Leveraging LLMs with LangChain for Supply Chain Analytics — A Control Tower Powered by GPT
<p>A Supply Chain Control Tower can be defined as a centralized solution that provides visibility and monitoring capabilities to manage end-to-end supply chain operations efficiently.</p>
<p>This analytical tool enables a Supply Chain department to track, understand and resolve critical issues in real time.</p>
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<p><a href="https://samirsaci.com/"><img alt="" src="https://miro.medium.com/v2/resize:fit:700/1*K7VD0trQXpFlzg9UT5HOeA.png" style="height:153px; width:700px" /></a></p>
<p>Supply Chain Control Tower with Python [<a href="https://towardsdatascience.com/automated-supply-chain-control-tower-with-python-17dbf93a18d0" rel="noopener" target="_blank">Link</a>] — (Image by Author)</p>
<p>In a <a href="https://towardsdatascience.com/automated-supply-chain-control-tower-with-python-17dbf93a18d0" rel="noopener" target="_blank">prior article,</a> I introduced a solution for an analytics control tower (developed with Python) capable of autonomously generating incident reports.</p>
<p>However, this approach encounters limitations in the <strong>range of indicators and reports provided</strong>.</p>
<p><a href="https://towardsdatascience.com/leveraging-llms-with-langchain-for-supply-chain-analytics-a-control-tower-powered-by-gpt-21e19b33b5f0"><strong>Read More</strong></a></p>