2013 - Smarter Analytics Leadership Summit
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2013 - Smarter Analytics Leadership Summit

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Smarter Analytics Leadership Summit.

Smarter Analytics Leadership Summit.
Improving Operational and Financial Results through Predictive Maintenance.

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2013 - Smarter Analytics Leadership Summit 2013 - Smarter Analytics Leadership Summit Presentation Transcript

  • Smarter Analytics Leadership SummitBig Data. Real Solutions. Big Results.Improving Operational and Financial Resultsthrough Predictive Maintenance © 2013 IBM Corporation
  • Introductions Jerry Kurtz Paul Hoy, CPIM Vice President - Industrial Sector Global Industrial Sector Executive Business Analytics and Optimization IBM Business Analytics Lester McHargue John Ward Business Solutions - Industrial Sector Global Industrial Sector Solutions Leader Business Analytics and Optimization IBM Predictive Analytics2 © 2012 IBM Corporation
  • Predictive Asset Optimization Jerry KurtzIBM Signature Solution Vice President Industrial Business Analytics and Optimization © 2013 IBM Corporation
  • IBM Signature Solutions bring together analytic industryexpertise, reusable assets and delivery skills to address high-value client initiatives A portfolio of outcome-based analytics solutions that address the most pressing industry and functional challenges by bringing together the breadth and depth of IBM’s intellectual capital, software, infrastructure, research and consulting services to deliver breakaway results. Tackle Deliver Accelerate High-value initiatives Proven outcomes Time-to-value Address industry imperatives Built on a rich portfolio of analytics Faster return on investment and critical processes capabilities and IBM innovations with short-term projects that implemented at clients worldwide support the long-term roadmap4 © 2012 IBM Corporation
  • Predictive Asset Optimization — optimizes performance andimproves quality by integrating IBM’s industry, services, softwareand research expertise Signature Solutions Customer Next Anti -fraud, CFO Performance Predictive Asset Optimization Best Action Waste & Abuse Insight New Signature Solution: Predictive Asset Optimization • Monitor, maintain and optimize assets for better availability, utilization and performance • Predict asset failure to optimize quality and supply chain processes Combined with user-friendly, industry dashboards, accelerators and methods Manufacturing Energy and Utilities Oil and Gas5 © 2012 IBM Corporation
  • Asset-intensive companies need help to solve complexoperational and process issues Asset Performance Process Integration  Lack of visibility into asset health  Difficulty separating the “signals” from  High costs of unscheduled the “noise” maintenance  Lack of visibility of predictors across  Inability to accurately forecast asset organizational silos downtime and costs  Inability to leverage analytical insights for asset optimization6 © 2012 IBM Corporation
  • Predictive Asset Optimization integrates Analytics Capabilities withEnterprise Asset Management (EAM)Business analytics can provide insights and actionable events to improveoperational efficiencies, extend asset life and reduce costs Enterprise Asset Predictive Asset Advanced Enterprise Management Optimization Asset Management Asset maintenance history  Optimized maintenance Condition monitoring and windows to reduce historical meter readings operating expense Inventory and purchasing  Efficient assignment of transactions labor resources Labor, craft, skills,  Minimize parts inventory certifications and  Improved reliability and calendars uptime of assets Safety and regulatory Requirements7 © 2012 IBM Corporation
  • IBM delivers business value with extraordinary differentiation inanalytics skills, products, innovation and marketplace experience IBM Services IBM Software IBM Research Client Value• GBS Consulting • IBM Software • Advanced • Addresses critical Services analytic Products* technology and industry expertise expertise imperatives • Deep information• Industry expertise and and analytics • Predictive analytics proven accelerators algorithms and • Accelerates time- capabilities techniques to-value• GBS software assets including dashboards, • Enterprise Asset • Real-world client Management • Outcome-based data flows and implementations methods capabilities approach + + + = IBM Systems and Technology Smarter Analytics Signature Solutions bring together these capabilities “…whole is greater than the sum of its parts.”8 * IBM SPSS, Cognos, InfoSphere, WebSphere, Maximo © 2012 IBM Corporation
