SAP Global Retail & Wholesale Execu6ve Council, October 1st, 2009 Walldorf, Germany

Transcription

1 SAP Global Retail & Wholesale Execu6ve Council, October 1st, 2009 Walldorf, Germany

2 Seman6c Product Memories Products keep their own diaries Prof. Dr. Antonio Krüger Walldorf,

3 Innova6ve Retail Laboratory Since October 2007 funded by Globus, DFKI and Saarland University 500 sqm lab and office space in St. Wendel, Germany Technology transfer of AI methods from University to Retail One of five living labs of DFKI

4 4 The roots of Globus Working for our customers for 181 years. Founded in 1828 by Franz Bruch, who promised: the cheapest and fairest service Franz Bruch Foto In 2007: We focus on our customers and on fulfilling their wishes! (Thomas Bruch)

5 5 Globus today Germany: 40 Hypermarkets 81 DIY centres 9 Electronics and electrical appliance stores Czech Republic: 13 Hypermarkets with DIY centres Russia: 5 Hypermarkets Luxembourg: 2 DIY centres Total employees: ca. 30,000 Total revenues 07-08: 5.1 billion euro

6 6 Globus today Average sales area GLOBUS 12,800 sqm HM* 7,000 sqm Average annual turnover GLOBUS HM* 46.0 million 23.7 million Average purchase GLOBUS HM* * HM: all Hypermarkets in Germany Source: GFK/HHP-Data 2007 AC Nielsen 2007

7 Research at IRL targeted at the customers e.g. by informing buyer s decisions.... the store employees e.g. to support customers... the stock employees e.g. to reduce search and find 6mes Retail Stock Consumer...the public e.g. by demonstra6ng innova6on IRL Research: Tangible Innova6ons

8 Research at IRL Interdisciplinary Research Computer Scien6sts and Electrical Engineers Social Science and Behavioural Psychology Economics and Informa6on Systems Scien6fic Excellence and Technology Transfer Worldwide unique concept of a Retail Laboratory with university- class research and interna6onal coopera6ons with high prac6cal impact.

9 Agenda The Innova6ve Retail Laboratory (IRL) Scien6fic Background: SemProm, Ubiquitous Compu6ng and the Internet of Things The concept of Seman6c Product Memories IRL- Research Prototypes which demonstrate the benefit of Seman6c Product Memory

10 Ubiquitous Compu6ng The Internet of Things Distribu6on Smartcard 3) Netbook, Smart 4) Things that think Phone, Smart Card 2) PCs 1) Main frames 1 Computer 1 User 1 User many computers 1 Computer Many users Time 1960 Based on Mark Weiser

11 The Internet of Things In retrospect it looks like the rapid growth of the World Wide Web has been just the trigger charge that is now setting of the real explosion as things start to use the internet Neil Gershenfeld, MIT

12 The combination of sensors and RFID allows on chip processing and storing of context information Functionalities Autonomous Sensornode Ad-hoc communication Logs Sensor Data Electronic Product Code RFID-tag Passive RFID- Sensor Semi- Active RFID- Sensor Active RFID- Sensor Sensor- Innovation Network Data storage (static) Data storage (dynamic) Continuous Data collection Autonomous Communication Large Scale Data collection Source: BRIDGE, Siemens/Auto-ID Lab DFKI GmbH

13 Miniaturiza6on of Sensors and Electronics Amount More and more small objects can be traced and tracked: Informa6on is stored in a seman6c product memory Costs Time

14 IRL research network Seman6c Product Memories Part of the Proposal Sohware Innova6ons for digital Enterprises to the BMBF- Spitzencluster - ini6a6ve More than 5 Mio of acquired funds from Public Hand and Industry

15 Research along the product life cycle Fabrik Transport Stock Proper6es of Transport CO2 Footprint Product Improvements Product Memory Storage Condi6ons Loca6on Storage 6mes Recycling Consumer Retail Dynamic best- before date Ingredients Recycling informa6on Usage Pamerns Cross- up selling Informa6on Price Sta6c Best- before date Automa6c ordering

