Data Analysis | Arkieva Supply Chain Blog

Category: Data Analysis

Rules-based Approach to Supply Planning: Matching Assets with Demand

In any supply chain, matching the supply assets to the demand in a consistent, reliable, and profitable manner is a key requirement. This blog discusses how to create supply plans based on a sequence of rules that makes the decision-making process fast, effective, and intuitive.

Part 3: How NLP Benefits the Supply Chain – Internal Structured Data

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.

Relationship Between MAD and Standard Deviation for a Normally Distributed Random Variable

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.

Supply Chain Management – Autonomous is Far Away and Resiliency is Needed Now

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...

Statistical Forensics: Manipulating Probabilities to Determine the Real Risk From Viruses

The risk of viruses is often a topic of conversation in current times. One of the dominant questions at social gatherings is - what is a bigger risk (defined as serious illness) this fall and winter: regular virus (REGVIR) or COVID-19...

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