Data Governance
Collect Process
Store
Manage Measure Consume
OLTP Systems
Summarize & Load
Big DataSources (Raw, Unstructured)
Alerts, Notifications
ERP CRM LOB
SQL Server
Parallel Data
Warehouse
Enterprise ETL with SSIS,
DQS, MDS
SQL Server
Data Marts
SQL Server
Reporting Services
SQL Server
Analysis Server
Business Insights
Interactive
Reports
Performance
Scorecards
Crawlers
Bots
Devices
Sensors
SQL Server
StreamInsight
Azure Machine
Learning
Intelligent
Systems Service
Hadoop on
Windows Azure
Hadoop on
Windows Server
OLTP Systems
Summarize & Load
Big DataSources (Raw, Unstructured)
Alerts, Notifications
ERP CRM LOB
SQL Server
Parallel Data
Warehouse
Enterprise ETL with SSIS,
DQS, MDS
SQL Server
Data Marts
SQL Server
Reporting Services
SQL Server
Analysis Server
Business Insights
Interactive
Reports
Performance
Scorecards
Crawlers
Bots
Devices
Sensors
SQL Server
StreamInsight
Azure Machine
Learning
Intelligent
Systems Service
Hadoop on
Windows Azure
Hadoop on
Windows Server
Collect Process
Store
Manage
Measure
Consume
Data Governance
More and more data is collected every day but …
… do we use them to make better decisions?
PREDICT FAILURESAsset downtime & maintenance costs
ROOT CAUSE ANALYSISProduct quality & brand perception
WARRANTY CLAIMSFuture claims & remaining useful life
…but there is extreme potential for improving our processes.
But what if you could:
What if you could:
Transform your business with Predictive Analytics
Predict failures before they actually happen
The ChallengeAcquiring a complete view of
the processes:
• Sensor data
• Operating conditions
• Telematics data
• Event data
The GoalPredict when and where asset
failures are likely to occur
Prevent costly production line
interruptions
Calculate reliability of assets
at any point in time
The SolutionDevelop a more profitable manufacturing process
by maximizing asset productivity using predictive analytics
to spot failures before they occurred
The ChallengeUnderstanding the business
flow and acquiring expert
knowledge
• Operator error
• Supplier issues
• Design problems
The GoalMinimize product quality
issues by understanding what
is happening and why it is
happening
The SolutionContinuously asses quality throughout the manufacturing
process by predicting problems early in the product cycle
Perform root-cause analysis of failures
The Challenge• Monitoring the condition
of the assets by collecting
telematics data about
operating conditions
• Integrating information
from operations, finance,
and customer systems
The GoalAvoid costly warranty claims
by providing resolution before
customers are aware of the
issues
Minimize the risk of
unpredicted failures
The SolutionPredict future warranty claims and avoid high services costs and
product recalls
Reduce warranty claims
Equipment
What’s transforming?
Deep & Continuous Engagement
Microsoft IoT Services Architecture : ISS+AzureML
ISS (Intelligent Systems Service)
Agent
Gateway
Event Hub & Azure Service Bus
Complex Event Processing &Rules Engine
TablesBLOBS
SQL AzureHDFS
IF {condition}
THEN {action}
Azure Service Bus
Design & Engineering
Manufacturing & Supply Chain
Service & Maintenance
Customer Relationship
ISS (Intelligent Systems Service)
ID
Industrial
Equipment
Operational excellence
IoT transformation with new business models
Intelligent products Service-centric relationships
Examples:
• Predictive Maintenance
• Asset Performance Management
• Energy Management
• Condition based Maintenance
Examples:
• Product performance and utilization patterns• X-functional information into R&D from
Customer and Marketing, Sales or Services
Examples:
• Event-driven marketing campaign and spent
• Customer and Sentiment analytics
• Predictive Maintenance
Connected OperationsConnected Product
Innovation
Connected Marketing,
Sales, Services
Microsoft Azure Intelligent Systems Service - Manufacturing
GLOBAL OPERATIONS
I can see my production line status and recommend adjustments to better manage operational cost.
I know when to deploy the right resources for predictive maintenance to minimize equipment failures and reduce service cost.
I gain insight into usage patterns from multiple customers and track equipment deterioration, enabling me to reengineer products for better performance.
MANUFACTURING PLANT
Aggregate product data, customer sentiment, and other third-party syndicated data to identify and correct quality issues.
Manage equipment remotely, using temperature limits and other settings to conserve energy and reduce costs.
Monitor production flow in near-real time to eliminate waste and unnecessary work in process inventory.
GLOBAL FACILITY INSIGHT
Implement condition-based maintenance alerts to eliminate machine down-time and increase throughput.
THIRD-PARTY LOGISTICS
Provide cross-channel visibility into inventories to optimize supply and reduce shared costs in the value chain.
CUSTOMER SITE
Transmits operational information to the partner (e.g. OEM) and to field service engineers for remote process automation and optimization.
Management
R&D
Field Service
CHALLENGE SOLUTIONS BENEFITS
Leading global manufacturer ThyssenKrupp Elevator maintains more than 1.1 million elevators worldwide, including those at some of the world’s most iconic buildings. ThyssenKrupp wanted to better compete in their industry by offering dramatically increased uptime, taking preventative maintenance a step further to predictive and even preemptive service.
• ThyssenKrupp teamed up with Microsoft and CGI to create a connected, intelligent asset monitoring system based on Microsoft Azure Intelligent Systems Service, Power BI for Office 365, and Microsoft Azure Machine Learning. The solution connects thousands of sensors and systems in its elevators to the cloud and draws this data into a dashboard available on PCs and mobile devices for a real-time view of key performance indicators.
• Increases reliability through predictive maintenance and rapid, remote diagnostic capabilities
• Reduces costs for ThyssenKrupp and their customers
• Rich, real-time data visualization
• Data continually feeds into dynamic predictive models
• Two-way flow of data enables diagnostics mode and remote elevator commands
Microsoft IoT with Machine Learning Case Study
www.InternetofYourThings.com
ThyssenKrupp Elevator: ThyssenKrupp Elevator is one of the world's leading elevator companies. With sales of €6.2 billion and more than 49,000 employees at 900 locations