Data continues to transform the manufacturing industry, and manufacturing analytics is what makes this possible. Information and insights from both operational technology (OT) and information technology (IT) sources are helping business leaders reshape how factories plan, make, and deliver products, increasing their efficiency and saving money.

Aggregating data from the factory floor and coupling it with data on materials, orders, and workforce status make it possible to monitor everything from equipment performance to customer demand in real time. As a result, manufacturers can optimize production schedules, reduce downtime, improve product quality, and make informed decisions quickly. Artificial intelligence, advanced analytics, and data modeling have been identified by Deloitte as the technology investments with the greatest potential ROI for manufacturers. These tools can help firms build a more resilient business today and be better prepared to grow amid the uncertainties of tomorrow.

What Is Manufacturing Analytics?

Manufacturing analytics is the discipline of converting manufacturing data into actionable insights. It refers to the systematic collection, integration, and analysis of data from machines, sensors, IT applications, and workers across one or more factories and their incumbent supply chains.

The combination of OT signals, such as temperature, torque, vibration, and cycle counts, with IT records, which include orders, inventories, and supplier lead times, gives rise to new types of analyses and pattern recognition. Manufacturers can further augment these analytics with dashboards and visualizations, diagnostic tools, predictive models, and prescriptive algorithms. Taken together, these features help create a closed-loop environment in which evidence drives decision-making both on the shop floor and in the boardroom.

Key Takeaways

  • Adoption of manufacturing analytics begins with aggregating and cleansing data from a variety of OT and IT sources.
  • AI, cloud computing, and Internet of Things (IoT) devices are critical components of the tech stack that enables analytics.
  • With a unified-data platform in place, manufacturers gain insights that bring greater efficiency, productivity, and flexibility to operations.
  • Analytics informs manufacturing activities, such as quality control, predictive maintenance, and product development.
  • In addition, analytics helps manufacturers better manage their supply chain, improve customer relationships, and gain a competitive advantage.

Manufacturing Analytics Explained

Like other forms of data analytics, manufacturing analytics uses a core set of methods, tools, and models. Generally, there are four stages of analytics maturity and adoption:

  • Descriptive analytics aggregates historical data into visual dashboards, displaying such information as the previous day’s production output and downtime.
  • Diagnostic analytics drills down by correlating multiple variables to determine the root cause of a problem, such as a delayed order or a high defect rate.
  • Predictive analytics combines historical data and machine learning models to forecast future events, such as machine breakdowns or shifts in customer demand.
  • Prescriptive analytics simulates future scenarios and provides recommendations for maintenance schedules, inventory levels, order quantities, and so on.

Data visibility is essential for manufacturers to reach more mature stages of analytics. The depth of insights or recommendations that manufacturers get from their data can only go so deep if data from the factory floor, the warehouse, the management team, and third-party suppliers remains in silos. Implementing a centralized, cloud-based data repository is a vital step toward bringing data into a single workspace and adding the forms of AI that make predictive and prescriptive analytics possible.

What Is the Purpose of Manufacturing Analytics?

At the highest level, manufacturing analytics exists to help business leaders continuously improve the effectiveness and efficiency of manufacturing operations—slashing waste, squeezing more value from every asset, and empowering workers to make faster, smarter decisions. Analytics connects once-isolated processes into a feedback loop of insight and action that supports managers in their efforts to improve agility and profitability.

On the production line, for example, it can uncover hidden capacity, reduce waste and defects, fine-tune parameters like temperature and speed of production, and prevent unplanned downtime by predicting when machines need repairs. Meanwhile, both in the warehouse and across supply chains, analytics provides end-to-end visibility into the flow of materials and products, helping firms better manage vendors, stock levels, and even warehouse layouts.

At a strategic level, analytics contributes to shorter and more dynamic decision cycles. Managers can model the impact of a tariff increase, logistics disruption, or change in order priority within minutes, rather than weeks, and make proactive adjustments to production workflows. When deployed throughout a company, manufacturing analytics aligns finance, operations, quality control, engineering, and other previously disparate business units according to the same key performance indicators (KPIs). This lets leadership drive continuous improvement and bring innovation to manufacturing processes.

What Is Manufacturing Analytics Used For?

