INSIGHTS|ANALYTICS|INNOVATIONS
Data Science & Big Data Practice
Amplify Serviceability
and Productivity by
integrating
machine /sensor data
with Data Science
Manufacturing Internet of Things (IoT)
What is Internet of Things (IoT)
The Internet of Things (IoT) connect all manner of end-point, unravelling a treasure trove of data
IoT
Ubiquitous networks and devices proliferation enable access to a massive and growing amount of
traditionally siloed information
Big Data
The Internet of Things is the next generation of
personal
computing,
whereby
objects
interact,
potentially independently, with each other and with
their environment…
The Semiconductor industry willdrive the adoption of IoT by:
– Providing smaller and cheaper semiconductors
– Increasing standardization
– Enabling Big Data/analytics
– Connecting sensors
New software applications for data management
Driven by the huge growth potential of connected devices and modest traffic
Main sources of demand:
– Transportation – Smart Cities – Smart Grid Software Big Data Analytics Telecom Metcalfe’s law Semi-conductors Moore’s law INTERNETOF THINGS
Data Science
Data Science empowers decision makers by extracting and presenting meaningful information real-time, thus
providing more pro-active decision making than reactive
Requirements of Internet of Things
Definition of Internet of Things
…with the flawless combination of sensors, actuators and
semiconductor based devices connected over internet
network
Impact of Internet of Things (IoT) on key Industries
Utilities
Airlines
Agriculture
Leisure
Factory Automation
Consumer
Wearable
Insurance
Capital Goods
Automobiles
Healthcare
Retail
Transport
Lighting
Mining
Smart meters and smart water allowing for better management of resources Behavior tracking and biometrics for Auto and
Life product lines Factory automation, autonomous mining
Precision agriculture using data analytics and real time recommendations
Enhancing clinical outcomes and reducing resource requirements
Extending brands’ digital ecosystems and engaging customers further
Improving customer satisfaction and cost control
Monitoring operational conditions and equipment efficiency
Connected Cars
Monitoring of equipment and autonomous mining Process Monitoring
Supply Chain Management
Sourcing Efficiency and downtime reduction
Smart lighting in residential applications
IoT will aid various industries to bring down process inefficiencies and reduce cost
How the IoT market will shape up in 2020
Business
Productivity Cost efficiency Regulations AssetsPeople
Lifestyle Convenience SafetySociety
Sustainability Safety Security Social CostTechnology
Network ubiquity Devices Application toolsDriv
er
s
Driv
er
s
Drivers
Source: IDCUtilities
Fleet
Machines
High Tech
Industrial/IOT
The Data Science Institute (DSI) Offerings
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
Business Impacts
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 •Impact to Bottom line -To help manufactures/Power
distribution companies to identify opportunities to reduce 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. 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.
Machine – Usages & Failure Analytics
Logs Collector
(Application,
Event Logs, Usages
Details)
Communication
Framework
Analytics EngineR
eal
Tim
e ,
B
at
ch
Business Impacts
Business Outcomes
1. Real-time event tracking and failure alarms
2. Utilization Analytics
3. Predictive Health Monitoring Dashboards 4. Cross-Sell/Up-Sell Opportunity identification
Real Time
Monitoring
• Real time Events/failure tracking • Predictive fault alarmUtilization &
Maintenance
•Machine Utilization •Machine Productivity •Predictive Maintenance ,Predictive FaultDesktop based Utilization & Predictive Health
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.
Fleet Monitoring
Unit
Communication
Framework
Desktop based Fleet Management Applications Real Time Alerts
Fleet Analytics
Cloud Based Applications
IOT enablement
• Asset Tracking and Location Services • Real Time Vehicle Performance
Monitoring Dashboards • Predictive Fault Alarming
• Performance Management Applications
Business Outcomes
Real Time
Monitoring
• Speeding ,Route and
Schedule
• Geo Fencing ,Real
time Events/Alarms
Utilization &
Maintenance
•Fleet Drive/Utilization time Monitoring •Predictive Maintenance ,Predictive FaultBusiness Impacts
Analytics EngineOur 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. 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.
Transforming your Data Chromosome
0 1
0 1
1 1
Case Study –Electricity Demand Forecasting
Business Questions
Value Propositions
Illustrative Solutions
Business Impact
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.
•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 ?
•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 .
•Forecasting accuracy improved by 10% •Revenue increased by 4%
•Load shedding reduced by 5% due to online load monitoring
•Better prioritization and triage for restoration of supply. •Planned purchase from grid based on accurate demand •Minimize load shedding to reduce penalty payments.
Case Study –Assets Predictive Health Monitoring
Business Questions
Value Propositions
Illustrative Solutions
Business Impact
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.
•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 the 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
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
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 an elevator(sub component) by using Real time usages and sensor data ?
Can we develop a Predictive model to predict the health of an elevator. For example – if it is not up to the mark , or running with desired quality?
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. At the same time, the 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.
Data Ingestion & Engineering (ETL, Metadata, Semantic Engineering)
Big Data LakeDeveloper Tools and Adhoc Analysis
Enterprise Unified Data View
Machine Log
Mining MultivariateAnalysis
Segmentation Machine Learning
2 3
1
Sensor
Data Process Data System Logs I/O units Failure Field Structured DataEnterprise Enterprise Un-structured Data
4
Hadoop NoSQL Spark External/Internal Data Ingestion - Real time/batch Quality, transformation Extract Transform LoadData Engineering 1
Parallel and clustered processing
Structured & un-structured data Hadoop/Spark/NoSQL
Big Data Lake 2
Data Integration for Unified View Data warehouse for
Viz, BI, Apps
Unified Data View 4
V
isuali
zat
ion,
BI
an
d
A
na
ly
ti
cs A
pp
s
Advanced Analytics, Machine Learning and statistical analysis Predictive Model, RecommendationData Science 3
Customers Products Finance Risk Sales Operations
Machine/Sensors Data
Digital Transformation Solution Framework
Digital Transformation IOT Solution Framework
INSIGHTS|ANALYTICS|INNOVATIONS
