Dynamics in Logistics

1. Dynamics in Logistics Shelf life prediction by intelligent RFID - Technical limits of model accuracy Jean-Pierre Emond, Ph.D. Associate Professor, Co-Director UF/IFAS Center for Food Distribution and Retailing University of Florida Reiner Jedermann Walter Lang IMSAS Institute for Microsensors, -actuators and systems MCB Microsystems Center Bremen SFB 637 Autonomous Logistic Processes University of Bremen

2. Outline • CFDR / University of Florida • Evaluation of quality • Case Study “Strawberries” • IMSAS / University Bremen • Integration of quality models into embedded hardware • Intelligent RFID • Feasibility / required hardware resources

3. Center for Food Distribution and Retailing

4. Laboratory evaluation of shelf life models • Several attributes have to be tested • color • firmness • aroma / taste • vitamin C content (Nunes, 2003)

5. Strawberries – Case Study Joint project between Ingersoll-Rand Climate Control and UF Temperature sensors were placed inside and outside the load at all locations in the trailers Quality was assessed from beginning to end How retailers evaluate the quality of a shipment? Economic impact of monitoring temperature and quality prediction

6. Strawberries – Case Study = 3 full days = 2 full days = 1 full day = 0 day RFID Temperature Tag + Prediction Models

7. Strawberries – Case Study FEFO = First expires first out = 3 full days = 2 full days RFID + Models decision: 2 pallets never left origin 2 pallets rejected at arrival 5 pallets sent immediately for stores 8 pallets sent to nearby stores 7 pallets with no special instructions (remote stores) = 1 full day = 0 day RFID Temperature Tag + Prediction Models

8. Strawberries – Case Study Results at the store level (22 pallets sent)

9. Revenue and Profit Strawberries – Case Study Actual RFID + Model REVENUE $47,573 $58,556 COST $49,876 $45,480 PROFIT ($2,303)$13,076

10. The idea of intelligent RFID • Avoid communication bottleneck by pre-processing temperature data inside RFID Temperature curve Function to access effects of temperature onto quality Only state flag transmitted at read out

11. Chain supervision by intelligent RFID Step 1:Configuration Step 2:Transport Step 4:Post control Step 3: Arrival Handheld Reader Manufacturer Reader gate Measures and stores temperature Calculates shelf life Sets flag on low quality • List • Temperature • Shelf life • Transport Info Full protocol

12. Modeling Approaches Reaction kinetic model (Arrhenius) • Different model types Tables for different temperatures Differential equation for bio-chemical processes d[P] / dt = −kPPO*[P] d[PPO] / dt = kPPO[P] − kbrown*[PPO] d[Ch] / dt = kbrown*[PPO]

13. Example Table Shift Approach • Only curves for constant temperature are known • How to calculate reaction towards dynamic temperature? • Interpolate over temperature and current quality to get speed of parameter change Temperature Change from 12 °C to 4 °C

14. Model accuracy • Measurement tolerances • Parameters like firmness or taste have high measurement tolerances • Question: Is this table shift approach allowed? • Yes, if all entailed chemical processes have the similar activation energies (similar dependency to temperature) • Otherwise testing for the specific product required

15. Simulation • Comparison of reference model (Mushroom DGL) with table shift approach • Parameter tolerances 1 % and 5%

16. Hardware Platforms Wireless sensor nodes • Tmode Sky from Moteiv • Own development (ITEM) • Goal • Integration into RFID-Tag • Comparable to RFID data loggers

17. Required Hardware Resources

18. Available Energy • Power consumption of model is not the issue • Multi parameter models are feasible on low power microcontroller • Reduce stand by current

19. Summary and Outlook • Case study (strawberries) showed the potential to reduce waste and increase profits • Quality evaluation of the level of RFID tags is feasible • Testing on existing hardware of sensor nodes • Development of new UHF hardware required

20. The End Thanks for your attention www.intelligentcontainer.com