Case Study: Ford Motor Company improves production uptime with TigerGraph

Ford improves machinery data reconciliation to 90% accuracy with TigerGraph

Ford Motor Company, one of the world's largest automakers, faced a challenge with data ambiguity from its global production machinery, which was stored in two relational databases. This issue resulted in production downtimes, and the company had struggled for months to remove data duplicates. They partnered with vendor TigerGraph to implement its graph database to address this through entity resolution.

The solution from TigerGraph involved using its graph database for entity resolution to consolidate data and remove duplicates. Subsequently, similarity matching algorithms were applied to predict part failures. As a result, Ford can now reconcile data to the same machinery with 90% accuracy, allowing them to pre-plan part replacements and avoid unnecessary breaks in the production assembly line.

Ford Motor Company