Demystifying Network Analytics: A Primer

Demand forecasting is not a new concept in the supply chain. In fact, it’s often referred to as ‘guessing between the goalposts,’ and for the most part, that was right. Now, using algorithms that not only predict demand to drive forecasts but also incorporate external data sources and continuously learn and optimize the algorithm without intervention we have the ability to significantly reduce the size of those goal posts. Using AI we can improve the accuracy of our forecasts and then allow us to predict where and when those products should be deployed ahead of customer demand. The result? Improved forecasts, reduced freight costs, better customer service levels, and optimized working capital.

Data-driven organizations use AI and ML, either natively within applications or infused into applications, to gain enhanced decision intelligence as well as automate processes for greater efficiency. But the proliferation of AI projects, ML models, APIs, and data sets to enable these processes present serious challenges that stand in the way of successful AI and ML deployments.

90% of WAN infrastructure deployed today was designed 20 years ago to address a singular requirement - to provide connectivity from branch to datacenter over MPLS. The WAN is at an inflection point and faced with unprecedented demands, including cloud and SaaS apps, UC as a service, and the ability to deliver new services rapidly and securely.

While there is a lot of buzz surrounding artificial intelligence (AI) in audit right now, some forward-thinking firms have already been using it for some time.