DDMA 14 mei 2009 Business Intelligence case Ahold

Event: DDMA CRM-BI Thema: Best of the Best Awards Spreker: Egbert Dijkstra - Ahold Datum: 14 mei 2009 – Hotel New York, Rotterdam www.ddma.nl

Business Intelligence at Albert Heijn Egbert Dijkstra Director Business Intelligence Information Management Europe Zaandam, April 2009 Information for Competitive Advantage 2008

Personal background ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

[object Object],[object Object],Characteristics Albert Heijn 12 million weekly customers 8,5 million loyalty cards 100 million ticket-line items per week.

Characteristics Albert Heijn ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Information as a corporate asset ,[object Object],[object Object],[object Object],[object Object]

Information at Albert Heijn in 2000: stand-alone solutions Slide Operational Environment Merchandising Sales Finance Logistics Stock WMS Item HR Etc. Cognos PowerPlay SAS Mainframe BO Oracle Market Expert SAS Mainframe SAS U n ix Nielsen/IRI Weather Supplier Buyer Replenish- ment Logistics Merchan- diser Marketing Store BO Oracle Customer

Slide Operational Environment Merchandising Sales Finance Logistics Stock WMS Item HR Etc. Many Informational “Solutions” Cognos PowerPlay SAS Mainframe BO Oracle Market Expert SAS Mainframe SAS U n ix Nielsen/IRI Weather Information at Albert Heijn in 2000: stand-alone solutions Supplier Buyer Replenish- ment Logistics Merchan- diser Marketing Store Customer BO Oracle

Slide One Informational Environment Operational Environment Merchandising Sales Finance Logistics Stock WMS Item HR Etc. Today: one copy of the truth Nielsen/IRI Weather Consistent, Single view of business over Multiple Dimensions Adding Value through Integration of Information Providing History and Performance The answer to any question – instantly Pallas Supplier Buyer Merchandiser Logistics Marketing Replenishment Store Customer Covering & supporting total value chain

Today’s situation “Pallas” - Goddess of wisdom ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

[object Object],[object Object],[object Object],[object Object],[object Object],Reasoning for BI-development

Pallas - Basic Architecture near real time automatic input to operational process Enterprise Data Warehouse Multi-subject oriented; total value chain Operational Source ODS Relational Data Mart MOLAP Flat File Transaction Repository Standard Reporting Analysis Ad-Hoc Reporting Data Mining Procedures & Organisation Meta Data Layer ,[object Object],[object Object],[object Object],[object Object],[object Object],Specific data marts per process layer Tuned & tools depending on specific business need Operational Source Operational Source Operational Source Operational Source

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Impression size & complexity Pallas ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],“ Pallas” is responsible for app. 50% of the company’s total IO User data: 20 TB, adding 300 GB per month - Metadata: 220 GB

Available functionality Analysis Why did it happen ? Monitoring What´s happening now ? HIGH Complexity HIGH LOW Business Value ,[object Object],[object Object],[object Object],[object Object],Prediction What might happen? Reporting What happened ? Query, reporting & search tools Dashboards, scorecards Olap and visualization tools Essbase Advanced Analytics

Spin-off ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Number of items sales price purchase price So, let’s give an example: sales 35.897.821.577 ticket line-items available since 2000 = 25% supermarket sales Place in Store Time Product Cashier Customer Location Promotion Weather Payment type Checkout type details details details details details details details details details details 35.897.821.577

Functional Coverings (1) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Functional Coverings (2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Functional Coverings (3) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Now it´s time to climb the top... ,[object Object],80% of sources, mature and stable environment, Supporting all business Processes. Realize all potential value Albert Heijn will take action in order to become a real analytical champion

Pallas Functionality Slide Analysis Why did it happen ? Monitoring What´s happening now ? HIGH Complexity HIGH LOW Business Value Now the focus is on these areas ,[object Object],[object Object],[object Object],[object Object],Advanced Analytics What might happen? Reporting What happened ? Query, reporting & search Dashboards, scorecards Olap and visualization tools Forecasting, Statistics, Modeling

From “one copy of the truth” to “one copy of the future” Slide Today Yesterday Tomorrow Reporting Descriptive Analysis Monitoring ,[object Object],[object Object],[object Object],[object Object],[object Object],“ Analytics” predicts what will happen tomorrow. The better the prediction, the better the actions of today are in line with what will actually happen. The better the business results will be.

[object Object],[object Object],[object Object],[object Object],[object Object],Our Ambition: An Analytical Culture

Analytics - characteristics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Almost all business area’s will profit ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Fact-based decision making at every level of the organization to drive superior performance

Example: Replenishment Process Replenishment Customer Demand Forecast Alerts & Monitoring Distribution Center Delivery Delivery Store Sales Store Order WH Order WH Order Simulation DC Replenishment Store Order Demand Forecasting Store Demand Forecasting Store Replenishment Goods In, Goods Out, Adjustments Store Demand Forecast Inventory Balance Replenishment Control Room Distribution Center Store Supplier Alerts & Monitoring Real time sales forecast every 5 minutes Continuous Customer Driven Retail

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Example: Business Intelligence & Strategy To measure how the second generation self check-out succeeded, a temporary analytical solution was developed within Pallas. Also: Doorbraak (no data visible intentionally)

Customers accessing Pallas The Albert Heijn customer retrieves a list of articles bought at the stores through the internet. It supports them in creating their new shopping list.

Integration with operational system

Pallas by PDA for Store Manager

Access for suppliers through internet

Operational Cost(app. € 3 mio) avg # reports per week avg # users per week Reports Users While usage multiplied by 10, operational cost stabilized

Competenc y Center Business Intelligence ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

DDMA 14 mei 2009 Business Intelligence case Ahold

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