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.
The current COVID-19 situation highlights the supply chain management challenges in any turbulent time. In this blog we identify five key points: preparedness, larger good, anticipate, and not react to events, responsiveness, and an intelligent stochastic estimate of demand.
We see graphs of COVID-19 events on a regular basis. One of the most common is a bar chart for daily new events (COVID-19 cases, hospitalizations, deaths). Recently in presentations, smoothing methods used to overcome limitations is presenting the raw daily data. This blog will take some of the mystery out of smoothing methods.
This blog provides some basic information on the curve, relates statistical concepts to policy and actions, and examines policy options for a safe restart relating them to the APEX curve. There are three essential groups of action to begin a safe restart: testing, detailed understanding of the impact of mitigation actions, and the ability to do detailed tracking.
We see graphs of COVID-19 events on a regular basis these days. Two common ones are bar charts for daily new events (COVID-19 cases, hospitalizations, deaths) and the “sweeping curve” to capture a cumulative number of events. Additionally, log transformations are mentioned. The purpose of this blog is to shed a bit of light on these curves and the role of the log transformation.