Neos Aware Modeling Methodology

 

learning-overview

Overview

The Neos Aware Modeling Methodology provides a systematic approach to creating models.  Regardless of skill level or experience with Neos Aware, modelers should consider using a quality process as it will save time and effort when designing models and often increase the utility and impact these models.  The process begins with a simple question and progresses to the new business understanding or insight. Neos Aware also ensures on-going understanding of the current and forward-looking business , quality issues, cost reduction or environment through the import data set loop for the new projects.

The Methodology

The Neos Aware Methodology diagram (above) is further explained by the following general descriptions:

Business Understanding (A)

This initial phase focuses on understanding the business objectives and questions, and then converting this objective/question into a data modeling problem definition. The result is a preliminary plan designed to achieve the objectives.

Parameterization (B)

This phase the user define the equipments used in the laboratory, the devise type, the units, and the procedure of each ceramic test.

The variable range is set up and the measured attributes are managed according the industry.

The permits for the users   are defined with the correct status according the customer policy.

Raw Material Data (C)

In this part, is introduced the raw material data as: supplier, description, technical data, and type of material, cost, and other proprieties.

Define the Project (D)

At this point, you are ready to start the project with the description, selection of raw material, percentage to be used and define the ceramic process to be used.

The attributes to be taken in consideration are defined and the laboratory equipment’s are confirmed.

Initial Formulations (E)

The software generates the initial formulations to ensure that interactions between the raw materials are verified.

After the run the calculations is set up the formulations for the laboratory tests.

Import data set (F)

After the first project you can import data set from the previous project reducing the required number of test for the new project.

Feed the results in the workbench(G)

The technicians, create the reference for test, check the procedures, and introduce the tests results and validation for calculation for each attribute.

For many attributes are available a tool to predict the next result according with the variable, this wizard help the technician reduce the number of tests.

Run the proprieties management (H)

This process unlocks the self-learning process, and is an obligatory step to simulate and optimize.

More tests are introduced, and proprieties recalculated, more the self-awareness are learning.

The error of laboratory tests is controlled and could be revised or ruled out.

Simulation of your formulas (I)

In this step the engineer can start to simulate with your formula and predict the result, these formulas are tested and introduced in the workbench, closing the cycle with the run of properties. This virtuous cycle, improve the knowledge for the engineer and for the platform.

Optimize you formula (J)

Multiple attributes in the ceramic formulations make it impossible to manage all simultaneously without this tool.

The engineer define all constrains without limitation, the objective in several dimensions and attributes, all in plain language.

After the calculation the results is confirmed in the laboratory and feed in the workbench.

Analyze Results (L)

With the dashboard, the engineer creates the graphics and tables to compare the results and control all attributes against the standard or another formulation.

Many graphics could be created by the user in accordance the objective of the development.

Answering the Question/Achieving the Business Understanding

The  business analysis answers the question and achieves the business/technology understanding.  The equation below illustrates the model combined with analysis and  supports the decision-making process.

Model + Analysis ⇒ Decision

Careful analysis of the model outputs completes the decision equation (cost reduction, improve quality, etc.).