simulating the evolution of business analytics at...
TRANSCRIPT
MIT Center for Digital Business CIO Conference May, 2011
Simulating the Evolution of Business Analytics at
SAP
© 2011. All rights reserved. / Page 2
AGENDA
Overview
Challenges & Opportunities: Innovating for Analytics
Partnering on Simulation Modeling for Strategic Management
MIT Simulation Approach
Recommendations
Conclusion
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EVOLUTION OF ENTERPRISE APPLICATIONS EMERGENCE OF BUSINESS ANALYTICS
1970s – 1980s 1990s – 2000s 2010 - Beyond
SAP invents ERP R3 and Business Suite Business Analytics
Automating Transactions
Automating Efficient Business
Processes
Optimizing Decision Making
THREE CONVERGING
FACTORS DRIVING THE
EMERGENCE OF
BUSINESS ANALYTICS
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VISION FOR BUSINESS ANALYTICS BRINGING ANALYTICS TO THE MASSES
■ Static Analytics for the Few
■ Focus on IT Requirements
■ Siloed Decisions
■ Information to Validate, Review and React
■ Proprietary Data Source
■ Traditional Deployment
■ Dynamic Analytics for Everyone
■ Focus on Business Needs
■ Collaborative Decisions
■ Insight to Anticipate, Share and Act
■ Any Data Source
■ On Premise, On Demand and On Device
To From
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Strategy Management Planning, Budgeting,
and Forecasting
Profitability and Cost Management
Financial Consolidation
Enterprise Performance Management
Disclosure Management
Enterprise Data Warehousing
Data Mart Solutions High-performance Analytic Solutions
Data Warehousing
Reporting and Analysis
Dashboards and Visualization
Data Exploration Mobile
BI Platform
Business Intelligence
Enterprise GRC Access Risk Management
Global Trade Services Continuous Transaction Monitoring
Governance, Risk, and Compliance
Data Services
Master Data Management
Event Processing
Content Management
Enterprise Information Management
Information Governance
Analytic Applications
By LoB Service, Sales, and Marketing
Procurement
Finance
Sustainability
IT, HR, and more…
By Industry Financial Services
Public Sector and Healthcare
Manufacturing
Consumer Products
Retail and Telco ….
COMPREHENSIVE PORTFOLIO NEW CATEGORY OF ANALYTIC APPLICATIONS
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Focused, purpose built niche applications
Tailored for the business user/ approved by IT
Solves specific use cases and user requirements
Captures deep domain expertise
Business Model Changes Required
Rapid development of new applications (6-8 month timeframes)
New pricing models
Volume play versus traditional play
Product lifecycle considerations
New channels & ecosystem approach
PURPOSE-BUILT ANALYTIC APPLICATIONS DIFFERENT APPROACH FROM CURRENT BUSINESS
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Executive Challenge “What Does Success Look Like?”
Partnership with MIT
MIT Center for Digital Business is the world's largest center for research focused on the digital
economy
Matched SAP resources with MIT researchers to form a collaborative research team
Simulation Modeling for Decision Making
Ability to validate overall approach while helping to make strategic decision
Allows for a common platform for communication and analysis of strategic options
Identifies high-leverage strategies that take organizational constraints into account
PARTNERSHIP WITH MIT ON SIMULATION MODELING
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A. Questions
What is the best speed, depth,
and quantity of applications?
What are the impacts of early
adopters on long-term revenue?
How long should the support
window be for short lifecycle products?
COLLABORATION WITH MIT
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A. Questions
What is the best speed, depth,
and quantity of applications?
What are the impacts of early
adopters on long-term revenue?
How long should the support
window be for short lifecycle products?
MIT APPROACH
Test
Base
Case time
Sales
What is the cause
of the difference?
Stage 1 Stage 2 Stage 3 Finished
ApplicationsBusiness Case
Identification
Business Case
Identification Rate
Advancing to Stage 2 Advancing to Stage 3 Packaging
Avg Time to DesignAvg Time to Produce
Avg Time to Package
Total Aware
New aware
Avg Deals per
Application
Total Revenue
New revenue
Avg Size of Deal
Avg cost of Stage 1
per Application
Total stage 1 costs
Avg cost of Stage 2
per Application
Total stage 2 costs
Avg cost of Stage 3
per Application
Total stage 3 costs
Total Costs
Increasing costs
Operating profit
Reference Time to
Design
Relative Time to
Design
Effect of Design
Time on Deals
Effect of Design
Time on Deals f
Base deals per
application
Removing stage 2
applications
Reference stage 2
applicationsRelative stage 2
applications removal
Effect of removing stage
2 applications on deals
Effect of removing stage
2 applications on deals f
Awareness Rate
per App
Leads
Generating leads
Installed BaseNew Sales
Support Costs
Fixed Costs per AppMarnigal Costs per App
Support Revenue
Support Revenue
per Deal
Supported Apps
Increasing
Supported Apps
Retiring
Supported Apps
Retirement Delay
Support profit
S2 = ò(AS2 - AS3)dt + S20
B. Modeling C. Analysis
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Background System Dynamics Modeling was developed at MIT in the 1950s.
Its been applied to numerous domains such as strategy, management, & process improvement;
its even been applied to insurgency & nation failure.
Approach It is designed to help addresses limitations of linear logic and over simplification caused by
typical human assumptions and behaviors.
In other words, its hard to manage complexity in our heads alone.
Key Features We can design simulations to experiment in complex systems.
