SYMIAN: Analysis and performance improvement of the IT incident management process
ABSTRACT Incident Management is the process through which IT support organizations manage to restore normal service operation after a service disruption. The complexity of real-life enterprise-class IT support organizations makes it extremely hard to understand the impact of organizational, structural and behavioral components on the performance of the currently adopted incident management strategy and, consequently, which actions could improve it. This paper presents SYMIAN, a decision support tool for the performance improvement of the incident management function in IT support organizations. SYMIAN simulates the effect of corrective measures before their actual implementation, enabling time, effort, and cost saving. To this end, SYMIAN models the IT support organization as an open queuing network, thereby enabling the evaluation of both the system-wide dynamics as well as the behavior of the individual organization components and their interactions. Experimental results show the SYMIAN effectiveness in the performance analysis and tuning of the incident management process for real-life IT support organizations.
Conference Proceeding: Business-impact analysis and simulation of critical incidents in IT service management[show abstract] [hide abstract]
ABSTRACT: Service disruptions can have a considerable impact on business operations of IT support organizations, thus calling for the implementation of efficient incident management and service restoration processes. The evaluation and improvement of incident management strategies currently in place, in order to minimize the business-impact of major service disruptions, is a very arduous task which goes beyond the optimization with respect to IT-level metrics. This paper presents HANNIBAL, a decision support tool for the business impact analysis and improvement of the incident management process. HANNIBAL evaluates possible strategies for an IT support organization to deal with major service disruptions. HANNIBAL then selects the strategy with the best alignment to the business objectives. Experimental results collected from the HANNIBAL application to a realistic case study show that business impact-driven optimization outperforms traditional performance-driven optimization.Integrated Network Management, 2009. IM '09. IFIP/IEEE International Symposium on; 07/2009
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ABSTRACT: The downsizing and closing of state mental health institutions in Philadelphia in the 1990's led to the development of a continuum care network of residential-based services. Although the diversity of care settings increased, congestion in facilities caused many patients to unnecessarily spend extra days in intensive facilities. This study applies a queuing network system with blocking to analyze such congestion processes. "Blocking" denotes situations where patients are turned away from accommodations to which they are referred, and are thus forced to remain in their present facilities until space becomes available. Both mathematical and simulation results are presented and compared. Although queuing models have been used in numerous healthcare studies, the inclusion of blocking is still rare. We found that, in Philadelphia, the shortage of a particular type of facilities may have created "upstream blocking". Thus removal of such facility-specific bottlenecks may be the most efficient way to reduce congestion in the system as a whole.Health Care Management Science 03/2005; 8(1):49-60. · 1.05 Impact Factor
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ABSTRACT: In this paper, we model the supply chain network as a closed Jackson queuing network and apply the properties of the closed Jackson network to explain some properties of a supply chain network. Under certain conditions, a simple throughput function that is the function of only the numbers of jobs and stations in the network, can be deduced. Based on the simple throughput function, we can deduce some properties of the supply chain network easily to explain some phenomena in reality.International Journal of Production Economics 01/2008; 113(2):567-574. · 2.08 Impact Factor