The Internet of Things (IoT) is transforming manufacturing through connected machines, sensors, devices, and data-driven insights. By implementing IoT technologies, manufacturers can gain new visibility into production operations, optimize processes, improve quality, increase automation, and enable new services and business models. This article examines key applications and use cases for leveraging IoT on the factory floor and throughout manufacturing IT systems.
Connecting Industrial Assets
The first step in any IoT solution is connecting relevant equipment, machines, and assets through sensors and communication networks. In manufacturing, this may involve:
- Sensors on production machines (e.g. temperature, pressure, speed)
- Sensors built into products and jigs/fixtures
- Actuators that can modify machine parameters
- Barcode/RFID scanners for tracking inventory
- Industrial controllers that automate processes
- Robotics integrated on the production line
In addition to production floor assets, enterprise IT systems like ERP, MES, and PLM can also be incorporated to unify the digital and physical. Connecting disparate assets provides the data foundation for IoT use cases.
Real-Time Monitoring and Visibility
By collecting and analyzing real-time IoT data from connected machines and products, manufacturers gain unprecedented visibility into the state of production operations. Insights from monitoring include:
- Overall Equipment Effectiveness (OEE) – Track machine availability, performance, quality
- Utilization – Identify underutilized/overburdened equipment
- Throughput – Monitor production counts by line/cell/machine
- Cycle Times – Optimize takt rates to meet demand
- Yield – Pinpoint causes of defects and downtime
- Energy Use – Profile equipment power consumption over time
- Product Location – Real-time WIP tracking via RFID/GPS
Dashboards displaying KPIs based on IoT data enable drilling down into specific systems or issues. Plant managers have an accurate picture of real-time operations.
Sensors collecting vibration, temperature, lubricant quality, and other real-time equipment health data coupled with analytics can detect machine issues before failure. This predictive maintenance allows:
- Greatly reduced unplanned downtime
- Maintenance based on actual wear rather than fixed schedule
- Just-in-time ordering of replacement parts
- Planning maintenance during planned outages/changeovers
- Avoiding catastrophic machine failures
Some issues like bearing wear or misalignment are detectable by sensors earlier than audible or visual signs appear. IoT data facilitates a move from preventive to predictive maintenance.
Automated Material Handling
IoT and embedded intelligence enable automated material transport with technologies like:
- AGVs – Automatically guided vehicles that follow routes
- AMRs – Autonomous mobile robots with more adaptability
- Goods-to-person ASRS – Automated storage and retrieval
- Pick-to-light and put-to-light systems
- RFID for check in/out of materials or tools
Automating material handling improves logistics efficiency and reduces material wait times as well as manual material handling labor.
Manufacturing Process Optimization
IoT data combined with machine learning can continuously fine tune manufacturing processes for improved quality, yield, and efficiency:
- Adjust equipment parameters in real-time to maintain tolerances
- Detect outliers and predict potential process failures
- Identify correlations between machine settings and product defects
- Shorten changeover times by tracking ideal equipment calibration values
Combining IoT data with simulation and digital twin models provides greater insights for process optimization.
Connecting inventory, MES, and supplier systems allows:
- Monitoring real-time inventory levels and consumption
- Automated triggering of orders based on usage and lead times
- Dashboards with inventory status, order tracking, and shortage alerts
This improves inventory turns and avoids shutdowns or delays from stock-outs. IoT data enables moving from fixed to dynamic reorder points.
Remote Monitoring and Control
IoT connectivity of production line equipment enables:
- Real-time line visibility from anywhere
- Remote diagnoostics and fast troubleshooting
- Over-the-air machine software updates
- Automated alerts based on machine state
- Some parameters can be controlled remotely
This allows manufacturers to leverage expertise across locations and proactively address issues before they cause slowdowns.
Digital Twin Modeling
IoT data combined with physics-based equipment models creates living digital twins of machines and lines. Digital twins enable:
- Simulating manufacturing processes virtually
- Predicting failures based on stress models
- Testing control logic changes digitally before deployment
- Training operators and AI agents in a digital environment
Digital twins will accelerate innovation as R&D can increasingly shift to simulation before physical prototyping.
Asset Tracking and Workflow
Connecting tools, jigs, fixtures, and inventory via IoT allows real-time location tracking across the factory floor. This enables:
- Tracking location and usage of all assets
- Reducing misplaced item search time
- Automated check in/out management
- Ensuring each worker has needed tools/fixtures
- Visual workflow instructions on tablets
- Managing inventory expiration dates proactively
Asset tracking reduces delays and indirect labor costs. Workflow improvements are driven by digitizing manual processes.
Safety and Compliance Monitoring
IoT devices like sensors, wearables, cameras, and environmental monitors provide insights into working conditions and safety:
- Detecting hazardous gases or chemical leaks
- Monitoring noise levels and machine emissions
- Tracking compliance with safety procedures
- Detecting unsafe environmental conditions like heat or chemical exposure
- Ensuring proper gear like gloves or respirators is worn
- Enforcing safe distancing practices between workers
IoT creates opportunities to significantly improve occupational health and safety. Compliance records can also be automatically maintained.
In 3D printing processes, IoT allows:
- Monitoring print bed temperature and material levels
- Tracking laser power, beam location, and scan patterns
- Detecting defects by analyzing sensor data for anomalies
- Adaptively controlling energy input to optimize quality
- Automatically managing powder recycling
- Storing print jobs and machine settings in the cloud for easy access
- Material usage monitoring and automated reordering
IoT both optimizes AM production and enables new cloud-based AM services.
