Manufacturing

With DataRobot, the quality of products can be modeled based on data about materials, production status, and other environmental variables. A good model reduces the lead time and cost of inspections by inspecting only areas that are higher risk. In addition, by visualizing models with DataRobot, warning factors are detected much earlier, improving the overall yield.

Models created with DataRobot predict signs of equipment failure well before they happen. By using historical data such as electrical current, vibration, and sound generated by manufacturing equipment, DataRobot quickly develops highly-accurate models that transform maintenance operations. These insights improve effectiveness while reducing overall maintenance costs.

Accurate demand forecasting makes production plans more efficient and helps eliminate waste. By applying end-to-end enterprise AI to market data, product specifications, and sales trends, DataRobot predicts future sales more accurately than traditional forecast methods. Companies use this information to predict sales in new market segments and plan accordingly.

Once the domain of highly-specialized practitioners, the tuning of processing conditions at production facilities can now be automated with DataRobot. By quickly generating models that offer highly-accurate predictions in a fraction of the time of manual methods, firms can leverage machine learning in manufacturing to speed the set up of new production lines and extend automation capabilities across the factory.

Manufacturing firms have a number of important considerations for R&D, including setting direction and priorities for the company and making build vs. buy decisions. DataRobot drives improvements and ROI for R&D through the visualization and comparison of DataRobot models, enabling more effective PDCA cycles and improving overall competitiveness.

With IoT, almost every device now connects to the Internet, constantly updating and providing value-added services. By leveraging models built by DataRobot in connected (or offline) line items, these Smart Products constantly learn through continual historical data analysis, allowing systems to evolve and improve faster than ever before.