Pallet-level Monitoring – Part One

Here in Part One of this two-part series, we look at why temperature monitoring should be done at a pallet level across the end-to-end cold chain, from the point of harvest to the point of final destination for the pallet.

Produce freshness, quality, and selection are primary drivers of why people choose one store or brand over another for groceries.¹ Maintaining maximum freshness at the point of sale to the consumer is a critical competitive competence for grocers and growers alike. Grocers must cull their fresh produce to assure an attractive presentation, but this entails a considerable loss to the grocer, typically over 10% of what they purchase.² Similarly, rejected shipments reduce the selection at the retailer and represent a big cost to the grower, typically 5%-10% of revenue.³

For fresh produce, total losses in the U.S. are estimated at ~33% of the harvest, with supply chain and retail loss accounting for around 18%, while post-purchase “plate waste” is about 15%.⁴ Lack of consistency in the end-to-end handling of produce is the primary cause of the supply chain and retail loss. Though most growers and grocers believe they are doing the best they can, a significant portion of this loss is avoidable. A different approach to temperature monitoring and freshness management throughout the supply chain can have a significant impact on the outcome.

To make a real difference in freshness, temperature monitoring needs to be done at the item/case-level or pallet-level. There are significant variations in the handling and temperatures that each pallet in a lot is exposed to. This results in significant shelf-life variations between pallets. A precise understanding of these per-pallet variations is foundational to improving freshness. Pallet-level monitoring enables a more intelligent approach to distribution—Intelligent distribution and FEFO inventory management (First Expired, First Out)—as well as providing the data needed to optimize end-to-end processes for maximum shelf life. Implementing these approaches can cut losses in half for retailers and growers.

Modern produce supply chains include a lot of environmental monitoring, such as monitoring the temperature in the field, the pre-cooler, the refrigerated trailer, and the refrigerated warehouse.

The assumption is that monitoring the environments in which produce has been stored and conveyed accurately, captures the temperatures that the produce has been subjected to. That assumption is wrong. In fact, pallet-to-pallet variations happen at many steps throughout the end-to-end journey, such as:

• Harvest—Cut-to-cool times can vary significantly by pallet, even those from the same field, using the same process. For instance, it can take roughly 15 minutes to pick and build a pallet of lettuce in a field operation. If the crew assembles 12 pallets to fill a flat-bed truck, the first pallet was harvested roughly 3 hours before the last pallet. At summertime temperatures, this can result in a variation of roughly 3 days⁵ of remaining freshness between the first and last pallets from the same load. Harvested crops often remain in the field even longer, severely impacting the freshness of the product.
• Pre-cool—In a variety of pre-cool equipment, the rate of cooling is often not uniform across all the pallets in the unit, varying by up to 4°F (2°C). Furthermore, pallets can enter pre-cool at significantly different temperatures (due to factors like different cut-to-cool times).
• In-transit—Studies have shown that the ambient temperature is not uniform throughout most refrigerated trailers and those differences vary over the length of a journey. It is not uncommon for the temperature of two pallets in the same trailer to be as much as 12° F (6⅔° C) different from each other.⁶
  

To understand the true temperatures that a product has been exposed to, it is therefore important to monitor the temperature at the product itself, rather than the surrounding environment. While case-level monitoring is possible, most produce travels from field to final destination on a pallet, and therefore the pallet is the most economical unit for measuring temperature very near the product itself.

Monitoring the End-to-End Process

Environmental monitoring also does not capture variations in how individual pallets are handled at each step throughout the end-to-end process. Consider for example the step of loading the reefer trailer. Often the driver has not pre-cooled the trailer; they turn on the reefer unit just before they start loading the trailer. The first pallets loaded into the trailer help to cool down the trailer, but in the process, those pallets are warmed up as well. Finally, after the last pallet is loaded, the driver puts the temperature monitor into the trailer and pushes the button to start recording the temperature. The trailer’s temperature recording device missed all of those events during the loading process and the dramatic variations of temperature that each pallet was exposed to.

The same types of variations and missed events happen at each process step in the end-to-end journey of each pallet of produce—whether it was how long it sat in the field, when and where it was placed in the pre-cool unit, periods of time outside cold storage during loading and unloading, and so forth. When only environmental monitoring is done, there are many missed temperature deviation events for every process step and handoff.

Figure 1 – End-to-End Temperature Monitoring

This is not just a question of temperature, but also capturing the time spent for each pallet at each process step at various temperatures. Time is an overall critical component to determine the net effect. For instance, how long was the product subjected to heat, dryness, or harsh light? Just knowing the peak temperature or an instance measurement is not sufficient to gauge the impact on the product.

Trying to stitch together an end-to-end picture from a series of separate, unintegrated environmental monitoring data does not work. Pallets must be continuously monitored from field harvest through final delivery. That is the only way to get a faithful end-to-end history of each pallet, required to accurately calculate the accumulated impact of temperature variations that the produce has been exposed to, and thereby correctly gauge delivered freshness. And that is what really matters.

In Part Two of this series, we explore details of the journey of distribution process transformation; from FIFO (First In, First Out) to ‘simple’ FEFO (First Expired, First Out) to true Intelligent Distribution for fresh food cold chains.

¹ For more on the competitive battle for freshness, see Winning the Freshness Wars — Return to article text above

² Dana Gunders, NRDC, Wasted: How America Is Losing Up to 40 Percent of Its Food from Farm to Fork to Landfill — Return to article text above

³ Pierson, Allen, and McLaughlin, MSU, Produce Losses in the U.S. Food Distribution System — Return to article text above

⁴ Why Quality Consistency Matters: Reducing Waste and Maintaining Shelf Life in Our Fresh Food Supply — Return to article text above

⁵ According to one source, Advances in Precooling techniques and their implications in horticulture sector: A Review, “as much deterioration can occur in 1 hour at 25˚C as in a week at 1˚C.” Other factors beyond the field temperature at the moment of harvest also impact the rate of shelf-life loss for produce sitting in the field or waiting to be cooled. This includes the temperature the night before, whether or not it rained in the last 48 hours, and other factors that increase respiration rates and thereby further constrain cut-to-cool time. — Return to article text above

⁶ Nunes, Nicometo, Emond, Melis, Uysal: Improvement in fresh fruit and vegetable logistics quality: berry logistics field studies — Return to article text above