reducing strawberry waste and losses in the postharvest supply chain via intelligent distribution...

Reducing Strawberry Waste and Losses

in the Postharvest Supply Chain via

Intelligent Distribution Management

Jeff Brecht and Francisco Loayza*University of Florida, Center for

Food Distribution & Retailing

Ismail Uysal, Cecilia Nunes, and

Ricardo Badia*University of South Florida

National Strawberry Sustainability

Initiative Grants Project Meeting,

Fayetteville, AR, May 21-22, 2014

J. P. Emond, CEO, Illuminate, LLC

Jeff Wells, CEO, Franwell, Inc.

Jorge Saenz, Dir. Cold Chain,

Hussman, Inc.

Gary Campisi, Sr. Dir. Quality

Control, Walmart

*Graduate students

BACKGROUND:

Remote Environmental Monitoring

and Diagnostics in the Perishables

Supply Chain*

Goal: Identify sensor-equipped RFID

technology and develop automated knowledge

system capability to determine the remaining

shelf life of operational rations in the DoD supply

chain based on remotely monitored temperature

history.

*Contracts W911QY-08-C-0136 and W911QY-11-C-0011; U.S. Army Natick

Research, Development, and Engineering Center

Meals Ready to Eat (MRE), First Strike

Rations (FSR), and Fresh Fruits &

Vegetables (FFV)We used wireless temperature sensors, remote monitoring

(RFID), algorithms, and diagnostics to demonstrate that

shelf life can be automatically calculated in real time using

web-based computer models.

Temperature data collection using commercially available

RFID tags and commercial handheld readers.

RFID-Enabled Temperature Tag

Accuracy & Reliability Testing

Accuracy

– Range Span Test

– Extended Requirement Limit Test

– Freezing Temperature & Recovery Test

– Two-Point Swing Test

Reliability

– Truck, Rail & Air Mode Vibration Test

(different temperature profiles)

– Sine Mode Vibration Tests

– Read Range Test

– Context-Based Temp Accuracy Metric

Best (Fewest) Tag Locations to Accurately

Estimate Product Temperatures

Temperature variation within pallets, trailers, containers

and warehouses results in shelf life variation

Effective shelf life estimation requires temperature

mapping Representations of the temperature

distribution within a FSR palletA snapshot after

12 hours of

cooling

Placement of Temperature

Recorders in FFV Loads

2 1

4 3

6 5

8 7

10 9

12 11

14 13

16 15

18 17

20 19BACK

FRONT Three temperature monitors:1. Inside the first pallet near the front

bulkhead of the reefer unit

2. Inside a pallet near the center of the load

(position 9, 10, 11, or 12)

3. On the outside rear face of the last pallet

at eye level. If only one temperature

recorder is being used, place it here.

Do not place temperature recorders

directly on trailer walls.

Shelf Life Estimation versus Tag

AccuracyTag accuracy varies with temperature

– For the most accurate shelf life estimation, tags

need to be most accurate in the temperature

range in which shelf life changes most rapidly

Thus, “context-based accuracy” (CBA) was

developed for shelf life modeling

– Improves shelf life estimation accuracy by

amplifying the effect of sensor error at

temperatures around which shelf life changes

rapidly

Shelf Life Estimation Model

A flexible model in complexity and accuracy

– Can work with mobile computers with low CPU power

– Increase in complexity and accuracy for

computers/servers with more CPU power

Complex learning model - yet simple operation

Can include multiple environmental factors as

needed such as temperature, humidity, etc. in

calculating product quality

Validated for different time-temperature profiles

Supply Chain Decision Support System

All sensory information available on the cloud

accessed through a web application

Each time an RFID temperature tag is scanned

by a reader:

– its location in the supply chain,

– its temperature records and,

– estimated product quality and shelf life

are recorded on a remote server

The web application also has decision making

and simulation capabilities with FIFO and FEFO

Supply Chain Decision Support System

Making logistics decisions using information from quality

parameters and shelf life models allows those decisions

to be based on a “First Expired, First Out” (FEFO)

model instead of “First in, First Out (FIFO)

