– R&D projects are often delivered late and over budget, which may result in missed opportunities, resource bottlenecks and budget constraints. There is a significant difficulty to accurately assess the actual complexity of a new project and the effort required to complete it, especially early in the project lifecycle when available data is limited but resources and delivery timelines have to be committed.
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Companies must rely on data-driven estimations rather than subjective guesswork in order to properly manage projects, allocate resources appropriately and make informed decisions regarding tradeoffs and acceptable risk levels. The main steps in leveraging analytics to optimize project plans are:
1. Establish a performance baseline – Assess complexity and analyze execution data of several completed projects to build a performance baseline for the organization
2. Assess complexity of new projects – Gather or estimate project characteristics (e.g. # of features, code reuse levels, platform maturity, etc.) to analytically determine the complexity of the project
3. Create or evaluate project plans – Use predictive analytics to estimate the required effort, time and resources, assess the risk and confidence level of the current plan, and create what-if scenarios to evaluate trade-offs between schedule, scope and staffing level to determine the optimal plan.
Numetrics is an R&D analytics solution by McKinsey & Company. It relies on a set of proprietary algorithms and a database of industry projects to offer R&D productivity measurements, industry benchmarks, root-cause analysis and project/portfolio planning solutions