Analytics can be applied to virtually every facet of production and supply chain management. From the warehouse to the assembly line to after-sale support, data-driven insights help optimize processes, anticipate issues, and guide strategic decisions. The most common use cases of manufacturing analytics and AI are discussed below.

Demand Forecasting

Demand forecasting helps manufacturers set production schedules, manage inventory and related costs, determine prices, allocate resources, and set business objectives. It boosts firms’ ability to pinpoint the right time to buy more materials or increase production rates to meet demand.

Demand forecasting depends on a reliable stream of high-quality, accessible data in a format that business intelligence and enterprise resource planning (ERP) systems can use. Reliable, trustworthy data fosters better analyses—thus providing managers with the superior information they need to make informed decisions and chart a path for future growth.

Quality Assurance

In the past, variability and defects in products were too often identified after the fact—after products were finished or, worse, already in customers’ hands. The ability to capture data at many steps in production, from material acquisition to machine settings to operator performance, helps manufacturers trace the origins of a defect and take steps to prevent it recurring in the future. It also helps businesses narrow down a defect’s root cause, which can reduce the scope and cost of a recall or fix to a specific lot, as opposed to an entire product run.

Predictive analytics can also apply to quality assurance. Firms can monitor whether factors, such as temperature, pressure, or humidity, contributed to a defect, so operators can adjust conditions accordingly. This type of feedback can be leveraged to inform product development and further reduce downstream errors, as designers and engineers can avoid known causes of errors.

Predictive Maintenance

Unplanned downtime costs Fortune Global 500 companies close to $1.5 trillion every year, as production must stop while malfunctioning equipment is repaired. Preventive maintenance can help keep machines running, but it may contribute to wasted resources if, for example, the parts scheduled to be replaced are still fully functional. Predictive maintenance goes further. It uses sensors to gather data on operational conditions, such as vibration, temperature, and pressure, and applies predictive algorithms to determine when equipment is likely to fail. This lets manufacturers plan maintenance before equipment malfunctions, yet reduces waste by replacing parts or refilling lubricants only when necessary. This strategy cuts operational costs and makes maintenance budgets more predictable.

Fault Prediction

Similar to predictive maintenance, fault prediction aims to forecast a failure in a system or process sequence. For example, if a chemical compound is too dry when it’s ejected into a molding machine, then that component may fall out of spec. Any product incorporating that molded component will be defective, and the production line will have to be shut down until the problem is resolved.

Fault prediction often involves complex, multivariate models in which the performance of equipment, the properties of raw materials, and the work of machine operators could individually or collectively cause faults. Analyzing early-stage production can help manufacturers identify the different conditions that develop faults. In turn, this may enlighten operators about which parameters need adjustment to prevent the occurrence of a fault.

Inventory Optimization

Manufacturing analytics can help turn inventory management from a series of periodic guesses into a continuously optimized process. By analyzing real-time data—sales orders, work-in-process counts, machine cycle times, supplier lead-time variability, and external demand signals—analytics systems can accurately calculate the levels at which inventory should be set to keep production flowing without tying up excess capital. Analytics can also improve visibility into inventory, boosting a business’s understanding of which stock is selling well and helping it predict and track emerging trends..

Energy Management

Reducing energy consumption can lower a manufacturer’s utility costs, propel it toward meeting corporate goals for carbon reduction, and help it build a competitive advantage derived from being energy efficient. Analytics can help manufacturers manage energy use, for example, by tracking and analyzing energy consumption of machines, processes, and facilities to identify opportunities where they might reduce waste. This may include shutting down equipment during lunch breaks or shift changes, staggering equipment startup times to avoid peak electricity demand charges, or running energy-intensive processes when renewable energy sources are available.

Monitoring energy use has an additional benefit: Since increased friction or wear can cause equipment to require more power to do the same job, a spike in a machine’s energy use could indicate an imminent maintenance issue in need of preemptive attention.

Product Development

Analyzing production data collected throughout the manufacturing process can help engineering and design teams identify product features that inject complexity or quality issues during manufacturing. They can consider avoiding those features in future products or redesign them to minimize the issues.

Furthermore, customer data, such as warranty claims, returns, and readouts from sensors in the field, offer insights into the performance of finished products, including the reasons causing them to break down. This helps R&D teams choose more reliable raw materials or create stronger components in future designs, making products more robust. The combination of data from manufacturing processes and customer reports contributes to an environment of continuous improvement.

