Predictive capability is driving the transformation of technological innovation. New materials and chemical processes are needed to meet demanding performance requirements across the broad spectrum of advanced technologies. It is a known fact that development of new materials, from discovery to deployment, typically required two decades.
Predictive modeling guides experiments in the most productive directions, to accelerate design and testing, and to understand performance. State-of-the-art computational tools allow scientists to calculate from first principles the interactions that dominate molecular behavior, while experimental tools can provide time-resolved measurements on real materials to validate these models.
Success of Predictive modeling in several industry sectors have demonstrated significant return on investment and reduced development times.
Knowledge management is the process of capturing, developing, sharing, and effectively using organizational knowledge.
Industries driven by science need to survive and deliver in an extremely competitive environment, which calls for the need to optimized operations, ever improving efficiency without compromising quality and adhering to regulations as well as drive innovation. These challenges also apply to the labs in academic and government setups, which in order to contribute to the organizational goals needs to remove inefficiencies and compliance risks from lab processes and create an environment conducive to collaborate for increasing innovation rates.
Collaboration within and between the labs will be achieved by eliminating paper-based experimental documentation processes that present lot of challenges to compliance but also offer difficulties access of relevant data throughout the research, development and manufacturing lifecycle which is vital to hone an environment which encourages collaboration in order to drive innovation, to rationalize research processes and make informed decisions whenever called for during the product development process.