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Leading Edge Technologies We Use as Building Blocks for Your Solutions
Improving productivity while safeguarding workforce health have traditionally been incongruent goals for all but the mammoth manufacturing complexes with established roles in the global supply chain. The effort requires the weaving of advanced technologies to keep companies aligned with best economies of scale practices enabling regionalization and localization of supply chains. This is a critical time to adopt or enhance digital manufacturing to transform the factory environment with tools supporting flexible labor, remote data, and connected management. Like top tier manufacturing complexes, small and mid-sized manufacturers must start integrating manufacturing performance platforms, autonomous materials movement, and Industrial Internet of Things (IIoT) into their systems to address requirements like variable production rates, mixed product demand – all with a cost basis equivalent to their larger competitors.
5G
Smart Manufacturing remains mostly wired with wireless on the shop floor applied for use cases where it makes business and technical sense. Operations of all sizes and in the automotive, consumer, and other supply chains are starting trials with cellular connectivity primarily deploying private LTE or 5G. Cellular Low Power Wide Area (LPWA) technologies which are included in 5G, are being integrated in condition-based monitoring solutions. 5G enables wider deployment of location technologies for asset and vehicle tracking and the upcoming convergence of OPC UA over TSN with 5G will deliver significant improvement in solutions for the fully connected, interoperable and automated production floor
- Ultra-reliable, high-speed wireless networking for the factory floor, warehouse, in-transit materials
- 5G is secure by design unlike previous cellular technologies where security was an overlay
- Converging with shop floor networking standards like OPC UA over TSN (OPC UA over 5G)
- Scalable from few devices to millions makes use possible for the small the
Internet of Things (IoT)
The factory is a data mine that can be prospected for efficiency improvement veins if properly connected and digitized. Expanded IoT sensor networks embedded in production equipment, moving inventories, utility panels, and personnel badges among others, can materially enhance operational efficiencies, particularly when their raw data is fed into well integrated and configured machine learning (ML) and artificial intelligence (AI) software. Industrial IoT adds value to industrial automation solutions as it
- Extends reach of traditional factory networks
- Integrates broader set of fixed and moving assets and goods into ERP and other enterprise management and planning systems
- Reduces costs of regulatory compliance monitoring and reporting operations
- Improves production line flexibility and utilization of production asset
Artificial Intelligence (AI)
Whether to surface operational insights from complex data lakes or system of systems; or to detect and prevent cyber-attacks, the use of artificial intelligence in manufacturing is quickly becoming an inevitability. Many systems already in use in manufacturing, incorporate some level of artificial intelligence. AI enhancements in systems designed for shop floor management, supply chain planning, preventive maintenance and other functional areas of the factory must be expertly selected and integrated to deliver on ROI targets. This peculiarity is an even bigger threat for small and medium manufacturing operations. When expertly applied, AI delivers
- Actionable analysis and dashboarding from the factory’s big data lakes
- Detects trends, potential points of failure, improvement areas
- Maximizes the use of production assets, personnel, and buildings
- Identifies opportunities to reduce inventory, consumption of energy and other resources
Machine Learning (ML)
Defects are one of the key wastes in manufacturing operations. Machine learning is a major ally in the fight against waste. If we understand systemic issues that lead to defects, identifying risks associated with them, it is possible to achieve material reductions in waste and acceleration of production timelines and batch scheduling. Machine learning can improve defect detection rates by as much as 90% according to FORBES. Machine learning solutions in manufacturing can
- Improve quality control processes, reducing their complexities
- Identify risks, issues predictively across the production chain
- Establish best routing of materials inside the factory as well as to and from neighboring supply chain partners
- Identify operational risks, resilience, tolerance to various possible machine failures