  • Why choose IBM’s Signature Solution: Predictive Asset Optimization? Industry Expertise • Predictive models for a number of specific industry use cases Big Data, Predictive & Advanced Analytics • An enhanced advanced analytics methodology, tailored to the needs of the predictive asset/maintenance space Accelerators • Pre-configured dashboard/visualization templates • Pre-integrated software tools, with connectors to a variety of asset management solutions Talent • A resource pool of highly talented advanced analytics SMES and Industry experts with Predictive Asset Optimization experience9 © 2012 IBM Corporation
  • Trends in Predictive Maintenance Paul Hoy, CPIM Global Industrial Sector Executive IBM Business Analytics © 2013 IBM Corporation
  • Analytic solutions enable optimization of asset performance 3x 83% Organizations that lead in analytics 83 percent of CIOs cited analytics outperform those that are just as the primary path to beginning to adopt analytics by 3 competitiveness times Asset Performance Process Integration  Improve quality and reduce  Optimize operations and failures and outages maintenance  Optimize service and support  Enhance manufacturing and product quality Source: IBM Institute for Business Value and MIT Sloan Management Review, “Analytics: Source: IBM CIO Study, "The Essential CIO" The New Path to Value”11 © 2012 IBM Corporation
  • Answering the questions associated with better asset andprocess performance How can I perform in depth root cause failure analysis on my process and equipment? How can I optimize my How can I detect warranty maintenance plan? issues sooner? ? What is the lifeexpectancy of an asset’s component or part? How can do I create highest quality products? How can I predict an Asset Process impending equipment Performance Integration How can I reduce failure and the cause? process variability? How do I achieve optimal How can I ensure equipment efficiency and supply is aligned with availability? demand? 12 © 2012 IBM Corporation
  • Predictive Maintenance Use Case – Key ExamplesPredictive Maintenance for Assets • Predictive Production Line Continuity • Utilize predictive analytics to identify when internally used production machinery, equipment, and assets are likely to fail or need service, and perform preventive maintenance in order to maximize production uptime and minimize disruptive, costly unscheduled downtime. • Predictive Optimization of assets in the Field • Utilize predictive analytics to identify when equipment in the field is likely to fail or need maintenance in order to maximize uptime/in-service time for equipment sold to customers or used to deliver service.Predictive Quality and Warranty Performance  Utilize predictive analytics to identify when goods and equipment sold to customers is likely to fail in order to identify root cause for problem correction, and to proactively address issues to reduce warranty cost and improve customer satisfaction.13 © 2012 IBM Corporation
  • Predictive Asset Optimization analyzes data from multiple sourcesand provides recommended actions, enabling informed decisions Conduct Root Cause 3 Analysis 2 Generate Predictive Predictive Asset 4 Display Alerts and Recommend Statistical Models Optimization Corrective Actions • Data agnostic • User-friendly model creation Collect & Integrate Data • Interactive 1 Structured, Unstructured, dashboards 5 Act upon Insights • Quickly make Streaming decisions Asset Performance ProcessMaintenance Asset Integration14 © 2012 IBM Corporation
  • Predictive Asset Optimization Architecture End User Reports, Dashboards, Drill Downs Statistical Statistical Decision Decision Business Business Analytics Analytics Management Management Analytics Analytics (SPSS Modeler) (SPSS Modeler) (SPSS DM) (SPSS DM) (COGNOS BI) (COGNOS BI) DB2 Analytic Datastore Analytic Datastore (Pre-built data schema for storing quality, machine and prod data, configuration) (Pre-built data schema for storing quality, machine and prod data, configuration) Industrial Enterprise Services Bus Industrial Enterprise Services Bus (Message Broker) (Message Broker) Telematics, Manufacturing Telematics, Manufacturing EAM System EAM System Execution Systems, Execution Systems, High volume streaming data High volume streaming data (Tivoli Maximo Tririga or (Tivoli Maximo Tririga or Legacy Databases, Legacy Databases, (InfoSphere Streams) (InfoSphere Streams) other) other) Distributed Control Systems Distributed Control Systems15 © 2013 IBM Corporation
