Measuring Produce Freshness: Part Two – Meeting Customer Requirements


Improving produce freshness and quality requires a system bringing together the end-to-end temperature history of each pallet, knowledge of the temperature response of different varieties, capabilities to match each pallet’s condition-based expiration date with different customers’ requirements, and finally, prescribing simple actions to workers and supervisors to ensure the best match between remaining shelf life and customer need.


( This article is excerpted from the complimentary report
Measuring Produce Freshness: The Key to Preventing Waste,
available for download here. )

In In part one, we highlighted the problem of freshness ‘blindness’ and the role of the Condition-based Expiration Date as a critical element to solving this problem. We also looked at what it takes to create a reliable and accurate algorithm that models deterioration of different varieties of produce exposed to different temperature histories. Here in part two we look at the need to know the customer’s minimum required expiration date, accounting for transit times, and why a prescriptive system is required.

Understanding the Customer’s Minimum Required Expiration Date

As illustrated in Figure 2 from Part One, an accurate actual remaining shelf life is only one piece of the puzzle. Transit time and freshness requirements for each customer order are also needed, in order to match each pallet to its optimal ‘home.’ Zest Fresh reflects how a system should calculate each customer’s freshness requirements. When a retailer places a purchase order (PO) with the grower, the Zest Fresh system will embed ZIPR Codes1 specifying the Minimum Required Expiration Date for items delivered for that order. The system calculates the Minimum Required Expiration Date for each order by adding the number of days of freshness required by that customer to the required delivery date. For example, if Grocer A (Order #123) requires 10 days of freshness for an order of lettuce to be delivered on July 10, then the Minimum Required Expiration Date for that order is July 20 or later. Suppose Food Service company B (Order #456), a customer of the same grower, requires six days of freshness and places an order for lettuce to arrive on July 8, then the Minimum Required Expiration Date for that order is July 14 (see Figure 1, illustrating this hypothetical example).

Source: Image by ChainLink Research
Figure 1 – Hypothetical Example of Differing Freshness Requirements

Process Mapping Key to Accurate Per-Location Freshness Requirement Calculation

To get an accurate forecast of requirements for each customer by location, realistic conditions on the ground need to be understood. Zest Fresh implements process mapping to assess each customer’s full supply chain, all the way to the store shelf display, to determine actual handling and conditions, which often vary from ideal conditions. For instance, most shelf displays are 40 – 45°F, not the ideal 32 – 34°F. This is why process mapping2 is so important. In the Zest Fresh system, each process has a forecasted value (based on actual conditions) and duration, for each grower and customer location, from the field, through to processing, distribution, customer DC storage, DC-to-store distribution, store backroom storage, and store display. The whole supply chain is mapped out.

Different location will have different process maps and forecasted freshness requirement values. A single PO may contain an order destined for a single DC, but that DC aggregates orders for multiple stores. Therefore, a single PO could have different ZIPR Codes based on the different freshness requirements of the different stores. Other factors that impact the freshness requirement include the velocity of the item and the store. A slow-moving item or a low-velocity store might require an additional day or two, on average, for sell-through.

Accounting for Transit Times

The system also needs to know the transit time to each customer’s site, as well as the required delivery date (specified in the PO). Transit time information is calculated in the Zest Fresh system based on the requested delivery date in each PO. In the previous example, suppose Grocer A’s DC is four days away and Food Service Provider B’s DC is two days away from the grower’s DC. On July 6, the grower is shipping lettuce out of their DC for both of these customers and for many others. The system looks at the Condition-Adjusted Expiration Date (aka ZIPR Code) for all lettuce available to ship and ensures that only pallets with a July 20 or later date are sent to Grocer A, whereas pallets with at least a July 14 date are sent to Food Service Provider B (shown in Figure 2).

Source: Image by ChainLink Research
Figure 2 – Hypothetical Example — Matching Pallet’s Remaining Shelf Life With Customer Need
(Transit + Freshness)

Prescriptive System Required

It is critical that the system is prescriptive — that is, workers, supervisors, and managers at the grower shouldn’t have to do any calculations or searching around the system to find a pallet whose Condition-based Expiration Date matches some customer’s required expiration date. Instead, the Zest Fresh system looks across all available pallets and all outstanding customer orders, and then matches them up, to ensure all customers are receiving at least their minimum required freshness. It then simply instructs the workers: ‘send these pallets here and those pallets there.

The only time a manager or supervisor needs to make a decision is when there is no feasible solution to meeting all customer requirements, which happens in supply constrained times such as early and late season. Then the Zest Fresh system presents alternatives for the manager to choose from, showing what the tradeoffs are in terms of missed deadlines or less-than-required freshness. Armed with this information, the grower may decide to call one or more customers to see who might have some flexibility (in exchange for some value, such as a discount). This kind of transparent process, with full insight into the remaining shelf life of orders being sent, builds trust and confidence. It is much better than the old way of blindly sending pallets that will be rejected or spoil at the store.

