© 2012 IBM Corporation

From insight to action - data analysis that makes a difference!

Heena Jethwa

Program Director

Predictive Analytics Product and Solutions Marketing

Agenda

What is Operational Analytics?

Operational Trends

Operational Challenges

Customer Examples

Operational Analytics: Insight to Action

Summary

What is Operational Analytics ?

3

Sales

Marketing

Customer Service

Finance

IT

Supply Chain

Product Development

HumanResources

Customer Analytics and Operational Analytics: Two sides of the same coin!

You can create a good customer experience if you internal organization isn´t ready

– Resources - Unresponsive customer facing staff

– Delivery – Goods and services not delivered in time

– Quality – Poorly manufactured goods

– Price – Too expensive compared to demand

– Expectations – Over marketed or over sold claims

– Flexibility – not able to adapt to market needs

What happens when we don´t manage these key areas ?

Market Trends for Operations Professionals

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Customer demands

Complex supply chains

Raw material price volatility

Compliance and scrutiny

Lean operations

Increasing customer need for product immediacy and uniqueness

Increased focus on organizational processes and transparency

Increasing complexity due to global suppliers and customers

Increasing volatility of supply and price of nickel, copper, and petroleum

All departments are expected to do more with less

Fraud Prevalence of fraud is becoming more widespread and expensive

A Balancing Act for Organizations

7

$$$

InventoryDemand Shaping

Costing

Real-time

Efficiency VariabilitySupply

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Assets

Optimization

Improvement

Processing

Sustainability

Waste

Abuse

FraudPrice Volatility

Compliance

Operational issues and challenges

Challenges Faced Daily

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Lack of VisibilityLack of Visibility Poor PerformancePoor PerformanceDisconnected Supply ChainDisconnected Supply Chain

• Manual processes & disparate sources

• Lack of insight into performance

• Data, Rich but insight poor

• Departments not working towards common goal

• Inability to accurately predict demand or preferences

• Manual processes & disparate sources

• Lack of insight into performance

• Data, Rich but insight poor

• Departments not working towards common goal

• Inability to accurately predict demand or preferences

• Need to control operational and service costs

• Emerging players and distribution channels providing additional choices for customers

• Inflexible and expensive systems

• Waste of resources and downtime

• Need to control operational and service costs

• Emerging players and distribution channels providing additional choices for customers

• Inflexible and expensive systems

• Waste of resources and downtime

Lack of VisibilityLack of Visibility Poor PerformancePoor PerformanceDisconnected

ProcessesDisconnected

Processes

• Connecting IT and Line of Business: Need to work together

• Resource complexity make it harder to respond to changing needs

• Difficulty synchronizing demand and supply

• Need to simplify back-office processes

• Connecting IT and Line of Business: Need to work together

• Resource complexity make it harder to respond to changing needs

• Difficulty synchronizing demand and supply

• Need to simplify back-office processes

IBM operations solutions help plan, manage, & maximize to INCREASE EFFICIENCY AND PROFITABILITY

Reporting, Analysis, & Predictions

Scorecarding & Dashboarding

Modeling

Planning, Budgeting & Forecasting

Resource Optimization

Predictive AnalyticsStatistical Analysis

Data & Text Mining

Business Rules & Optimization

Forecasting & Simulation

Real-time Decisions

PlanPlan

MaximizeMaximize ManageManage

OperationsSolutions

Plan for Operational Success• Allocate future expenditures in most efficient manner• Ensure the right quantity of the right product is available at the

right time and location

Manage Day to Day Operations• Enhance existing operational processes• Improve employee productivity and effectiveness

Maximize Operational Performance • Extend longevity of infrastructure and equipment• Improve asset and employee performance

Capab

ilities

Operational Analytics: Customer Examples

Car Manufacturer•Finding the actual root cause•Saving 30m a year •Reduction of 5% in warranty claims

• ROI of 629%; Payback in 2 months• Increased percentage of emergency

investigations from 49% to 93%• Reduced customer calls by 36%• Reduced contract labor costs by $1.8M• Reduced gasoline truck costs by 20%• Enabled recapture of $3.8M in revenues]

Challenge

Solution

Results

• Identified the need to take proactive steps to limit the effects of an inevitable downturn in sales

• Realized that identifying and predicting sales patterns could result in up-front cost-savings

• Improve the service it provides to customers

• IBM SPSS Modeler

A €658.4 million business, the Brammer Group is Europe’s leading supplier of quality industrial maintenance, repair and overhaul products. The company employs over 2,000 people in more than 300 locations, across 16 countries.

