Manufacturing

Dramatically improve core operational functions and help products, brands and services stand out in the marketplace.

Why AI Now?

Amidst the Industry 4.0 revolution, manufacturers must fully embrace cyber-physical systems and use all available data and predictive analytics to innovate every aspect of their business. Those that don’t will find themselves quickly outperformed by aggressive and nimble competitors emerging from around the world. Today’s manufacturing is not just about volume and efficiency. Manufacturers must use machine learning to design smart products, run smart factories, forecast demand, ensure quality, reduce production downtime and manage supply chain risk. RapidMiner empowers manufacturers to wield data science as a powerful tool, dramatically improving core operational functions and helping products, brands and service stand out in the marketplace. 

Manufacturing Industry Use Cases

Drive Revenue

  • Predict and forecast demand to allocate resources most profitably
  • Discover deep customer insights to enhance product design & support
  • Create intelligent, connected products that generate new and innovative business models

Cut Costs

  • Predict maintenance needs before they arise and address proactively
  • Optimize production while keeping costs low and product quality high
  • Increase supply chain efficiency to maximize profitability
  • Analyze service patterns to improve product design & cut warranty costs

Avoid Risks

  • Detect product issues early and improve QA to reduce liability
  • Manage production risks to ensure smooth, consistent product delivery
  • Assess customer service and fix issues before widespread impact
  • Minimize EH&S risk by predicting the likelihood of harm

Highlighted RapidMiner Impact

A global computer equipment manufacturer increased the accuracy of demand forecasting by identifying high volume customers and predicting future purchases.

A CPG manufacturer used text analytics on competitors’ product content and reviews and increased sales by revising its pricing & positioning accordingly.

An aircraft manufacturer combined sensor data and text analytics on repair and service reports to improve its maintenance resource allocation.

A tire manufacturer optimized its production recipe, increasing output and lowering cost while still achieving its target physical properties.

A cement mill operator predicts drilling machine failure and can take preventive action, enabling it to make repairs proactively at lowest cost.

A semiconductor manufacturer predicts the optimal settings of production equipment to maximize throughput while maintaining quality.

An electronics manufacturer achieved a zero tolerance QA policy by augmenting inspection with predictive analytics on upstream component production metrics.

A turbine manufacturer used root cause predictive analysis to reduce product failure by identifying complex, interdependent component relationships.

What Our Customers Say

5/5

“Very rich software with highly advanced features, yet very fast to use and apply models. Software is very broad in its coverage of models, with Auto Model capabilities, I can quickly complete model building and testing for different projects with little effort.

– Senior Data Scientist in the Manufacturing Industry

Read the full review on Gartner Peer Insights
Gartner Peer Insights reviews constitute the subjective opinions of individual end users based on their own experiences, and do not represent the views of Gartner or its affiliates.

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Tuesday, September 24, 11am EST

Predictive Maintenance – A Powerful First Step Towards Adopting AI for Manufacturing

Manufacturers can use AI systems to design smart products, run smart factories, forecast demand, ensure quality, reduce production downtime, and manage supply chain risk. But one use case rises above the rest In terms of feasibility and impact – predictive maintenance. It addresses the age-old challenge of ensuring maximum availability of critical manufacturing systems, while simultaneously minimizing the cost of maintenance and repairs.

Let us show you how data science can help improve core operations so your organization can better drive revenue, cut costs, and avoid risks.