Whether this is disparate systems within a plant, data from different lines/ locations or tracking of products across the supply chain. With the influx of smart items and IOT technologies across the manufacturing and supply chain, the range and variety of data sources is increasing. This data has the potential to be highly valuable, but also presents many challenges.
Analytics Engines XDP™ provides a platform that enables companies to integrate manage and analyse their data in a simple and scalable manner. Intelligent analytics can optimise processes, reduce costs and create actionable insights from the data.
Analytics Engines XDPTM is a fully featured data platform that provides data management and analytics capabilities allowing you to capture and house your raw data, meta-data and interim analysis results.
Analytics Engines XDP is designed from the core to deal with the challenges of integration and management of data. Through the use of multiple data stores we ensure that the right storage methodology is used for the data and deals with the non-standardised nature of the data. This means we don’t just provide infrastructure for your data landscape today but also futureproof your organisation.
Security and auditability are critically important and are key components of Analytics Engines XDP. We provide a full audit trail of all data and analytics in the system with a full audit of components. This enables track and trace of the full manufacturing and supply chain process for lots, batches and processes. This also enables rapid diagnostics of control points when batch or process issues occur.
The real value in data comes when it is utilised to provide actionable insights. Analytics Engines enables industrialized analytics to be carried out on the integrated data to provide real-time insights on the full supply chain and manufacturing process. Obviously each industry faces its own challenges, but through the use of intelligent analytics and trend analysis, is it possible to improve yields, track batch efficiencies and inform predictive maintenance.