored to client needs.

We plan to incorporate AI-powered analysis using existing PM (Preventive Maintenance) data, integrating equipment condition and historical maintenance data to automatically prioritize maintenance tasks. This will allow technicians to focus on the most critical equipment and make maintenance scheduling and resource management more efficient.

In addition, we will improve the BI-based user experience by allowing customizable dashboard layouts and highlighting equipment based on priority, depending on field requirements. This will enable us to provide optimized data for each client’s operational environment and goals, supporting more strategic asset management.

guardione® pdx is an industrial AI platform that continues to evolve alongside the ever-changing industrial landscape. We focus on resolving the pain points that matter most to our customers, striving to enhance their operational competitiveness. Stay tuned as the guardione® pdx team grows together with our clients in the field.