Pallet-level Monitoring for the Fresh Food Supply Chain – Part Two

Intelligent Distribution


Intelligent distribution optimizes distribution decisions, at each stop of the cold chain, based on the remaining shelf life of each pallet. It creates uniform shelf-life pallets and shipments and is key to maximizing the shelf life of produce at each process step in the end-to-end supply chain. Implementing these approaches can cut losses in half for retailers and growers.


In Part One of this 2-part series, we looked 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 final destination point of the pallet. Here in Part Two, we explore the details of the journey from FIFO (First In, First Out) to ‘simple’ FEFO (First Expired, First Out) to true Intelligent Distribution for fresh food cold chains.

Translating Pallet-level Monitoring into Freshness Improvements

There are several ways that pallet level monitoring can be used to improve freshness and reduce losses:

  • FEFO Inventory Management—Inventory and stock rotation based on First Expiring, First Out – optimizing for remaining freshness.
  • Intelligent Distribution—Optimized distribution decisions, based on the remaining shelf life of each pallet.
  • Uniform Shelf-life Shipments—Shipments contain pallets of similar remaining shelf life.
  • Shelf-life Maximizing Process Optimization—Each process step optimized to maximize the shelf life of the product.

FEFO Inventory Management

Most warehouses and distribution centers (DC) use a First In, First Out (FIFO) method for determining which pallets to ship next. This is based on the false assumption that all pallets have been handled the same, and therefore the oldest pallets have the shortest shelf life and should be sent first. Lacking pallet-level visibility, all the pallets in a lot look the same and are treated the same with no opportunity to expedite and recover product with shorter remaining shelf life. Pallet-level end-to-end monitoring gives a much more precise picture of the remaining shelf life, enabling a First Expired, First Out (FEFO) approach, which is smarter and more accurate. The remaining shelf life is calculated, based on the product/pallet level data captured throughout the life of the pallet; the complete temperature history inside the pallet from the time of harvest to the present. Various shelf-life models have been developed that can be used to calculate the remaining shelf life for each specific variety of produce.[1]

In its simplest form, a FEFO approach will ship the pallets with the least remaining shelf life first, as shown in Figure 1 – FIFO vs. FEFO Distribution. In this highly simplified example, the traditional FIFO approach (shown on the left) ships pallet 1 first, since that pallet arrived first and is assumed to have the least remaining shelf life. With FEFO, that same pallet is shipped third, because two other pallets have shorter remaining shelf life and therefore should be shipped first, even though they arrived at the DC later than pallet 1.

Source: ChainLink Research
Figure 1 – FIFO vs. FEFO Distribution

Intelligent Distribution

Intelligent distribution improves on the simple FEFO approach by adding intelligence about the destinations. Specifically, decisions are made matching the remaining shelf life of each pallet to the transit time and consumption velocity of each destination. Pallets with the lowest remaining shelf life will be sent to the destinations that require the least transit time and/or have the highest velocity of consumption.[2] Longer shelf-life pallets are sent to destinations that are further away and/or slower in consuming the product. Other attributes can be factored into an intelligent routing algorithm, such as the importance, type, and/or meticulousness of each customer or contractual shelf-life obligations. For example, food services (restaurants, cafeterias, food processing) require less shelf life than grocery retailers.[3]

Figure 2 – Simple FEFO vs. Intelligent Distribution, shows that a simple FEFO approach sends pallet 2 and pallet 4 to Destination 1, since those two pallets have the least remaining shelf life, and Destination 1 was the next shipment to be sent. Intelligent Distribution understands that Destination 1 should not be sent any produce that has less than nine days of shelf life left (4 days in transit + 5 days consumption velocity). So it sends those two pallets to Destination 2, which is closer and has a higher consumption velocity.

Source: ChainLink Research
Figure 2 – Simple FEFO vs. Intelligent Distribution

When FEFO is combined with intelligent distribution, it results in significant reductions in spoilage and losses. Figure 3 below shows that for strawberries, losses are reduced from 37% to 23% and even greater improvements for other products.

