OptimalAI
AI POLICY
Manufacturing
Shorten development cycles, improve engineering efficiency and increase safety
Improve product design yields and assembly line process. Improve after sales maintenance services.
OptimalAI enables the manufacturing industry to leverage the rapid growth in the volume of data to optimize processes in real time. Organizations can shorten development cycles, improve engineering efficiency, prevent faults, increase safety by automating risky activities, reduce inventory costs with better supply and demand planning, and increase revenue with better sales lead identification and price optimization.
Increased Productivity
Improve processes by the task,
automate assembly lines, reduce errors, limit product rework,
and reduce material delivery time. Using machine learning
to determine timing of
goods’ transfer can increase
material delivery time by 30% and improve production yield by 3–5%.
Sale of Maintenance Services
Predict sales of maintenance services, optimize pricing and refine sales-leads prioritization. Improve EBIT by approximately 13% by using machine learning to predict sources of servicing revenues and optimize sales efforts.
Optimized Fleets
Optimize flight planning and route and fleet allocation;
enhance maintenance engineer and pilot training. Automate 12% fuel savings for manufacturers’
customers and airlines by using machine learning to optimize flight routes.
Procurement
Procuring thousands of parts from tens of thousands of global suppliers can be a complex challenge for the manufacturing industry. OptimalAI helps manufacturers digitally link to their suppliers’ systems to enable live transparency of supplier availability, performance and downtime. This helps balance the supply chain and real time optimize inventories. Key differentiators between suppliers are easily identified to and support effective procurement levers and reduce administrative costs. Discrepancies between what is paid to suppliers and what it owed them are easily identified
Program Management
Program management can often fail to detect emerging problems or prioritise critical decisions, resulting in delays costing millions of dollars. OptimalAI deep learning optimizes the key performance indicators (KPIs) of program reviews in real time. Deep learning networks use live and /or historical data to predict, identify, and prevent an array of challenges including material and human resource bottlenecks and optimized energy consumption.
Assembly Lines
Assembly line inefficiencies cost manufacturers billions of dollars every year. Existing industry fault detection and classification tools can be inaccurate and cause expensive and unnecessary interruptions on the assembly line. Using advanced analytics to review data across assembly line processes can assist in rewriting the manufacturer's operating procedures. Machine learning, deep learning, collaborative robots, and self-driving vehicles can then all improve warehouse costs and reduce inventory levels. Automating the times to leave a warehouse can reduce material-delivery times by 30 percent and improved production yield by 3 to 5 percent. Applying deep learning to real-time information flows enables a live transparency of component availability and risk management, including accurately predicting defaults and production interruption. Utilizing virtual agents to deliver instructions and information on smart phones reduces assembly errors and reduces the learning curve for new operators.
After Sales Maintenance and Services
With real-time feedback between IoT connected products and support facilities, machine learning can optimize when to deploy the augmented field force, drones and robots as a service to conduct asset inspections, quality checks and preventive maintenance. This enables customized pay-as-you-go services and better prevents service disruption and downtime. It also allows manufacturers to offer service contracts to customers that provide better performance at a lower cost. Improving the accuracy of forecasting MRO work and focusing sales efforts on the most promising leads improves profits.
Engineers
Allow infrastructure maintenance and optimization to be repeatable and auto-scalable. Seamlessly integrate siloed data to provide engineers with live transparency of total assets and information from across data multiple sources. Enable on-demand analysis to identify opportunities for performance enhancement.
Digital Twins
OptimalAI's digital twins span entire ecosystems to promote effective risk management, resilience and protection against supply chain disruption. We integrate traditionally siloed, cross-domain data into an end-to-end seamless environment enabling live situational awareness and optimal decision-tree predictions.
Supply Chains
Combining data-driven insights with AI-powered operational network models of the supply chain reduces cost, optimizes capital expenditure and minimizes operational expenses. This can be further enhanced by seamlessly linking IoT connected infrastructure to create real time intelligent supply chains that monitor and predict optimal decision-making.