Shelf life depends on a multiplicity of

variables and their changes…

– type of fruit or vegetable

– environmental conditions

– packaging

Temperature low, high, fluctuating

Humidity low, high, fluctuating

Atmosphere oxygen, carbon dioxide

Packaging packed, bulk

Postharvest history

Postharvest treatments (pre-cooling, quarantine

treatments, fumigation, heat, ozone…)

+ All factors combined

Maturity/ripeness at harvest

Shelf life can be limited by different

things…

– Appearance color, texture…

– Flavor aroma, taste

– Nutritional value sugar content, vitamins, antioxidants…

Storage for 8 days

Shelf Life Prediction of

Fresh Fruits and Vegetables

0 °C

5 °C

10 °C

15 °C

20 °C

Shelf Life Modeling

Many possible algorithms for shelf life estimation

For example, Arrhenius:

A typical shelf life plot for an imaginary product

Methods to Predict Shelf Life

Predictive microbiology based on microbial growth.

Sensory quality limits the shelf life and not microbial growth (Labuza & Fu 1993;

Riva et al. 2001; Jacxsens et al. 2002; Sinigaglia et al. 2003; Corbo et al. 2006).

Time-temperature indicators use chromatic variation

that depends on temperature-time exposure and assumes a relationship

with the loss of quality. Monitors the temperature history in response to the

cumulative effect of time and temperature (Wells & Singh 1988; Riva et al. 2001; Giannakourou

& Taoukis 2003).

Bio-indicators direct use of a microbial culture that displays the

same temperature characteristics as the food spoilage organism (McKeen &

Ross 1996).

Methods to Predict Shelf Life

Respiration rate by measuring the oxygen consumed and the

carbon dioxide released, but not appearance, texture or composition(Rieblinger et al. 1977).

Changes based on single quality factors are

assumed to be a measure of average biological aging or development

patterns: firmness (Rieblinger et al. 1977; Aggarwal et al. 2003), color (Ishikawa & Hirata

2001; Hertog 2002; Schouten et al. ; Hertog et al. 2004), shriveling (Hertog 2002)

Changes based on multiple quality

factors as a function of individual commodity characteristics,

handling temperature, humidity, temperature & humidity and time

(Nunes and colleagues, 2001-2012)

Time (days)

0 2 4 6 8 10 12 14 16

Ascorb

ic a

cid

(m

g/1

00g

dry

weig

ht)

300.0

400.0

500.0

600.0

700.0

800.0

Time (days)

0 2 4 6 8 10 12 14 16

Qualit

y r

ating

(1-5

)

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

0C

5C

10C

15C

20C

Time (days)

0 2 4 6 8 10 12 14 16

Weig

ht

loss (

%)

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

Time (days)

0 2 4 6 8 10 12 14 16

SS

C (

% d

ry w

eig

ht)

50.0

60.0

70.0

80.0

90.0

100.0

13 days

shelf life

@ 10C

4%

weight

loss

42%

reduction

in SSC

48%

reduction

in AA

Papaya

More than visual/tactile indicators need to be considered in

determining the shelf-life and limiting quality factors

Modeling to predict shelf life

The ultimate goal of Modeling is to

provide reliable predictions of occurrences

that have not yet taken place, for any

product, from any source and in any

situation.” (Tijskens and Luyten, 2003)

Modeling to predict shelf life

Challenge Predict shelf life of produce

throughout the distribution system non-constant

environmental conditions

Use data available and collect more data on quality changes based

on constant environmental conditions

Use time-temperature tracking technologies that allow a

constant monitoring of the environmental conditions during distribution (i.e., RFID)

Shelf Life Estimation Based on

Quality Curves

A dynamic versus a static system

– Accommodates real-world fluctuating temperature

conditions

– Polynomial trendlines chosen that result in the

strongest correlations

– Different quality curve equations are used for each

time step based on the limiting quality factor for that

temperature (interpolated for intermediate temps)

