In this session, DataProphet - an advanced machine learning company focused on developing and deploying bespoke solutions for industry, will cover how they're using machine learning and AI to optimize complex manufacturing processes to improve both quality and yield

1. Proudly developed by

2. Established 2014
With the goal of providing
state-of-the-art machine
learning solutions
A Team of 35+
Consisting of post graduate
engineers, computer
scientists and statisticians
OMNI is AI Process Control
OMNI in deployment at
foundries, steel mills,
automotive assemblers and
ore refineries
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3. VISION
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Expert system powered by AI to
actively prescribe optimal control
parameters.
Machine vision for quality control -
detects, classifies, and locates
surface defects.

4. 4

5. Example:
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6. Aggregation platforms
Control plan software
Log books
Quality inspection sheets
Historians (sensor logs from PLCs / SCADA):
GE Historian
Generic SCADA
Direct from machine:
Siemens
Honeywell
Schneider
ABB
Additional sensors and databases:
IIoT
Shopware
Many databases (Oracle, SAP, Postgres, MySQL,
Microsoft SQL, etc...)
Machine / Process Data Quality Data
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7. 7
Implementing OMNI:

8. 8
Fully declarative infrastructure using
Terraform.
Configuration management via SaltStack.
Model training, data pipeline and API
serving via auto-scaling EC2 instances.
Model Inference Service
Elastic Container Registry provides
reliable model versioning.
OMNI models deployed as ECS
services.
Efficiently scale individual model
inference capacity.
Rapidly deploy and serve new
models.

9. Example:
Atlantis foundries: horizontal grey iron foundry producing truck engine blocks;
casting approximately 75,000 tonnes per annum across 2 main products.
Shipped defect rate for 2016 averaged six percent and ten to fifteen percent
internal rework.

10. OMNI discovered optimal operating region for the casting line; and
prescribing optimal parameters to operators.
The application of OMNI at the plant
achieving an ROI on our license fees within the first month.
Example:

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12. Example:
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13. Control Plan for the Operator Site overview for Plant Head
Process Variable 16
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14. 14
Alerts prioritise
control parameters
by their measure of
effect on defects

15. Data ETL and Warehousing
Generate
Prediction /
Prescription
Aggregate
Results
Automate
report
feedback
Process
Owner
Process
Owner
Implementing OMNI:
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16. 16
Airflow
Data aggregation
Process
Parameters
Site 3Site 2Site 1
Site 1
Custom Dashboard
Site 2
Custom Dashboard
Site 3
Custom Dashboard
Implementing OMNI:

17. A market–product fit is critical and there are several overserved industries. Finding the
right one in which you can have impact is as important as the product itself.
Product architecture for congruency with market requirements is also important. How
you store, manipulate data and share insights from it must be in a way that is compatible
with the clients values and ethos.
We leveraged many components from the AWS offering to build this system, providing a
vibrant and valuable user experience.
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