The Benefits of Manufacturing Analytics

Analytics can deliver many tangible benefits that directly affect a manufacturing firm’s performance. By analyzing data effectively, manufacturing businesses can improve productivity, cost management, quality control, and more.

  • Increased productivity: Collecting data from throughout the production lifecycle and using it to analyze simulations of equipment constraints can help manufacturers lift overall equipment effectiveness (OEE)—the productive capacity a business actually gets from its equipment as a percentage of its theoretical maximum. A higher OEE can unlock additional capacity without involving new equipment purchases. Furthermore, analytics can boost worker productivity, as well, by reducing operator idle time and providing real-time data and dashboard views to accelerate worker problem-solving.
  • Reduced operational costs: Similarly, analysis of production data can identify hidden waste in energy and material usage or labor performance. Data-driven changes can contribute to meaningful margin improvements.
  • Improved product quality: Closely monitoring processes can detect anomalies in real time and provide insights into the root causes of defects, such as issues traced to a single supplier’s materials.
  • Enhanced decision-making: The combination of real-time KPIs and prescriptive analytics tools can provide better information to business decision-makers and help shorten the time it takes them to make operational decisions. Broader analytics, including analyses of evolving demand, competition, and market conditions, can help senior managers make strategic business decisions—including pivots—when called for.
  • Increased supply chain visibility: Analytics can help stitch together supplier lead-time data and IoT sensor feeds from shipments and warehouses, and it can scan events to flag delays early, reroute stock in motion, and keep production on schedule.
  • Prevented downtime: Predictive maintenance can anticipate and prevent equipment failures. Streaming machine-sensor data through predictive-maintenance models can unearth wear patterns days in advance, turning surprise breakdowns into quick, scheduled fixes.
  • Better-managed supplier performance: The ability to assess and rank vendors on factors like lead time and defect rate helps firms identify top suppliers. It also provides leverage when renegotiating contracts.
  • Increased employee engagement: By making data available across the enterprise through self-service dashboards, employers are empowered to view and act on analyses. This also helps nourish a culture of process ownership and continuous improvement.
  • Improved service quality: For manufacturers that handle post-sales maintenance, analyzing data from devices in the field, as well as service history, can help predict service needs, manage spare parts inventory, and set technicians’ schedules. This can lead to faster response times and improved customer service.
  • Sharpened competitive edge: Integrating manufacturing analytics into day-to-day work and strategic decision-making helps businesses spot demand shifts, cost swings, and competitors’ moves early, letting teams pivot on pricing, production, and sourcing before rivals can react.

Manufacturing Analytics Best Practices

Successfully implementing manufacturing analytics requires more than the right technology; it also demands the right approach to managing people, processes, and company culture. Manufacturers can take the following steps to make the most of their analytics initiatives:

1. Inventory Your Data Sources

The data that supports manufacturing analytics comes from many places. The first step toward leveraging analytics is knowing where data is being generated. Data sources can include sensors, machines, a wide range of business applications, and even paper forms, such as maintenance logs or faxed order confirmations. Data outputs from these sources may be structured (for example, sensor data uploaded to a database) or unstructured (such as free-text notes), and they are likely to be stored in several locations on-premises and offsite.

Data inventory is a critical first step toward understanding how to consolidate data in one location. It can also reveal important gaps in available data. For example, manufacturers might discover that some equipment hasn’t been outfitted with sensors and, therefore, isn’t providing valuable readings.

2. Connect Your Data Pipelines Together

Once companies have inventoried data sources, the next step is integrating them into a central data repository. Data integration tools, APIs, or other forms of middleware can automate this process. This reduces a company’s reliance on manual data transfers and curbs errors. It’s especially beneficial if a manufacturer has grown through acquisition or otherwise operates sites that use disparate applications.

Connecting multiple data streams helps create a single source of reliable data accessible throughout the enterprise. This is a prerequisite for achieving cross-functional insights, because it lets manufacturers connect the dots among factors, such as a supplier’s batch, production yields, and final customer returns, and use them to pinpoint opportunities to increase productivity or better manage costs.

