Karel Lintermans

About Karel Lintermans

Karel is a consultant who joined the Arkieva team in November 2020. At Arkieva, his focus is mainly on demand and RCCP implementations. In his free time, he loves to program different pieces of code (preferably in the Python or SQL language) that can help other consultants in the company and his team. Karel graduated Cum Laude at the University of Ghent as a Master of Science in Business Administration with a specialization in management and IT. His favorite holidays are in the mountains where he loves to eat raclette and ski down the mountain.

Part 3: How Natural Language Processing (NLP) Can Benefit the Supply Chain – Internal Structured Data

This blog discusses how utilizing a semantic parsing method can help a less experienced user transform their data questions into advanced database queries, and how it can help detect errors in datasets.

Part 2: How Natural Language Processing (NLP) Can Benefit the Supply Chain – Internal Unstructured Data

We know how to deal with structured data but working with unstructured data might be a bit more time consuming and challenging. There are multiple solutions that NLP offers to transform your unstructured enterprise data to structured data.

Part 1: How Natural Language Processing (NLP) Can Benefit the Supply Chain

As more and more individuals utilize supply chain software, there is a need to simplify its usage. The next step in evolution can be Natural Language Processing (NLP) where the user expresses a desire in plain language, and the software translates it to queries in the background. This and other use cases such as the automation and analysis of content have made NLP an area of prominent growth.

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