The chatter about Artificial Intelligence’s (AI) and its recent companion, machine learning (ML) is ever-present. In a wide range of economic activities, AI/ML methods are now standard practice...
There is plenty of material being written and posted on the challenges, estimating the growth in COVID19 incidences, and thoughts about the economy. The purpose of this is to pull from experiences in the trenches in shifting OPCURVE to provide some guidance on actions to take to help the nation achieve both critical goals.
An ongoing challenge for any firm is estimating demand for new products. This is especially true when the product has new technology or is replacing an existing product and the additional function in the new product is limited. Often a mathematical construct called an S curve is helpful. This blog provides an overview of S curves and why they can be helpful.
The spread of the COVID19 virus is a major concern of everyone. Typically, two critical questions are being asked: why it seemed to grow so quickly over the last few weeks and what is the impact of social distancing. This blog provides a kitchen table explanation of “rapid growth” and how social distancing might dampen growth.
Probabilities are persuasive in supply chains and analytic methods – especially in machine learning where conditional probability is a dominant underlying structure that makes or breaks the success of an application. In this blog, we will learn how to take the mystery out of the term ‘conditional probability’.
Most software packages show current views of current forecasts, sales, production, and inventory. But what if you wanted visibility of the underlying trends, changes, patterns in all systems...
Shutdown days are either planned well in advance or inadvertent and unwelcome manufacturing excursion- this is a factory issue, not a demand issue. The answer is simple...
In Part 1 of this blog, we closed with the following question: “OK, intermittent demand creates a challenge, but I still need a demand estimate, what do I do!” Below we will provide an answer, but with a different orientation that begins with the question: “what is the purpose of the demand estimate?”
Historically, most of the key planning and computational activities (models, time series, machine learning, and other analytics) that support extended supply chain management (SCM) are “deterministic models”.
With each storm, there comes a bevy of forecasts put out by different computer models. These forecasts begin about 10 days out and change as the storm gets closer and closer. This blog tries to extract some learnings from this process of forecasting.