16 Intelligent Business Processes and the Internet of Things Business Processes= Strength: Enterprise Software Business Processes BPM / Execution Complex Events Producthistories Application- Services Product Types Product type Events Typed Product History Product type Services Product Instances Instance-based Events Instance-based Product histories Instance-based Services Dynamic Information Real World Static Information Internet of Things = Strength Embedded Systems DFKI GmbH

17 Example of Product Memories The fridge reads the product memories and provides an explora6ve interface.

18 Product Memories helps to calculate the CO2 Footprint of Goods (CEBIT 2009)

19 Example: A product memory of a deep frozen pizza Time Stamp Data link Interpretation T freezer-1 /temp/_32 TEMPERATURE T freezer /event/pizza_out_of_freezer EVENT T Cart /event/pizza_in_cart EVENT T Cart +1 /temp/4 TEMPERATURE ( ) /temp/2 TEMPERATURE T Cool_box-closed /event/box_closed EVENT T Cool_box-closed bis T Cool_box-removed /temp/xx TEMPERATURE T cool_box-removed /event/box_removed EVENT T cool box_removed bis T box_recognized /temp/xx TEMPERATURE T box_recognized /event/box_in_kitchen_detected EVENT T box_recognized bis T pizza_on_work_top /temp/xx TEMPERATURE T pizza_on_work_top /event/pizza_removed EVENT T pizza_in_fridge /event/pizza_in_fridge EVENT T pizza_in_fridge bis T NOW /temp/xx TEMPERATURE DFKI GmbH

20 The three pillars of Semantic Product Memories Backend DB, ERP etc. Data Collection and Processing Sensor data integration Event-based analysis, controlling, rules and processes Instrumented Environments Intelligent, self-organized networked components Modeling, Storage and Retrieval Data Federations Semantic Interoperability through ontology based information models Multimodal Interaction Object centric, contextsensitive, personalised, tangible User Interfaces. Mobile assistance services Production Maintenance DPG Consumer Logistics Retail Smart Item Memory, processing power, embedded sensors Instead of a Closed-Loop: Transfer of product memory content across domains DFKI GmbH

21 The Layered SemProM Architecture Applications Semantic Queries Semantics SemProM Structural Representations Physical Memory Structural Queries Hardware-Level Interface Class 1 Objects Class 2 Objects Class 3 Objects Class 1: No Embedded Processing Class 2: Automated Data Collection by Products. Class 3: Embedded Processing of Information. Applications do not differentiate between classes Barcode, RFID, etc. SunSpots, Motes, etc. Next Gen. Smart Items DFKI GmbH

22 The IRL Demonstrators IRL Labor

23 Intelligent fruit shelf: Automatic identification of fruit crates and adapting of customer information

24 Intelligent fitting room: personalized assistance according to semantic color scheme DFKI GmbH

25 Multi-Source Product recommendations Product Tag Mobile Internet / Store/ Product Memory 1. Ratings of other users 2. Recommendation of friends 3. Independent Recommendations 4. Rankings 5. Information on product instance 6. Share experiences

26 Mobile Product Lens Standard Hardware (Nokia Phone N95) Personalized customer informa6on appears on package Real- 6me recogni6on of camera pose and product packaging allows an augmented reality approach to realize advanced customer product informa6on.

27 Intelligent Shopping Cart and instrumented wine shelf with digital sommelier service Informa6onsdisplays RFID- Technologie Navigation and Positioning of Cart through RFID technology

28 The intelligent DFKI beer-mat: automatic ordering of next round! Weight- and Acceleration sensors Polls through beer mat DFKI GmbH

29 Conclusions Semantic Technologies enable the Interoperability of the Internet of Things. The Semantic Product Memory goes far beyond RFID-Technology, including sensors and embedded processing as well as storage. SemProM is an important mile stone to enable future business and product processes. It allows real-time adjustment of processes by direct product observations. DFKI GmbH

30 IRL Partner Thanks for your attention