For example, we can ramp up workforce faster than doable in the real world
MIT SIMULATION METHODOLOGY
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Conducted interviews with a wide variety of
stakeholders
Directed literature review across multidisciplinary topics: software
development, process improvement & strategy
Formulated dynamic models and simulation environments
Identified broad strategic concerns and articulate initial
recommendations
JUNE 2010 JANUARY 2011
MIT LIFECYCLE
PROGRESSION
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Enhance product quality and the ability to better select and cull applications to improve market performance
DEVELOPMENT
Early stage application development with alignment to sales improves outcomes and reduce risks
Balance product retirement and support window for positive ROI
FOCUS AREAS OF MIT MODEL FOR SAP
SALES
SUPPORT
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FOCUS AREAS OF MIT MODEL FOR SAP: DEVELOPMENT
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Total Deals
10,000
7,500
5,000
2,500
0
0 8 16 24 32 40 48 56 64 72
Time (Month)de
alTotal Deals : base Total Deals : test1
Result 1: Enhanced attention in early stages improves deal flow
Finished Applications
200
149.9
99.8
49.7
-0.4
0 8 16 24 32 40 48 56 64 72
Time (Month)
appl
icat
ion
Finished Applications : base
Finished Applications : test1
Total Deals
8,000
1,,000
1,,000
1,999
-0.8
0 8 16 24 32 40 48 56 64 72
Time (Month)
deal
Total Deals : base Total Deals : test1
Result 2: Even with fewer application, better application selection increases deals
10k
2010 2015
BASE
TEST
(Increase development time)
200
2010 2015
DEALS
APPLICATIONS
10k
2010 2015
DEALS
BASE
TEST
(Cull less
promising
applications)
BASE
TEST
SIMULATIONS
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DEMO
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Most weight (KPIs) Most applications Quality
Cost
Time
RELATIONSHIPS
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Applications
Deals Size
DYNAMICS
STRATEGY
LEVERS • Application weight
• Development time
• Time to market
OPTIONS
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DEVELOPMENT
RECOMMENDATIONS
Big opportunity for performance enhancement from improving early stage development
Information feedback from market into product development More KPIs and more accurate KPIs
There are advantages of not moving every application to SKU and selecting more promising applications
Reduced production costs More deals per application Lower support costs and support risk
Entering many applications into the pipeline can help mitigate risks of removing applications
More leeway for selection Less pressure to finish each application
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OTHER FOCUS AREAS OF MIT MODEL FOR SAP: SALES AND SUPPORT
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SALES
RECOMMENDATIONS
Need to seed and build the sales pipeline upfront Improved alignment of the sales plan to the execution plan Early planning needed to fill the 4x ratio
Early adopters help drive both product quality and awareness and leads
Collaboration helps to pre-set the revenue process Helps to close revenue via leads and application attractiveness
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SUPPORT
RECOMMENDATIONS
Maintenance and support of applications is a key input to overall ROI
Inputs to support include: Fixed costs per application Marginal cost per deal supported Revenue income per deal Lifespan of support costs (7 years) and revenue (years)
Dynamics of number of applications and number of customers are key to support performance
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A. Questions
What is the best speed, depth,
and quantity of applications?
What are the impacts of early
adopters on long-term revenue?
How long should the support
window be for short lifecycle products?
MIT COLLABORATION RESULTS
Test
Base
Case time
Sales
What is the cause
of the difference?
Stage 1 Stage 2 Stage 3 Finished
ApplicationsBusiness Case
Identification
Business Case
Identification Rate
Advancing to Stage 2 Advancing to Stage 3 Packaging
Avg Time to DesignAvg Time to Produce
Avg Time to Package
Total Aware
New aware
Avg Deals per
Application
Total Revenue
New revenue
Avg Size of Deal
Avg cost of Stage 1
per Application
Total stage 1 costs
Avg cost of Stage 2
per Application
Total stage 2 costs
Avg cost of Stage 3
per Application
Total stage 3 costs
Total Costs
Increasing costs
Operating profit
Reference Time to
Design
Relative Time to
Design
Effect of Design
Time on Deals
Effect of Design
Time on Deals f
Base deals per
application
Removing stage 2
applications
Reference stage 2
applicationsRelative stage 2
applications removal
Effect of removing stage
2 applications on deals
Effect of removing stage
2 applications on deals f
Awareness Rate
per App
Leads
Generating leads
Installed BaseNew Sales
Support Costs
Fixed Costs per AppMarnigal Costs per App
Support Revenue
Support Revenue
per Deal
Supported Apps
Increasing
Supported Apps
Retiring
Supported Apps
Retirement Delay
Support profit
S2 = ò(AS2 - AS3)dt + S20
B. Modeling C. Analysis D. Takeaways
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Enhanced existing portfolio process. Ideas that did not meet market needs would not be launched as a final application, but remain in demo/accelerator form
DEVELOPMENT
Validated importance of making co-innovation customers successful and referenceable prior to final launch
Carefully weighed product release and associated costs with years of support offered
APPLYING THE INSIGHTS TO THE BUSINESS
SALES
SUPPORT
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Simulation is a good approach for modeling a new business
Allows understanding of business and organizational changes required
Examines cost factors impacting the business
Captures critical linkages and dependencies across business functions
Strong partnership and executive sponsorship critical to success
Requires participation of senior leadership team in the process
Importance of dedicated champion that can bridge multiple domains
Results & Next Steps
SUCCESSFUL APPLICATION OF SIMULATION
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Thank you!
Contributors:
Daniel Goldsmith, Research Scientist
Michael Siegel, Principal Research Scientist
Shivani Govil, VP, Product & Bus. Strategy
Sandra Ballew, Sr. Operations Expert