Use CaseR and D

Optimise experimental efficiency through refining research process and operations

Identify critical factors to improve R&D efficiency - e.g. to reduce the number of required experiments for research and testing process (an examples might include component testing). Optimising configuration of processes and operations will work better where the parameters under development remain stable.

⚙

Operational

Operational - Lower error rates

Cost

Cost - Reduce wastage

Cost

Cost - Optimise resource allocation

Description

Optimise experimental efficiency through refining research process and operations

Benefits & ROI

0.4

Linked Case Studies

Case Study

Berkeley Lab

Berkeley Lab releases reinforcement learning training platform for training autonomous vehicles on traffic regulation

→

Case Study

MIT

MIT scientists develop system that crowdsources data to speed up drug discovery using neural networks

→

Case Study

Facebook

Facebook optimises experiments for server performance and ranking improvement using reinforcement learning

→

Case Study

Roche

Roche plans to reduce time-to-market for oncology medicine by streamlining clinical trial process with machine learning

→

Case Study

National Institute for Materials Science Japan

Researchers from the National Institute for Materials Science Japan automate creation of knowledge graphs regarding the materials design process using natural language processing

→

←Back to Use Case Library

Source: mckinsey.com · Editor: original-sdg