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 constructed called an S curve is helpful. This blog provides an overview of S curves and why they can be helpful.
Taking the mystery out of the rapid growth of COVID19 and the purpose of social distancing – some basics for Octogenarians
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.
Scientific and system-driven Inventory Projection facilitates a quick decision-making process and enables a prompt analysis of alternative what-if scenarios. The following are the top 7 benefits of system driven inventory projection.
When trying to forecast demand for the future, it is important to understand the variability in the underlying dataset.