ABSTRACT
Balancing the financial needs of the firm with the responsiveness needs of customers requires that queues be actively managed. The use of an analytical queuing model can evaluate queuing systems that will be found wherever critical resources are needed to serve customers. Managing queues for services is complex because service times almost always vary, usually significantly. And, customers often arrive in random patterns. Random customer arrivals and service times can be represented by a probability model. A queuing model can be used to determine the most effective number of servers in a business process that balances the customer desire for fast service and service provider desire for low resource costs. The M/M/s queuing model is easily implemented using an Excel template. A standard threshold for capacity buffering can be developed based on the nature of demand variation and service time variation. Lean methods can lower queue times dramatically by reducing service activity times, which can have dramatic effects on customer service.
