Digital twins: Replicating reality

A leading semiconductor manufacturer was looking to apply AI/ML through a digital twin across the design and manufacturing value chain. Through the implementation of digital twin simulations, we enabled significant improvement and efficiency in design model generation, package design, and yield enhancement.

Impact achieved included a 25 percent increase in first-time-right designs, a 20 percent increase in engineering capacity freed to address additional programs and a 30 percent shorter average root cause analysis cycle time.

A global payment player had their heavily outsourced back-office operating on a 24-hour service level agreement (SLA), with manual workforce management practices only able to provide business-unit level monthly forecasts. We developed an AI forecasting engine that leveraged digital twin simulations to help meet their goal of a four- to eight-hour SLA target. Their new AI-informed location strategy led to a 25 percent to 30 percent productivity unlock and changed the way the workforce is managed.

A European city aimed to use aggregated data to make informed decisions on its urban planning and mobility concept to optimize for sustainability, efficiency, and affordability. We co-created a simulation model that replicated real-time traffic conditions to create a geospatial model with data from millions of commuters and built graphic visualizations to aid decision makers. These interactive models allowed policy markets to understand and evaluate the future impact of traffic initiatives and current citizens to optimize their own mobility behavior.

A global airline wanted to improve aircraft maintenance, optimizing for variables including heavy check cost and performance, spare parts allocation and maintenance program intervals. Through a series of sprints, we developed a digital maintenance history platform to unify data sources, creating an end-to-end view of an individual aircraft’s maintenance history, operational data, and conditions. We then defined failure modes and built a predictive reliability model that predicted 40 percent to 60 percent of aircraft on ground events in the riskiest 20 percent of aircrafts with an impact of roughly $100 million in combined value across the fleet.