Smart Manufacturing (SM) is regarded a vital step toward optimal and more cost efficient operations with respect to future offshore oil platforms, oil fields and chemical / refinery production. A reliable and functional Early Warning (EW) technology is a fundamental condition for a successful intelligent oilfield and smart-plant operations. Such event early warning system can give the operators several operational options and various degree of freedom to act in advance to identify false alerts and to prevent unplanned production deviations and shut downs.

Today, although large quantities of data are being collected at production facilities, it is difficult to obtain insight from this data in order to support actions to improve production. While manual approaches to visualizing, characterizing, and understanding data can in principle be effective, in practice a manual approach is far too labor intensive in the current data-rich environment. Automated methods are essential to help focus limited human resources and expertise and to improve decision making process.

In this paper, we will review how new emerging technologies in advanced data analytics, pattern recognition, semantic data modeling and knowledge management - can collect, capture, predict, prevent and correct plant problems even before they occur. It also examine the progress of Grid Computing to today's Cloud Computing environment of how they would impact IT Infrastructure in to manufacturing industries to deliver and support data-intensive applications.

At the conclusion, we will discuss on industry best practices to implement these the technologies with Business Process Modeling.