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.

Tools of the Trade: How to Compare / Combine Diverse Time Series – “Normalizing”

A reoccurring challenge in comparing and combining diverse time series in demand forecasting is the “scale” – as it is in combining metrics. Rescaling is a powerful but simple method to help with this issue enabling demand planners to focus on similarities of shape. This blog provides an example of one method called normalization.

Data Science Without Modeling Impact is a Path to Disaster – Simulation to Explore the Impact of Group Size on COVID-19 Spread

In this blog post, we will briefly review some examples of being “COVID-19 adrift” with just data and then focus on the primary task – demonstrating how modeling can be used to understand the impact of group size on COVID-19 spread.

Pin It on Pinterest