Showing posts from September, 2016

Beginning, evolving, and concluding R2O projects

An enterprise or organization could become throttled by its own success when research is routinely transitioning to operations, new ideas are arising from both research and operational workforce segments, and improvements within existing projects are continuing. Leaders with fixed budgets and workforces with little unallocated time will complain that they cannot do everything or they cannot continue everything. This is a particular challenge for enterprises with substantial government participation because market conditions do not necessary guide the path ahead. Should we put time, money, and effort into this , or that ? Where is the greatest benefit? What will improve our services the most? When should we wind down a project? Beginning R2O projects For new R2O projects, it is important to decide whether an idea or concept has the potential to evolve an enterprise or organization strategically or resolve an existing challenge tactically. R2O projects that are strategic in nature will

Operations-driven R2O with science-integrated training

The ideas that initiate a research to operations transition cycle need not always originate as part of the research process. Some suitable concepts for research development can and, with the right governance structure in place, do come from operations. This constitutes operations-driven R2O. Operations can serve as a strong driver for R2O in cases where practitioners proffer an idea because the potential application and need is understood. Therefore, early adoption of byproducts or techniques resulting from operations-inspired research is easier. Arguably, well-founded ideas—those formulated with a preliminary “how” to accompany the “why”—for R2O projects that originate in operations have the potential to be more successful and more impactful than those that come from the normal progression of research projects because they escape an improvement sequence. However, the challenge is that operations may not have a sufficient knowledgebase to fully appreciate how certain observations and