“Fast Forward” Industry 4.0 with Enterprise Big Data Analytics and Machine Learning with PTC and Hortonworks

This blog was co authored by Simon K. Lutzenberger, Manager Strategic Partnerships at PTC

Today, PTC and Hortonworks announce a strategic partnership to “fast-forward” the realization of Industry 4.0 benefits including improved manufacturing quality and yield, enhanced asset and plant uptime, and optimized production flexibility and throughput. This collaboration is directed at a state-of-the art solution comprised of complementary offerings from Hortonworks and PTC that, together, provide:

• Seamless connectivity to manufacturing sensors and industrial equipment
• Comprehensive management of Enterprise Big Data, foundational for next generation manufacturing insights
• Class leading Data Analytics and Machine Learning, essential for predictive analytics and Artificial Intelligence (AI) enabled manufacturing

Foundational to the solution is an integration between key Hortonworks and PTC solutions to enable ingesting data from industrial devices, storing and processing it within an enterprise Data Lake and then making this data available for the creation of next-generation IOT analytics, Machine Learning, and AI-enabled manufacturing applications. More specifically, pre-built integration templates result in a best-of-breed solution comprised of the following products:

• PTC Kepware: a portfolio of industrial connectivity solutions to help businesses connect their diverse portfolio of industrial automation devices and software applications;
• PTC ThingWorx: an Industrial Innovation Platform for the Internet of Things (IoT), enabling the rapid creation of IoT applications, including powerful Analytics capabilities;
• Hortonworks Data Platform (HDP): an open source framework for distributed storage and processing of large, structured and unstructured, multi-source data sets; and
• Hortonworks DataFlow (HDF): a scalable, real-time IoT streaming data management platform that ingests, curates and analyzes data for key insights and immediate actionable intelligence.

This resulting solution can provide significant value to customers including best-of-breed data management and analytics solutions, rapid time to value for the implementation of IoT-enabled manufacturing use cases and reduced risk via a pre-integrated solution template.

For more information, see the Hortonworks IoT website.

Background

Despite decades of continuous efforts to improve manufacturing operations, the total cost of poor quality to manufacturers amounts to a staggering 20 percent of sales revenues according to the American Society of Quality. Further, for process-oriented industries, manufacturing yield dramatically impacts company profitability. In the semiconductor industry, for example, a 1% increase in semiconductor yield results in approximately $1 million additional revenue per plant/month.

In addition, equipment and plant downtime costs manufacturers an inordinate amount of money. According to Deloitte, unplanned downtime costs manufacturers approximately $50 billion per year. Deloitte has also found that poor maintenance strategies can reduce plant capacity by 5 percent to 20 percent.

Solution: Big Data Analytics for Manufacturing

Recent advances in Big Data Analytics, leveraging machine learning, provide the ability to improve manufacturing performance in two key areas:

• Optimizing quality and yield outcomes by analyzing large volumes of historical manufacturing data to identify the factors (i.e., sensor values) that correlate to quality issues (either within the plant or within the field).
• Predicting impending equipment failures by analyzing large volumes of historical manufacturing data and then identifying the factors (i.e., sensor values) that precede actual equipment failures (from maintenance management systems, quality management systems, etc.).
• Once these factors are identified, equipment performance can then be continuously monitored with respect to these factors, foreshadowing the need for maintenance actions.

Requires Wide-Ranging Data

Optimizing the effectiveness of Big Data Analytics for Manufacturing requires a combination of sensor data (i.e., time-series data) and broader enterprise data (from ERP, MES, maintenance management, quality management, CRM systems, etc.).

Architecture for Success

This architecture improves quality, yields and equipment / plant uptime with real-time connected manufacturing analytics lifecycle.  PTC and Hortonworks are uniquely qualified to enable this real-time connected manufacturing analytics based upon an architectural framework emphasizing big data ingestion and management, machine learning and streaming analytics. The following illustration examines the real-time connected manufacturing analytics lifecycle in greater detail.

Enabling Customer Success

Through this integrated solution from PTC and Hortonworks, customers can achieve improved product and process quality and yields through reduced equipment downtime and maintenance costs compared to time-based maintenance, and through lower total solution cost and risk via single, bundled solution for Industrial IoT analytics including data management and advanced analytics.

Next Steps

• Stop by the Hortonworks booth #134 and attend our joint presentation with PTC called “modernization of the factory shop floor” on November 28^th, at 14:30 – 15:00 at HPE Discover 2018 in Madrid to learn more about Hortonworks and PTC and the exciting combination of Big Data and IoT.