“Flower power” and “cool baby” were popular phrases in the 1960s, and those of us in the fresh and perishable food supply chain industries still like to think about flowers and being cool (or cold). But, it’s a downer when we read that we’re still wasting 40% of our food every year. It bums me out. Why?

Because it doesn’t have to be this way. We have the ability to reduce pre-consumer perishable food waste by 50% or more. The reason we, as an industry, haven’t made much of a dent in this problem is that too often we approach it by using tools and methodologies born out of days long gone.

We need to get hip!

Old style data loggers, which have been around for a while, placed in trucks or trailers, only provide limited information – perhaps letting you know after the fact that there’s been a temperature excursion. By then, it’s too late to do anything. And, visual inspection is misleading in terms of actual days of remaining freshness since it is a lagging indicator and only useful right before the product spoils.So, when we see there’s been an excursion what do we do? We reject the entire load and throw it away into the landfill and someone takes the loss (and maybe there’s an out-of-stock too).

It gets worse though. Too often, we reject the entire trailer load when, in reality, the vast majority of the load may be perfectly fine. The old way of thinking is that, if I put a temperature data logger in the trailer, it will tell me the temperature history of the trailer. What it tells you is the temperature history of that location in the trailer. It’s not representative of the entire trailer, where it is generally cooler near the refrigeration source and warmer further away and on the sunny-side of the trailer. But, more important, it’s also not at all representative of the temperature of the produce in the trailer, which is really what you want to know. And it’s not proactive. By the time you know something, it’s too late to do anything about it.

In fact, Zest Labs’ research has documented that temperatures vary at the pallet level and not the whole trailer. Pallet-level variation starts at harvest based on harvest conditions, processing and temperature. This means each pallet has its own unique freshness capacity and shelf life that changes over time, from harvest to precool, precool to shipment and once it’s in the trailer en route to the distribution center or store. In our white paper, Comparing Pallet- and Trailer-level Temperature Monitoring, we include data that shows just how much pallet temperatures (and by implication the dynamic remaining shelf life) vary because of this. In fact, one pallet had temperature issues before it was even loaded into the trailer (due to inadequate precooling) and actually heated up adjacent pallets, negatively impacting their remaining shelf life.

How do we know this? We insert autonomous IoT condition sensors into each pallet of produce at the time it is harvested. Because we know what the freshness capacity or total available shelf life of the produce is when it’s harvested, we can then use the data collected by the sensors to dynamically calculate the remaining shelf life at every step of the way. This enables members of the fresh food supply chain with the information they need to intelligently make decisions concerning pallet routing and prioritization. In the example shown in the white paper, an alert could be generated indicating that one of the pallets was insufficiently precooled and shouldn’t be placed into the trailer. In fact, five of the 26 pallets had temperature (and shelf life) issues. So, it’s a game of roulette for you to figure out, was the data logger on one of the five bad pallets or on one of the 21 good pallets. Guess wrong and you’re either accepting five pallets that will spoil, or rejecting 21 perfectly fine pallets.

Using the knowledge provided by IoT condition monitors and cloud-based predictive analytics, we can proactively prevent this waste from ever happening. This has positive impacts on customer satisfaction, sustainability and, ultimately, your margins.

Right on!