What is Intelligent Manufacturing?
Manufacturers today face a lot of challenges. Customers are demanding more and more customizations which results in having smaller production batches, frequent changes, and more waste. Supply lines are also decreasing and there is increased outsourcing. To compete, manufacturers need to optimize the productivity of their expensive equipment, reduce waste, maximize yields, and reduce cycle times. New capabilities for processing sensor data, along with big data, machine learning and artificial intelligence, cloud and edge technologies are enabling a shift from reactive problem solving towards increasingly proactive management of equipment, processes, product and factories.
Tomorrow's factories today
Smart factories or Intelligent manufacturing is the utilization of real-time data analysis, artificial intelligence (AI), and machine learning in the manufacturing process to accomplish the above optimizations. Using sensors on equipment to acquire and process real-time data, intelligent manufacturing allows manufacturers a complete, 360-degree high-fidelity virtual data-driven integrated view of all operations—from suppliers and supply chains, through equipment, processes, and manufacturing practices, to final product testing and customer satisfaction.
By processing real-time data from machine sensors and applying AI and ML, it’s possible to predict critical events and take preventive action to avoid problems. A smart factory can monitor streaming sensor data using business rules and ML models to tell us about the health of our equipment and processes. A wide range of solutions can be used to better understand equipment, processes, products, operations, customers and sales; and then help act on the insights gained. Many manufacturing companies throughout the world are already using intelligent manufacturing in the following industries: semiconductor, electronics and medical devices; automotive & aviation; equipment manufacturing, pharmaceuticals; chemicals, metals and mining and consumer packaged goods.
Perhaps most of the promise and success of Industry 4.0 and IoT technologies for manufacturing depends on effective ML, AI, big data, and other advanced analytic technologies, comprehensively implemented to provide digital twin virtualization, insight, and predictability. In addition, organizations also need to understand the details of how customers use their products in the field and how products age or their reliability deteriorates or even when they would need maintenance. Today, some manufacturers offer an additional service for proactive maintenance. Think about elevators (Kone, Schindler, OTIS, ect) that send back information and alert when assistance is necessary. Such insights will almost always open new business opportunities to enhance the customer experience. All of this needs to happen while manufacturers are becoming more transparent and adhering to regulatory requirements as they are common and increasingly relevant in most industries for managing consumer risk.
Here are some tangible Smart Manufacturing use case areas
- Product Quality and Reliability
- Machine learning to accurately model and predict equipment, process and product results
- Process Control and Capability with alerting
- Equipment maintenance: Predictive, condition-based and scheduled with alerting
- Factory Monitoring including Management dashboards , KPI charts and OEE.
- Supply Chain: Demand forecasting, inventory optimization, supplier performance
- Resource modeling and optimization
- Customer Analytics – customer & product segmentation, cross-sell / up-sell opportunities
- Sales - Pricing optimization and Account management
- Yield Prediction, Predictive Maintenance, Virtual Metrology
- Uni / Multi-variate Control Charts, Time Series
- Anomaly Detection – AI: Deep Learning
Image & Pattern Classification
- Defect image classification, Wafermap patterns
- Multi-image, Multi-media, equipment sounds
- AI: Deep Learning
- Advanced Process Control: Sensor Analytics & IoT
- Fault Defect Classification, Run-to-Run Control
- Equipment Health Monitoring
- Factory Map Dashboards & Alerting
Supply Chain & Factory Digital Twins
- Predictive Scheduling – Fab Tools & Supply Chain
- Material & Vehicle Routing
- Linear Programming, Genetic Algorithm
Digital Factory Platform
In order to be successful, a digital factory platform to enable intelligent manufacturing must have the following:
- Data Integration: Historical & Streaming data
- Interactive Visual Analytics & Dashboards
- AI & Machine Learning: no-code visual workflows
- Edge & Sensor Analytics
Benefits of Intelligent Manufacturing
The benefits of intelligent manufacturing include the ability to proactively detect and respond to events, which improves quality, yield, and reduces the downtime and also leads to better overall equipment effectiveness (OEE). By having a digital twin of the factory, it is possible to simulate beforehand new productions and understand the bottlenecks. Intelligent manufacturing allows for proactive changes in the supply chain and a smart inventory, optimizing other factory logistics including packaging and transportation. Intelligent manufacturing can uncover new business opportunities, revenue streams, and monetization of assets for a sustained competitive advantage. It can also automate, orchestrate, and predict product failures for preventative maintenance to prevent downtime. With intelligent manufacturing, you can process and analyze data in real time near the point of data generation for rapid response to process anomalies.
In sales and marketing intelligent manufacturing can enable your organization to understand markets, predict and adapt to customer preferences. For supply chain optimization, intelligent manufacturing can help with forecast demand, optimize inventory, and monitor suppliers. Analytics has always been used in supply chain organizations for forecasting and inventory management but in the age of the IoT, we now know the position of just about everything and that requires more real-time capabilities. 5G networks could take factories to the next level. 5G has the ability to support high connection density with tens of thousands of endpoints, thereby truly enabling the use of industrial data at scale.
Smart manufacturing can be used to enhance product and process quality with intelligent statistical process control, yield management, and reliability analysis. Being able to understand and demonstrate that processes are in control is at the heart of initiatives using Quality by Design (QbD) and good manufacturing / documentation / security practices (GxP). Intelligent manufacturing can help with regulatory compliance to standardize, automate, and monitor QbD and GxP initiatives. Being able to demonstrate to regulators that processes are understood and in control can tax even the most sophisticated of organizations. Analytics can be used for automated and validated regulatory reporting, complete audit trail, version control, and electronic signatures to document changes to analytic processes, procedures, and reports to monitor and automate workflow and approvals.
Implementing intelligent manufacturing is vital to digital businesses, because simple automation is no longer sufficient to keep up with the market and Industry 4.0. To survive digital disruption being brought by the IoT and Industry 4.0, manufacturers must apply field and customer centric analytics.
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