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.
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.
If you are thinking “machine learning and AI” will save you from data disasters – think again as the pandemic behavior is playing havoc with machine learning models.
As previously discussed, being only data-driven can be a road to disaster for COVID-19 or supply chain management. To avoid this disaster requires skill sets from operations management (OM). In this blog we demonstrate that the probability a person actually has COVID-19 antibodies depends heavily on other factors besides the “raw data” of the test results.
An often-heard theme in supply chain management (SCM) and COVID-19 is “data-driven” – being data-driven is the path to success. For COVID-19 “science-driven” is often said in the same sentence. For SCM demand or customer-driven replaces “science”. This blog will point out a few examples in the COVID-19 challenge demonstrating COVID-19 is an OM challenge.
For those that work regularly in the supply chain or managing the demand-supply network (DSN) models are commonplace to help with similar questions. This blog will provide some basics about models that all will find helpful...