3. Use Software to Cleanse Your Data

Since raw manufacturing data can be noisy, incomplete, or inconsistent, it’s important to adopt tools and processes that filter out errors, fill in or discard missing values, standardize formats, and validate accuracy. Doing this will remove known outliers from a sensor’s data stream or create standard formats for the alphanumeric codes used to label components.

As part of the data cleansing process, manufacturers should also set up automated data quality checks. Quality engineers and other domain experts can help companies define the acceptable values for specific data fields and create business rules for addressing discrepancies. This will help prevent a batch of data without issues such as ingesting a time stamp or flagging values outside their acceptable range.

4. Standardize Data Acquisition

Besides coming from different sources, manufacturing data presents itself in different formats and at inconsistent points in time. For effective analysis, companies should standardize how they collect and record data. Machine data should use a common protocol, such as Open Platform Communication Unified Architecture. Similarly, dates and times should all be recorded in the same time zone while temperatures should be in the same scale. Standards should also indicate how often a given data type should be captured and recorded, as some parameters are more critical to operations than others.

As with data cleansing processes, data collection standards require documentation and informed input from domain experts. Manufacturers also may benefit from sharing their standards with suppliers, so that data associated with raw materials and product components don’t arrive in the wrong format. Standardized data acquisition reduces errors and helps businesses ramp up new analytics projects quickly.

5. Start Small and Scale Up

As with any other potentially disruptive change, it’s best to start manufacturing analytics on a small scale. A pilot project on a particular production line or a specific problem, such as a bottleneck on one machine, is a good place to start. Any issues will thus be relatively narrow in scope (unlike those that might arise during an enterprisewide rollout), iterations can be performed quickly, and risk is contained.

Clear and measurable metrics for a pilot are a must. Examples include decreased defect rates, equipment failures predicted and prevented, and reduced downtime. When positive results are achieved—and shared throughout the company—then internal demand will grow for future larger-scale analytics initiatives.

6. Use AI to Detect Performance Anomalies

Even well-trained human eyes can miss patterns and anomalies, such as the slow impact of wear and tear on mechanical equipment, subtle shifts in calibration, or minor changes in energy use. AI, including machine learning tools, can monitor performance data to discover early indicators of problems by automatically flag unusual behavior in machines or processes. Leading tools are also capable of looking at combinations of metrics at the same time.

Effective AI usage involves deployment in ways that foster collaboration with human experts. AI’s main advantage is its ability to monitor thousands of signals in real time and flag anomalies. From there, well-trained engineers can confirm whether the red flag was a true issue or a false alarm and choose the right action to take.

7. Make Data Reports Accessible

For manufacturing analytics to have a high-value impact on your business, decision makers throughout the organization need access to data and insights. That requires user-friendly reports and dashboards that show key metrics in visual forms that operators and executives alike can view and act on. At the same time, analytics solutions need to offer different levels of detail for different users; while process engineers may be interested in detailed machine data, managers will want to see high-level KPIs.

This kind of accessible and actionable data helps shift the culture of a company. When operators see defect rate data in their dashboards, or maintenance teams receive predictive alerts about equipment requiring attention, workers are prompted to take proactive steps to correct the issues. Teams no longer need to wait to read summaries produced days, even weeks after data has been collected before taking action. Transparent and accessible data puts everyone on the same page, allowing the organization to move forward as a cohesive unit.

8. Leverage Real-Time Analytics

Timing is vital in fast-moving production environments. Real-time analytics can help manufacturers shift from retrospective, reactive changes to proactive action. A sensor indicating the need for changes to fill weights, for instance, can trigger an alert to modify a machine during the current production run. Or real-time supply chain analytics can warn of a late delivery, allowing managers to shift workers’ tasks until the shipment arrives. Linking analytics and automation workflows lets manufacturers take things a step farther. Quality control adjustments can be triggered or maintenance workers can be dispatched as soon as a fault is detected—or anticipated.

