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  Queueing Analysis      
         
 

Queueing AnalysisHow do you decide how many bank tellers, airline ticket agents or postal clerks you need to serve customers waiting in lines? In the 1990s JP Morgan Chase Bank was faced with that question and in response, SDC developed SwiftLine ? With SwiftLine ?, the staffing and scheduling decisions are based on customer service. For example, a bank manager tells SwiftLine ? that he would like 95% of the bank's customers to wait in line less than 5 minutes; in turn, SwiftLine ? tells the manager how to schedule his available tellers to meet that standard and at the lowest cost to the bank.

Unique among scheduling tools, SwiftLine ? is able to generate service-based schedules because it first analyzes the behavior of the waiting lines or queues. Using transaction data from the bank's teller system - or reservation system in an airline - SwiftLine ? calculates by day-of-week and time-of-day, how many customers waited and how long they waited. Not only are these data used for scheduling tellers, they are also invaluable for understanding and managing the queues.

Another of our queueing models, the Desktop Hypercube ?, is used to deploy both police and emergency medical service resources and is currently at the core of a family of emergency response planning tools we're developing for the Department of Homeland Security.

Measuring Customer Waiting Line
Queueing
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