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