The encyclopedia · Strategy & Leadership · Financial decision · 2017–2020
Verizon tamed thousands of small suppliers with analytics, saving millions
Descriptive, predictive and prescriptive analytics plus text mining rationalized Verizon's tail-spend suppliers and cut spend by millions.
Verizon
the move
Verizon's strategic sourcing teams could manage a few large suppliers by hand, but thousands of small tail-spend suppliers were expensive, risky and impractical.
The supply chain organization combined descriptive, predictive and prescriptive analytics with machine learning, text mining and NLP to rationalize that tail.
The approach centralized contracts and relationship management while still pursuing the best price per unit.
why it works
- It collapses the long tail into a manageable set of supplier blocks.
- Text mining/NLP turns unstructured contract data into signal.
- Prescriptive analytics tells sourcing where to consolidate.
- It reduced spend by millions and cut contract lead time.
what transfers
When a problem is too big to staff, the fix is usually to cluster it; analytics can collapse thousands of small deals into a few rationalized blocks.
what came after
Verizon reduced spend by millions of dollars and achieved the lowest price-per-unit for the sourced products and services; it also gained centralized and transparent contract and supplier management, lower overhead, reduced contract execution lead time, and improved service quality.
references
- Verizon Uses Advanced Analytics to Rationalize Its Tail Spend Suppliers
- Verizon Uses Advanced Analytics to Rationalize Its Tail Spend Suppliers
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