Infosys' session on IoT World - Systems Integration in an IOT world: A practitioner’s view
Download as PPTX, PDF11 likes8,173 views
The installed base of IOT products is growing exponentially along with its economic impact. Innovators are seeking a new generation of technology solutions that will help them create, operate and maintain smart connected products. It is a system of systems world that is complex, heterogeneous with a mix of incompatible, non-standard, multi-vendor, smart & not-so smart, connected and not-connected products that generate incredible amounts of data to be analyzed for insight and value. In this presentation, Jayraj Nair, AVP and Head of IoT, Infosys Engineering Services will share his experience’s building real world IOT solutions, innovative ways in which a system integrator can enable the integration between the physical and digital worlds and accelerate the adoption of Internet of Things.
Ad
More Related Content
What's hot (20)
Similar to Infosys' session on IoT World - Systems Integration in an IOT world: A practitioner’s view (20)
Ad
More from Infosys (20)
Ad
Recently uploaded (20)
Infosys' session on IoT World - Systems Integration in an IOT world: A practitioner’s view
- 1. System Integration in an IOT world Jayraj Nair, Head – IOT Practice and AVP – Engineering Services, Infosys
- 2. Content 2 • Infosys at a glance • Smart, connected products and operations • Industry 4.0 and survey results • Capability levels – monitoring, control, optimization • Rethinking predictive analytics – IIP • Ecosystem partnership – IIC
- 3. Infosys at a glance 3 Infosys Intellect ValuesPeople Clients Founded in Pune, India in 1981 $8.7 billion revenues 950 clients 50+ countries $40.3 billion market capitalization 176,000+ staff 98 nationalities 97 percent of staff are university educated 22 percent hold masters degrees or doctorates 94 percent are consultants and engineers World’s largest corporate university 2 percent of revenues invested in R&D More than 300 researchers Investing $500 million in Innovation Fund 505 patents pending and 204 granted Transparency, ethics, and respect 98.3 percent of projects delivered on time 96.6 percent business is repeat business 2 percent of profits go to the Infosys Foundation Topped Asiamoney’s Corporate Governance Poll in the Domestic Country category 4 out of top 5 US banks 6 out of top 10 global CPG 8 out of top 10 global pharma 4 out of top 5 global aerospace and defence 6 out of top 10 global telcos
- 4. Infosys Engineering Services 4 250+ clients 20+ years Global operations 8000+ engineers Keyengineeringservices Mechanical, structural and manufacturing engineering Product lifecycle management Knowledge based engineering and analytics Electronics / embedded / electrical engineering Value analysis and value engineering Plant automation control services Networking and telecom engineering Industries Aerospace Discretemanufacturing Automotive OEM,SemiconductorandHi-tech Telecomserviceproviders Medicaldevicesandhealthcare Retail,CPGandtransport ISVandservices Energyandutilities
- 5. Integrating smart, connected products 5 1 2 543 Hardware and software embedded design of intelligent devices, intelligent gateways devices, device security, and multiple protocols such as MQTT, AMQP, STOMP MODBUS, PROFINET, OPC Connectivity solution around Wi-Fi, ZigBee, Bluetooth, Zwave, 3G, and 4G moving to 5G SIM; M2M connectivity management; traffic engineering; network and device operations Management of end devices for state and status; data modeling; cloud hosting; device software management; policy rules engine; complex event processing; security; and integrity of data Database integration, CRM integration data warehouse, workflow management, workforce management, enterprise mobility Stream processing, actionable analytics, data visualization, statistical computing, business dashboards, predictive and prescriptive analytics, business insights, monetization, and continuous improvement Intelligentdevices Connectivity Devicemanagement Enterpriseintegration Businessanalytics
- 6. Smart, connected products and Industry 4.0 6 Operations efficiency Production optimization (OEE) Production planning and scheduling Productivity modelling Statistical quality control Inventory optimization Maintenance efficiency Condition monitoring Predictive maintenance Maintenance planning and scheduling Reliability-centered maintenance Root cause analysis / anomaly detection Service efficiency Remote management / remote services Field service management Materials management (spare parts / inventory) Service life cycle management Supply chain analytics Information efficiency iDashboard Data quality framework Asset life cycle information model Machine-born data management and analytics Knowledge management Energy efficiency and sustainability Energy management Resource efficiency Asset sustainability index Safety performance (Alarm management, EHS) Regulatory / standards compliance Impacts value chain Transforms asset efficiency
- 7. Industry 4.0 survey results 7 Study : FIR Institute at the University of Aachen, Germany + Infosys Participants : 433 Industrial Manufacturing executives
- 8. Industry 4.0 survey results 8
- 9. Deploying varying levels of IoT capability Monitoring visibility E.g., factory visibility Control operations E.g., Health Safety Optimization E.g., Predictive analytics 9 Reference solutions @ booth #220
