IBM PREDICTIVE MAINTENANCE AND QUALITY. Overview

Transcription

1 IBM PREDICTIVE MAINTENANCE AND QUALITY Overview

5 Senturus Webinar Series Topics Chapters in the BA Demystified Series Architectures & Data Transformation Solutions Tools Methodologies & Techniques People & Processes Our Approach: Our webinars are delivered by Senturus team members and industry experts (guest speakers) Copyright 2013 Senturus, Inc. All Rights Reserved. 5

6 Today s Webinar Architectures & Data Transformation Solutions Predictive Analytics applied to Maintenance & Quality Tools Chapters in the BA Demystified Series Methodologies & Techniques People & Processes Our Approach: Our webinars are delivered by Senturus team members and industry experts (guest speakers) Copyright 2013 Senturus, Inc. All Rights Reserved. 6

7 Today s Presenters John Peterson CEO, Senturus Anuj Marfatia IBM Program Director, Solutions Marketing

8 SENTURUS INTRODUCTION Who we are

9 Senturus: Business Analytics Consultants Business Intelligence Enterprise Planning Predictive Analytics Our Team: Business depth combined with technical expertise. Former CFOs, CIOs, Controllers, Directors Copyright 2013 Senturus, Inc. All Rights Reserved.

11 The Senturus IBM Cognos Relationship Senturus has been a IBM Cognos Premier Partner for 12 years (One of 10 such Partners in North America) Senturus has won a number of IBM and Cognos awards over those years Senturus sits on the board of the Cognos User Group of Northern California A number of Senturus consultants are former IBM Cognos employees IBM Cognos professional services has often staffed their consulting engagements with Senturus people We have delivered over 1,000 successful projects using IBM technologies

12 PREDICTIVE ANALYTICS Why now?

13 The Evolution of Business Analytics Rear View Mirror Integrated Planning & Forecasting Predictive Analytics Advanced Visualization Dashboards & Scorecards SQL Reports OLAP & Ad-hoc Forward Looking Operational Reports Past Present Future

14 Today s Situation Why the time is ripe Organizations have spent over a Trillion dollars on operational systems (ERP, CRM, etc.) which contain massive amounts of valuable digital data Organizations have spent Billions of dollars on Business Intelligence systems and data marts/warehouses New IP-based devices are throwing off Billions of records ( data exhaust ) daily I.E. The Data Is There!

15 Today s Situation (cont.) Why the time is ripe Moore s law has driven massive increases in computing power, storage space, and network bandwidth. while reducing cost Thus, driving esoteric applications into the mainstream. I.E. The Power and Software is There!

16 Today s Situation (cont.) BUT, most BI systems still simply pump out canned reports showing past activity AND, expect humans to sift through it all in order to make impactful business decisions and take appropriate action for the future We can do so much more

17 PREDICTIVE MAINTENANCE AND QUALITY Shifting from Reactive to Proactive

18 Marketplace Forces are Amplifying day-to-day Issues Predictive and Business Intelligence Predictive Maintenance and Quality Lean operations Poor asset performance Limited process integration Complex supply chains Customer demands Aging workforce Lack of visibility into asset health High costs of unscheduled maintenance Inability to accurately forecast asset downtime and costs Resultant unnecessary process proliferation Lack of visibility of predictors across organizational silos Difficulty synchronizing demand and supply Too many manual processes and information sources Losses in processes have become normal Raw-material price volatility Compliance and scrutiny Aging assets pushed to limits to meet consumer needs Resource complexity makes it harder to respond to changing needs

19 Opportunities Exist to Improve the Bottom Line Predictive and Business Intelligence Predictive Maintenance and Quality Interconnected growth, lower data-capture cost Risk of asset failure Focus on operational processes 1 trillion, USD0.03 Number of sensors by Estimated price of average passive sensor by 2021, representing a 66 percent decrease in eight years 2 #1 Failure of critical assets was the top risk stated by executives as having the biggest impact on operations 3 99% Percent of CIOs with mandates to transform the business who are looking to simplify key internal processes 4 1Making Markets:Smarter Planet. IBM Investor Briefing, Big Data-Startups, The Great Sensor-Era: Brontobytes Will Change Society, April 16, Aberdeen Group, Asset Management: Using Analytics to Drive Predictive Maintenance, March 19, IBM, The Essential CIO: Insights from the Global Chief Information Officer Study, May 2011.

20 IBM Predictive Maintenance and Quality Predictive and Business Intelligence Predictive Maintenance and Quality Improve asset productivity Reduce Operational costs Increase process efficiency Accelerate time to value Real-time capabilities Big data, predictive and advanced analytics Quicker and more-accurate decision making IBM Maximo integration Open architecture Business intelligence

21 PMQ Enables Better Business Outcomes Predictive and Business Intelligence Predictive Maintenance and Quality Helps monitor, maintain and optimize assets for better availability, utilization and performance Helps predict asset failure to better optimize quality and supply chain processes Reduces guesswork during the decision-making process Combined with out-of-the-box models, dashboards, reports and source connectors

22 PMQ offers business value for organizations Predictive and Business Intelligence Predictive Maintenance and Quality BUSINESS USE CASES BUSINESS VALUE Predict asset failure Assess failure based on usage and wear characteristics Use individual-component information, environmental information or both Help identify conditions that can lead to high failure Estimate and extend component life Increase return on assets Improve maintenance, inventory and resource schedules Predict poor quality parts/components Help detect anomalies within processes Compare parts against a master Conduct in-depth, root-cause analysis Improve quality and reduce recalls Reduce time to identify issues Improve customer service