• Reduced total inventory £31.1 million in one year by reducing the need to carry surplus stock

• Inventory turnover improved from 3.2x in 2008, to 3.7x by the end 2Q 2009

• Accelerated report creation by up to 97 percent, providing near-real-time analysis

• Improved customer satisfaction

Customer Profile

Brammer Group Increases Inventory Turns

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Challenge

Solution

Results

• Keep employee culture during exponential growth• Could not gain insights from its employee surveys about

employee needs and general work climate• Lost touch with employees as departments expanded

• IBM SPSS Statistics

The family-owned company began its successful pastries business in 1938. It is the market leader and the most popular pastries brand in Austria. The company has stores and warehouses in Austria, Germany, and Switzerland, and in 2010 had sales of €183M.

• Introduction of more flexible working hours• Changes to the rules governing stand-ins• Changes to cross-departmental communication

procedures• Led to increased collaborative culture

Customer Profile

Rudolf Olz Meisterbacker GmbH Improves Employee Satisfaction

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© 2012 IBM Corporation

Is your company being

PROACTIVE OR REACTIVE ?

...based on business processes and external events…

View specific, personalized business dashboards….

OPERATIONAL VISIBILITY

Operational visibility paired with performance optimization and analytics is driving new levels of DYNAMIC DECISION MAKING

PERFORMANCE OPTIMIZATION

…augmented with advanced analytics to suggest next best action, creating an environment of competitive agility that is game-changing.

…configured based on business rules and business policies…

Predictive Operational Analytics Leverages Every Aspect of the Analytical Process

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Reports,KPIs, KPPs

Analyses

SegmentsTime series analysis

ProfilesScoring models

Anomaly detection...

Scoring

Detect & Capture Analyze & PredictEngage &

Act

Define Thresholds

Determine the level of Risk

Define List

Assign weight (points) to each

indicator...

Domain Expertise

Demographic data

Transaction data

External data

AgeGenderAddress

...

SKUPrices

QuantityDate

...

Product responsesWeather infoSupplier info

...

System notificationsEmail

ReportsDashboards

...

Schedule costs are greatly reduced, less service interruption and increased customer satisfaction

• How do costs vary by region? Why do they vary?• What is the total cost of ownership of each piece of equipment?• What will repairs cost me next year?

What is the cost of maintenance & failures?

Identify anomalous production data and show the specific data that is out of tolerance

Peer group profile compared to anomalous runs

Anomaly Detection

Operating conditions, service duration and manufacturing quality all play a role in the failure likelihood

4 specific combinations of factors that are driving failure are identified automatically

Root Cause Analysis of FailuresWhat parts are failing? What is driving the failure?

How Likely Is a Failure at Time X?

Leverage all available data – Sensor logs, maintenance logs, condition

monitoring data, etc.

Build predictive models – Estimate the failure likelihood at any point in the

future for every piece of equipment– Neural Nets, Logistic Regression, Decision

Trees, SVM, SLRM, etc.

Apply models to new data– Generate updated failure likelihood values

Real-time condition monitoring based on reliability prediction

Improving Competitiveness with Predictive Operational Analytics

23Source: Operations Management Consulting Marketplace 2010-2013; Kennedy Consulting Research & Advisory

Time

Utilizing a phased approach to becoming proactive

ANALYTIC-DRIVEN ORGANIZATIONS are distinguished by their ability to leverage …

All perspectivesPast (historical, aggregated)

Present (real-time)Future (predictive)

At the pointof impact

All decisionsMajor and minor

Strategic and tacticalRoutine and exceptionsManual and automated

All informationAll information

Transaction data Application data

Machine data Social data

Enterprise content

All peopleAll departments

Experts and non-expertsExecutives and employees

Partners and customers