Number of Case Studies72
Big Data and Predictive Maintenance
Big Data and Predictive Maintenance
Predictive maintenance refers to techniques that help determine the condition of in-service equipment in order to predict and/or optimize when maintenance should be performed. Predictive maintenance is one of the most important benefits of the Industry 4.0 revolution. 
IIC Condition Monitoring & Predictive Maintenance Testbed
IIC Condition Monitoring & Predictive Maintenance Testbed
The current state of condition monitoring requires manual measurements that are compounded with aging equipment and the retirement of knowledgeable personnel.
Large Oil Producer Leverages Advanced Analytics Platform
Large Oil Producer Leverages Advanced Analytics Platform
Approximately 17,000 wells in the customer's portfolio have beam pump artificial lift technology. While beam pump technology is relatively inexpensive compared to other artificial lift technology, beam pumps fail frequently, at rates ranging from 66% to 95% per year. Unexpected failures result in weeks of lost production, emergency maintenance expenses, and costly equipment replacements.
Number of Software266
Teamcenter
Teamcenter
Teamcenter provides cross-domain design data management through integrations with the MCAD, ECAD, software development, and simulation tools and processes your design teams use every day. You can manage, find, share and re-use multi-domain data across geographically distributed design centers through a single, secure source of product design and simulation data. You can understand the complex relationships and dependencies between requirements and all the subsystems and design domains across all the possible configurations of the product, even as changes are introduced. You can also create assemblies from parts generated by multiple suppliers that involve complex interactions of subsystems, then prepare and validate the readiness of the design and bill of materials for fabrication, assembly and test.By integrating your current multi-domain design tools with Teamcenter, you can transform otherwise disconnected tools and processes into a single, cross-domain design data management environment that enables you to lower costs, improve quality, and increase design productivity.Multi-CAD Design Data Management for MCADTeamcenter provides design data management with multi-CAD support so that your design teams can create, manage, visualize, validate and re-use native design data across a wide selection of MCAD systems, including NX and Solid Edge from Siemens PLM Software, as well as AutoCAD®, CATIA®, Inventor®, Pro/ENGINEER® and SolidWorks®. Using our JT 3D visualization standard, you can create integrated multi-CAD designs using parts and components from different MCAD tools. You can collaborate on designs, even if you don’t have access to the MCAD tools that authored them.ECAD Design Data ManagementTeamcenter supports integrations with all major ECAD systems. The rich design data management capabilities for printed circuit board (PCB) and wire harness release management enable you to find the right data quickly. The enterprise-wide ECAD parts library management reduces costs by eliminating inconsistent and inaccurate ECAD part data. The ECAD viewer, ECAD-MCAD exchange support and assembly/test analysis tools promote close collaboration within and across domains and organizational functions.Software Design ManagementTeamcenter provides software design data management by integrating software engineering data and processes with product lifecycle management (PLM). Leveraging a multi-domain lifecycle integration framework, Teamcenter enables the seamless integration of application lifecycle management (ALM) tools, data and processes. With this ALM-PLM integration, you can manage your software designs in a holistic product view, and manage software design processes as an integral part of the overall product lifecycle.Simulation Data and Process ManagementTeamcenter can help you validate performance targets by simulating products across a variety of multi-domain and multi-physics issues. Using Teamcenter capabilities specifically designed for managing models, simulation data, and simulation processes, you can quickly derive and generate the computer-aided engineering (CAE) structure from the MCAD or ECAD structures. For complex products, you may use tens or hundreds of different simulation tools to verify performance targets and meet validation contracts. Teamcenter provides a framework for codeless integration with these tools so that data from Teamcenter can be delivered to the tools. Results can then be captured and stored in Teamcenter along with all the correct associations to design and requirements data.Get Up and Running Quickly with Preconfigured PDMIf you need PDM for your small- or medium-sized business, and your primary focus is to take control of multi-CAD and ECAD data and processes, consider the Teamcenter Rapid Start deployment option. With preconfigured groups, roles and processes based on PDM best practices, you can get up and running with Teamcenter quickly and cost-effectively.
Apama
Apama
Imagine how responsive your enterprise could be if you could glean real-time insights from all that big fast data—data streaming in from global markets, mobile devices, the Internet of Things (IoT), internal transactional systems and a myriad of other sources. You can be that event-driven enterprise by using Apama. Software AG's Apama Streaming Analytics—supporting predictive analytics—is the world’s #1 platform for streaming analytics and intelligent automated action on fast-moving big data. With Apama, you can analyze and act on high-volume business operations in real time.Apama Streaming Analytics is built on an in-memory architecture that enables real-time processing of extremely fast, large data volumes—orders of magnitude larger than traditional database-based IT architectures. Data science teams build predictive models in whatever data mining tools they prefer to use, then use Predictive Analytics for Apama to load the models in the PMML format. This step takes a fraction of the time that model deployment ordinarily requires, and eliminates the need for manual coding, cross-checking and error correction. Apama allows for ingesting these models rapidly, making them instantly organic to the business process that they support, as defined by various Apama applications.Apama then probes incoming event data from any device, social media stream or business system with extremely low latency against the imported predictive models for real-time scoring. Predictive Analytics for Apama analyzes this streaming data, which can also be enriched with historic and contextual data-at-rest where necessary, to identify business patterns that have happened or are likely to happen. The platform’s visualizations and visual analytics for business users support both human-oriented and automated intelligent actions, alerts and notifications.
