Transaction processing systems
Examples of decision-support systems include applications for analysis of sales revenue, marketing information, insurance claims, and catalog sales.
Office Automation Systems
E-mail(Lotus Notes, and Microsoft Outlook.) , Voice mail, Facsimile. Desktop videoconferencing , Electronic collaboration
Knowledge Work Systems
omputer-aided Design (CAD)/Computer-aided Manufacturing (CAM) Virtual reality systems Virtual Reality Modeling Language (VRML) Investment workstations
Decision Support Systems
SS supports more collaboration on a shared task. Examples include integrated tools like Microsoft's NetMeeting or Groove.
Management information systems
Data warehouses, Enterprise resource planning, Enterprise systems ,Expert systems, Geographic information system ,Global information system ,Office Automation
The global trend in developed economies from manufacturing towards services has led to an explosion in the call centre industry. With constant advances in the enabling technologies allied to changing business strategies, call centre management has become a critical business success area. Call centres can be thought of as stochastic systems with multiple queues and multiple customer types, resulting in great challenges associated with managing these systems. It is very difficult to understand the dynamics of call centres using purely analytical techniques due to the operational and mathematical complexities involved. This paper introduces and gives a detailed overview of call centre functions and their operations as the basis to promote the use of Discrete-Event Simulation (DES) for modelling purposes. The effects of calls routing and prioritizing to specific agents with multiple skills in an inbound call centre are discussed and modelled using the Witness simulation software.
Call centre managers are increasingly expected to deliver both low operating costs and high quality of service. To meet these potentially conflicting objectives, call centre managers are challenged with deploying the right number of agents with the right skills to the right schedules in order to meet an uncertain, time-varying demand for service . Despite many analytical approaches for modelling call centres, the gap between these models and the call centre’ s reality is still quite large. These analytical approaches cannot be accurate enough, as they do not mimic randomness. Therefore, DES appears to be the most viable option for accurate performance modelling and subsequent decision support.
The demand for decision support models is continuously increasing, due to the increased complexities in call traffic management and the use of multiple channels in call centre operations. Although many researchers have already explored the use of DES in call centre environment, they have not directly addressed the issues of effective routing policies that often incorporate priority rules for the calls and agents. Specifically, the number and types of agents, who handle the calls and the working schedules of these agents under constraints on the quality of service and on admissible schedules is one of the main optimization problems encountered in managing these multi-skill call centres . Without proper DES models, it is very difficult for the managers to deal with such problems and to explore ‘ what-if’ scenarios on a daily basis. In absence of such models, managers cannot visualize the consequences of different process changes before they are implemented.