Similar prescriptive actions are provided by Zest Fresh to locations and participants throughout the supply chain:

  • Receiving/Quality control — The system can advise the retailer (or other customer) on whether or not to accept each pallet, based on whether the pallet’s current Condition-based Expiration Date (ZIPR Code) still meets the requirements on the order. A pallet may have been within spec when shipped, but was exposed to high temperatures in transit, so now does not meet the freshness requirements. This may not be visually evident, but the temperature exposure history tells the story.
  • Consolidation/Multi-level distribution — Growers, Distributors, and Retailers may all operate within a multi-level distribution paradigm. They operate DCs that are continuously receiving pallets from various growers and locations. The pallets are cross-docked to outbound trucks. Now, the system can orchestrate these consolidation centers, to ensure each pallet meets the requirements for the destination. Normally these consolidation centers are not performing quality checks. Now the system can ensure that all pallets meet freshness requirements and can reject (or reroute) pallets that don’t.
  • Retail distribution — Stores vary both in distance from the DC and sell-through rates. Using ZIPR Code data (Condition-based Expiration Dates), retail WMS systems can employ FEFO inventory management, matching pallets to store transit and freshness requirements and automatically advise workers which pallets to send where.
  • Store freshness management — Zest Fresh advises store managers which pallet to sell first, which ones they might want to put on special to sell quickly, and which ones will last until later.

Benefits of a Freshness-Aware Intelligent Cold Chain

Operational realities in produce supply chains result in significant variations in freshness between different pallets, even when they were harvested from the same field on the same day. Recognizing and managing that variance is the key to maximizing value and minimizing waste. This is best accomplished via intelligent routing of pallets, matching measured remaining freshness with accurately assessed customer requirements. This approach creates a number of benefits:

  • Improved quality consistency — Produce is delivered with freshness and quality consistently meeting the end customer needs.3
  • Brand value — The value of both the grower’s and the retailer’s brand is significantly enhanced by delivering more consistently fresh produce.
  • Higher ‘delivered yield’ – higher percentage of delivered product is accepted, sold, and ultimately consumed. Rejections and soft claims are reduced. Waste is avoided.
  • Significantly higher delivered yield to distant customers — In particular, deliveries that have to travel long distances, such as berries going from Mexico to Canada, will see an even more dramatic increase in delivered yield, as they previously had the highest rejection rates, but now can be reliably served.
  • Reduced waste — Mapping of actual remaining freshness to each customers’ need reduces waste across the supply chain.
  • Reduced inventory buffers — Extra inventory buffers are built in to compensate for waste. By providing more accurate visibility into freshness and product quality, both remotely (when the trailer is loaded) and locally (when the trailer unloaded), greater confidence is achieved, waste is reduced, and hence safety stocks can be reduced. For retailers, this significantly reduces the cash burden of inventory.
Source: Image by ChainLink Research
Figure 3 – Compliance Improvements4 with Freshness-aware Intelligent Cold Chain

To realize these benefits, temperature tracking alone is not enough. What is needed is a system bringing together the end-to-end temperature history of each pallet from harvest to shelf, knowledge of the temperature response of different varieties grown in different locations under different conditions, knowledge of variety-specific criteria for freshness, capabilities to match each pallet’s Condition-based Expiration Date with different customers’ requirements, and finally, prescribing simple actions to workers and supervisors, based on all of that knowledge to ensure the best match between remaining shelf life and customer need. Zest Labs provides the only solution we know of that brings together all these required elements.

Growers and retailers alike have a huge stake in maximizing consistent reliable freshness and minimizing waste. Using the approaches and system described in this paper, they can create freshness-aware intelligent cold chain that achieves these substantial benefits to help achieve their strategic goals.


1 Unlike the ZIPR Code attached to a pallet of produce, the one attached to a PO is not dynamic. It does not change over time, but is fixed, based on the customer’s requirements for that order. — Return to article text above
2 For more on process mapping for produce supply chains, see Preemptive Freshness Management: Empowering Workers to Improve Delivered FreshnessReturn to article text above
3 For more on this see Why Quality Consistency Matters: Reducing Waste and Maintaining Shelf Life in Our Fresh Food SupplyReturn to article text above
4 With traditional visual inspection, most of the ~70% of non-compliant pallets of produce are accepted by the retailer anyway. Some will be returned, some will be discarded, some will be sold at markdown, and some will be sold at full price, but not meet the customers’ expectations of freshness. — Return to article text above

To view other articles from this issue of the brief, click here.

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