Packaging and Labeling
For packaging stations, IoT delivers:
- Monitoring packed item counts
- Validating correct packaging material is loaded
- Detecting and rejecting improperly sealed packages
- Reading unique IDs to match right box with product
- Checking label content and print quality
- Tracking packed cartons and pallets
- Managing packaging inventory
Automation and oversight improves packaging line efficiency and customer experience.
IoT provides multiple opportunities to boost product quality:
- Monitoring process parameters in real-time
- Identifying correlations between defects and machine metrics
- Performing real-time quality checks via automation
- Tracing defective items back to exact production conditions
- Closing the loop from customer complaints to root cause
- Reducing inspection labor through electronic parameters
- Training AI algorithms on quality data at scale
Higher first pass yield reduces rework costs and speeds delivery of orders to customers.
|IoT Quality Management Benefits| |-|-| |Fewer defects| |Less rework| |Lower COGS| |Faster delivery| |Improved customer satisfaction|
##Connected Products and Assets
Smart connected products enabled by IoT assist throughout the product lifecycle:
- Accelerated prototyping via digital twin simulations
- Gather field data from pilots to finalize designs
- Automated configuration based on customer specs
- Monitoring product state during assembly
- Updating firmware/software over the air
- Real-time location tracking of finished goods
- Condition monitoring like temperature or vibration
- Dynamic routing based on changing demands
- Monitoring product health and utilization
- Predictive and preventive maintenance
- Remote diagnostics and over-the-air updates
- Feature additions or upgrades
End of Life
- Recovery and refurbishing of returned items
- Harvesting working modules from defective units
- Automated disassembly and recycling
- Reuse of components in remanufacturing
New Business Models
IoT enables new data-centric business opportunities for manufacturers:
- Outcome-based models – Customers pay for performance/output rather than asset ownership
- Predictive maintenance – Provide insights to customers to optimize servicing
- Monitoring services – Manage installed product performance for customers
- Usage-based design – Redesign products based on actual customer use data
- Data monetization – Develop new analytics products from aggregated data
- Added functionality – Provide software upgrades or new features for additional fee
- Circular supply chain – Increase reuse, remanufacturing, and recycling
These opportunities create new recurring revenue streams beyond the initial product sale.
Integrating IoT with Other Systems
To maximize benefits, IoT deployments must be integrated with other organizational systems:
- ERP – Exchange planning, inventory, and production data
- MES/MOM – Coordinate manufacturing execution processes
- PLM – Share product definitions and bills of materials
- SCADA – Interface with industrial automation
- CRM – Incorporate customer information and field data
Well designed interfaces avoid silos and enable enterprise-wide circulation of IoT data.
With increased connectivity and data sharing, IoT also creates security risks that manufacturers must address:
- Securely authenticate users, devices, applications, robots, etc.
- Authorize appropriate access to connected machines/systems
- Encrypt network communications and data
- Protect edge devices and their software
- Detect anomalies and cyber intrusions
- Maintain safe OT-IT segmentation
- Update vulnerabilities throughout product life
A holistic cybersecurity strategy reduces risk as IoT solutions scale across the factory and enterprise.
Overcoming IoT Adoption Challenges
To successfully implement IoT, manufacturers should consider common challenges:
- Cultural resistance to new technologies and processes
- Integration with legacy equipment and siloed data
- Unclear ROI for comprehensive IoT initiatives
- Immature standards and competing vendor solutions
- Cybersecurity threats and regulatory compliance
- Lack of skilled workers to implement and operate new technologies
- Technical challenges of data processing and edge analytics
A phased roadmap focusing on practical use cases, strong change management, and external partnerships can help overcome barriers.
IoT presents game-changing opportunities for manufacturers in efficiency, quality, automation, and new services. But realizing the full potential requires connecting disparate assets, extracting insights from data, optimizing processes, and transforming workflows. Companies that strategically apply IoT across the factory floor and enterprise will gain long-term competitive advantage as well as closer customer relationships. With thoughtful implementation, manufacturers can position themselves at the forefront of the next industrial revolution powered by the Internet of Things.
How is IoT used in Manufacturing Industry? – FAQ
Q: What are the key benefits manufacturers see from implementing IoT solutions?
A: Major benefits include increased operational visibility, improved equipment reliability, higher quality and yield, greater automation, faster innovation cycles, and opportunities for new data-driven services. IoT allows manufacturers to gain insights not possible with manual data alone.
Q: What are examples of sensors used in industrial IoT applications?
A: Common sensors include temperature, pressure, flow, vibration, sound, current, voltage, position, acceleration, image, gas, proximity, level, humidity as well as RFID and barcode readers. Sensors generate data about machine state and industrial processes.
Q: How does IoT enable predictive maintenance in manufacturing?
A: By analyzing real-time sensor data on vibration, temperature, and other indicators of equipment health, manufacturers can detect issues prior to failure and schedule proactive maintenance. This reduces downtime costs.
Q: What networking technologies connect IoT devices in factories?
A: Standard networking protocols like EtherNet/IP, ProfiNET, CC-Link, and OPC UA allow sensors, controllers, and equipment to communicate. Wireless networking like Wi-Fi, cellular LPWAN are growing for flexibility. 5G may emerge for deterministic performance.
Q: What are the key challenges manufacturers face in implementing IoT solutions?
A: Challenges include dealing with legacy equipment data, unclear ROI, cybersecurity risks, integration complexity, technical talent shortage, and organizational culture obstacles. Vendors like Sierra Wireless, Litmus Automation, Bright Wolf and Cumulocity help overcome these issues.