Maximize Manufacturing Potential with AI

The best time to focus on AI-driven smart manufacturing was yesterday.

The DataRobot AI Platform delivers unparalleled Experimentation and Production capabilities, empowering manufacturers all over the world to achieve operational excellence, reduce downtime, improve product quality, and enhance customer satisfaction. The combined power of generative AI and predictive AI to reduce costs and improve customer satisfaction is unparalleled.

Leading global manufacturers are actively embracing AI and all of its potential to stay ahead of the game.

Modernizing Demand Forecasting, Supply Chain, and Predictive Maintenance

Learn how to solve your most urgent manufacturing and business needs with an end-to-end AI solution focused on delivering real business value.

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Manufacturers Unlock Real Value with AI

63%

Industry 4.0 companies using AI/ML that credit it with boosting profits

Source: SAP

75%

Manufacturers that benefit from AI/ML optimization

Source: Deloitte

$500 billion

Amount that smart factories will add to the global economy in next 5 years

Source: Manufacturing Tomorrow

AI Is Solving a Variety of Challenges in Manufacturing

Demand forecasting

AI can be used to forecast demand for products, based on historical data, trends, and external factors such as weather, holidays, and market conditions.

Quality Control

AI-powered cameras and sensors can quickly identify defects and errors in the manufacturing process, leading to increased first-pass yield. Using large language models to extract textual information from reports, refined through quantitative measures, can improve QC modeling outputs.

Supply Chain Optimization

AI can help enhance supply chain activities, such as optimizing inventory levels, and identifying potential supplier issues.

Process Optimization

AI can integrate data from various sources, including machines and sensors, to optimize manufacturing processes and increase yield in continuous processes.

Predictive Maintenance

AI can help predict equipment failures before they happen, saving significant downtime and maintenance costs.

Trusted by 6 of 10 Top Global Manufacturers

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When a leading Turkish cement maker turned to AI for process optimization, the company identified and rolled out improvements that reduced cost, and most importantly, brought down CO2 emissions nearly 200,000 tons per year.

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Robust Potential for Manufacturing Applications of AI/ML

Supply Chain Management

AI allows you to predict late shipments at all supply chain phases. DataRobot allows you to nest AI models to overcome multimodal supply chain complexity. With DataRobot, you can also mitigate risks and ensure model accuracy as economic conditions change through advanced monitoring capabilities.

Inventory Status Predictions

AI helps manufacturers predict inventory at all stages of the production process, further optimizing economic order quantity (EOQ) and economic production quantity (EQP):

On-Hand Inventory
Work in Progress
Raw Material
Safety Stock
Transit Inventory
Deadstock Inventory

Predictive Maintenance

Predict the likelihood of individual processes and machines causing downtime, allowing you to plan maintenance and other preventative activities.

Predict Downtime

Optimize scheduled maintenance based on unscheduled downtime with predictions for mean time between failures (MTBF), mean time to repair (MTTR), and overall equipment effectiveness (OEE).

Quality Control

Multimodal and image analysis allows you to monitor the production process, detecting outliers and deviations from established quality standards and alerting production managers about potential issues in real time.

Root Cause Analysis (RCA)

Examining important variables and factors that contribute to defects using a variety of data, including sensor inputs. Using an enterprise knowledge base to capture internal documents, like standard operating procedures, allows to identify defects more quickly.
Automated Quality Assessment

Automatically identify whether a finished product adheres to your quality standards, based on a set of predefined metrics for a part or finished good. Using generative AI to explain predictions and model behavior allows to address quality defects more proactively.