{"id":27510,"date":"2026-04-19T14:08:15","date_gmt":"2026-04-19T21:08:15","guid":{"rendered":"https:\/\/this.fish\/?post_type=blog&p=27510"},"modified":"2026-04-28T07:14:17","modified_gmt":"2026-04-28T14:14:17","slug":"a-deep-dive-into-ai-and-seafood-cost-accounting","status":"publish","type":"blog","link":"https:\/\/this.fish\/fr\/blog\/a-deep-dive-into-ai-and-seafood-cost-accounting\/","title":{"rendered":"A Deep Dive into AI and Seafood Cost Accounting"},"content":{"rendered":"
\"Raw<\/figure>\n\n\n\n

The only thing as unpredictable as the weather in the seafood industry is cost accounting. From the uncertain nature of fishing to fluctuating market prices and variable yields, managing profitability is a constant struggle for seafood CFOs and financial controllers. At the heart of this challenge lies a fundamental business question:What does it cost to produce each seafood product you sell?<\/em><\/p>\n\n\n\n

Figuring out the profit margin on a salmon fillet is, I would argue, more complex than calculating the cost of goods sold (COGS) for an iPhone. The difference lies in the nature<\/em> of the complexity: iPhone complexity is controlled and predictable. It’s about managing a staggering number of known variables across global supply chains. Seafood complexity has far fewer variables, but they are wildly<\/em> unpredictable. It’s about constantly adapting to fish price volatility, yield variation, \u201cgive-away\u201d variance and uneven quality.<\/p>\n\n\n\n

This unpredictability is a perfect problem to be solved by AI, whose core functionality is to be a \u201cprediction machine.\u201d AI finds underlying patterns in data, providing you with insights that you otherwise wouldn\u2019t have. AI can predict a value (price, cost or yield), classification (grade) or anomaly (problem or error) based on pattern recognition in data.<\/p>\n\n\n\n

To illustrate my point, I\u2019ve generated a month of production data in a fictional seafood processing plant using five raw material lots. Production runs from 1,800 to 3,500 kg of raw material per day. During the month, fish prices rise. Initially, there are three lots in the inventory valued from $3.8 to $4.9 per kg. After a week, a fourth lot (D) is purchased at $5.5 per kg and a fifth lot (E) for $5.6 per kg around mid-month, raising the value of the inventory.<\/p>\n\n\n\n

\"Raw<\/figure>\n\n\n\n

As the raw material inventory is depleted, lot D and E are purchased to replenish quantities, maintaining a healthy balance, as the chart below illustrations.<\/p>\n\n\n\n

\"Raw<\/figure>\n\n\n\n

I then used three different methods to calculate costs. (See Seafood Cost Accounting Methods below for more details.)<\/p>\n\n\n\n