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Identified as a strategic area, supply chain transformation plays a critical role in today's IBM business. In this paper, we introduce an effort in IBM Research Division named SmartSCOR, which provides a comprehensive framework and methodology for On-Demand SCM problem-solving based on the cross-industry process standard Supply Chain Operations Reference(More)
Business process modeling is the basis of business process management. The target of business process modeling is to get an abstract representation of the actual business processes. Although there are many business modeling methods, no well established modeling standard is available in this area. This paper reviews major business process modeling methods.(More)
Business process is crucial to the success of any business. Business process modeling enables a common understanding and analysis of a business process, and simulation is an effective way to diagnose and evaluate complex business processes. There are lots of software tools in market for business process modeling and simulation, however, a common issue for(More)
—In this paper, we discuss the modeling and prediction of power frequency. Power frequency is one of the most essential parameters in the monitoring, control, and protection of power systems and electric equipments because when a significant disturbance occurs in a power system, the frequency varies in time and space. It is critical to employ a dependable(More)
IBM General Business Simulation Environment (GBSE) is a supply chain simulation tool developed by IBM China Research Lab. It can capture supply chain dynamics with finest level of granularity and provides great insights to a supply chain's real operations. GBSE is designed for tactical level decision making; it is proper for supply chain what-if analysis(More)
Explicit model reduction for nonlinear systems with no prior information about the type of nonlinearity involved is difficult and challenging. It is easier to reduce nonlinear systems which nonlinearity is known. In this paper we introduce two nonlinear model reduction techniques for quadratic nonlinear systems. The first technique is nonlinear balanced(More)