Oxilate is the successor of the successful ITEA 14035 Reflexion project which supported changing the industry’s R&D way of working revolutionary by introducing and integrating wide-spread available data analytic solutions from the open source / data science communities. By evangelizing and accelerating the adoption of data analytics in R&D product engineering, R&D engineers are activated to unleash the value out of the system data harvested from operational high-tech systems.
The Oxilate project takes the next step in digital enablement by expanding this innovative way of working to impact the complete life-cycle of products and associated services: Oxilate focuses on providing support for well-performing systems fully integrated in the customer’s operational workflow. This is done by development and integration of ‘actionable’ data analytics with expert knowledge into widely available support for professionals, creating direct business value in the product life-cycle they serve.
This ‘actionable’ support will empower professionals to become more proactive in their respective life-cycle phase, taking the need for expert involvement out of the loop, while still keeping business effective and achieving operational excellence. It consists of operational data-driven tools, methods, processes, models and / or platforms, futureproof and resilient with respect to changes over time and in the product life-cycle:
1. Mechanisms to create value from operational data by integrating data and domain expertise.
2. Systematics to make this integrated knowledge ‘actionable’, by empowering professionals from all business activities to achieve impact (action) in their respective business processes without a direct need for expert support.
3. Ways of working to achieve operational excellence by taking the ever-changing product life-cycle and customer environment into account and adapt to optimize system operations respectively.
4. Methods to exploit user interaction and operational usage for the benefit of product improvements and business prediction.