A Data-Driven Approach to Intelligent Resource Optimization

All businesses are resource-constrained in some way. To succeed in today's digital business arena, organizations must
make intelligent decisions that maximize the return on finite resources such as vehicle fleets, mobile field workforces and
production facilities. Decision optimization is becoming a new competitive differentiator, and industry leaders are pursuing
artificial intelligence and machine learning (AI/ML) to make data-driven decisions that create a competitive edge.
Historically, resource optimization has been a challenge. Especially when some assets are of higher value and in very
limited supply – their availability becomes unpredictable in times of uncertainty and high demand, or when the number of
variables that constrain their use are numerous and complex.
Decisions must be based on analysis of current data and the success rates of prior decisions and outcomes. This requires
examination of resource availability, limitations and the dynamic circumstances under which resources must be deployed.
What's needed are decision optimization tools, an evolving class of which are now on the market horizon. These tools will
be less complex, practical to use, and can quickly guide decision-makers in how best to employ critical business resources
– a class of tools we refer to as intelligent resource optimization (IRO).