Our latest webinar examines the varying definitions of machine learning and artificial intelligence while discussing how they can be leveraged to improve your statistical forecast.
The adoption of machine learning and artificial intelligence is on the rise, but not at the pace of other transformations. Here are a few reasons why.
Are you a supply chain planning firefighter? Learn more about the value of keeping your technology current and the dangers of relying on an outdated system.
This blog discusses how utilizing a semantic parsing method can help a less experienced user transform their data questions into advanced database queries, and how it can help detect errors in datasets.
Part 2: How Natural Language Processing (NLP) Can Benefit the Supply Chain – Internal Unstructured Data
We know how to deal with structured data but working with unstructured data might be a bit more time consuming and challenging. There are multiple solutions that NLP offers to transform your unstructured enterprise data to structured data.
As more and more individuals utilize supply chain software, there is a need to simplify its usage. The next step in evolution can be Natural Language Processing (NLP) where the user expresses a desire in plain language, and the software translates it to queries in the background. This and other use cases such as the automation and analysis of content have made NLP an area of prominent growth.
Industry analysts, big-time consultants, and your peers are all talking about technology, digital transformation, and the future of the supply chain. It can seem like a lot of noise given the day-to-day pressure you feel while working to ensure that inventory is on hand and positioned where it is supposed to be. With all you have on your plate, are you aware of the signs that it is time for a change?
A reoccurring challenge in comparing and combining diverse time series in demand forecasting is the “scale” – as it is in combining metrics. Rescaling is a powerful but simple method to help with this issue enabling demand planners to focus on similarities of shape. This blog provides an example of one method called normalization.
In this blog, we point the reader to a recent article “Humachine”, which identifies the general challenge of implementing decision technology to improve SCM decision making resulting in improved organizational performance and the importance of experience in the trenches.
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.