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Industry 4.0 Vision & Reference Architecture
• Industry 4.0 AI & ML
• Industry 4.0 eBIW Product & Expertise
• Q&A – Wrap up
2
Data Rich SiloedPerfectCustomer Aware
Insight-Driven ConnectedFastCustomer Led
Transform the
business, placing the
customer at its heart
Use data to make
decisions that improve
customer experience
Accelerate the pace of
innovation, working
with the customers,
partners and the supply
chain
Break down internal
barriers, with a focus
on the customer and
not the org chart
The Digital Enterprise
4
Manufacturers Are Focusing Outside In, With Customers At The Center
From Grease to Code, They Are Learning How to Differentiate with Digital
Detect• Track movement
• Read temperature
• Gauge humidity
• Sense vibration
Shop Floor Applications
Asset Monitoring Production Monitoring
Fleet Monitoring Connected Worker
IT Applications
Revenue and Billing Manufacturing
Logistics Service
IoT Devices
Building Sensors Equipment
Logistics Mobile Devices
Device Signals
Analyze• Visualize status
• Contextualize events
• Predict failures
• Trigger alerts
• Update device parameters
Act• Dispatch service
• Reroute shipments
• Substitute materials
• Re-plan supply
• Initiate billing events
Business Events
Focus on business outcomes
Industry 4.0 Information FlowBetter products, operating efficiency, happy customers
Integration&
GovernanceStructured Data
UnstructuredData & IOT Data
Predictions & ActionsPatterns & Correlations Genealogy & Trace
ERP
CRM
HCM
SCM
MES
QualityLIMS
Manufacturing Data HouseData Management, Preparation & Contextualization
T&A
Machine Learning & Other Data Science
Manpower Machine ManagementMaterial Method
Insight Models Predictive Models Feature Significance Models
Model Deployment
Contextual Data
Model Definition
Manufacturing Data
Model Training Model Performance Evaluation
Root Cause Analysis Impact AnalysisProactive Management
Time series Images Maps TXNs Parametric & bin
Data Types
Equipment Sensors
Scada
MQTT
OPC-UA
Data Historian
Product sensors and Test
Environmental
Industry 4.0 - Connected Machine, Method and ProcessConvergence of Operational Technology (OT) with IT Systems
Industry 4.0 - Connected Machine, Method and Process
Realtime Data Streaming
Batch Streaming
AI/ML Models Realtime Anomaly Detection
Using AI/ML Models
Realtime Alert/Notification/API
Predictive Analytics for
Condition Based MonitoringInitial & Retrained
Industry 4.0 – Architecture (AWS)
Build-Train-Deploy-Retrain-ML Model & Real-time Insight Driven Action
Industry 4.0 - Connected Machine, Method and Process
Realtime Data
Streaming
• Continuously generates
manufacturing process event
data in real-time which
describes something that
occurred at a given time.
• The real-time data helps the
machine learning models to
identify anomalies across
different processes and
variables.
• The data can be aggregated
with other data points in a
single real-time stream
processing data pipeline.
• Easy translation and
communication between
diverse devices, applications
(new and old), and integration
technologies.
Industry 4.0 - Connected Machine, Method and Process
Realtime Anomaly Detection
• Monitors production processes
continuously and detects anomalies
using artificial intelligence, machine
learning, and statistical process
control methods.
• Finds top features contributing to a
process failure using artificial
intelligence and machine learning on
historical data.
• Detects anomalies on real-time data
using the statistical process control
method.
• The data-driven approach can be self-
adaptive, it has a high dimensional
learning capability and is, by nature,
deployable on many different
application cases (i.e. machine types).
Industry 4.0 - Connected Machine, Method and Process
Predictive Analytics for
Condition Based Monitoring
Using AI/ML
• Real-time monitoring of industrial
equipment performance using IoT,
IIoT, and Artificial Intelligence.
• Automated scheduling of
maintenance processes and
procurement requests for parts.
• Fixing the wear and tear before the
point of failure of the asset.
• Reduced chances of downtime by
50%.
• 40% increase in production/service
delivery.
Predictive Maintenance Explained
Real Time monitoring Predictive Maintenance
Industry 4.0 - Connected Machine, Method and Process
Industry 4.0 - Connected Machine, Method and Process
Realtime Useful Life(RUL)
Prediction Using AI/ML
The method used to calculate RUL
depends on the kind of data available:
• A known threshold value of a
condition indicator that detects failure.
Degradation models estimate RUL by
predicting when the condition
indicator will cross the threshold.
• Lifetime data indicating how long it
took for similar machines to reach
failure
• Run-to-failure histories of machines
similar to the one you want to
diagnose
Industry 4.0 - Connected Machine, Method and Process
Real-Time Alerts/
Notifications/APIs
• Lightweight, out-of-the-box
mobile alerting solution that
addresses urgent and
targeted communication
needs of operations and
maintenance teams out on the
shop floor.
• It creates a direct pathway to
modern Industry 4.0-style
communication and
complements your SCADA,
MES, ERP, and IoT platforms
in minutes with critical mobile
alerting capabilities.
• Manage timely availability of
teams(on-call duties, shift) to
route alerts automatically.
