Data Science & Big Data Practice INSIGHTS ANALYTICS INNOVATIONS Manufacturing IoT Amplify Serviceability and Productivity by integrating machine /sensor data with Data Science
What is Internet of Things (IoT) Definition of Internet of Things The Internet of Things is the next generation of personal computing, whereby objects interact, potentially independently, with each other and with their environment IoT The Internet of Things (IoT) connect all manner of end-point, unravelling a treasure trove of data Requirements of Internet of Things with the flawless combination of sensors, actuators and semiconductor based devices connected over internet network The Semiconductor industry will drive the adoption of IoT by: Providing smaller and cheaper semiconductors Increasing standardization Enabling Big Data/analytics Connecting sensors Driven by the huge growth potential of connected devices and modest traffic Main sources of demand: Transportation Smart Cities Smart Grid Big Data Ubiquitous networks and devices proliferation enable access to a massive and growing amount of traditionally siloed information Semiconductors INTERNET OF THINGS Telecom Data Science Data Science empowers decision makers by extracting and presenting meaningful information real-time, thus providing more pro-active decision making than reactive Source: The Internet of Things Is Now Connecting the Real Economy New software applications for data management Big Data
IoT market in 2020 Drivers Drivers Business Productivity Cost efficiency Regulations Assets People Lifestyle Convenience Safety Technology Network ubiquity Devices Application tools Source: IDC Drivers Society Sustainability Safety Security Social Cost
The Data Science Institute (DSI) Offerings Utilities Fleet Machines High Tech Industrial/IOT Demand Forecasting Real-time outage Predictive Health Fleet Utilization & Usage Optimization Real-time Monitoring Predictive Health Preventive Downtime Field Failure/ utilization Analysis Predictive Health Preventive Downtime Field Failure/Utilization Analysis Predictive Health
Utilities Energy Demand Forecasting Inaccurate estimation of electricity demand and generation results in expensive power purchase from distributed energy resources. Hence near real time utility purchasing and distribution decisions are required. DSI s Advanced forecasting models take various inputs such as real time demand, outage pattern, weather, calendar events etc. to come up with near real time forecasting. Business Impacts Impact to Bottom line -To help manufactures/power distribution companies to identity opportunity areas to reduce the energy consumption and carbon footprint across production lines. Better Demand and Consumption allocation, planning for energy/utilities. Automated Demand Response Improved Serviceability by proper understanding of real time demand pattern. Business Outcomes Increased revenue due to advanced real time forecasting model incorporating real time weather change,supply surge, and other environmental factors. Deep dive analytics of consumption pattern (segmented consumer space across geo and time
Machine Usages & Failure Analytics Our expertise and experience of Machine, Operations,Sensor logs etc. can help Customer/OEM to gather insight on operational,maintenance activity. DSI solution can help customer to set up data ingestion/processing/consumption framework for real/periodic performance monitoring, predictive health monitoring and operational reporting. Real Time, Batch Communication Framework Logs Collector (Application, Event Logs, Usages Details) Analytics Engine Desktop based Utilization & Predictive Health Business Impacts Real Time Monitoring Utilization & Maintenance Business Outcomes Real time Events/failure tracking Predictive fault alarm Machine Utilization Machine Productivity Predictive Maintenance, Predictive Fault 1. Real-time event tracking and failure alarms 2. Utilization Analytics 3. Predictive Health Monitoring Dashboards 4. Cross-Sell/Up-Sell Opportunity identification
Fleet Analytics IOT enablement Our Fleet Analytics option provides a clean snapshot of all things related to engine performance, including a close look at fuel consumption and costs, idle time and travel trends for a single vehicle or a sub-fleet. Business Impacts User-friendly, data-rich dashboards illustrate comprehensive data, while allowing one to drill down on key indicators for an in-depth fleet and safety analysis along with predictive health of fleet. Communication Framework Fleet Monitoring Unit Real Time Monitoring Utilization & Maintenance Speeding,Route and Schedule Geo Fencing,Real time Events/Alarms Fleet Drive/Utilization time Monitoring Predictive Maintenance,Predictive Fault Analytics Engine Business Outcomes Real Time Alerts Cloud Based Applications Asset Tracking and Location Services Real Time Vehicle Performance Monitoring Dashboards Predictive Fault Alarming Desktop based Fleet Performance Management Applications Management Applications
Transforming your Data Chromosome DSI Case Studies on Assets Predictive health Monitoring 1 10 01 1
Case Study Electricity Demand Forecasting A state owned electricity distribution company was constantly loosing significant amount over electricity purchase. Due to poor planning during summer time, the unplanned load shedding was impacting market share as well. Broadly there were three concerned areas : 1.Increased Purchase cost 2.Higher Load Shedding instances 3.Poor distribution planning The concerned stakeholders were eager to have a improved forecasting,consumer segmentation for appropriate distribution,and real time monitoring of load and supply. Business Questions Whether improved forecasting model can come up with 90%+accuracy? Whether demand of Urban and Non urban customers can be monitored in real time? Whether clusters of high consumption of urban consumers can be separated out? Whether real time alert mechanism can be placed to avoid load shedding? Value Propositions Better prioritization and triage for restoration of supply. Planned purchase from grid based on accurate demand Minimize load shedding to reduce penalty payments. Illustrative Solutions Previous 5 years records were collected and cleansed for better understanding and modeling. Weather dataset was always studied together with consumption dataset. Consumption patterns of Urban/Non Urban/etc. are studied together with weather and socio-economic factors. Advance forecasting models were employed to improve accuracy to the extent of 95% For real time demand fluctuations, big data platform based real time event based processing incorporated. For online metrics monitoring, online monitoring dashboard was created. Business Impact Forecasting accuracy improved by 10% Revenue increased by 4% Load shedding reduced by 5% due to online load monitoring
