System and method for integrated resource scheduling, task allocation and agent work management

#The task of generating optimized schedules for contact center agents and other related resources has been known for years to be a complex one, and has spawned an entire industry of companies which provide products, of varying sophistication, which attempt to provide optimal resource scheduling for contact centers. Known as workforce management systems, the products known in the art generally perform a common series of sequential tasks in order to accomplish the scheduling function. Referring to FIG. 1, the first step of workforce management is to gather historical data on call volumes 100 for the contact centers in question; this data is generally broken out by call activities or skills, such as sales, service, complaints, etc. Data 101 is also obtained, similarly segregated by activity or skill, for the average handling time of calls, which is the total time a call takes within the contact center from start to completion (which is usually defined as the time when the agent who handled the call last completes any call-related work after the caller is released). Note that “call” as used in discussing the present invention can be a traditional phone call carried by the public switched telephone network (PSTN), a voice-over-IP (VOIP) call, an e-mail, or any other customer interaction arriving at or commencing in a contact center.

#Second, the data on call volumes and handle times is run through a forecasting engine 102 to generate a forecast 103 of anticipated call volumes and handle times for the period to be scheduled. The technique used to create the forecast can be one of a multitude of techniques known in the art, including but not limited to neural network forecasting, statistical forecasting, template-based forecasting, genetic algorithms and neural networks where the weighting functions are determined by genetic algorithms. The forecast is then modified by the inclusion of the anticipated effects on call volume and handling times resulting from upcoming events such as catalog mailings, television advertisements, and so forth. The modified forecast is then used to generate a forecast of agent resource demand 104, based on the forecast and the desired agent occupancy rates 105 (that is, if you need 9 agents on calls and agents are intended to be busy 90% of the time they are on duty, then you will need 10 agents available to handle the expected traffic). Note that “agent resource” usually refers to human agents but can also refer to interactive voice response (IVR) ports against which scripts have been loaded to handle customer interactions automatically. Usually but not necessarily, this agent resource demand forecast is generated in 15 minute increments for the entire schedule period.

#Next, a scheduling engine 106, which takes into account a plurality of business rules 107 concerning break policies, shift schedules, maximum and minimum workweeks, meal scheduling, and other related scheduling influences, creates an optimized overall schedule 108 which includes specific work times for each agent. Often agent preferences and vacation requests are included in this scheduling step. These scheduling engines 106, like the forecasting engines 102, use a plurality of well-known optimization techniques to generate a schedule which ensures that the expected traffic is handled within established service level limits while meeting as many of the business rules and agent preference constraints as possible. Finally, during the period the schedule is in effect, an adherence monitor 109 measures conformance to the schedule in real time, usually by integration of the workforce management system with one of the many interaction management engines 110 known in the art (including for example automated call distribution systems, computer-telephony integration systems, e-mail management systems). Usually a graphic display of the current performance of the contact center relative to the forecast is provided, showing where deviations from the forecast and the schedule are occurring, and showing what their effect is on service levels.

#The present invention is described in conjunction with the appended figures:

#FIG. 1 is a diagram of a typical prior art workflow management system.

#FIG. 2 is a diagram of a typical prior art routing system.

#The ensuing description provides preferred exemplary embodiments only, and is not intended to limit the scope, applicability or configuration of the invention. Rather, the ensuing description of the preferred exemplary embodiments will provide those skilled in the art with an enabling description for implementing a preferred exemplary embodiment of the invention. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the invention as set forth in the appended claims.

#Embodiments of the present invention disclose a system and method for integrating the call routing, agent scripting and workforce management functions described above, in order to enable them to work together without opposing the influence of one against those of the others.

#In one preferred embodiment of the present invention, and referring to FIG. 4, a workforce management (WFM) system of the type described above and well-known in the art is modified by the addition of feedback means 400 and 401 from the routing engine 203 and the agent scripting engine 301, respectively, to the forecasting engine 102 or the adherence module 109, or both. The information passed via this mechanism can be any information available to the routing engine 203 or the agent scripting engine 301, and can be passed in any of a number of ways well established in the art. In an embodiment of the invention, the information is passed from the routing engine 203 or the agent scripting engine 301 to the forecasting engine 102 or the adherence module 109 or both via data that is “attached” to the call, or added to the data about the call that is maintained by the predefined interaction management engine 110. This is a common method in the art of passing contextual data concerning an ongoing interaction from one part of an interaction management system to another, in this case from routing to WFM. In another embodiment, data is passed directly from the routing engine 203 or the agent scripting engine 301 to the forecasting engine 102 or the adherence module 109 or both in the form of an XML data tree, another method well established in the art. In yet another embodiment, information can be passed from the routing engine 203 or the agent scripting engine 301 to the forecasting engine 102 or the adherence module 109 or both by insertion into a database table which is periodically read by the recipient components.