  • Predictive Asset Optimization generates business value Business Use Case Business Value Predict Asset Failure/Extend Life Determine failure based on usage and Optimize Enterprise Asset wear characteristics Management maintenance, inventory and resource schedules Utilize individual component and/or Increase return on assets environmental information Identify conditions that lead to high Estimate and extend component failure life Predict Part Quality Detect anomalies within process Improve customer service Compare parts against master Improve quality and reduce recalls Conduct in-depth root cause analysis Reduce time to identify issues16 © 2012 IBM Corporation
  • Deriving Value From Predictive Asset OptimizationKey Metric Business Benefit How Advanced Analytics Enables ValueMaximize Revenue Products and Services New Products and Services. Up Sell Opportunities, Higher product qualityCompetitive Advantage High Availability Better Asset Utilization, More Production Cycles Lower Start Up Costs Fewer Reworks, Fewer Installation RepairsCost Savings Less Un Planned Downtime Fewer Failures, Faster Problem Identification, Better process throughputIncreased Reliability Better Productivity Issues Cost Avoidance, Faster Root Cause, Higher equipment utilization Better Quality Proactive Monitoring, Predictable Performance, Identification of factors likely to result in diminished qualityO & M Costs Non Production Costs Fewer Failures, Fewer Emergencies, Less need for excess MRO inventoryIncreased Efficiency Shorter Maintenance Predictive Maintenance, Better Planning Lower Warranty Costs Fewer Part Failures, Shorten Issue ResolutionCustomer Experience Proactive Management Fewer Surprises, Proactive CommunicationIncreased Satisfaction Individual Experience More Focused Communication, Holistic View Better Collaboration Information Integration Across Industry, Better Insight Across Silos17 © 2012 IBM Corporation
  • John WardCustomer Case Studies Global Industrial Sector Solutions Leader IBM Predictive Analytics © 2013 IBM Corporation
  • Predictive Quality and Warranty PerformanceBMW © 2013 IBM Corporation
  • Case Study Overview: BMW Customer Overview Proven Business Value• German manufacturer of quality vehicles for worldwide markets• Manufacturing plants in Germany and elsewhere Reduced warranty cases from 1.1 to 0.85• Service / Warranty agencies worldwide per vehicle 5% reduction in warranty cases Business Challenges Annual savings of €30m approx. • Needed to gain deeper insights into the causes and combinations of circumstances which led to warranty issues in each geography • Needed to increase customer satisfaction through increased product quality and reduced warranty issues Solution Implemented  Implemented a data mining capability to gain actionable insights across a wide range of warranty issues  Fed back issue findings into product design process for improvements and modified service patterns where these were demonstrated to have contributed to warranty issues20 © 2012 IBM Corporation
  • Predictive Quality: IBM Predictive Asset Optimization (PAO) is used inthe BMW light-alloy foundry for the production process to better understandand eliminate problems quickly.Reduced scrap rate by 80% in 12 weeks Recycling not OK Processing and Cooling Testing OK Process info Database Cooling Processing Customer Online scoring Information flow Material flow21 © 2012 IBM Corporation
  • Predictive Maintenance: Reduce warranty claims for new cars byanalyzing historical information and vehicle data using IBMPredictive Asset Optimization (PAO).Reduced warranty costs by 5%, Repeat repairs by 50%22 © 2012 IBM Corporation
  • 23 © 2012 IBM Corporation23
  • Predictive Optimization of Assets in the FieldA Large Construction EquipmentManufacturer © 2013 IBM Corporation
