For decades, cost-plus pricing has been the dominant approach in industrial spare parts businesses. Simple, auditable and easy to explain, it provided organizations with a sense of control in complex environments.

Artificial intelligence has become a recurring theme in pricing discussions. In industrial contexts, it is often presented as a way to automatically optimize prices, reduce manual effort and uncover hidden margin opportunities.

In industrial organizations, pricing decisions are often expected to be data driven. Yet, many pricing initiatives struggle not because of a lack of models or tools, but because the underlying data foundation is fragile.

For industrial manufacturers, spare parts pricing represents a significant margin lever. Yet, as catalogs grow larger and more complex, pricing control becomes increasingly difficult to maintain.