Nano Version Control and Robots of Robots: Data Driven, Regenerative Production Code

TL;DR

This paper presents a data-driven, regenerative approach to automated production code creation using layered robots, patterns, and prototypes to enhance sustainability and reduce fragility in manufacturing systems.

Contribution

It introduces a novel method transforming complex production code challenges into manageable problems using data, patterns, and prototypes, enabled by agent-based simulation and NanoVC repositories.

Findings

Enhanced sustainability in production systems.

Reduced fragility of automation code.

Legacy encoding of designer expertise in robots.

Abstract

A reflection of the Corona pandemic highlights the need for more sustainable production systems using automation. The goal is to retain automation of repetitive tasks while allowing complex parts to come together. We recognize the fragility and how hard it is to create traditional automation. We introduce a method which converts one really hard problem of producing sustainable production code into three simpler problems being data, patterns and working prototypes. We use developer seniority as a metric to measure whether the proposed method is easier. By using agent-based simulation and NanoVC repos for agent arbitration, we are able to create a simulated environment where patterns developed by people are used to transform working prototypes into templates that data can be fed through to create the robots that create the production code. Having two layers of robots allow early implementation choices to be replaced as we gather more feedback from the working system. Several benefits of this approach have been discovered, with the most notable being that the Robot of Robots encodes a legacy of the person that designed it in the form of the 3 ingredients (data, patterns and working prototypes). This method allows us to achieve our goal of reducing the fragility of the production code while removing the difficulty of getting there.