Leveraging analytics to improve predictability and reduce schedule slips of R&D projects
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 life cycle. 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 by analyzing the complexity and execution of several completed projects 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 risk level of the current 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
Dorian is a Data Scientist and subject matter expert with Numetrics, an advanced analytics solution by McKinsey. His current focus includes the application of big data, advanced analytic and artificial intelligence techniques to improving the efficiency of R&D teams in Semiconductor and Software Development. He is a recognized authority on applying leading edge analytic techniques to big data at enterprise level. Before joining McKinsey Dorian consulted to Fortune 50 and other enterprises applying advanced data analytic techniques in areas including ecommerce, CRM, supply chain and pricing. He is the author of two books on advanced data analytics.
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