The Internet of Things (IoT) and data analytics are two technologies swiftly transforming the manufacturing industry. According to Mordor Intelligence, the IoT analytics market size is estimated at $47bn in 2025 and is projected to reach $143bn by 2030. This growth is driven by the adoption of automated and smart technologies, increased consumer demand, and a strong focus on optimisation.
Today, data analytics for IoT is a key tool for transforming production processes as it opens up new opportunities to increase efficiency, reduce operational costs, and improve product quality. In this article, we’ll explore major applications of IoT analytics in manufacturing and the benefits the technology brings to industrial companies seeking to stay more competitive.
What is IoT data analytics in manufacturing?
IoT data analytics in manufacturing involves processing and analysing vast amounts of data generated by IoT devices, sensors and equipment to deliver actionable insights into production processes and forecast potential issues.
By combining IoT analytics with advanced technologies, like AI and machine learning, industrial companies can quickly process real-time and historical data to detect complex patterns and trends. This fusion expands predictive analytics capabilities and fosters more informed, data-driven decision-making, enabling manufacturers to optimise diverse processes using insights from IoT data analytics.
5 main IoT data analytics use cases for smart manufacturing
Predictive maintenance
Predictive maintenance is one of the most impactful applications of IoT analytics within the manufacturing sector. By integrating IoT devices with Manufacturing Execution Systems (MESs), factories can analyse historical and real-time data from equipment and production lines. It allows them to predict potential failures and bottlenecks before they occur and take immediate action early on.
As a result, it helps prevent downtime and assists with maintenance scheduling, resulting in more accurate and efficient production planning. For example, sensors can detect signs of degradation in a machine component, allowing tech specialists to repair or replace it before it breaks down and causes production delays or quality issues.
Benefits:
- Improved planning of production processes
- Increased equipment life cycle
- Reduced costs for unscheduled repairs
Real-time quality monitoring
Quality control is a key element of any production process, and IoT analytics facilitates consistent quality assurance by enabling real-time monitoring of product quality and production conditions along the whole production cycle.
IoT devices monitor product parameters, such as weight, colour, or density. The analytics system defines if a parameter is out of the allowed range and automatically alerts the operator. This enables factories to identify deviations or defects as quickly as possible, before they affect the entire product series.
IoT sensors are also used to monitor environmental parameters, such as temperature, humidity, and air quality, to ensure optimal production conditions. It’s especially critical in food, chemical, and pharmaceutical sectors, where even minor fluctuations can compromise product quality.
Benefits:
- Reduced risks of defects
- Quick diagnosis of quality problems
- Increased customer satisfaction
Asset management
With IoT sensors and devices, manufacturing companies can monitor equipment performance and utilisation rates in real-time throughout the asset lifecycle. Knowing which assets are frequently used, their health status, real-time availability, and other details helps production managers make informed production scheduling decisions, allocate maintenance budget more accurately, and meet high safety standards.
In addition, by monitoring equipment for signs of overheating, leaks, or other potentially dangerous situations, IoT data analytics systems enable instant detection and response to potential threats, thereby supporting high safety standards in factories.
Benefits:
- Improved equipment utilisation rate
- Extended equipment life cycles
- Improved employee safety
Energy management
Smart sensors are used to track how much energy various equipment and systems are using in real time, while IoT analytics systems help promptly detect areas with high or insufficient energy consumption. Based on these insights, factories adjust equipment operation – turn off unused ones or switch them to an energy-saving mode.
Also, with IoT analytics systems in place, factories can dynamically adjust ventilation, heating, and lighting based on worker activity and temperature. For example, if the temperature outside is low, but indoor conditions are satisfactory, the energy management system will reduce the operation of air conditioning or heating to maintain comfortable conditions for employees.
Benefits:
- Reduced energy costs
- Improved energy efficiency
- Sustainable use of energy resources
Inventory management
IoT data analytics is being used widely in inventory management, enhancing the accuracy, efficiency and transparency of warehouse processes. IoT sensors, such as RFID tags or GPS trackers, installed on shelves, racks, and containers collect real-time data on the quantity and location of goods. This data is fed into analytics systems to track inventory in real-time, predict demand, and automatically initiate replenishment if product levels fall below a predetermined threshold. This helps avoid both inventory overstocking and shortages.
Benefits:
- Prevention of stockouts
- Optimised inventory allocation
- Reduced storage costs
Conclusion
The manufacturing industry is rapidly evolving, and IoT analytics serve as an enabler to transforming traditional industrial processes into intelligent, data-driven ones. Established market leaders such as Tenaris, Volkswagen, and General Electric (GE) that have integrated IoT analytics into their workflows are already gaining tangible advantages, such as optimised processes, better decision-making, and reduced operational costs.
However, the successful implementation of IoT and data analytics requires a strategic approach, a clear understanding of business needs and objectives, and readiness for technological change. For companies that lack the in-house IoT expertise, it’s reasonable to reach out to an experienced vendor who will help them define an implementation strategy, develop a detailed roadmap, and implement the most suitable IoT solutions, thereby helping achieve tangible benefits shortly.
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