Since the beginning of time, alternate but related views of production have existed: historically called “starts” and “outs”.
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.
Successful management of inventory is a hot topic in supply chain management. While it's easy to measure, the cause may be complicated to uncover.
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.
In many areas of supply chain management, analytic methods generate estimates with “pesky decimals”. The traditional method to eliminate the decimals is rounding. In this blog, we demonstrate the importance of this method and how to calculate these improved integer estimates.
Learn how a simple binomial model can help anticipate the future including COVID-19 breakthrough cases just as models help a firm estimate it's future.
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.
For a new technology to be successful it requires the correct seasoning which requires time in the trenches. This blog captures lessons from two past gurus.
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.