Modular Adoption

Go To: Advancing Competitiveness Homepage

Credit: Pixabay

A Conceptual Framework for Economic Decision Making in Advancing Manufacturing Industry Competitiveness: Modular Adoption

For more information, see NIST AMS 100-80.

Credit: Pixabay

An organization can increase the probability of successful implementation by providing tools to facilitate adoption (e.g., guides and software); reducing individual risks by, to the extent possible, decoupling funding from impact evaluation; recognizing and praising adoption; and spreading costs of adoption out by modularizing the framework. Modulization might include breaking the framework into smaller components where each component has benefits. For instance, the modules might include the following:

• Project Characteristics Tracking: This module implements a systematic approach for tracking and characterizing projects and the components of projects (e.g., categorization). The benefit of this module is that it provides a clean way to group and discuss projects.
• Rough Order of Magnitude Impact Tracking: This module implements the tracking of fast-moving noisy impact indicators. The benefit of this module is that it provides leading indicators of impact that can be used to guide decisions and communicate potential impact.
• Deep impact analysis: This module includes implementing impact analyses of projects. The benefit is that it can aid in understanding the accuracy and precision of fast-moving indicators and provides a more robust estimate for communicating impact.
• Prediction Data System (Industry Data): This module includes developing industry data to guide in the design and selection of projects.
• Formalized Predictions and Tracking: This module includes formalizing and tracking predictions. The benefit being that it aids in understanding, designing, and selecting projects.
• Analysis and Recalibration: This module includes the analysis of collected data to generate actionable insight with the potential benefit being that impact is increased over time.

This modularization spreads out costs into bite size pieces where each piece has its own benefits. 

Framework Components, Logic, and Implementation

Credit: Pixabay The Framework	Credit: Pixabay Framework Logic	Credit: Pixabay Implementation Infrastructure
The Framework's Process	Background: Impact Forecasting	Notes and Cautions
Data Layers and Feed Back Loops	Supporting Logic and Evidence	Standards and Platforms
Metrics and Units of Observation	Economies of Scale	Modular Adoption

Credit: AMS 100-80

Collaboration is a key component to reducing change agent costs and enabling compound learning. If you are considering adopting this framework, consider reaching out to the author Douglas Thomas, Economist: douglas.thomas [at] nist.gov (douglas[dot]thomas[at]nist[dot]gov)