Thingworx Analytics
Thingworx Analytics
ThingWorx Analytics enables enterprises to find the true value in their IoT data – to learn from past data, understand and predict the future, and make decisions that will enhance outcomes.WatchMonitor edge devices and provide real-time pattern and anomaly detection on real-time data streams.PredictProvide automated predictive modeling and operationalization for a variety of different outcomes. Pattern and anomaly detection on real-time data streams.AdaptDeliver prescriptive and simulative intelligence that identifies factors that contribute to an outcome and explains how to change a predicted outcome.OptimizeAutomatically operationalize and maintain predictive and simulative intelligence to deliver to end-users.
Number of Suppliers60
Humaware
Humaware
Humaware have developed a suite of innovative data driven tools that provides users with a preventative maintenance capability that detects and diagnoses defects to predict and prevent asset failure. Implementation of our data driven toolset enables organisations to develop effective asset management strategies to enhance condition monitoring systems and realise the benefits of investments in predictive maintenance.
DataRPM
DataRPM
DataRPM is an award-winning predictive analytics company focused on delivering the next generation predictive maintenance solutions for the Industrial IoT. DataRPM platform automates data science leveraging the next frontier in machine learning known as meta-learning, which is machine learning on machine learning. The platform increases prediction quality and accuracy by over 300% in 1/30th the time and resources delivering 30% in cost savings or revenue growth for business problems around predicting asset failures, reducing maintenance costs, optimizing inventory and resources, predicting quality issues, forecasting warranty and insurance claims and managing risks better.DataRPM's advisory board consists of industry luminaries from companies such as Google, Hortonworks, Oracle, Cloudera, EMC, Facebook and Booz Allen Hamilton. Headquartered in Redwood City, California, the company is privately held. Customers of DataRPM include companies like GE, Cisco, Jaguar, Orange and similar Fortune 500 companies.
FogHorn
FogHorn
FogHorn is a leading developer of “edge intelligence” software for industrial and commercial IoT applications. FogHorn’s software platform brings the power of machine learning and advanced analytics to the on-premise edge environment enabling a new class of applications for advanced monitoring and diagnostics, asset performance optimization, operational intelligence and predictive maintenance use cases.
Number of Use Cases12
Predictive Maintenance (PdM)
Predictive Maintenance (PdM)
The aim of predictive maintenance (PdM) is first to predict when equipment failure might occur, and secondly, to prevent the occurrence of the failure by performing maintenance. Monitoring for future failure allows maintenance to be planned before the failure occurs. Ideally, predictive maintenance allows the maintenance frequency to be as low as possible to prevent unplanned reactive maintenance, without incurring costs associated with doing too much preventive maintenance.Predictive maintenance uses condition-monitoring equipment to evaluate an asset’s performance in real-time. A key element in this process is the Internet of Things (IoT). IoT allows for different assets and systems to connect, work together, and share, analyze and action data.  
Asset Health Management (AHM)
Asset Health Management (AHM)
Asset Health Management refers to the process of analyzing the health of an asset as determined by operational requirements. The health of an asset in itself relates to the asset's utility, its need to be replaced, and its need for maintenance. It can be broken down into three key components:Monitoring: Tracking the current operating status of the asset.Diagnostic Analysis: Comparing real-time data to historical data in order to detect anomalies.Prognostic Analysis: Identifying and prioritizing specific actions to maximize the remaining useful life of the asset based on analysis of real-time and historical data.
Industrial Digital Thread
Industrial Digital Thread
The digital thread refers to the communication framework that allows a connected data flow and integrated view of the asset’s data throughout its lifecycle across traditionally siloed functional perspectives. The digital thread concept raises the bar for delivering “the right information to the right place at the right time.”The Industrial Digital Thread (IDT) testbed drives efficiency, speed, and flexibility through digitization and automation of manufacturing processes and procedures. It collects information in the design, manufacturing, service and supply-chain setup, and provides access to intelligent analytics for industrial manufacturing and performance data.The Digital Thread integrates design, engineering, manufacturing, and MRO (maintenance, repair, and overhaul) systems, establishing a seamless flow of information. This type of integration employs data and analytics across the complete product life cycle, optimizing efficiency, from design to manufacturing, operations, and maintenance to service in a closed loop. 
72 Case Studies
266 Software
60 Suppliers
12 Use Cases
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