Smart manufacturing uses internet-connected machines to monitor production processes. Analyzing this data can help manufacturers quickly adapt to changes throughout their manufacturing and supply chain processes.
Why is Smart Manufacturing Important?
Traditional manufacturing cannot respond to environmental changes because it was not designed with flexibility in mind. From the outset, smart manufacturing is designed to be adaptable. Analyzing data from sensors and digital feeds about factors such as demand helps manufacturers quickly respond to changing conditions.
Benefits of Smart Manufacturing
Below are some of the benefits of using smart manufacturing over traditional manufacturing technologies and processes:
- Plants can boost productivity because reliable real-time data helps predict failures and identify safety and quality assurance issues.
- Predictive analytics can optimize logistics and improve on-time delivery with real-time information on traffic conditions, shipping container bottlenecks, adverse weather, and more. Plus, smart sensors can identify issues with vehicles and drivers to help prevent breakdowns and accidents.
- Manufacturers can realize labor improvements by optimizing and automating processes to carry out projects more efficiently.
- In the supply chain, predictive analytics can optimize inventory replenishment, quickly scaling production up or down as needed.
- Analyzing data on carbon emissions, energy and water use, and waste can lead to insights on reducing your environmental footprint.
Potential Challenges for Implementing Smart Manufacturing
Smart manufacturing projects can encounter the following challenges during approval and rollout:
- Perceived risk is a major obstacle often addressed through an incremental or phased rollout that assesses the risk at multiple milestones.
- The need to reengineer existing processes to adopt smart technology can slow down a project.
- Technology integration in IT and OT can be complex due to differing APIs and network requirements.
- Automation can be challenging due to the many robotic alternatives that manufacturers must assess based on various use cases.
- Employees often need training, including change management.
Automotive
The automotive industry is evolving from a model of refinement of existing processes practiced for decades. Companies such as Tesla simplify production by removing steps wherever possible. Parts such as the heat exchanger serve more aspects of the vehicle, and components are constantly changed to allow faster automation. Ultrasonic sensors on the bumpers have been eliminated, and their function has been assigned to cameras aided by more advanced image processing for depth perception.
Mercedes places barcodes on the windshields of partially built trucks in Mexico to make them easier to find in parking lots where they are held, awaiting parts that were unavailable during initial vehicle manufacturing. Cameras and drones confirm locations after the vehicle is parked.
Power Generation
Power generation uses expensive machines such as nuclear reactors, hydro turbines, and offshore windmills. These machines use IoT sensors to allow manufacturers to monitor their use in production. The analytics driven by the sensor data streams allow 3D service monitoring applications to predict proactive maintenance intervals.
Einzelhandel
Retailers such as Sainsbury’s and Cost Plus use smart algorithms to predict consumption to drive replenishment orders, with managers simply monitoring automated orders. Insights from real-time customer behavior and streamed point-of-sales data help retailers understand demand as it’s happening.
Smart Logistics
Carriers of refrigerated goods from farm to store are excellent examples of smart logistics. They can include a cellular-connected temperature sensor with produce packed and chilled in the field to detect overheating in transit and alert the shipper of potential spoilage. The flagged goods can be assessed at arrival to prevent the spread of disease.
Farming
Farming makes broad use of smart technologies—drones survey fields to map crops that are ready to harvest. Organic farms use robots that wander the fields day and night using video recognition to identify weeds and zap them with a laser, maximizing yield without pesticides.
Quality Control
Farming makes broad use of smart technologies—drones survey fields to map crops that are ready to harvest. Organic farms use robots that wander the fields day and night using video recognition to identify weeds and zap them with a laser, maximizing yield without pesticides.
Data Analytics
Smart manufacturing relies on data analytics to improve the efficiency and effectiveness of manufacturing processes. Sensors collect data, and data streaming services share data using a publish and subscribe model. The data is stored in a data platform where artificial intelligence (AI) techniques such as machine learning (ML) can perform advanced analytics whose output is used to prescribe or directly make operational changes on the factory floor.
Actian and the Data Intelligence Platform
Actian Data Intelligence Platform is purpose-built to help organizations unify, manage, and understand their data across hybrid environments. It brings together metadata management, governance, lineage, quality monitoring, and automation in a single platform. This enables teams to see where data comes from, how it’s used, and whether it meets internal and external requirements.
Through its centralized interface, Actian supports real-time insight into data structures and flows, making it easier to apply policies, resolve issues, and collaborate across departments. The platform also helps connect data to business context, enabling teams to use data more effectively and responsibly. Actian’s platform is designed to scale with evolving data ecosystems, supporting consistent, intelligent, and secure data use across the enterprise. Request your personalized demo.
FAQ
Smart manufacturing refers to the use of advanced technologies—such as IoT sensors, AI, analytics, automation, and digital twins—to optimize production processes, improve efficiency, and enable real-time decision-making across industrial environments.
Smart manufacturing integrates real-time data from machines, sensors, and systems into a unified environment. AI models, analytics engines, and automation tools analyze this data to predict failures, optimize workflows, adjust production schedules, and improve quality control.
Key technologies include IoT devices and machine sensors, industrial control systems (ICS), edge computing, cloud and hybrid data platforms, predictive analytics, robotics, digital twins, computer vision, and 5G connectivity for low-latency data transfer.
Benefits include improved production efficiency, reduced downtime, predictive maintenance, better product quality, faster issue detection, safer operations, lower operational costs, and enhanced supply chain agility through real-time visibility.
Challenges include integrating legacy systems, managing high-volume streaming data, maintaining cybersecurity, ensuring data accuracy, scaling AI models across plants, and aligning OT (operational technology) and IT teams on data standards and governance.
