About 40 percent of America’s food gets wasted between farm and table. IoT sensors are being deployed to track food through supply chains to reducing waste.
Forty percent.
That’s how much food America wastes from farm to fork, according to the National Resource Defense Council. It’s analogous to going to the grocery store, buying five bags of groceries, leaving two of them in the parking lot, and driving away. This decades-old food waste problem has environmental, social, and financial implications. Until now, technology has been ineffective at solving it.
Before we talk about where IoT plays a role in all of this, let us discuss why new technology is necessary to address the problem.
The Massive Impact of Food Waste Today
Food waste contributes eight percent of global greenhouse gas emissions to our atmosphere. Chad Frischmann, Vice President of Project Drawdown — a coalition of scientists, researchers, and writers who have calculated how to cool the planet over the next 30 years by reducing the amount of greenhouse gas in the atmosphere — shared in a Washington Post article:
“If food waste were a country, it would come in third after the United States and China in terms of impact on global warming.”
On Drawdown’s website, reducing food waste ranks third on their list of solutions to reduce greenhouse gasses.
Additionally, food waste going to landfills has subsidiary environmental impacts that often go overlooked. For every piece of food wasted, the resources that went into growing and producing that food — the land, water, labor, fertilizer, and energy — is wasted along with it.
40 million people struggle with hunger in the United States, including more than 12 million children, according to Feeding America. Fortunately, some of the food that would be destined for landfills is repurposed and made available to food banks and impact organizations, although this only addresses a fraction of overall waste.
The financial impacts are also significant because wasted product drives up costs. Rejected shipments due to premature spoilage force all parties to absorb the cost of the wasted product. As a result, margins decrease for retailers, logistics providers, and growers alike. Unless retailers raise their prices to offset the loss, they can face bankruptcy or go out of business. At the consumer level, losing one grocery store may not have a big impact on those living in major metropolitan areas where stores abound, but if there is no grocery store with fresh food for 30 miles in a rural region, it’s a huge problem.
Despite the epic scale of waste, the problem has continued unabated.
Why? Two reasons:
- Many in the industry believe the food waste problem can’t be solved — that it’s simply considered an industry-wide “cost of doing business.” In that mindset, they factor the cost of waste into their pricing.
- The industry has lacked the necessary technology and tools to fix the problem.
Fortunately, there are technologies like autonomous IoT sensors and cloud-based analytics available today to make a difference. When properly applied, IoT-based analytics platforms can reduce food waste by 50 percent or more.
How IoT Can Reduce Food Waste
Step One: Understanding the Cause of Food Waste
Much of food waste at the retail and consumer level is a result of premature spoilage — the product goes bad earlier than expected. To tackle this problem head on, we must first identify and understand where the cause of food waste originates. Most retailers believe the primary cause of fresh food waste can be linked to poor in-store handling. Why? Because it was at the store where the produce spoiled, it’s generally at the physical store that spoiled food is culled and tossed out. As such, the blame is assigned to the last person who handled the produce — the grocer — unbeknownst to them that the primary causes of waste typically occur much earlier in the supply chain before the produce has even reached the grocer.
Most of the factors leading to fresh food waste happen upstream in the supply chain and even within the first 24 hours after harvest. Studies indicate that improper or inadequate temperature management — starting at harvest — is the primary contributor to early spoilage and food waste.
Freshness Capacity and Why it Matters
All produce has a definable maximum shelf-life, or “freshness capacity.” This capacity varies based on harvest quality, conditions and processing, and the products’ temperature through distribution. Those differences — which occur at the individual pallet level — can lead to product harvested from the same field on the same day to have shelf-life that varies by as much as five days. Additional variations can occur throughout the supply chain due to a variety of factors, so it’s critical to monitor and manage product at the pallet level from harvest to store delivery to reduce waste.
The impact of temperature on produce often cannot be seen until it’s too late. In order to prevent waste, we need granular, pallet-level data — and that means using a data-collection technology that scales. Physically inspecting each pallet is inefficient, impractical, and a big undertaking in an industry where there aren’t enough workers and as the cost of employing people is on the rise. Therefore, automated data collection is necessary in order to gather the insights needed to have an accurate view of the supply chain’s effectiveness.
Legacy Approaches
The food industry has, until now, focused on technology that identifies waste, rather than preventing it. Currently, there’s little data about the condition of each pallet from harvest to shipment to the store. There are assumptions made about important factors like cut-to-cool time and pre-cool efficiency, but these assumptions often diverge significantly from reality.
Traditionally, when produce leaves the supplier about one to two days after harvest, a low-tech USB data logger is typically inserted into the back of the refrigerated trailer to monitor the ambient trailer temperature from the supplier to the retailer and ensure cold chain compliance. This approach is often paper-based, labor-intensive and potentially provides inaccurate or incomplete data. If there’s an “excursion” where the temperature spikes in transit, the load will be checked and potentially rejected and wasted. This could mean all 26 pallets of strawberries in a typical trailer load, for example, go from the trailer into landfill — when perhaps only the pallet that was sampled was bad.
This legacy approach does not solve the waste problem, only quantifies and documents it, providing a forensic record that something went wrong along the way long after there’s time to do anything about or prevent the problem. In those scenarios, distributors and end users (i.e. grocers) are left with little choice but to throw out the baby with the bathwater.
However, by combining a forensic record with technology like IoT sensors and artificial intelligence (AI), we can determine the root cause of vast portions of food waste and provide insights and alerts across the supply chain to prevent it.
How IoT Data and Analytics Solve the Problem
When IoT sensors are placed in each pallet in the field at the time of harvest, they immediately start collecting data about time and temperature: the two most critical factors impacting freshness and potential waste. By placing readers or access points at each waypoint in the supply chain — at the receiving dock, coolers, warehouses, and loading docks — data can be autonomously collected and fed into a cloud-based analytics system. This system not only provides instant access to the data, but it can also be applied to predict when product will expire so that we can apply intelligent pallet routing to reduce waste by ensuring each pallet is delivered to the retailer with sufficient remaining freshness (shelf-life) to meet their requirements. In the image below, an IoT condition sensor has been placed in each pallet.
Image Credit: Zest Labs
Equipped with this level of data capture and insight, a warehouse manager knows which pallets have reduced shelf-life, for example, as a result of waiting in the yard for several hours before being cooled. They can also identify an “at risk” pallet and prioritize it for cooling. Additionally, by applying AI-based predictive analytics, they can know each pallet’s actual remaining shelf-life and route it accordingly, such as re-routing a pallet with only six days of shelf-life to a local grocer versus across country, so it’s delivered with sufficient freshness for sale and consumption.
Using IoT sensors and cloud-based analytics is cost-effective and scalable. Other than placing the IoT device in the pallets, there’s no manual intervention. Therefore, there’s no increased labor costs and no change to supply chain processes. It’s the most effective way to provide intelligence and insight while improving freshness management across the supply chain, drastically reducing waste and mitigating its environmental, social, and financial impact.