- 10. 10 Implemented a real-time plant visibility solution for one of the largest food and beverage companies in the world Solution • Real-time capture of production data from factory floor equipment and systems • Mobility solution implemented for ‘on-the-go’ quality inspection • Intelligent dashboards with real-time and historical performance reporting Results • Overall line and plant effectiveness increased from 45 percent to 60 percent (which has significantly impacted plant throughput and profitability) • Manual paperwork reduced by 70 percent • Enterprise data duplication reduced to 0 percent • Reduction in frontline resource fatigue and equipment downtime • Implemented root-cause analysis for quicker troubleshooting • Increased speed and accuracy in decision-making • Significant productivity improvement in quality inspection (mobile on-the-go) Challenge A global FMCG company needed to modernize its factories across multiple locations to improve operational efficiency and effectiveness
- 11. 11 Developed a manufacturing intelligence system for one of the largest manufacturers of heavyweight motorcycles in the world Solution Implemented a leading-edge manufacturing integration and intelligence system, fully integrated with ERP and shop floor systems Results • Real-time visibility into production rejects and start-up rejects, reduced scraps, and improved the quality measure of production systems significantly • Enhanced the overall equipment effectiveness (OEE) of the plant • Implemented preventive maintenance by tracking the order inflow into production systems and proactively resolving them • Engaged in innovation co-creation, partnering in their innovation strategy Challenge A global automotive company needed better insight into its manufacturing systems in order to build an intelligent manufacturing enterprise
- 12. 12 Enabled a real-time information management system for a global mining major Solution • Enabled integration and interoperability between multiple manufacturing systems, historical data and legacy systems • Critical systems moved to newer converters – without disruption to business continuity • Established a more stable platform for the existing quality system for smelter operations Challenge A global mining major needed real-time visibility across the mining value chain – extraction, transportation, beneficiation and processing Results • Improved day-to-day scheduling, resulting in increased efficiency and productivity • Improved availability of business-critical applications • Global roll-out of new KPIs • KPI standardization across similar operations
- 13. Deploying varying levels of IoT capability Monitoring visibility E.g., Factory visibility Control operations E.g., Health safety Optimization E.g., Predictive analytics 13 Reference Solutions @Booth 220Reference solutions @ booth #220
- 14. 14 Implemented a biometric field engineer safety monitoring solution for one of the largest global manufacturers of escalators and elevators Challenge A global manufacturer of escalators and elevators needed real-time visibility into the safety of their field engineers Solution • Implemented a biometric field engineer safety monitoring solution • Real-time monitoring and reporting of hazardous situations for multiple individuals and teams simultaneously • Designed for and deployed in harsh and remote environments • Developed and deployed: • Mobile app for field engineers • Remote monitoring sensors • Mobile app for facility managers • Integrated directly with other enterprise systems and / or emergency services Results • Ensured faster response times in emergency environments • Reduced the number of accidents as the vital parameters of each field engineer is constantly monitored
- 15. 15 Development of a centralized dashboard to implement continuous and remote monitoring of field-based personnel for a leading mining company Challenge A leading mining company needed to continuously monitor their field-based personnel and miners, and provide assistance during life-threatening conditions Solution • Developed a centralized dashboard to continuously monitor field workers • Identified the severity of the breach or hazardousness of the situation • Delivered immediate evacuation alerts and tracked high-risk workers Results • Faster evacuations • Faster mobilization of specialist resources • Reduction in the cost of health insurance cover for miners • Improvement in the health and safety matrix
- 16. 16 Infosys built a demonstrator to establish the relevance of Industry 4.0 in the delivery of smart services together with a global leader in power and automation technologies and the largest technical university in Germany Solution • Android-based mobile application • Applied to a technically complex class of instruments whose functionality is critical in the process industry • Real-time QR code scanning to understand current device status and maintenance needs • Multiple workflows to manage and maintain complex gas analyzers • Cloud-based self-service experience • Integrated security solution Results • Enhanced user experience for both the customer and the field engineer • Faster and best-in-class service support from OEM • Significant reduction in mean time to repair (MTTR) • Enhanced quality monitoring and reporting
