What a Difference a Defect Makes: Streamlining Decision-Making with a Platform for Detecting Fresh Fruit and Vegetable Defects and Evaluating Quality
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In life, two things are certain, death and taxes. In the world of produce, there’s just one: defects. You can’t always avoid them. But as a supply chain stakeholder, whether you are an exporter, importer or a retailer, there are steps you can take to minimize their impact. The first of these steps is gaining insight into the kinds of fruit and vegetable defects you are dealing with. That’s key to establishing a clear understanding of what will satisfy your customers, and what will cause them to look elsewhere.
These are the two pillars of successful quality management: understanding your produce, and understanding your customers’ quality standards. Gaining this comprehensive overview of the fresh produce supply chain depends on the availability of large volumes of accurate data, in real time. And that’s where Artificial Intelligence (AI) makes all the difference, giving stakeholders across the supply chain a common language of quality that they can use to identify defects and make effective decisions.
Understanding Types of Defects
Each fresh produce category has its own list of defects. This depends on the type of produce, where it’s grown, when it’s grown, and the harvesting method. But in general, there are two main families of defects to consider. We will call them progressive and permanent defects.
1. Progressive fruit and vegetable defects
The key differentiator of a progressive defect is that it will degrade the quality of the fruit over time, often very quickly. Think about defects such as mold, or an open wound. If a fruit or vegetable has an open wound, it will be inedible within a couple of days. If fruit with mold makes it into a shipment, it can quickly spread, extending to the rest of the produce and causing a whole container to spoil. Even when selecting a single piece of fruit for purchase, this is still not fit for sale – it’s produce that a consumer would never intentionally purchase. When it comes to a whole shipment, this can be catastrophic.
2. Permanent fruit and vegetable defects
A permanent defect is non-progressive, a defect that won’t change over time. Consider defects such as scars or closed wounds. This defect doesn’t evolve over time, and it won’t impact the entire shipment through contagion. But a large percentage of permanent defects could impact the price you get for the produce, or the likelihood of renegotiations or waste. This is because ultimately, a consumer wants fruits without any defects at all. However, unlike a progressive defect, most shoppers will buy fruit and vegetables with permanent defects such as some discoloration or bruising if they cannot find any without.
How defects impact the fresh produce supply chain
As a trader or an importer, you know that shipments with a high percentage of progressive defects may be impossible to sell to your customers. But permanent defects trigger a more complex decision-making process. After all, you may still be able to sell the shipment, or at least part of it, depending on your customers’ quality thresholds, or current market conditions. If the produce is in short supply in the market, your chances of selling are better.
The question is, how can your inspectors ascertain this ahead of time? How can they ensure quality in line with each customer’s quality standards – especially when these vary from customer to customer?
The short answer is that they can’t, if they’re still relying on traditional methods. The fresh produce industry needs to modernize, fast, in order to keep up with growing customer demands. Let’s take a look at those traditional quality assurance and quality control (QC) methods to understand why digitalization is such an urgent priority.
The Inspection Process for Fresh Fruit and Vegetable Defects
Most inspections involve sampling only one part of a shipment. Inspectors manually spot defects from this sample to gather information. They then use this data to work out statistical significance across the whole shipment. For example, if you test 100 samples of table grapes and find that two of these have scarring, you can say that 2% of the shipment has scarring.
The risks of traditional quality management
This method clearly involves a lot of guesswork. For one thing, extrapolating from a small sample is not always accurate. In addition, the exporter generally doesn’t know which customer each shipment is going to. Even when they do, they don’t know what the buyer is expecting, or what their standards for quality are.
Each inspection is an isolated event, without access to information about what came before. For exporters, this means they have a gamble on their hands. They can send the shipment to the high-quality buyer. This might earn a better return, but it also carries a higher risk of rejection. The other option is to send it to the open market. This is less risky from the point of view of rejection, but the low price they get may not be worth the effort.
Plugging the gaps with real-time data
What’s missing in this picture is certainty. And certainty only comes from having an accurate, detailed picture of your entire operation. That picture needs to include data about the production process, inspections, testing, and the final product. This level of detail is now attainable – at scale – through AI and ML technology. In place of best guesses and risky gambles, companies can use AI-powered quality management to make clear and accurate decisions that ensure everyone downstream gets the quality they expect.
It also lays the foundation for continual improvement, because ML algorithms are able to learn from the data they ingest, generating insights and recommendations based on this historical perspective.
The Clarifruit Difference: One Centralized System for Historical and Real-time Data
This single, centralized quality control system is equipping fresh produce businesses with the control and oversight they need.
Finally, the exporter we met earlier has real-time access to previous data and accurate information about the quality expectations from each buyer. They can go back into the system and see what happened the last time they sent table grapes with a similar defect or quality profile, and ensure that only high quality products make it into the supply chain. They can also use advanced analytics to grade the inspected produce based on specific customer specifications. This all leads to better and smarter decisions based on actual information rather than guesswork. In short, the exporter can find the optimal customer in terms of price and quality for each specific shipment.
That exporter’s customers also gain more control over how they manage their imports. The customer can categorize defects any way that they choose, such as by progressive or permanent defects, or even within these categories, by low, medium or critical defect status. For example, in a particular season, a customer may be happy to accept a shipment with permanent defects such as scarring, but only up to a certain percentage. At a later date, when this produce is harder to come by, they can adapt their requirements to accept fruit with a medium status.
Making quality mismatch a thing of the past
Both of these parties can also manage their requirements according to specific produce types. What is acceptable in lemons might not be in tomatoes, cherries or grapes. This level of control brings intelligence and insight to the quality system for the first time.
Benefits for Supply Chain Stakeholders: Growers, Wholesalers & Retailers
Clarifruit’s AI-powered fresh produce quality control solution brings significant advantages to every link in the fresh produce supply chain, from growers to wholesalers and retailers.
- Growers: Streamlines quality assessments to ensure that product quality matches customer requirements, reducing rejections and maximizing profitability.
- Wholesalers: Enhances operational efficiency, speeds up sorting, and reduces quality disputes.
- Retailers: Ensures consistent quality, optimizes inventory, and minimizes waste.
The AI-Enhanced Future of Fruit and Vegetable Quality Management
Quality assurance (QA) and QC for fresh produce is transforming from cost-intensive manual processes to digitalized and scalable systems that can support the growing demand for high quality fruits and vegetables. AI-powered QCAAS solutions like Clarifruit are leading the way, equipping the industry with the same technological capabilities that are revolutionizing almost every product or service in the Fourth Industrial Revolution.
The Evolution of Fruit and Vegetable Defects Detection
As the fresh produce industry continues to evolve, the detection and classification of fruit and vegetable defects has become increasingly sophisticated. Modern AI systems can now identify subtle variations in defects that might escape human detection, such as early signs of internal decomposition or minute color changes that indicate the onset of disease. This enhanced capability allows supply chain stakeholders to make even more informed decisions about produce handling and distribution, ultimately leading to better quality control and reduced waste throughout the entire supply chain.
The Economic Impact of Proactive Defect Management
Understanding and managing fruit and vegetable defects has significant economic implications for the industry. Research indicates that proactive defect detection and management can reduce post-harvest losses by up to 25%, representing billions in potential savings globally. By implementing AI-powered quality control systems, businesses can not only identify defects earlier but also predict potential issues before they become critical, allowing for more strategic inventory management and optimal pricing strategies. This proactive approach to managing fruit and vegetable defects is becoming increasingly crucial as global supply chains become more complex and consumer expectations for quality continue to rise.
Try out Clarifruit for free for a taste of automated quality control, enhanced efficiency, and consistent customer satisfaction.