23 Israel Electric Increases Grid Reliability Predictive and Business Intelligence Predictive Maintenance and Quality 20% cost reduction by avoiding the expensive process of reinitiating a power station after an outage USD80,000 savings per turbine on petrol combustion costs by avoiding malfunctions of turbine components Increased efficiency of preventive maintenance schedules, costs and resources, resulting in fewer outages and higher customer satisfaction Business problem: The company s research institute is charged with improving the safety and reliability of power generation and transmission while fueling innovation. That includes planning for disruptive events such as solar storms, making improvements in transmission efficiency, incorporating new sources of renewable energy into the grid and analyzing growing volumes of data from an increasingly smart grid. Solution: This energy provider uses powerful predictive analysis to understand when and why outages occur so it can take steps to prevent them.

24 Honda R&D Co., Ltd Uses Predictive Analytics Predictive and Business Intelligence Predictive Maintenance and Quality 50% reduction in carbon dioxide emissions by commercializing EV technology Boosts confidence and customer satisfaction with EVs by improving performance Improves design by analyzing massive amounts of operating data Business challenge: Because all-electric vehicles (EVs) do not use gasoline as do traditional or hybrid cars, they rely entirely on their batteries for power. Honda R&D Co., Ltd., a division of Honda Motor Co., Ltd., wanted to better understand what factors had the greatest effect on battery performance and longevity. The smarter solution: Honda R&D can now gather and analyze near-real-time battery data from Fit EVs on the road in Japan and the United States. Analysis can identify which operating factors, such as road conditions, charging patterns and trip length, have the greatest effect on battery life. Further analysis can help the automaker predict when batteries need to be replaced so it can alert owners in advance. Data gathered from the real-world operation of our vehicles is critical to predict the longevity of current batteries and greatly influences future product design. Senior chief engineer, Automobile R&D Center

25 PMQ Analyzes Data from Multiple Sources Predictive and Business Intelligence Predictive Maintenance and Quality Generate predictive and statistical models #1 Collect and integrate data Structured and unstructured, streaming and at rest #2 #3 Predictive Maintenance and Quality Data agnostic User-friendly model creation Interactive dashboards Enables faster decisions Attain analytical insights #5 #4 Display alerts and recommend actions Act upon insights Asset performance Process integration

26 PMQ uses data from raw format to action Predictive and Business Intelligence Predictive Maintenance and Quality End user reports, dashboards, drill downs IBM Predictive Maintenance and Quality Telematics, manufacturing execution systems, existing databases, distributed control systems High-volume streaming data Enterprise asset management systems

27 With a proven architecture Predictive and Business Intelligence Predictive Maintenance and Quality End user reports, dashboards, drill downs Predictive analytics Decision management Analytic data store Business intelligence (Prebuilt data schema for storing quality, select machine and production data, and configuration) Integration bus (Prebuilt data schema for storing quality, select machine and production data, and configuration) Advanced analytics powered by IBM SPSS and Cognos software Data integration provided by IBM Integration Bus and IBM InfoSphere Master Data Management Collaborative Edition software, which feeds a prebuilt, data schema based on IBM DB2 software Process integration with automatic work-order generation from Maximo software Telematics, manufacturing execution systems, existing databases, distributed control systems High-volume streaming data Enterprise asset management systems Data models, message flows, reports, dashboards, business rules, adapters and key performance indicators

28 Converges Asset Management & Analytics Capabilities Predictive and Business Intelligence Predictive Maintenance and Quality Enterprise asset management + Predictive Maintenance and Quality = Better outcomes Asset maintenance history Condition monitoring and historical meter readings Inventory and purchasing transactions Labor, craft, skills, certifications and calendars Safety and regulatory requirements Supply chain processes Asset lifecycle management Analytical insights Staff planning Facilities operation Better maintenance windows to reduce operating expense More efficient assignment of labor resources Enhanced capital forecasting plans Enhanced spare parts inventory Automated analytical techniques, including anomaly detection for assets and sensors Improved reliability and uptime of assets

29 PMQ Key Features Predictive and Business Intelligence Predictive Maintenance and Quality Real-time capabilities Big data, predictive and advanced analytics Quicker, more accurate decision making Maximo integration Open architecture Business intelligence Accelerated time to value

30 PMQ is a Comprehensive Solution Predictive and Business Intelligence Predictive Maintenance and Quality Infrastructure activities Program and project management Setup and installation Hardware Software Specialists Hosting Analytical activities Solution impact assessment Business case development Use case definition Data integration Information modeling Predictive modeling Specialized skills Integration skills Business consulting Industry skills Maintenance experts Maximo specialists Industry expertise Scientists and mathematicians Prof. services Vendor software Vendor research Vendor systems and technology Client value

31 Let s Get Started with Better Business Outcomes Predictive and Business Intelligence Predictive Maintenance and Quality 4 1 Visioning workshop Whether via web seminar, at your facility or in an IBM solution center, we can begin charting a course. 2 Business value assessment Align business capabilities with business strategy, and recommend a road map for improved value. 3 Solution workshop Lay out the path ahead, from immediate improvements to a common future vision. Proof of concept Prove the path forward, starting small and scaling up.

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