Every manufacturing asset—from machines and systems to sensors and software—generates data. According to recent research, industrial enterprises worldwide already create more than 1.9 ZB of data per year and are on track to produce 4.4 ZB by 2030.
The challenge is making big data actionable. As noted by a Dun and Bradstreet survey, just 36% of manufacturers say they can make informed business decisions with their existing data.
Manufacturing data analytics helps bridge the gap between raw data and actionable insight. Analytics frameworks can identify immediate operational concerns, track emerging trends and provide recommendations to optimize production line performance. Analytics are essential to deliver operational excellence and stay competitive across evolving industrial markets.
What is manufacturing data analytics?
Manufacturing data analytics is the practice of using data to evaluate, predict and optimize manufacturing performance. Analytics isn’t restricted to production processes; it also applies across maintenance, quality control, supply chain and technology operations.
In practice, analytics helps companies gain a deeper understanding of how assets act and interact across the organization. Consider a manufacturer seeing a sharp increase in failed quality control for a highly specialized component. Over the last six months, the number of components failing quality checks has risen fivefold. Cursory analysis of the issue shows no consistent failure point; issues appear random and unconnected.
Deeper data analysis, however, suggests that an intermittent fault in assembly line systems is the root cause. Further investigation shows that this fault is becoming progressively worse over time. Equipped with this information, teams can take targeted action to resolve the problem and reduce the need for rework.
Types of manufacturing data analytics
There are four common types of manufacturing data analytics: descriptive, diagnostic, predictive and prescriptive. Used together, these analytic types help companies understand what’s happening, why it’s happening, what is likely to happen next and what action to take.
- Descriptive analytics: Descriptive analytics help companies understand what’s happening; they are a description of current or historical events. Here’s an example. A packing machine has been experiencing unexpected downtime. Descriptive analytics evaluates operations and provides a description: On average, every two days, the machine receives an invalid input, which causes it to fail until being restarted by a technician. While most manufacturers have access to descriptive analytics, many stop there. This effectively leaves them flying blind. They know what’s happening, but they don’t know why, they don’t know what comes next and they’re not sure how to solve the problem.
- Diagnostic analytics: Diagnostic analytics dig deeper to uncover why events are happening. In the case of our packing machine, diagnostic analysis reveals an issue with programmable logic controller (PLC) instructions that triggers this fault under specific circumstances.
- Predictive analytics: Predictive analytics evaluate potential outcomes and how likely they are to occur. Doing so requires access to both current and historical data, which allows manufacturing analytics software to evaluate multiple factors simultaneously. Conducting a predictive analysis indicates that the same failure type will persist and will likely become more frequent over time. In addition, the continual restarting of the packing machine will negatively impact its remaining useful life (RUL).
- Prescriptive analytics: Prescriptive analytics help identify what actions to take; they prescribe a course of treatment that should solve the problem. In the example above, this may be a reprogramming of the PLC or it may be a replacement if the device is out of date or out of support.
Key data sources in manufacturing analytics
Effective analytics depends on data from multiple sources across equipment, maintenance and production systems. While single-source data offers some insight into machine operations and system performance, it provides limited value. This is because single data sources have a narrow scope: Data collected from an electrical subsystem can tell teams exactly what’s happening with power connections and voltage changes, but if the cause of the issues lies outside the system itself, the trail goes cold.
By using multiple sources, manufacturers are better equipped to track, analyze and manage key trends. Common sources include:
- Equipment and sensor data: Equipment itself is a source of data. PLCs connected to computerized maintenance management systems (CMMS) and enterprise asset management (EAM) solutions, provide near real-time updates about machine condition and performance. Connected IIoT sensors, meanwhile, such as those designed to measure temperature, vibration, pressure and friction, can help companies spot potential problems before they occur.
- Maintenance and reliability data: Historic data, such as maintenance records and reliability evaluations, provide context for current operations. For example, machines that are not serviced regularly are more prone to fail without warning.
- Production and throughput data: Workload data offers additional insight. If equipment runs continuously without scheduled breaks for cleaning or maintenance, its failure rate increases. This may be tied to increasing production targets or throughput expectations that see companies adopting a more reactive approach to breakdown maintenance.
- Quality inspection and test data: Just because machines don’t break down, this doesn’t mean they’re working as intended. Product quality and test data provide key information about equipment output. For example, if regular testing shows that assets are meeting production targets but 1 out of every 10 components fail quality control inspections, it may be worth taking machines offline temporarily for a full inspection.
- Inventory and supply chain data: Larger-scale trends also play a role in manufacturing analytics. If supply chain issues leave companies without necessary materials, even high-performing machines won’t reach output goals. And if MRO asset management strategies are limited (or non-existent), equipment repairs may require additional time to source, ship and receive essential components.
How manufacturing analytics improves operational performance
Analytics helps companies connect the dots: If X occurs, Y is the likely result, while Z is possible. Factors A, B and C influence the probability and repeatability of the event. This pattern recognition offers multiple benefits, such as:
- Improved efficiency and throughput
- Reduced downtime and variability
- Better resource utilization and more predictable operations
- Faster, more informed decision-making
The role of data analytics in maintenance and reliability
Higher uptime directly supports production performance. Reduced downtime, meanwhile, means less effort and fewer resources spent on reactive maintenance. Advanced data analytics enables both. Equipped with timely and accurate data, companies can:
- Identify patterns and trends: Patterns and trends provide visibility into what’s already happened and help companies predict what happens next. Equipped with this information, maintenance teams can take action to reduce unplanned downtime.
