In my last post, I talked about the relationship between the mean absolute deviation (MAD) and standard deviation (STDEV) for a normal distribution. Apparently, many people had never seen the math behind it, and I got questions about the same relationship for the case where the demand was not normally distributed.
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.
When and how to invest in supply chain management technology is a critical question for all firms that is not a one-time question, but ongoing...
When and how to invest in supply chain management technology is a critical question for all firms that is not a one-time question but ongoing. This blog will observe the real business value is “survival and responsiveness” and elaborate on the challenges with this evaluation.
The right technology can provide a solution to maintaining optimal supply chain planning, but how can you justify the investment?
A colleague and I were talking recently, and the conversation turned to what is the relationship between Mean Absolute Deviation (MAD) and the Standard Deviation (STDEV). In this post, I will explain to you the math behind that approximation, which by the way, only applies to normal distributions.
Over the past few years, the chatter about the role of AI to “optimize” supply chain has been almost endless. Some of the material is great, other is hype, some conjecture, and in most cases, we will not know the impact...
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.
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.