Predictive Analytics in the Electronics Industry: how data is transformed into failure predictions

Predictive analytics has proven to be increasingly efficient and necessary across various markets, especially in the electronics industry. Through it, organizations can optimize time, avoid material and financial losses, and predict failures based on historical data and machine learning techniques.

In the first article of the series on predictive analytics in the electronics industry, the concept of this approach and its main applications were presented. In this second stage, the focus is on the primary data sources, how this information is collected, and how it is used to generate reliable predictions.

Data in predictive analytics

Data is what brings predictive analytics to life. It allows computers to identify patterns and anticipate future situations. Therefore, the effectiveness of this process depends almost entirely on the quality, consistency, and diversity of the data sources used, ensuring that recorded information is neither incomplete nor inaccurate.

Main data sources

Machines and equipment: data collected directly during the production process, reflecting real-time operational behavior. Examples include machine temperature, operating speed, and energy consumption, enabling the identification of failures and process deviations.

IoT sensors: physical data captured by sensors installed on machines, capable of recording information that often goes unnoticed by operators, such as vibration, noise, and humidity. The goal is to identify small changes, since failures rarely occur abruptly; they usually present early warning signs.

MES (Manufacturing Execution System): a system that connects machines, products, processes, and time. It executes and monitors production almost in real time, making it a valuable data source for capturing granular data, recording events as they occur, and linking technical information to process context.

Quality systems (AOI, SPI, ICT, FCT): responsible for detecting defects and indicating whether a process has failed. These systems identify the type of failure, its location, and rework rates. By enabling root cause analysis, they directly improve process yield.

Maintenance systems: record the history of actual failures, maintenance orders, and replaced components. In predictive analytics, they are essential for estimating time-to-failure, prioritizing interventions, and reducing downtime.

ERP and corporate systems: management systems that connect technical data to financial impacts such as inventory, purchasing, costs, and sales. In predictive analytics, they are widely used for demand forecasting, production planning, and cost reduction.

Engineering and product data: include design information and specifications such as PCB revisions, engineering changes (ECOs), datasheets, and tolerances. These data define the product’s technical limits and directly affect the production process. In practice, analyzing this information enables comparing product versions, anticipating process adjustments, and identifying designs more prone to failure.

Field and after-sales data: represent the real use of the product by customers, including operational logs, reported failures, and usage conditions. These data extend visibility across the entire product life cycle, enabling the prediction of field failures, product design improvements, and reduced warranty costs.

The most significant value of predictive analytics emerges when different data sources are integrated, creating a connected view of process, quality, and maintenance. This integration allows companies to go beyond simple data collection and transform information into operational and strategic decisions. In this context, it is essential to distinguish between preventive and predictive maintenance, which, although often treated as equivalent, have different impacts on operational efficiency and business results.

How does predictive maintenance differ from preventive maintenance?

Preventive maintenance is based on predetermined time intervals, such as every six months, to prevent failures in advance. Although widely used, this approach has significant limitations because it does not account for the equipment’s actual condition.

Main limitations of preventive maintenance:

• Premature replacement of components still in good condition – Parts are replaced based on fixed schedules rather than their actual condition, which may lead to unnecessary replacement of components that are still in good working condition.
• Unnecessary machine downtime – Maintenance activities are performed even when equipment is operating properly, causing avoidable production stoppages.
• Increased operational costs – Frequent scheduled interventions, labor, and early part replacement increase maintenance costs without fully preventing failures.
• Difficulty in preventing unexpected failures – Since equipment condition is not continuously monitored, failures may occur between scheduled maintenance intervals.

Predictive maintenance, on the other hand, is guided by the equipment’s actual condition, using data from multiple sources to anticipate failures and determine the optimal time for intervention.

Benefits of condition-based predictive maintenance:

• Interventions performed at the right time – Interventions are carried out only when equipment condition indicates degradation.
• Reduction of unplanned downtime – Early fault detection helps prevent sudden breakdowns.
• Better planning of resources and teams – Maintenance activities can be scheduled more efficiently.
• Increased equipment lifespan and availability – Reduced unnecessary stress and optimized operation extend equipment life and improve reliability.

In practical terms, by migrating from time-based maintenance to a data-driven approach, the industry stops acting reactively. It begins making more accurate decisions, reducing operational risks and costs over time.

How does failure prediction work?

Failure prediction is the core objective of predictive analytics. Generally, it proceeds in three stages: data collection, analysis of behavior over time, and estimation of the equipment’s remaining useful life (RUL).

Another important aspect is identifying anomalous patterns—behaviors that deviate from a machine’s expected operating pattern. A key point is that not every anomaly immediately results in a failure, but every failure originates from an anomaly.

In this context, machine learning stands out by allowing systems to learn from data rather than relying solely on fixed rules. The main models used in predictive maintenance include:

• Supervised learning: uses historical data of known failures. Models such as regression, Random Forest, Gradient Boosting, and neural networks are applied to predict time to failure and identify probable causes.
• Unsupervised learning: used when there are no labeled failures. Models such as K-Means, Isolation Forest, Autoencoders, and PCA are employed to detect anomalies, cluster operational behaviors, and identify unknown degradation patterns.
• Time series models focus on the evolution of signals over time. Examples include LSTM, ARIMA, and Prophet, which are applied to forecast trends and detect progressive degradation.

Component degradation represents the wear that occurs before functional failure, such as bearing wear or the gradual increase in equipment shutdowns. This degradation is identified when models detect persistent deviations that form patterns over time.

As a result, failures can be detected weeks or even months in advance, enabling more efficient planning and a significant reduction in operational risks.

Business impact

Predictive analytics goes beyond technology or innovation; it represents a strategic management model that directly impacts revenue, operational costs, operational reliability, brand reputation, and even safety. Unexpected failures always result in significant losses.

One of the main benefits is the reduction of downtime, or unplanned stoppages, which may occur due to sudden failures, emergency maintenance, or lack of spare parts. These stoppages can interrupt production for indefinite periods, directly affecting operational results.

Predictive maintenance transforms this scenario by enabling the anticipation of failures through data analysis and multiple data sources, reducing or even eliminating downtime. As a consequence, equipment lifespan is also extended, since operating machines with undetected failures, overheating, or unnecessary interventions tends to accelerate wear.

Conclusion

By connecting data from different stages of the production process and transforming it into actionable information, predictive analytics establishes itself as a key element of modern industry. More than predicting failures, this approach enables more intelligent decisions, more reliable operations, and more efficient resource use, creating a solid foundation for the continuous evolution of electronics manufacturing.

In the electronics industry, companies specialized in manufacturing solutions, such as ASM, play a direct role in integrating machines, systems, and data. Through equipment, software, and digital platforms, these solutions enable the structured collection of information across the entire production process, laying the foundation for advanced predictive analytics and condition-based maintenance applications.