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Reducing Strawberry Waste and Losses in the Postharvest Supply Chain via Intelligent Distribution Management

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2014 National Sustainable Strawberry Initiative Project Leader Meeting

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Reducing Strawberry Waste and Losses in the Postharvest Supply Chain via Intelligent Distribution Management

  1. 1. 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
  2. 2. 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
  3. 3. 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.
  4. 4. 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
  5. 5. 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 pallet A snapshot after 12 hours of cooling
  6. 6. 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.
  7. 7. Shelf Life Estimation versus Tag Accuracy Tag 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
  8. 8. 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
  9. 9. 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
  10. 10. 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)
  11. 11. Shelf life depends on a multiplicity of variables and their changes… – type of fruit or vegetable – environmental conditions – packaging
  12. 12. 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
  13. 13. Shelf life can be limited by different things… – Appearance color, texture… – Flavor aroma, taste – Nutritional value sugar content, vitamins, antioxidants…
  14. 14. Storage for 8 days Shelf Life Prediction of Fresh Fruits and Vegetables 0 °C 5 °C 10 °C 15 °C 20 °C
  15. 15. Shelf Life Modeling Many possible algorithms for shelf life estimation For example, Arrhenius: A typical shelf life plot for an imaginary product
  16. 16. 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).
  17. 17. 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)
  18. 18. Time (days) 0 2 4 6 8 10 12 14 16 Ascorbicacid(mg/100gdryweight) 300.0 400.0 500.0 600.0 700.0 800.0 Time (days) 0 2 4 6 8 10 12 14 16 Qualityrating(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 Weightloss(%) 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 SSC(%dryweight) 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
  19. 19. 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)
  20. 20. 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)
  21. 21. 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
  22. 22. 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)
  23. 23. 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
  24. 24. 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
  25. 25. Strawberry Validation Test FEFO Recommendation from backroom to store shelf Retail Store FEFO Recommendation shipping to stores Contract Strawberry Farms Contract 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 hrs Batch 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
  26. 26. Strawberry Validation Test FEFO Recommendation from backroom to store shelf Retail Store FEFO Recommendation shipping to stores Contract Strawberry Farms Contract 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 hrs Batch 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
  27. 27. Strawberry Validation Test FEFO Recommendation from backroom to store shelf Retail Store FEFO Recommendation shipping to stores Contract Strawberry Farms Contract 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 hrs Batch 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
  28. 28. 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
  29. 29. 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.

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