Manufacturing

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

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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

Mobirise

Drive Revenue

1. Predict and forecast demand to     allocate resources most                   profitably

2. Discover deep customer                    insights to enhance product            design & support

3. Create intelligent, connected          products that generate new            and innovative business                    models

Mobirise

Cut Costs

1. Predict maintenance needs             before they arise and address         proactively

2. Optimize production while                keeping costs low and product      quality high

3. Increase supply chain efficiency      to maximize profitability

4. Analyze service patterns to              improve product design & cut        warranty costs

Mobirise

Avoid Risks

1. Detect product issues early and     improve QA to reduce liability

2. Manage production risks to              ensure smooth, consistent              product delivery

3. Assess customer service and          fix issues before widespread            impact

4. Minimize EH&S risk by                        predicting the likelihood of              harm

Business Value

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.

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

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