RVIA Economic Impact Study

The Recreation Vehicle Industry Association commissioned an Economic Impact Study on the RV industry, released on June 7, 2016. The study found that the RV industry contributes about $49.7 billion in economic output or 0.28 percent of the Gross Domestic Product. Through its production and distribution linkages, the industry impacts firms in 426 of the 440 sectors of the United States economy.

Nationwide, the industry is responsible for 216,170 jobs, both directly and inderectly, creating an economic impact of $37.5 billion. The full study results, along with each individual state and congressional district's economic impact is available on the website by clicking here .

Oracle Launches AI Cloud for Manufacturing to Increase Yield

Tue May 15, 2018
Author: RV News Staff

152642017455256.jpgOracle launched its new artificial intelligence (AI) cloud applications, designed to help manufacturing organizations reduce costs and increase yields. The system provides rapid analysis and actionable insights that can improve production efficiency and performance, the company says. Essentially, the software finds patterns and makes predictions by analyzing data from IT platforms and IoT sensors.

The new Oracle Adaptive Intelligent Applications for Manufacturing leverage machine learning and AI to process vast amounts of data from production environments and rapidly identify issues, enabling improved operational efficiency.

The system helps manufacturers spot anomalies during production, pinpoint the root cause of issues, and predict events before they occur. Managers can look into every stage of the production process, foresee faulty processes and elements, and trace the impact of issues from production through to customer delivery.

Built on the Oracle Cloud Platform with embedded machine learning capabilities, this solution includes a manufacturing-aware data lake that brings together and analyzes structured, semi-structured, and unstructured data from multiple data sources on the shop floor.

Oracle Adaptive Intelligent Applications for Manufacturing include:

-- Pattern and correlation analysis: Discover key patterns and correlations between a complex set of multi-variate influencing factors across manpower, machine, method, material, and management related information. Users can then align these insights with manufacturing business metrics such as yield, quality, cycle time, cost, scrap, rework, and returns to help quickly identify root causes.

-- Genealogy and traceability analysis: Using highly intuitive user interfaces and a self-driven ad-hoc analysis paradigm, the solution sets the foundation for “smart recall” analysis by providing comprehensive capabilities for backward and forward tracing of products and processes to quickly identify impacted products, services, and customers.

-- Predictive analysis: Leveraging the foundation of patterns and correlations analysis driven by machine learning and AI algorithms, this solution predicts the likelihood of occurrence of critical outcomes such as yield, defects, scrap, rework, cycle time and costs for ongoing production activities. This provides business users with the lead-time needed to intervene in a timely fashion to minimize losses.

-- Oracle Adaptive Intelligent Applications for Manufacturing are designed to work in a complex and heterogeneous mix of IT systems such as Manufacturing Execution Systems (MES), Quality Management, Enterprise Resource Planning (ERP), Human Capital Management (HCM), Customer Relationship Management (CRM) and Operational Technology (OT) systems that include sensor and log data from equipment and machines as well as external environmental data such as humidity, temperature etc.

“Traditionally, pattern and correlation analysis and predictive analysis are done by a small group of specialist data scientists,” says Ramchand Raman, vice president of Oracle Product Development. “Oracle Adaptive Intelligent Applications for Manufacturing dramatically simplify the output of complex machine learning and AI algorithms and present these insights to average business users to drive better, faster decision making.”