  • Predictive Asset Optimization for Heavy Equipment A condition monitoring system that will: A condition monitoring system that will: Combine multiple data sources associated with a piece of equipment Combine multiple data sources associated with a piece of equipment Machine Data Apply predictive analytics to highlight possible problems Apply predictive analytics to highlight possible problems Wired or Wireless Utilize interpretive expertise to confirm the problem and identify a solution Utilize interpretive expertise to confirm the problem and identify a solution View 5 CM Elements and make Maintenance and Repair Work Inspections and RecommendationsEvents / Orders Fluid SamplesTrends /Payloads Dealer Condition Condition Monitoring Monitoring Analyst Elements 1. Machine Data Off-board Analytics Recommendations Fed into 2. Fluid Analysis To Detect Slower Maintenance & Repair Moving Problems (M&R) Process 3. Inspections Analysts Validate 4. Site Conditions Recommendation 5. Repair History Effectiveness 25 © 2012 IBM Corporation 25
  • Typical PAO Heavy Equipment Use Cases Priority Model Description Business Value Modeling Data Sources Business Questions Addressed Techniques Predict Major Machine health score used Classification Repair History Are there indications that a major Component Failures to predict impending Models (Dealer Source component failure is likely to occur 1 failures TBD), Fluid in the immediate future? Analysis, VIMS, Events, Other Predict Component Life Understand impacts of Regression Repair History How do low level failures Based on Specific individual low level failures, Models (Dealer Source cumulatively affect the life span of 2 Machine History estimate component life TBD), Events components? What are the site specific effects? Identify Failures that Based on history, identify Association Warranty Data, What kinds of failures are likely to Often Occur Together machines that have a high Models Repair History occur together (e.g., failure x probability of experiencing happens n hours after failure y, 3 similar failures failure x is usually followed by y and z, failure x happens every n hours, when failure x happened, condition y is usually present)? Detect Anomalies within Detect groups of machines Clustering Models (Trends, Fluid, What machines are behaving the Fleet experiencing anomalous Events) or Other differently from the others in the 4 behavior Electronic Data fleet or at a site? Source Utilize Statistical Process Detect statistically rare Runs Chart, Trends or Other What are the rules by which Control conditions that bear further Range Chart Electronic Data observed changes in electronic data 5 investigation Source will trigger alerts? Predict Component Life Extend component life, Weibull Analysis Warranty Data, How can component life history 6 Based on Population better MARC analysis Repair History data be used to make decisions about PM or PCR intervals,26 © 2012 IBM Corporation26
  • Predictive Maintenance Demonstration © 2013 IBM Corporation
  • Lester McHargue Business Solutions Industrial Business Analytics and OptimizationPredictive Asset OptimizationLarge / Capital Equipment Manufacturers © 2013 IBM Corporation
  • Capital Equipment Manufacturers Manufacturers need to be able to identify potential component failure as well as machine health of in-service equipment by identifying early signs of potential downtime and enterprise component issues.  The Opportunity  What Makes it Smarter • Difficulty in separating the signal from the This system uses a variety of Advanced Analytical techniques to monitor Time-Series noise to detect enterprise component Machine Data, Site Conditions and Service History to predict component well as system problems failure • Unscheduled maintenance and downtime are critical issues costing the end customers from Hundreds of Thousands to Millions of Structured Data Unstructured Content Dollars per hour • Process Data • Work Orders • Most relationships with the user community • Alarm Data • Technical Support if reactive in nature. Quality issues are often • Manufacturing Data • Warranty Claims identified at the site. Monitor &  Business Case Track Critical • Early identification and mitigation of enterprise Information Sense & component and quality issues Implement Measure • Provide insight to the health and probability of Corrective failure for in service equipment maximizing Metrics Action uptime Early • Establish a proactive relationship with the user Detection community to increase asset availably, reduce Propose Detect & costs, and create new product and service Corrective Share Critical offerings Issues Actions • Better maintenance planning Measure & • Identification of new product and proactive Decide on services. Actions29 © 2012 IBM Corporation