Machine Learning and AI Embedded in
Smart Manufacturing
Statistical trend and pattern
detection for Business KPIs
Prescriptive Actions based
on root cause analysis
TREND
DETECTION
RECOMMENDATIONS
ANOMALIES
PREDICTIONS
Auto Detect deviation
from normal behavior
Predict future state by
learning from machine
data and business context
Actionable
Analytics
Industry 4.0 - Connected Machine, Method and Process
MQTT
BrokerConsumer &
Publisher
Cloud Gateway Stream
Processing
Azure Functions
HTTP REST
End PointWeb Portal/ Mobile App for
User Input/ Configurations
Smart Welding – An eBIW’s
Product Offering (HW & SW)
Industry 4.0 - Connected Machine, Method and Process
Routing Layer
Data Acquisition Layer
Machine/ Transducer Layer
Product Offering & Application
Connected
Enterprise
Connected
asset
Connected
worker
Yield Predictive
Maintenance
Quality
Insights for quality
improvement
Defect rate reduction
Intelligent Analytics and Gamification Layer
Production & OEE
Management
Autonomous
MaintenanceUtility Management
Cloud Layer
IOT Gateway Data Conversion and shaping
Key Deliverables - MIS
1. IMPROVING OPERATIONAL EFFICIENCY
2. OVERAL EQUIPMENT EFFECTIVENRSS (OEE) ENHANCEMENT in Following Domain:PRODUCTIVITY: Measurement of actual production against Norms or Captivity in real time. Suitable Management action to track the CAUSE of short-fall (if any) and initiate immediate counter measure to improve machine output close to ideal OEEQUALITY: Checking Whether Output Parameters are within the Set Limit or not. This will enable shop floor to achieve target with quality conformance. Management will be assured about shop process consistency. Same Data can be used to generate SQC parameters throughDigitised QC system. This can be used as Sales & Marketing tool to demonstrate to it’s customers. COST: Cost of operation can be drilled down to every level and can be used to minimise wastage. This will help the organisation to determine Lean KPIs for operation and ensure continual improvement of Cost optimization.
3. DIGITAL PREDICTIVE MAINTENANCEMACHINE HEALTH : In place of routine periodic maintenance and replacement of components, this system will enable Predictive maintenance of machines resulting in major saving in operational cost a t the same time ensure maximum uptime and prevention of unwarranted breakdown.
4. PLANT UTILITY
Real time monitoring of plant utilities like water, power, oil and gas will help reduce plant operational cost.
5. ASSET TRACKINGa.Enables tracking Asset Location & activity on real time basis. Useful for Construction/ Mining industries.b.Can unleash maximum asset payback by renting out during non productive hours/ period.
Deliverables
• Creates a Real Time Dashboard to facilitate LEAN implementation in operation to
improve OPERATIONAL EFFICIENCY every hour by checking actual data with
BENHCHMARK DATA and improve on the same.
Plan-Do-Check-Act- CYCLE
• Ensure M/C uptime & minimise spares cost by Predictive maintenance.
• Provide Asset sanity.
• Linked with SCM will ensure JIT delivery system of Consumables & Raw Materials.
• Quality incidence data will be used to monitor the jobs where rigorous post inspection
will be needed.
• Variation in the Output data will lead to replacement of Wear Parts proactively.
AI/ML Expertise – Platform & Solution
Data Management
Database – Data Lake - Access – Integration
- Preparation
Infrastructure
CPU – GPU – Storage -
Network
Model Training
Projects
Open Source Libraries
Model Deployment
JupyterLabNotebook
Model Management
Model Explanation
Data ScienceLibrary Auto ML
ModelCatalog
Data Access
Data Transformation
Data Visualization
Model Training and Tuning
Platform & Methods
Communications &
Media
Industrial
Manufacturing
Retail & Consumer
Goods
Airlines &
Transportaion
• Predictive Analytic for
Loading pre-paid cards
• Best-Fit Offers for Postpaid
Customers
• Revenue Leakage
• Churn Propensity
• Customer Segmentation
• Customer Fraud Detection
• Heat-zones of High Traffic
and Network Insights
• Smart Network Constraint
• Real-time Anomaly
Detection &
Visualization Product
Innovation
• Predictive Analytics for
Condition-based
Monitoring (COBM).
• Real-time Useful Life
(RUL) Prediction
• Right Product Mix &
Promotional
Effectiveness
• Flexible Supply by
demand signals and
sentiment.
• Customer
Segmentation
• Customer Sentiment
Analysis
• Market-Basket
• Employee Basket
• Employee Combination
• Product Category Mix
• Product Affinity
• Shrinkage
• Up-selling & Cross
Selling
• 360° customer profiling
• Delay Prediction and
Impact
• IATA Compliant Real-
time Baggage Tracking
• Data Monetization
• Identifying Ancillary
Revenue opportunities
• Up-selling and Cross
Selling
• Heat maps to visualize
the connection between
retail sales and departure
gates
AI/ML Expertise – Platform & Solution
Delivery of Industry Specific Use cases
Full Stack Development Expertise
• Front End Frameworks
• Angular, React
• Backend Development
• Java, Springboot, Nodejs, Python
• Databases
• Oracle, Mongo DB, Elasticsearch, Postgresql, Mysql, Dynamo DB
• Mobile Development
• React Native, Ionic
• Dev Ops / CICD
• Docker, Kubernetes, Jenkins, Git, Cron,
• Lambda, EC2, Cloudfront, Route53, Cognito, S3
A Must for Making Industry 4.0
Into a Reality