01 / Common assumption
Management believes sales are constrained by insufficient assortment, stock shortages, or price competition from marketplaces.
Trading company / industrial components
The assortment grows while buyer confidence declines
Problem
The company has a broad range, available stock, and competitive prices, yet loses orders, produces quotes slowly, relies on discounts, and receives compatibility-related returns. The conventional response is to stock even more products.
02 / What the diagnostic revealed
The flow — search → compatibility check → availability → quote → order — stops at compatibility. Supplier data uses inconsistent formats; attributes, equivalents, and substitution rules are not standardized; and only two technical experts can validate a selection. The catalogue is large, but buyers, salespeople, search engines, and AI systems cannot confidently match a component to the job.
03 / Core constraint
The throughput of compatibility confirmation. In practice, the company sells confidence in the technical decision, not merely components.
04 / COFB
COFB intervention scenario
- 01
Start with the most profitable use cases and SKU groups instead of digitizing the entire catalogue.
- 02
Create one data model for key attributes, compatibility, equivalents, permitted substitutions, certificates, stock, and lead time.
- 03
Turn expert knowledge into rules, leaving specialists only new and high-risk exceptions.
- 04
Do not publish an SKU without a verified minimum data set; launch guided selection first, then a grounded AI assistant that answers only from verified data.
- 05
After the model stabilizes, expose structured data through APIs to partners, marketplaces, and AI procurement systems.
05 / How to verify the hypothesis
Time from enquiry to correct quote, specialist workload, return rate, catalogue conversion, automatically confirmed enquiries, and sales completed without extra discounting.