In this blog we briefly cover some key insights for successful time series forecasting: (a) Profiling the Shape of the Curve is the first stage, and the first step is assessing if the time series is stationary. (b) The forecast method identified must capture the shape and be able to project the shape across time. (c) There are limits in historical and no amount of “fancy math” can overcome them.
Right-sizing inventory affects earnings before interest, tax, depreciation, and amortization (EBITDA) in several ways. Understand why it's both good and bad.
In this blog, we will illustrate through an example of these potential pitfalls (unanchored, random variation, and narrow metrics) and potential negative impact on a firm.
In the simple version of supply chain management (SCM) the goal for demand forecasting in the tactical decision tier is prediction accuracy. In the wiser version, the purpose is expanded to include an understanding of your demand (demand profiling)...
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.
The S-curve is an indispensable tool for demand planning for new products or technology. Learn the steps to create the right model for your market growth.
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.
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...
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.
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