The growing global awareness of climate change has promoted a tighter focus in the manufacturing sector around sustainable work practices and environmentally friendly initiatives such as the so-called circular economy. Such initiatives are encouraging companies to seek out greater efficiencies around manufacturing yields, primarily to reduce wastage through tighter control of the manufacturing scrap rate.
For this reason, there is a significant demand for a generic and low-cost analytics solution to assist small to medium scale enterprises in identifying sources of manufacturing defects and facilitating the implementation of targeted quality improvements. This represents the target market for the Quantic defect analytics suite.
The goal of the Quantic project is to enable and empower companies to capture, understand and generate business value from their manufacturing process defect data.
The primary project objective is to research an analytics suite that takes defect data, such as from the Quantic defect data capturing app or from existing internal systems and carries out some generalised and advanced analytics to support quality improvements.
Through the use of these analytical techniques (e.g. artificial intelligence, machine learning) it is envisioned the project can highlight fundamental process defect information and identify the most critical signals that are showing anomalous trends or revealing details never observed before. It is further envisioned that this approach will lead to better manufacturing yield outcomes, increased digitisation and overall increased efficiency of member company operations.
Specific project objectives are as follows:
The manufacturing scrap rate is a major concern for plant managers because of the waste produced and the resources that are consumed during the production of defective parts (i.e. energy, raw material and equipment uptime). While many companies collect scrap information, few if any collect this information in an automated manner with the intention of transforming it into intelligence. As a result, companies are often left reliant on paper-based data collection methods and/or minimal digital records, resulting in a limited view of the scrap rate without having visibility on why, how, when and where the scrappages occurred.
The project consortium consists of:
• Grant Engineering
• McHale Engineering
• Decotek Automotive
Software Engineering Project Manager
This project is co-funded by the European Regional Development Fund (ERDF) under Ireland’s European and Investment Funds Programmes 2014-2020.