My operations team wants to build a custom machine learning model to optimize our warehouse layout, but I am worried it will turn into an expensive science project. How do we reframe this as a simple operations-improvement project instead of an ML project?

Question

Tyler Smith · Accepted Answer

When your operations team proposes a complex machine learning project to optimize your warehouse layout, you must step in and reframe the conversation. Do not let your company get bogged down in expensive technology projects that fail to deliver a clear operational result.

Instead, follow the simple rule of never selling AI. Refrain from calling this a machine learning project, and instead frame it strictly as an operations-improvement project that uses machine learning.

Start by defining the exact business goal you want to achieve. This could be reducing order picking times by fifteen percent or increasing your daily shipping capacity.

Your team must present the project in terms of these physical results, placing the machine learning technology in a minor technical footnote.

To keep the initiative simple and realistic, set a clear, bound quarterly Rock. The goal of the Rock is not to build a massive, custom software platform. The goal is to deploy a simple data model that analyzes your historic order patterns and suggests a more efficient physical layout for your high-velocity inventory.

By focusing on the physical operational outcome rather than the underlying technology, you ensure that your team remains focused on driving real efficiency rather than playing with shiny new tools.