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.

Data Science Tools of the Trade: Monte Carlo Computer Simulation

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.

By |2020-11-23T19:58:42-05:00November 24th, 2020|COVID-19, Data Science, Machine Learning|0 Comments

Lessons for COVID-19 and Supply Chain Management – Six Months Later

Over the past six months, significant progress has been made in understanding COVID-19 and reducing its health and economic impact. In this blog, we will not rehash this information, but to identify open issues in handling the current challenge of tackling COVID-19 and relate this to the challenge of managing supply chains.

Optimization and Effective Use of Space – COVID-19 Challenges

For most involved in Supply Chain Management, optimization is viewed as one of the three primary methods to create a supply or central plan that matches or balances assets with demand. Historically effective use of space involved minimizing unused space or maximizing revenue from a fixed amount of space. COVID-19 has upset the social order.

Statistical Forensics – The Danger of Being Data and Not Operations Management Driven

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.

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