- Support predictive and condition-based maintenance: Real-time condition analysis enables condition-based maintenance. For example, if sudden temperature spikes are detected, teams can proactively take equipment offline to solve the issue. If more gradual changes are identified, teams can create a predictive maintenance plan to address the problem during regularly scheduled repairs.
- Prioritize maintenance activities: Data helps teams prioritize maintenance activities. Minor issues can be addressed during monthly or quarterly repairs, while mission-critical concerns can be solved as soon as necessary parts are on-hand.
- Improve asset lifecycle management: The RUL of equipment varies based on workloads, environmental conditions and part failures. Data analysis helps identify possible problems that lower RUL and resolve them to extend the remaining useful life, in turn improving asset lifecycle management.
- Reduce reactive maintenance: Reactive maintenance is expensive and time-consuming. Why? Because it doesn’t start until machines stop. When failures occur, teams start the process of root cause failure analysis (RCFA) and remediation, which may take days or weeks, leaving machines offline and tanking production performance.
Big data analytics help predict and prevent common failures to reduce reactive maintenance. Put simply, data forms the foundation of proactive and preventive maintenance strategies that let businesses act before issues impact production performance. These strategies are essential to optimize production lines, reduce reactive spending and improve equipment lifespans.
Data analytics and Manufacturing 4.0
Data analytics also plays a foundational role in Manufacturing 4.0 initiatives. Often used as a manufacturing-specific way to describe Industry 4.0 initiatives, Manufacturing 4.0 connects assets, processes and systems to produce interconnected and interoperable production frameworks that enable digital transformation at scale.
This digital transformation is necessary for companies to effectively manage evolving customer expectations, changing supply chain requirements and always-connected workflows. Data analytics underpin this transformation.
First, data analytics allows organizations to connect IIoT sensors with other connected assets. This provides a holistic view of operations that enables equipment operators and maintenance teams to quickly identify and report issues. In this same vein, analytics enable real-time performance monitoring. This monitoring can be customized on a per-device basis, allowing teams to track specific metrics or KPIs such as the mean time between failures (MTBF) or the mean time to repair (MTTR).
Data analytics also supports the deployment of artificial intelligence (AI) and machine learning applications. First, companies can use data analysis to evaluate and verify AI outputs. While intelligent tools excel at spotting patterns, their outputs still require validation against operational data.
Analytics can also help companies identify best-fit functions for AI. The nature of intelligent tools makes it easy for manufacturers to overspend on new programs and platforms that have a low bar to entry but offer limited business line value. Using analytics, teams can pinpoint and evaluate potential AI use cases.
Finally, data analytics sets the stage for closed-loop optimization and continuous improvement. Many processes in manufacturing are naturally closed loops. For example, while it’s worth understanding how production line assets interact with each other, improved performance starts with closed-loop analysis of equipment efficiency, reliability and accuracy. Analytics help companies get the big picture of smaller, closed-loop processes.
Combining data from multiple closed-loop processes, meanwhile, sets the stage for the development of continuous improvement roadmaps that pair real-time data with long-term strategy.
Getting started with manufacturing data analytics
For many companies, getting started with manufacturing data analysis can feel overwhelming. With so much data from so many assets, chasing actionable insights can feel like a waste of time and money.
Five best practices can help streamline the process.
1. Start with clear business questions: Ask first, then implement. Identify critical equipment with high failure rates and then create clear questions that need data-driven answers, such as “Why is X failure happening?”, “When did Y problem start?” or “What is the best course of action to resolve Z?”
2. Focus on high-impact use cases: Not all machines are equally important to production. While failure on a backup packing machine may lower throughput volumes, it doesn’t derail operations. Sudden stoppages of key assembly equipment, meanwhile, create both immediate impacts and downstream bottlenecks. By focusing on high-impact use cases, companies can reduce the risk of expensive downtime.
3. Use pilot projects to prove value: Start small to prove value. Select a critical machine to analyze, then identify key data sources. Run the numbers, implement the suggestions and track the outcomes. If successful, scale up. If not, try again.
4. Build capabilities incrementally: Because manufacturing processes are naturally interdependent, trying to do too much, too fast can create complexity and lower data visibility. Instead of going wide, think deep; build capabilities incrementally by focusing on key equipment first and taking a measured approach to expansion across production lines.
5. Align analysis with operational goals: Data analytics offers the most value when aligned with operational goals. If high-quality outputs are your top priority, don’t focus on speed. Instead, evaluate data through the lens of quality control and weight quality-related KPIs higher than their speed or cost counterparts.
Turn information into a manufacturing advantage
Data analysis of manufacturing operations, performance, efficiency and connectivity is a strategic capability that enables real-time decision making, improves equipment resilience and paves the way for new solutions such as AI and automation. Bottom line? Data analytics drives modern manufacturing industry excellence.
ATS helps manufacturers apply data analysis to drive smarter decisions and support digital transformation. Let’s talk.
References
ABI Research. (Q3 2024). Data generation by manufacturing industry. https://www.abiresearch.com/news-resources/chart-data/manufacturing-industry-amount-of-data-generated
Dun & Bradstreet. (2025). Manufacturing’s Data Confidence Crisis. https://www.dnb.co.uk/blog/supplier-risk/manufacturing-data-quality-ai-failure-gap.html