Manufacturing Analytics Technologies

Manufacturing analytics relies on a suite of advanced technologies to collect, process, and analyze data, and then visualize the results. The following are key technologies that contribute to a robust analytics solution:

  • Artificial intelligence and machine learning: AI systems can ingest streams of sensor, production, and market data to predict machine failures, optimize process parameters, flag quality anomalies, and forecast demand far more accurately than static rules can. They can help business leaders continuously fine-tune schedules, inventory levels, and machine settings for higher efficiency and faster response to changes. All of this contributes to process automation and data-driven decision-making.
  • Cloud computing: The cloud’s combination of elastic storage, flexible deployment, and scalable computing resources enables the integration of data across global operations. Thus, it supports consistent use of analytics services and collaborative decision-making among previously disconnected stakeholders.
  • Internet of Things: Connected devices on machines, conveyors, and vehicles continuously collect such data as temperature, pressure, speed, vibration, and location, whether in transit or from the factory floor. This real-time data stream is essential for studying production processes that previously generated only static or delayed data.
  • Computer vision: Cameras and sensors on the production line can inspect products, spot defects, monitor equipment, and improve safety. This provides valuable information about process performance, such as how materials move through a warehouse, and can inform operational improvements.
  • Manufacturing execution systems (MES): These software applications manage and monitor production on the factory floor. MES systems typically handle scheduling, dispatch orders, track materials, and collect production data, making them a vital conduit of information for analytics and decision-making.
  • ERP software: Manufacturing ERP systems provide end-to-end visibility into the manufacturing process. They gather and analyze production planning, inventory management, quality control, and order tracking data to help business leaders improve efficiency and reduce costs. They can also integrate that operational data with data from finance and accounting, procurement, and other business functions, creating a single integrated repository for all business data.

The Future of Manufacturing Analytics

Numerous technological advances are poised to further embed manufacturing analytics into factory operations and strategic decision-making. One is the expansion of edge computing and high bandwidth connectivity. This could, for example, allow AI models to run directly on factory-floor devices and locally handle time-sensitive tasks, such as an adjustment to valve pressure. Another is the expanded adoption of generative AI. This could let workers ask natural-language questions when querying data sets, allowing those without extensive training in data science to take advantage of analytics. Companies will also benefit from further integration of data sources such as information from suppliers, customers, and utility companies; this consolidation can contribute to insights that spark further savings and operational improvements.

As data continues to flow, manufacturers will need to invest heavily in data governance and standardization to maintain high-quality data. Taking the time and effort to create data standards and governance policies up front will reap dividends in the long run.

Turn Insights Into Results With NetSuite for Manufacturing

NetSuite’s manufacturing ERP is a cloud-based solution built to support data aggregation and analysis from the shop floor to the top floor. It lets engineers build no-code dashboards that stream run rate, scrap, and OEE data while also taking in data from global workflows and supply chains. The companion NetSuite Analytics Warehouse—powered by Oracle’s Autonomous Data Warehouse technology—can pull that shop-floor telemetry and integrate it with finance, CRM, and IoT feeds to run margin and capacity what-if scenarios. In short, NetSuite can help manufacturers turn analytics into direct gains in throughput, cash, and margin.

The manufacturing industry is poised to benefit from advances in data technologies, including AI, that accelerate collection, standardization, and integration of previously disparate data sets to support analytics. Real-time data availability can influence momentary decisions on the factory floor, permitting small changes to equipment settings or operational workflows that ultimately produce long-term productivity and cost savings. Analytics also assists long-term planning, as leadership can run simulations on everything from customer demand to raw material price trends, then make informed decisions that stimulate business growth. Such improvements represent a key competitive advantage for manufacturers that boldly embrace analytics in an increasingly dynamic economy.

Manufacturing Analytics FAQs

What is manufacturing intelligence?

Manufacturing intelligence helps firms analyze and refine the production process by transforming raw data gathered throughout the production lifecycle into actionable insights. Companies accomplish this by integrating data sources and software tools on a centralized platform and leveraging a wide range of manufacturing analytics models to improve efficiency and productivity while lowering costs.

How is data collected in manufacturing?

In manufacturing, data is typically collected from machines and sensors that monitor operational technology signals on the factory floor, such as temperature, torque, vibration, and cycle counts. Data is also collected from information technology systems that aggregate customer orders, materials inventory, and supply chain performance.

What is an example of manufacturing data analytics?

One example of manufacturing data analytics is the study of past orders, seasonality, and economic indicators to predict an upcoming surge in demand for certain products and then proactively ramping up production. Another example is to look at lead time data from suppliers and adjust the level of safety stock on hand if lead times have increased in recent weeks.