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insideBIGDATA Guide to Computer Aided Engineering – Part 2

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The Manufacturing industry is in the middle of a transition to what’s being called the fourth
industrial revolution: Industry 4.0. Industry 4.0, or Industrial Internet of Things (IIoT), is enabled by smart or connected manufacturing and brings together physical production and operations with digital technologies, machine learning, and big data analytics. It creates a more connected and holistic ecosystem of machines, assets, and processes capable of autonomously exchanging information, identifying anomalies, and triggering actions.

The essential first step for manufacturers is to consider how much data the enterprise has at its disposal. Most manufacturers collect vast troves of process data but typically use it only for tracking purposes, not as a basis for improving operations. The challenge is for these players to invest in the systems and skillsets that will allow them to enhance their use of existing process statistics. This Guide, “insideBIGDATA Guide to Computer Aided Engineering,” sponsored by Dell Technologies, will walk through some of the ways to expand the scope of analytics to further increase business value.

With the high rate of adoption of sensors and connected devices, there has been a massive increase in the data points generated in the digital Manufacturing industry. These data points can be of various types. In manufacturing, operations managers can use analytics to drill down into historical process data, discover previously unidentified knowledge among discrete process steps and inputs, and then optimize the factors that are shown to have the greatest effect on yield. Many manufacturers across a broad range of industries now have an abundance of real-time shop floor data and the capability to conduct sophisticated statistical learning assessments.

They are taking previously siloed data sets, aggregating and joining the data before analyzing them to reveal key insights. Even when considering manufacturing operations that are thought to be best in class, the use of analytics may reveal further opportunities to increase yield above industry benchmarks. In addition, companies can reduce their waste of raw materials, reduce energy costs and increase profitability by rigorously assessing production data, all without having to make additional capital investments or implementing major change initiatives.

How Dell Technologies Helps

Today, in the era of the Internet of Things, manufacturers’ data management challenges are growing in scope as products continually generate data related to their performance, functionality and quality. The challenge is not only to capture all this data, but to manage and analyze it to generate product and process insights. Hadoop provides an ideal solution to these challenges.

Dell Technologies has invested to create a portfolio of solutions designed to simplify the configuration, deployment and management of Hadoop clusters. These trusted designs have been optimized, tested and tuned for a variety of key Hadoop use cases. They include the servers, storage, networking, software and services that have been proven in our labs and in customer deployments to meet workload requirements and customer outcomes. The modular solution building blocks provide a customized yet validated approach for deploying new clusters and scaling or upgrading existing environments. Dell Technologies Solutions for Hadoop have been jointly engineered to optimize investments, reduce costs and deliver outstanding performance.

Companies working with CAE software can greatly benefit from Hadoop because these companies have previously struggled to manage the constantly growing volume of data. The datasets are large and often unstructured. Dell Technologies solutions for Hadoop provide a powerful infrastructure platform for processing and managing these kinds of heterogeneous data sources, creating more value through effective and consistent collaboration.

PREDICTIVE MAINTENANCE

Once a product is manufactured and shipped, companies may have little information on its performance. One of the most significant ways that data is playing a role in manufacturing is in the repair and maintenance of equipment. With a reactive maintenance process, valuable uptime can be lost. However, with IoT and sensor data streaming from equipment, predictive maintenance enables organizations to effectively predict or respond faster to machine outages.

Manufacturers can use analytics and machine learning to identify patterns that may indicate a potential breakdown. This enables early and corrective measures to be planned (e.g., such as ordering new parts for the make and model of the equipment) and introduced in the most effective way, avoiding unplanned downtime and costly staff and resources. This data can also be used to predict more than just equipment failure.

For many manufacturing processes, it’s important to predict the remaining optimal life of systems and components to ensure that they perform within specifications. Falling out of tolerance—even if nothing is broken—can be as bad as failure.

In order to be able to predict potential product component failures, companies can continue to leverage Hadoop technologies, such as Cloudera®, to process, and analyze streaming and operational data, gaining performance and maintenance insights across the asset lifecycle. They can integrate data from multiple connected machines and other enterprise data sources such as maintenance and quality management systems to build predictive models.

Data scientists can build, test, and deploy machine learning models, utilizing this data to predict when and how an equipment failure or outage might happen. They can even push these models out to the edge to make intelligent decisions, in real time, close to where the data is generated. With this visibility, manufacturing organizations can identify issues before they occur, take corrective actions, and optimize production schedules according to machine availability—reducing the downtime that can significantly impact the bottom line.

How Dell Technologies Helps

Dell Technologies and Cloudera have been collaborating for many years to provide customers with guidance on optimal hardware to streamline the design, planning, and configuration of their Cloudera deployments. The Cloudera Data Platform (CDP) on Dell EMC Infrastructure is based on the collective experience of both companies in deploying and running enterprise production environments.

With the objective of reducing time to value and deployment risk, the preconfigured, validated designs offer architecture guidance for data analytics infrastructure managers and architects to run CDP on optimized yet flexible configuration of Dell EMC hardware infrastructure. This enables organizations to efficiently extract insights from data and make better business decisions.

SUPPLY CHAIN AND PRODUCTION OPERATIONAL EFFICIENCY

In an increasingly global and interconnected environment, manufacturing processes and supply chains are long and complex. Efforts to streamline processes and optimize supply chains must be supported by the ability to examine every process component and supply chain link in granular detail. Data analytics gives manufacturers this ability.

With the right analytics platform, manufacturers can focus on every aspect of the production process and examine supply chains in detail. This ability to narrow the scope allows manufacturers to identify bottlenecks and reveal underperforming processes and components. Analytics also reveal dependencies, enabling manufacturers to enhance production processes and create alternative plans to address potential pitfalls.

The beauty of this is that it can be done leveraging both historical and real-time data. In addition to enabling historical data analysis, data can drive predictive analytics, which manufacturers can use to schedule predictive maintenance. This allows manufacturers to prevent costly asset breakdown and avoid unexpected downtime.

With the help of these advancements, the manufacturing process involves minimal human interaction. Everything from delivering raw materials to quality control of finished products is executed through advanced algorithms powered by data analytics.

To be able to support all this data, requires a powerful and scalable storage solution.

Over the next few weeks we will explore these topics:

Download the complete insideBIGDATA Guide to Computer Aided Engineering technology guide courtesy of Dell Technologies and AMD. 

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