Inventory Category
Challenge
The description field in the procurement system contained vital information about each purchased item but was populated with free text, lacking any standardized format. This led to inconsistencies and inefficiencies in inventory management, complicating the process of matching items with internal categories for reporting and optimization.
Solution
- LLM for Text Normalization: A Large Language Model was trained to read and normalize the free-text descriptions into a standardized format. This made it easier to analyze and categorize the inventory items.
- Category Matching Engine: The normalized text was then fed into a category matching engine. This engine used machine learning algorithms to automatically associate each item with one of the organization's internal categories.
- Data Validation and Feedback Loop: To ensure accuracy, a feedback loop was established where category matches were reviewed and validated, refining the machine learning model over time.
Result
The deployment of the LLM and category matching engine brought transformative changes to the non-profit’s procurement system. Inventory could now be automatically categorized and managed, leading to more efficient reporting and operational processes. By automating this critical aspect of their operations, the organization could focus more resources on their core mission, improving the lives of people worldwide.
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