Dynamics in Logistics

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Dynamics in Logistics
Shelf life
prediction by intelligent RFID - Technical
limits of model accuracy Jean-Pierre Emond,
Ph.D. Associate Professor, Co-Director UF/I
FAS 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

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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

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Center for Food Distribution and Retailing
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Laboratory evaluation of shelf life models

• Several attributes have to be tested
• color
• firmness
• aroma / taste
• vitamin C content

(Nunes, 2003)
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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
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Strawberries Case Study
3 full days
2 full days
1 full day
0 day
RFID Temperature Tag Prediction Models
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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
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Strawberries Case Study
Results at the store level (22 pallets sent)
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Revenue and Profit
Strawberries Case Study
Actual RFID Model REVENUE
47,573 58,556 COST 49,876
45,480 PROFIT (2,303) 13,076
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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
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Chain supervision by intelligent RFID
Step 1Configuration
Step 2Transport
Step 4Post control
Step 3 Arrival

Handheld Reader
Manufacturer
Reader gate
Full protocol

• List
• Temperature
• Shelf life
• Transport Info

Measures and stores temperature Calculates shelf
life Sets flag on low quality
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Modeling Approaches
Reaction kinetic model (Arrhenius)

• Different model types

Tables for different temperatures
Differential equation for bio-chemical
processes dP / dt -kPPOP dPPO / dt
kPPOP - kbrownPPO dCh / dt
kbrownPPO
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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
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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

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Simulation

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

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Hardware Platforms

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

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Required Hardware Resources

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Available Energy

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

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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

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The End
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