AI-optimiztaton for Supply Chain Level 1

We start with attacking common findings through real discovery and reality.

This would lead is to an estimated $10M-$50M in savings through the first 12 months.

The savings would casscade into an addtional $50M when the network effects are reaching the end points in the targedt supply channels.

The market effects are estimated to net $100M to 500M in market sentiment though a healither balance sheet and income statement.

ROA/ROE will state a leaner operationg while actually having more capacity and capabilities "unseen" yet in the 10K.

The next benifits are an autonouns learning and audjtsing supply chain.

A well networked and unified workforce working closer togther on the responsibilies which they were hired, vs challnges and fire fighting.


symptomology mapping tools as a means to resolve uncharacteristic AI-behaviour.

bundling artificial memory packages to support an episodic based prediction pattern.

Episodic memory is a neurocognitive (brain/mind) system, uniquely different from other memory systems, that enables human beings to remember past experiences. The notion of episodic memory was first proposed some 30 Years ago. At that time it was defined in terms of materials and tasks. It was subsequently refined and elaborated in terms of ideas such as self, subjective time, and autonoetic consciousness. This chapter provides a brief history of the concept of episodic memory, describes how it has changed (indeed greatly changed) since its inception, considers criticisms of it, and then discusses supporting evidence provided by (a) neuropsychological studies of patterns of memory impairment caused by brain damage, and (b) functional neuroimaging studies of patterns of brain activity of normal subjects engaged in various memory tasks. I also suggest that episodic memory is a true, even if as yet generally unappreciated, marvel of nature