IBM Software Group Predictive Maintenance A smarter way to manage assets Tim Ricketts Smarter Infrastructure Team September 2014 IBM Software Group Traditional Way of Doing Asset Management Challenges §  Assets are managed and maintained according to a fixed schedule §  Unplanned issues with assets are manually reported, delaying the response time. §  Responses are reactive, typically only after a failure has occurred. Question §  How can we use data from networked assets to reduce the response time, and enable proactive action rather than waiting for failures? Solution §  Use the real-time metric and event data coming from instrumented and networked assets to automatically create tickets to address issues or respond to failure warnings. IBM Software Group Predictive Maintenance is the next step towards Maintenance Excellence Maintenance Maturity Model Managing budget costs while improving reliability and safety Predictive Maintenance Conditionbased Maintenance Preventive Maintenance Reactive Maintenance (machine fails, then fix) Source: Gartner 3 (based on manufacturers’ schedules, time, or operational observations) (based on monitoring to assess condition of assets) (based on models of evolution of the condition of assets) Predictive Maintenance uses analytics to model foreseeable evolutions of the characteristics of individual systems or assets IBM Software Group IBM Predictive Maintenance and Quality • Reduce operational costs • Improve asset productivity • Increase process efficiency July 23, 2013! Q1 2013 2012 Singular software capabilities (Maximo, Cognos) 4 Customizable, cross-IBM, software and services solution (Analytics with real-time data integration) Accelerate Time-to-Value §  Real-time capabilities Packaged, cross-IBM, software product (Analytics with real-time data integration) §  Big data, predictive, and advanced analytics §  Quick and accurate decisioning §  Maximo integration §  Open architecture §  Business intelligence IBM Software Group A proven architecture based on best practices underlies Predictive Maintenance and Quality Advanced analytics powered by IBM SPSS and Cognos §  End User Reports, Dashboards, Drill Downs Data integration provided by Websphere Message Broker and Infosphere Master Data Management Collaborative Edition, which feeds a pre-built, DB2-based data schema §  Predictive Analytics Decision Management Business Intelligence Analytic Datastore (Pre-built data schema for storing quality, select machine and prod data, configuration) §  Integration Bus Process Integration with Maximo – automatic work order generation (Message Broker) §  Telematics, Manufacturing Execution Systems, Legacy Databases, Distributed Control Systems 5 High volume streaming data Enterprise Asset Management Systems Includes data models, message flows, reports, dashboards, business rules, adapters, and KPIs IBM Software Group Predictive Maintenance and Quality generates significant business value for organizations Business Use Case Business Value Predict Asset Failure/Extend Life §  Determine failure based on usage and wear characteristics è Estimate and extend component life §  Utilize individual component and/or environmental information è Increase return on assets §  Identify conditions that lead to high failure è Optimize maintenance, inventory and resource schedules Predict Part Quality §  Detect anomalies within process è Improve quality and reduce recalls §  Compare parts against master è Reduce time to identify issues §  Conduct in-depth root cause analysis è Improve customer service IBM Software Group Predictive Maintenance and Quality converges Enterprise Asset Management (EAM) and Analytics capabilities Enterprise Asset Management + Predictive Maintenance and Quality §  Asset maintenance history §  Inventory and purchasing transactions §  Labor, craft, skills, certifications and calendars §  Safety and regulatory Requirements Better Outcomes §  Optimized maintenance windows to reduce operating expense Asset Lifecycle Mgmt §  Condition monitoring and historical meter readings = §  Efficient assignment of labor resources Supply Chain Processes Analytical insights Facilities Operation §  Enhanced capital forecasting plans §  Optimized spare parts inventory Staff Planning §  Automated analytical techniques, including anomaly detection for assets and sensors §  Improved reliability and uptime of assets IBM Software Group Predictive Maintenance and Quality provides several key features Real-time capabilities Quick and Accurate Decisioning Big Data, Predictive and Advanced Analytics Maximo integration Business Intelligence Open Architecture Accelerated Time-to-Value IBM Software Group Integration Overview §  IBM PMQ contains adapters for IBM Maximo, which allow data integration §  IBM PMQ uses existing Maximo infrastructure for data integration §  Maximo Upstream Module (Master Data Loading) §  IBM PMQ can consume master data residing in IBM Maximo §  IBM PMQ mirrors the asset data that is managed in IBM Maximo. §  An automated process can be designed to synchronize data between IBM PMQ and IBM Maximo §  Data that comes from IBM Maximo must be updated and maintained in IBM Maximo. It is not possible for changes that are made in IBM PMQ to be propagated back to IBM Maximo §  Maximo Downstream Module (Work Order Creation) §  IBM PMQ generates recommended actions which can be passed to IBM Maximo §  IBM PMQ can be customized to import IBM Maximo work orders as events to record activities such as inspections and repairs. IBM Software Group Questions §  Tim Ricketts Cloud & Smarter Infrastructure Team IBMSoftware Group Dubai §  Phone: 971-4-3907067 §  Mobile: 971-(50)1881945 §  E-mail: [email protected] © IBM Corporation 2011