  • Capital Equipment Manufacturers -Overview of Cross-Industry Standard Process for Data Mining (CRISP-DM)  The methodology include six phases: • Business Understanding • Data Understanding • Data Preparation • Modeling • Evaluation • Deployment  The first phase, “Business Understanding,” is critical for successfully turning qualitative and quantitative data into valuable insights that lead to value-added actions.  Phases 2 through 5 can occur in any order and almost always include multiple iterations back to the Business Understanding stage.  The methodology by itself, however, does not guarantee success with advanced analytics.  Success requires deep expertise and experience in evaluating and using a wide range of advanced analytical techniques.A large equipment manufacturer saved $1 million in just two weeks by using predictive maintenance to proactively identify problems and take actionbefore failure occurred. By minimizing downtime and repair costs across all its manufacturing operations, the manufacturer achieved a 1400% return oninvestment in just four months. 30 © 2012 IBM Corporation
  • Capital Equipment Manufacturers - Creating Client Value ThroughIntegrated Operations Visibility Prediction Collaboration Optimization Integrated Others… Operations Services Production and Technical Support Maintenance and Warranty and Collaborative Performance Reporting And Engineering Service Quality Decision Making Turnaround Predictive Modeling Maintenance Warranty Optimization & Maintenance Service Analytics Planning Optimization Business Consumables Early Event Enterprise Asset Advanced Analytics Solutions Warning Management and Discovery Others… Inventory Planning Downtime Enterprise Document Data Mining for Knowledge Optimization Management Anomaly Detection Management Unstructured Content Maintenance Manuals Downtime Reports Standards Trade Publications Call Logs Service Records Reports Real-time Data Capturing and DCS, PLCs RFID & MES Application Engineering Equip Historians Remote Sensing & Facility Monitor & Process Doc Actuation Physical Assets31 © 2012 IBM Corporation
  • US Capital Equipment Manufacturers: A Matrix/Horizontal Approach To Analytic Value Maintenance Production / Field / Plant Technical Support Warranty Process Service / Engineering  Fewer Claims  Reduce Downtime  Better Efficiency  Product Design  Incident Avoidance  Part Cost  Better Collaboration  Higher Quality  New Products  Case Reduction  Supplier Recovery  Efficiency  Predictive Performance  New Services  Case Resolution  Reserves  Asset Utilization  Holistic View  Proactive  Efficiency  Consistent Standards  Root Cause Revenue Growth: Availability, Asset Utilization, New Products, New Services, Proactive Cost Savings : Reduced Downtime, Higher Quality, Better Efficiency, Root Cause Analysis O&M Costs: Enterprise View, Fewer Emergencies, Value Based Decisions, Warranty Costs Customer Satisfaction: More Availability, Better Collaboration, Proactive Communication Integration of Analytics Across Functional Areas Yields the Best Results32 © 2012 IBM Corporation
  • Value of Integrated Analytics Faster Alarm Detection Better Optimization + Next Best Action Earlier Failure Prediction Better Asset Utilization Proactive Improvements Process Optimization Enterprise Analytics Customer Impact Analysis Dependencies +Value Based Decisions Faster Alarm Detection Better Optimization Earlier Failure Prediction + Better Asset Utilization Proactive Improvements + Process Optimization Enterprise Analytics + Customer Impact Analysis Dependencies Faster Alarm Detection Dependencies Capability Earlier Failure Prediction + Better Optimization Proactive Improvements + Asset Utilization Fi n Enterprise Analytics an Su ce pp Faster Alarm Detection + Enterprise Analytic ly Early Failure Prediction + Dependencies Ma rod an Proactive Improvements + Optimize dD int uct P en io em an n En roce an ce P ter ss d Re roce Alarm Detection & pr P Early Failure Prediction al ise Reactive Improvements Tim s s e Value33 © 2012 IBM Corporation