- 17. Deploying varying levels of IoT capability Monitoring visibility E.g., Factory visibility Control operations E.g., Health safety Optimization E.g., Predictive analytics 17 Reference Solutions @ Booth 220
- 18. 18 Enabled predictive maintenance and corrective action for a mining major Analyzed 300 million records in less than 4 seconds Reduced the cost by 1000 percent Helped customer save on costly downtime caused due to equipment failure Provided predictive analytics to identify when equipment on field is likely to fail or needs maintenance in order to maximize uptime / in- service for equipment
- 19. 19 Improved service levels of ATMs for a leading ATM manufacturer Cleansed 4 million records within 27 seconds Reduced cost by 18 percent, from increasing mix to 40 percent staged calls Increased efficiency by 14 percent, from 3 to 4 service calls per technician per day Reduced time to identify chronic defects from weeks to hours
- 20. 20 Prevented network faults using predictive analytics for a telecom major Analyzed 16.7 million records in 5 seconds Impending network faults in the next week predicted with a high degree of accuracy to fix the network failure points Solution based on open-source stack, including Apache Spark and prebuilt Infosys components, was delivered in just 5 days Improved price-to-performance ratio
- 21. Rethinking predictive analytics 21 Scope All data – not just structured, not just internal Speed @ scale Handle massive scale without compromising speed (decision time) Insights Move from request-response to self-discovery
- 22. Prebuilt Components from Infosys & Partners Open Source Data Scientists Technical Experts Infrastructure Management Analysts Functional Experts 9 Building blocks – Infosys Information Platform
- 23. Ecosystem partnership – IIC 23 • Infosys will focus on the development of future IIC testbeds, leveraging its expertise in predictive analytics to enable Industry 4.0 essentials of maintenance, operations, information, service, and energy efficiency • Solutions will leverage the recently announced Infosys Information Platform (IIP) and be anchored on open-source and open-access ingredients for rapid innovation by the partner community Reach out to us [email protected] to collaborate and co-innovate
- 24. © 2015 Infosys Limited, Bangalore, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosys acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this document. Except as expressly permitted, neither this documentation nor any part of it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printing,photocopying,recording or otherwise, withoutthe prior permission of InfosysLimited and/ or any named intellectual property rights holders under this document. Thank you
Editor's Notes
- #5: PLM – Product Life-cycle Management, OEM – Original Equipment Manufacturer, CPG – Consumer Packaged Goods, ISV – Independent Software Vendors
- #19: Rio Tinto use case: A mining company operating a fleet of thousands of trucks wanted a predictive maintenance platform to prevent losses incurred due to breakdown of vehicles. The trucks carrying mineral ore were fitted with sensor technologies. The fleet of trucks pinged data that was of little use in the absence of a mechanism that analyzed data in real time. Infosys developed a proof of concept (PoC) using open-source tools and technologies such as Hadoop and cloud-based hardware. It enabled the company to process voluminous amount of data and glean meaningful insights. Predictive maintenance resulted in reducing vehicle downtime. It delivered significant improvements in downstream supply chain operations and profitability. The Infosys solution offered processes-related data in real time. The scalable solution can support the company’s business plan to increase the fleet of trucks by more than 300 percent with a corresponding increase in sensor data load.
- #20: Diebold – A leading ATM manufacturing and service company wants to reduce its cost in maintaining ATMs also provide better customer service and SLAs Predict that an ATM is going to fail in the next week (over 80 percent confidence level) based on alert and incident data from XMS and machine data Ticketing data from 8500 ATMs / 4M records was ingested into Spark and then cleaned out (date, spaces, null fields etc.) in just 27 seconds on 10 node AWS cluster (32 CPU, 64GB RAM, 640 GB SSD storage) Enriched data to create the parameters required for analysis. Spark Logistic Regression run on top of this data with data prediction in 60 msecond Data ingested to Oracle and visualization done using Tableau
- #21: BT – A global telecom company offering data services to customers using xDSL technology partnered with Infosys. Network faults in the xDSL backbone caused disruptions in data services. The company sought a model to predict the network faults proactively. Infosys developed a ‘control signature’ of healthy lines using signal processing and statistical techniques based on the Hadoop-based Infosys Information Platform. Infosys created a ‘fault signature’ from the connection properties in the days leading up to the reporting of a ‘fault’. The team built a predictive model that reported the probability of an impending fault within a week. The formula for the predictive model was applied to 16.7 million network line characteristic data records, processed within 5 seconds on a medium-sized 5 node cluster. The outcome was a subset of network faults that could be predicted with a high degree of accuracy