Category: Data Science

Since the beginning of time, alternate but related views of production have existed: historically called “starts” and “outs”.

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.

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.

Natural language processing (NLP) in supply chain software can increase accessibility to more users. Find out how your organization can benefit.

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.

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.

Monte Carlo Discrete Event Computer Simulation is a particularly powerful and flexible “tool of the trade” for a wide range of challenges in supply chain and operations management. This blog provides some basics on MCDECS, the next blog will use MCDECS to explain why limiting the size of a gathering helps reduce the spread of COVID-19.

There are a number of data analysis “tools of the trade” that have proven effective in exploring data to get it to tell a story. One such method is the first-order difference (FOD). The purpose of this blog is to provide an overview of FOD with respect to profiling demand history.