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This is the bilingual post in English and Vietnamese. Vietnamese is below.

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English

A technology move, a long-standing operational problem

In late September 2026, Amazon introduced a set of AI agents to help sellers on its platform carry out two core operational tasks: planning inbound stock and handling aged inventory, meaning stock held in the warehouse longer than its normal selling pace. In form, this is a technology story. In substance, it addresses a management problem that every business holding inventory must solve: limited resources do not allow managing every item at the same level, so the key issue is determining where to concentrate control.

Notably, even a large-scale business with strong technological capability still returns to a foundational question: among the thousands of item codes in the warehouse, which account for most of the value, and which are tying up working capital at low efficiency. Amazon brought AI to this step not because the problem is new, but because it is common and systemic, with a standard tool to address it existing since the middle of the last century and remaining fully valid today.

That tool is ABC analysis. Its principle is a quantitative observation: in almost every portfolio, a small proportion of items generate most of the value, while the rest contribute negligibly. A few item codes generate most of the revenue. A few customers generate most of the profit. A few causes generate most of the incidents. In daily operations, businesses tend to allocate resources evenly, which leaves the most important items controlled at a level not proportional to their contribution.

This article takes Amazon’s move as the analytical case, but the scope of application is not limited to technology or e-commerce. The principle applies to every kind of business that must carry out resource allocation across a portfolio with many items, from retail chains to manufacturers, from service firms to small operations. The content covers the founding principle, the classification method, application to aged inventory, practical implementation, and considerations in use.