CLICK AI helps manufacturers quickly integrate data from enterprise systems, operational sources, sensor networks, and external providers to deliver deep learning models that generate predictive insights. The introduction of AI through deep learning models can help meet important goals such as reducing inventory and eliminating waste due to poor quality, creating an annual economic value of $100 million for manufacturers around the world.
It is important to make the right decisions quickly. In a typically one-way manufacturer's process, planning and data can be organically linked to each stage via CLICK AI, enabling rapid and agile development.
Research shows that 79% of companies with good performing and optimized supply chains have achieved greater sales growth. Deep learning helps you optimize your supply chain by identifying problems across individual and collective supply networks, and streamlining operations to address supply network variability. Artificial intelligence models trained through CLICK AI continuously analyze supply chain data to find new patterns. This pattern provides an opportunity for companies to optimize their supply chain processes by identifying with predictive accuracy a set of factors affecting the supply chain.
Revenue optimization is important in advanced IC design and manufacturing. You can use deep learning to quickly detect and mitigate urgent process issues to improve sales and product quality. It can also predict revenue issues for downstream products to spot problematic areas in the manufacturing process. The artificial intelligence model created by CLICK AI can find new patterns by analyzing manufacturing data and sales data. This provides an opportunity for companies to optimize their manufacturing processes and, consequently, to optimize product quality and sales volume, by identifying product sales with predictive accuracy.
CLICK AI improves service satisfaction and reduces downtime by identifying components that are expected to be dangerous and proactively warning customers of potential problems. Deep learning processes are commonly applied to fraud detection and process automation. Training data used as a deep learning training model can be easily obtained from most warranty claim management applications. It assists claims handling through process automation and resolution suggestions, and helps to make better decisions by performing consistent assurance work. By optimizing warranty with deep learning process through CLICK AI, it is possible to maintain visibility of individual components in the manufacturing and distribution process at the customer's location.
CLICK AI reduces inventory holding costs, improves cash flow and supply chain visibility, and improves inventory analysts' productivity. The inventory optimization of CLICK AI applies state-of-the-art deep learning to analyze demand, supplier delivery time, quality issues, and product line interruptions to establish real-time recommendations and monitoring for each level of confidence level and receive real-time notifications. As a result, CLICK AI enables you to perform scenario planning and root cause analysis, optimize inventory levels, and manage suppliers comprehensively.
CLICK AI can accurately predict asset failures by aggregating data from sensors, device enterprise systems, and operating systems. CLICK AI provides planners and operators with a comprehensive insight into asset risk to maintain higher asset availability, service-based differentiation, and lower maintenance costs.
CLICK AI can leverage deep learning in real-time to improve operational efficiency and profitability across sales, marketing, and customer services. CLICK AI's CRM can provide real-time AI generation forecasts and scores with insights from the integration of internal and external datasets.
Predicting defects in products produced by a manufacturer is very important because it is directly related to the profits of the company. The traditional method of identifying defective products is to identify defective products manually by humans, which is expensive and is only a post-processing method because the possibility of human error is high and the need to depend on many people. If you use the deep learning of CLICK AI technology by collecting data such as pressure, speed, and temperature that may affect product quality using sensors, you can quickly and accurately predict product defects.
Deep learning can be used to analyze and predict video and image-based data to detect and warn safety risks in real-time. Customers can remotely verify remote operational security and detect asset integrity through upstream, midstream and downstream operations. You can avoid dangers through fast processing and feedback in the field by monitoring risks such as smoking behavior with image classification or object detection AI using CLICK AI.
The manufacturing industry is affected by the consumption behavior of the market. From consumer trend analysis, new product planning, competitor analysis, and brand positioning, CLICK AI uses deep learning to analyze and predict consumer data to improve services and optimize manufacturing product quality and manufacturing processes.
Demand forecasting is a complex process that requires large-scale work by experts, such as accountants, as well as data analysis, but is an important part of the manufacturing process. Leveraging demand forecasts for manufacturers eliminates the need to store unnecessary inventory by efficiently managing inventory and optimizes the overall manufacturing process. The deep learning of CLICK AI can leverage external factors such as economy, market, raw material availability, and data such as demand, inventory, and supply to predict how much demand will occur in the future. It gives manufacturers a deeper insight into their business performance and future plans.