  • Some Example Results Use Case PAO Approach Results Provide Early Detection of Combine operational, environmental, and Identification of the factors impacting availability maintenance information to identify causal leading factors factors impacting up time Provide Early Detection of Integrate process, technical support, and 10 to 13 month early Enterprise Wide component warranty information to identify enterprise detection failures, impacting warranty, wide patterns in component failures and asset availability. Provide Early detection of Integrate process, technical support, and Identification of trends that impact quality and maintenance information to identify primary and performance multivariate patterns that lead to poor results secondary causal factors Provide an indication to the Integrate process, environmental, operational, 80% to 90%+ health of in-service equipment, maintenance, and engineering support accuracy in predicting and the probability of failure information for a complete picture to health of equipment downtime. between maintenance windows an asset. Enable proactive customer Integrate process, technical support, Identification and management through better maintenance, and warranty information to proactive delivery of understanding of individual provide individualized products and services products and equipment issues services. Direct and through dealers34 © 2012 IBM Corporation
  • Predictive Asset OptimizationImplementation Methodology © 2013 IBM Corporation
  • Predictive Maintenance Value Financial Impact Value Drivers Key Performance Indicators Improved revenue growth % improvement / ton Cost per Ton Revenue Production Higher Growth Productivity Tons per Hour % improvement / Hour Component Rebuild Cost Comp replacement cost Renewal Cost Component Life Comp Life Target Achieved Maintenance Mechanical Availability Availability Index Improved Cost position Increased Up % scheduled Maintenance % scheduledShareholder Operating Time Value Margin Mean Time Between Stops MTBS Mean Time to Repair MTTR Maintenance cost Labor resources Maintenance Ratio Parts consumption Parts Ratio Improved Working Average Inventory value, capital position Number of Spares major components Fixed Asset Fewer Spares Capital TCO Per Spare Efficiency Improved Infrastructure Dedicated Shop space efficiency of capital outlays Inventory Average inventory value Fewer Spare Parts Average Spare Parts / Spare Parts / Components Reduced Risk / Components Components Inventory Inventory Risk Safety / Compliance % field repairs Mitigation 36 © 2012 IBM Corporation
  • Predictive Asset Optimization’s pre-integrated solution offersflexibility to meet your company’s needs Flexible Deployment Flexible Purchases OPTION 1: BUSINESS VALUE ACCELERATOR Define Options Assess Develop Solution Requirements Business Case Roadmap • Pre-configured 2-3 Weeks 2-3 Weeks 1-2 Weeks individual products andStart Here services, or as OPTION 2: SOLUTION PROOF-OF-CONCEPT or PROOF-OF-VALUE Define and • GBS hostedStrategy EvaluateWorkshop Implement Pilot solution Pilot 6-8 Weeks 1-4 Weeks OPTION 3: SOLUTION IMPLEMENTATION Establish Conduct Design, Define Solution Solution Impact Build and Use Cases Components Assessment Deploy37 © 2012 IBM Corporation
  • IBM Proven Methodology: A Parallel ApproachIBMs approach is tailored to deliver immediate value via a Proof of Value (POV)application as well as provide short and long-term strategic development for initiativeplanning and capabilities development. Strategy Development Develop Strategy, Initiative Roadmap & Value Case Strategy & Roadmap Strategy Assessment & Development Formulize Strategy Roadmap & Value Case Analytics Proof Of Value Address high-priority business challenge through advanced analytics techniques Analytics POV Use Case Requirements POV POV Build Track & Evaluate POV & Data Understanding38 © 2012 IBM Corporation
  • How to learn more  For additional information including whitepapers and demos, please visit:  IBM.com Predictive Maintenance http://www-01.ibm.com/software/analytics/solutions/operational-analytics/predictive-maintenance/  Smarter Predictive Analytics: http://www.ibm.com/analytics/us/en/predictive-analytics/  Smarter Analytics Signature Solutions http://www.ibm.com/analytics/us/en/solutions/business-need/index.html:39 © 2012 IBM Corporation