The risk of viruses is often a topic of conversation in current times. One of the dominant questions at social gatherings is - what is a bigger risk (defined as serious illness) this fall and winter: regular virus (REGVIR) or COVID-19...
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.
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.
The current COVID-19 situation highlight 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.
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...
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.