– The model predicts the final quality index and the

residual shelf life

Shelf Life Estimation Based on

Quality Curves

Different quality factors limit shelf life at different

temperatures

– Initial quality indices are measured to set the starting

point

– The shelf life limiting quality factors at different

temperatures are known for each product and

– Residual shelf life is based on calculated time to

reach a pre-defined lower threshold quality index

(The residual shelf life calculation can be based on the

current temperature or a future temperature regime)

Strawberry Validation Tests

Fruit were inspected to validate the quality prediction

versus physical inspection

At the DC:

The worst case out of 6 tests was a 9.5-hour difference

between predicted and observed (over a 7-day shelf life)

Test RFID

Tag #

Date/Time

Predicted Shelf-life =

0

Date/Time

Observed Test Shelf-

life = 0

Difference

(hours)

Timing of Model

vs. Observed

1 DC 1 lb. 500304 10/28 13:30 10/28 23:00 9.5 before

1 DC 2 lb. 500243 10/28 13:30 10/28 16:30 3 before

2 DC 1 lb. 500372 10/29 17:00 10/29 18:30 1.5 before

2 DC 2 lb. 500315 10/30 13:00 10/30 7:30 5.5 after

3 DC 1 lb. 500435 10/31 14:00 10/31 6:00 8 after

3 DC 2 lb. 500430 10/31 13:00 10/31 16:30 3.5 before

Strawberry Validation Tests

Each flat had a RFID temperature tag

At the Retail Store:

The worst case out of 4 tests was a 8-hour difference

between predicted and observed (over a 7-day shelf life)

FEFO decision making was estimated to result in

30% less shrink than FIFO

Test RFID

Tag #

Date/Time

Predicted Shelf-life =

0

Date/Time

Observed Test Shelf-

life = 0

Difference

(Hrs)

Timing of Model

vs. Observed

2 Store 1 lb. 500317 10/29 17:00 10/28 23:30 7.5 after

3 Store 1 lb. 500411 10/30 11:00 10/30 14:00 3 before

3 Store 2 lb. 500416 10/31 5:00 10/31 11:00 6 before

4 Store 1 lb. 500233 10/26 16:30 10/27 0:30 8 before

Strawberry Validation Test

FEFORecommendation from backroom

to store shelf

Retail Store

FEFORecommendation shipping to stores

Contract Strawberry

FarmsContract

Strawberry Farms

Pilot Process Map

Contract Strawberry

Farms

Strawberry Supplier

Regional DC

Retail Store

Grocer’s Perishable DC

3rd Party Transportation

1 to 10 hrs Batch Data

Up to 48 hrs Real-time Data

Up to 24 hrs Real-time Data48 – 72 hrs

Batch Data

Up to 2 hrsBatch Data

Up to 24 hrs Real-time Data

Retail Store

CRITICAL:

Association of

RFID Tag ID to

Warehouse Pallet

License Plate

Program tag, add Lot # and Start tag

Automatic Reading of Tag into Facility

Automatic Reading of Tag out of Facility

Stop tag and end consignment

Information Flow

Product Flow

Leg 1 Leg 2 Leg 3 Leg 4 Leg 5 Leg 6

Grocer’s Transportation

Baseline

Quality

Score/Shelf

Life Estimate

Shelf Life

Estimate

Shelf Life

Estimate

Shelf Life

Estimate

Final Points

To model FFV shelf life, consider all possible

shelf life limiting quality factors over a wide

temperature range

Dynamic shelf life modeling accommodates real-

world fluctuating temperature conditions

Accurate determination of initial product quality

is crucial

To complete our project, we need to confirm that

our shelf life model accurately predicts

strawberry quality at the store level

Thanks for your attention!

Questions?

This project was funded by a grant from the Walmart Foundation and

administered by the University of Arkansas System, Division of Agriculture,

Center for Agricultural and Rural Sustainability.

reducing strawberry waste and losses in the postharvest supply chain via intelligent distribution...

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