Image source: Jedermann, Nicometo, Uysal, Lang: Reducing food losses by intelligent food logistics

Figure 3 – Comparison of Product Loss using Traditional Approach vs. with FEFO and Intelligent Distribution  [4]

Uniform Shelf-life Shipments

Without pallet-level knowledge of actual remaining freshness, loads may be built with significant variations in freshness from one pallet to the next pallet. In many if not most cases, the downstream distribution center may not have implemented FEFO or intelligent distribution, in which case those variations will result in some potential spoilage, since the downstream facilities assume uniform freshness for the entire shipment. Delivering uniform freshness in a shipment accommodates current downstream distribution and inventory management capabilities that typically have not been optimized for either intelligent distribution or FEFO inventory management.

Shelf-life Maximizing Process Optimization

Pallet level visibility enables the grower and others handling the product to better understand the impact of their current processes on shelf life and thereby make improvements. For example, today the loss of shelf life resulting from longer cut-to-cool times is not being measured in most farm operations. Lacking visibility into the impact of cut-to-cool time, the grower optimizes logistics efficiency instead, waiting until their truck is full before taking product from the field back to the pack house. Once the grower understands the impact in reduced shelf life, they may decide to make more frequent trips, with smaller loads, so that the product has less time at elevated temperatures.

If they similarly understand the impact of shelf-life variations between pallets, the grower might also fine-tune their processes by prioritizing the pre-cool of warmest pallets first, thereby reducing those variations and achieving more consistent quality. They might also take steps to ensure more uniform pre-cooling, such as maintaining sufficient airflow through proper positioning of pallets and correctly working fans. Similarly, if truck drivers have real-time visibility on how their actions are impacting shelf life, they would be much more likely to turn the trailer’s reefer unit on sooner, to be sure that it is brought down to the right temperature before loading. Similar improvements are possible at each process step in the fresh food supply chain.

Of course, shelf-life maximizing process improvements don’t just happen automatically once pallet-level temperature monitoring is done. They require that the measurements and impacts of actions are made visible to the managers and workers, and that their performance metrics (and ideally compensation) are at least in part based on how they are impacting shelf life.

Advantages of Pallet-level Monitoring Over Visual Inspection

Source: | © Dmitry Kalinovsky | ID 43791177

Remaining shelf life is invisible until near the end of a product’s usable shelf life. That’s why visual inspection often does not provide real insights into the remaining shelf life of product. Furthermore, random sampling completely misses per-pallet variations. An entire shipment may be rejected because the worst-case pallet was inspected … or accepted because the best-case pallet was inspected. Pallet-level monitoring allows much more precision in a grocer’s decision on which pallets to accept or reject.

Solving the Freshness Challenge Requires Pallet-level Monitoring

End-to-end pallet-level monitoring captures the actual conditions across the life of the product from the moment of harvest until delivery to the retailer. It encompasses all the pallet-level variations, to provide a much more realistic estimate of actual remaining freshness. With accurate actual freshness data, FEFO inventory management and Intelligent Distribution can be implemented. This proactively avoids waste, by managing product at the pallet level, based on the actual freshness. Further improvements are possible by looking for opportunities at each process step in the chain to optimize processes for maximizing shelf life. When combined, these efforts have been shown to cut losses in half or more for grocers and growers, adding significantly to their bottom and top lines, as well as to the ultimate goal of end-consumer delight with the freshness of the produce.

1 For example, A generic model for keeping quality of vegetable produce during storage and distribution describes parameters for shelf-life models for 60 different fresh fruits and vegetables. Improvement in fresh fruit and vegetable logistics quality: berry logistics field studies reviews several existing shelf-life models for berries. — Return to article text above

2 For example, different retail locations (DCs or stores) will have different velocities of produce sales (higher or lower inventory turns). — Return to article text above

3 Food processors are high velocity and will usually process produce on the day of receipt. In contrast, the consumers purchasing produce from a retail grocer expect several days of remaining shelf life after they have purchased it from the store, where it may have already sat for a few days. — Return to article text above

4 Jedermann R, Nicometo M, Uysal I, Lang W. Reducing food losses by intelligent food logistics. Philos Trans A Math Phys Eng Sci. 2014 June 5;372(2017):20130302. doi: 10.1098/rsta.2013.0302. PMID: 24797131; PMCID: PMC4006167. — Return to article text above

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