Digital twin analytics value accelerator

• advanced predictive techniques combining various types of time-series and ML techniques that are rapidly tested for suitability
• external geo-coded data and signals from KPMG Signals Repository along with your internal financial or operational drivers to add context and predictive power
• continuous learning and evolution that incorporate structured feedback loops, which can enhance prediction accuracy over time.

• generate recommended scheduling and resource allocations to optimize enterprise operational performance and profitability
• quickly identify risk-mitigating alternatives by leveraging geo-localized predictive supply and demand models that react to rapidly changing conditions and new system dynamics.

• highly dynamic and randomized interactions, including complex systems such as business processes, facilities, warehouses, fleet, traffic, pedestrians and supply chains with complex knock-on effects and interconnections
• partial or no baseline data due to unexpected “black swan” events such as local or global weather or public health disruptions.

The digital twin analytics value accelerator is designed to help you innovate, experiment and act using intuitive simulations of facilities, processes or end-to-end systems to optimize operational and financial outcome metrics. Using sophisticated “what if” scenarios, you can explore potential options, each optimized for different factors such as cost or risk, or stress test current plans with disruptions or by challenging assumptions under different best- or worst-case scenarios—with far more realism, insights and scale than even the most elaborate spreadsheets can provide.