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  • Jordan Gunn

More for Less: How to Protect Your Seafood Brand by Making Your QC Budget Go Further

In this blog we’ll discuss the fallout of the seafood industry’s QC budget cap of 1% of total sale value, and show you how to make that money go further to protect your product and brand image.


It’s a great time to be in seafood. A recovering economy with high per capita disposable income, regained consumer confidence in the cleanliness of seafood after a decade characterized by heavily publicized oil spills, and a continued increase in dietary preferences for seafood spell big profits in the years to come.

But the $2.5 billion market prize this industry represents doesn’t come without its challenges. Razor-thin margins and high volumes create unique challenges for producers looking to cash in. As a rule of thumb, seafood processors have tried to cap QC budgets at 1% of the total sale value of their products to limit production costs. That strategy makes sense from a strictly monetary viewpoint, but cutting money from QC budgets can have unintended consequences downstream.

To keep a QC budget below 1% of the total sale value of seafood you have to drop the taste panels and complex chemical analysis and aim for maintaining a QC regiment that simply keeps QC complaints from customers and officials at “an acceptable level”. After all, aiming for zero complaints is a pipe dream that can quickly drain your budget. But being satisfied with an “acceptable level” so your numbers work out is problematic for the following reasons:

  1. There is no acceptable level of complaints in reality, every complaint hurts your brand image and should therefore be avoided.

  2. Limiting the use of organoleptic testing (sight, smell, taste, and touch) to individuals without any group averaging in the form of taste panels is inherently unreliable due to human error and its subjective nature.

  3. Skipping chemical tests because of timing and cost issues is dangerous because they’re the most objective, reliable QC options at your disposal.


It’s not like these are purely theoretical concerns. These concerns manifest in tangible poor business outcomes such as:


  1. Low quality control benefit/cost ratio

  2. Poor quality product due to limited parameter oversight

  3. Loss of product due to recalls and products not meeting final specifications

  4. Poor brand image due to the present of defects and inconsistencies


But what if there was a way around all of these problems and to avoid the negative consequences for your seafood operation?


Luckily technology has provided us with a solution in the form of a state-of-the-art sensor-based quality control system that has the power to retrieve better data, for a larger percentage of your product, at a lower price. Too good to be true? Not really, here’s why... AI-Assisted Hyperspectral Imaging technology (referred to as AI-HSI) is a solution to the low cap/high risk problem within the seafood industry because:


  1. It can help replace unreliable individual-based organoleptic tests with reliable, objective analyses.

  2. It can replace expensive complex chemical tests with cost-effective optical-based methods.

  3. It can perform analysis faster than both human-based and chemical tests.

  4. It covers a larger sample area than chemical-based spot tests.

  5. It can perform analysis on multiple parameters simultaneously, further increasing its speed and coverage.


Again, these aren’t theoretical benefits that only your QC team would appreciate. These advantages manifest themselves in business benefits in the form of:


  1. High quality control benefit/cost ratio

  2. High quality product due to extensive parameter oversight

3. Low recall rates and high percentage of products meeting final specifications.


4. Better brand image due to a lack of defects and inconsistencies


In summary, AI-HSI is a solution to the problem of low QC budgets hurting your brand. It can give you better quality control, over more of your outgoing product, for less money allowing you to do much more with much less.



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