Improving Product Uptime through Analytics
Working with an industry-leading manufacturer, the goal was to improve the uptime of products in use. Using more than a million repair records across 15 different datasets for three different products, we developed several solutions to identify signals that an issue might be brewing. This enabled us to solve the underlying cause well before it escalated to a problem that required widespread repairs (which would lead to downtime). During this talk Alison will share details on the use case and the solutions developed that helped identify the largest and fastest growing potential issues within product lines.
Alison (Ali) O’Connor is a Senior Data Scientist at QuantumBlack, a McKinsey Company. Ali’s advanced knowledge of predictive analytics, machine learning, and probabilistic theory allow her to lead and contribute to a number of projects that require the construction of accurate models of static and/or dynamic environments. She has significant expertise developing relational models used primarily in decision-support tools. Her modeling expertise extends beyond probabilistic modelling, and also includes standard statistical modeling. Prior to joining QuantumBlack, Ali spent five years working for Charles River Analytics, an R&D US Department of Defense contractor. At Charles River, Ali won contracts with the US Army, Navy, NASA, and DARPA. Ali holds an M.S. in Computer Science from Tufts University and a B.A. in Cognitive Science from the University of the Pennsylvania, with a concentration in Computation and Cognition.