In today’s intricate supply chains and with consumers who are digitally adept, even a single error in data, like an incorrect product weight, can trigger a ripple of inefficiency across the entire system, resulting in a dissatisfied customer. Retailers worldwide are constantly striving to enhance search and browsing experiences, with the goal of boosting sales and reducing returns from customers who may have inadvertently purchased products due to incomplete or inaccurate information.
Retailers require a strategic approach for Item catalogue management and attribute enrichment, not only to deliver the appropriate products to their customers, but also to eliminate inefficiencies in their supply chain. However, the implementation of Item Catalogue & attribute enrichment often poses challenges, mainly due to the existence of silos and incomplete data scattered across different departments within an organization, and the difficulty in understanding the extensive and detailed item information provided by the suppliers.
Coforge is driving Item catalogue management and attribute enrichment using large language model (LLM) by building AI models to automate the E2E Item Catalogue Management process.