Case Study Assets Predictive Health Monitoring A leading Elevator OEM was struggling with service quality and customer satisfaction. Frequent failures without any alarms were impacting the end customers and hence customers dissatisfaction was eroding market share. Many times its field engineers were struggling with prioritization and resource allocation. The OEM s Competitors were leading with new IOT enabled solutions. To turn the tide, the strategy and operations team came up with an ambitious plan to enable digitization of elevator system. The concerned business team wanted to do the pilot with certain locations elevator connected to centralised monitoring system almost getting synchronized with each elevators in near real time. Business Questions What are the frequent failures customers are facing.spread across geo? How different elevators can share usages data to cloud based system and further can be analysed? Can we predict the life /status of a elevator(sub component) by using Real time usages and sensor data? Can we develop a Predictive model to predict the health of elevator. For example if its not up to mark, or running with desired quality? Value Propositions Real time integrated Elevator systems with runtime predictive alarms Illustrative Solutions Analysed fault/alarm patterns of historical service data. Elevators usage patterns integrated with other sensor data. Periodic application of machine learning algorithm to predict life of critical components.this helped service team to maintain inventory of critical and costly parts. Enabled monitoring performance of critical components, raising alarm when performance starts deteriorating. Elevator keeps sending error codes and few of these error codes can be studied together to get a health index of that elevator Creating knowledge base of faults/repairs helpful to provide specific inputs to field technicians to understand failure correlations Business Impact 1.Reduced maintenance cost, improved elevator availability thus reduced downtime. Improved MTBF. 2.It became possible to prioritize and optimize service and repairs. 3.Predictive fault alarm helped technicians to be available for proactive servicing and failure identification. 4.IOT enablement helped to monitor each elevator with near real time
Case Study Machine s Predictive Health Monitoring & utilization A leading medical OEM deployed different types of medical devices at various diagnostic sites. OEM was struggling with machine utilization.machine performance and abrupt failures.same time,oem wanted to increase peripheral revenue by selling up new version of device components,routine service contract etc. The OEM wanted to have a robust big data analytics system which will consume data (~1TB) from across geos over night and ultimately will throw insights on utilization,machine behaviour and overall health index of each device. Business Questions How automatic ingestion of application logs can be done overnight? Whether the TB data can be processed over night and how it can ETL for analytics consumption? How we can see utilization pattern by processing application/component logs? Benchmarking of two different machines for cross/up sell. Can we predict the life /status of a medical device by processing application logs of week? Value Propositions Periodic health indexing of devices and utilization record for cross sell/up sell. Data based selling. Illustrative Solutions Setup the automatic data ingestion mechanism. Big data technology stacks were employed for voluminous data processing. ETL mechanisms were implemented for device parameter extractions. Creating knowledge base of faults/repairs helpful to provide specific inputs to field technicians to understand failure correlations. Statistical based reporting mechanism were implemented to capture weekly performance metrics and utilization visualizations Benchmarking analytics were done to capture operational efficiency for two different sets of machine. Analytics as consumption layer helped many adhoc analytics and advance analytical modeling. Business Impact Predictive health alarm Reduced the intervention time by identifying the frequent error patterns. Increases component selling
Digital Transformation Solution Framework Digital Transformation IOT Solution Framework Developer Tools and Adhoc Analysis 2 Big Data Lake 3 Spark Hadoop NoSQL Machine Log Mining Segmentation Multivariate Analysis Machine Learning 4 Enterprise Unified Data View Customers Products Finance Risk Sales Operations Visualization, BI and Analytics Apps 1 Data Ingestion & Engineering (ETL, Metadata, Semantic Engineering) Sensor Data Process Data System Logs I/O units Field Failure Enterprise Unstructured Data Enterprise Structured Data Machine/Sensors Data 1 Data Engineering 2 Big Data Lake 3 Data Science 4 Unified Data View External/Internal Data Ingestion - Real time/batch Quality, transformation Extract Transform Load Parallel and clustered processing Structured & unstructured data Hadoop/Spark/NoSQL Advanced Analytics, Machine Learning and statistical analysis Predictive Model, Recommendation Data Integration for Unified View Data warehouse for Viz, BI, Apps
INSIGHTS ANALYTICS INNOVATIONS INSIGHTS. ANALYTICS. INNOVATIONS.