How A Self-Organizing Data Structure Enables Faster Release and Time to Market
As the manufacturing industry leverages emerging technologies to digitize their production systems and their entire supply chain, the data generated from this initiative will drive the digital factory of the future. But as more manufacturing companies enable their Smart Factory, data integration and contextualization is becoming their number one challenge. Data will drive the Smart Factory initiatives, but it will also be its biggest hurdle if it is not integrated into the factory’s digital footprint.
A Self-organizing Data Structure
The concept of an autonomous, self-organizing manufacturing system is now possible using a “digital twin” of a factory. A digital twin can simulate the manufacturing system’s operations. It is configured with a self-learning algorithm that continuously learns and adapts to its environment changes. For the real-life manufacturing system to have self-organization capability, the digital twin model of a factory must itself be self-organizing. To do that, process data is critical.
With the help of a self-organizing data structure, there is no need to create or change data models for your digital twin. Its self-learning algorithms are so effective that there is no reason for any change, retest, or revalidation, and it also minimizes human intervention. It can monitor and manage the manufacturing process and verify possible process restructuring scenarios. Because of this, data contextualization is streamlined. It can easily convert all numerical parameters into a usable form instantly without much data wrangling. This manufacturing system enables an entire plant in months to produce high-quality products quickly and reliably.
Reduce your Data Hurdles in Smart Manufacturing with Mareana
Mareana’s Manufacturing Data Hub (MDH) uses Machine Learning (ML) and Artificial Intelligence (AI) algorithms to contextualize, harmonize, and curate manufacturing data creating a digital twin that powers the factory of the future.
MDH’s self-organizing data structure capability eliminates the need for creating or changing data models as it is designed for constant change, accuracy, and speed. Its pre-built genealogy-driven complex calculations are made run-time with data tied at every level, making model building or testing extremely simple. It has user-friendly navigation for batch release and quality investigation with end-to-end visibility of bottlenecks, constraints, and waste. Thus, there is no need for change, retest, or revalidation.
Furthermore, companies can leverage historical data for new products with MDH’s digital knowledge base of manufacturing data (Lab, Design of Experiments, commercial manufacturing). Its powerful python libraries allow for flexible modeling and analysis, shielding users from ETL and wrangling. This avoids costly, time-consuming experimental batches and speeds up decision-making, enabling faster release and time to market for your products.
Let Mareana’s MDH help you increase your production efficiency and quickly and reliably produce high-quality products to serve to markets. Schedule your demo today!
