Workforce management with skills-based call routing: The new challenge
Leamon, Paul H
In today’s competitive environment, many call centers are being asked to provide better service with fewer people. Skills-based call routing promises to meet these needs. As a result of this promise, many call centers are currently using skills-based call routing or will be in the near future.
Skills-based call routing substantially complicates the management tasks of creating forecasts and schedules. The strategies currently being used for these tasks will be described and their strengths and weaknesses analyzed. Recommendations will then be made for accommodating the complexities of skills-based call routing.
Forecasting Uslw Erlang e
In a call center that is not using skills-based call routing, Erlang C can be used to calculate the number of agents needed from call volumes, average handling time (AHT) and service level goals.
Erlang C assumes each agent handles a single call type on a first-come, first-served basis. This assumption is not valid for skills-based call routing because:
All agents do not have the same skills,
Call types may have different priorities, so all calls are not handled on a first-come, first-served basis, and Skills-based call routing delivers greater economies of scale, thereby reducing the number of agents needed.
Using skills-based call routing, the percentage of time an agent will handle each call type depends on call routing rules (such as conditional queuing), changing call priority, queuing to backup skills and time of day and day of week. Availability also depends on other agents’ skills and schedules. Since scheduled agents may be used for a variety of call types, the number of agents needed for each call type depends on which agents are scheduled. This revelation is very significant since it identifies a circular problem. That is, the exact number of agents can only be determined after schedules are created; however, the schedules needed depend on the number of agents required.
Erlang C can be used as a single step of the process of calculating agent requirements for skills-based call routing. One option is to calculate agent requirements for each call type independently, then apply an efficiency factor to lower the requirements, since multiskilled agents provide greater efficiency. Another approach is to add the call volume of all call types and calculate the weighted average handling time (AHT) for each interval. Since the workload was combined, the Erlang C calculation incorrectly assumes that all agents are cross-trained. Therefore, the number of agents must be increased by a factor to account for lower efficiency.
The strength of these approaches is simplicity. Their weakness is accuracy. The degree of accuracy is limited by the method used to adjust requirements so enough agents are scheduled for each call type without overstaffing or understaffing. To be accurate, this adjustment must consider the true ACD call routing and the impact of which agents are scheduled and which skills and skill levels agents possess.
Forecasting Using Multiserver Queuing
Another forecasting method uses multiserver queuing formulas to calculate agent requirements. This approach is used for call centers with ACDs that assume agents within agent groups possess identical skills and provide an approximation of the multiskill effciency gained by skills-based call routing. However, these formulas include assumptions that may not be true. Multiserver queuing formulas assume that calls are routed to separate queues for each agent group or a common queue for all agent groups. These assumptions are incorrect if calls are simultaneously or conditionally queued to agent groups or if agents possess different skill levels. The accuracy of calculated agent requirements would be negatively affected if these assumptions are not valid.
This approach has other limitations, since agent requirements are calculated for skill sets rather than call types. For example, if a call center handles English and Spanish callers and agents have the English, Spanish or bilingual skills, then these formulas would calculate requirements for English-speaking agents, Spanish-speaking agents and bilingual agents (instead of just the requirements needed for English calls and Spanish calls). This approach removes the flexibility to determine the best set of schedules (i.e., using the best mix of English-, Spanish- and bilingual-skilled agents). Another difficulty is determining how many total agents are needed for the English and Spanish calls, since the percentage of time each bilingual agent will spend handling English and Spanish calls is not known.
Multiserver queuing formulas cannot accurately calculate multiskill efficiency. Additionally, these formulas cannot be used to forecast agent requirements when agents are assigned to individual skills and skill levels.
The forecasting problem is not “How many agents are needed per agent group?” but “How many agents are required per call type considering the multiskill efficiency gained through skills-based call routing?” The multiserver queuing approach does not solve the problem.
The difficulty of scheduling for skillsbased call routing is determining how each multiskilled agent will be utilized for each call type throughout the day in order to determine if enough agents have been scheduled for each call type. There are a few scheduling techniques that attempt to solve this problem.
Skill scheduling for a single call type at a time – Beware of skill scheduling solutions that assign multiskilled agents to one call type per scheduling interval. For example, agents with sales and service skills might be scheduled for sales calls from 8:00 a.m. to noon and service calls from 1:00 p.m. to 5:00 p.m. These methods do not match ACD skills-based call routing, which would route sales and service calls to these agents throughout the day.
Schedule from most to least skilled agents – If agents are trained first for Skill 1, then Skill 2 and then Skill 3, the following approach may be used for scheduling:
First, schedule the most skilled agents (agents with Skills 1, 2 and 3) against the call type forecast requirements that use Skill 3.
Next, add the requirements of call types that use Skill 2 to those that use Skill 3 (and apply a factor to decrease the combined requirements) and schedule agents with Skills 1 and 2.
Finally, add the requirements of call types that use Skill I to those that use Skills 2 and 3 (again decreasing the combined requirements) and schedule agents with Skill 1.
This approach does not consider routing rules that affect agent availability such as conditional queuing or queuing to backup skills (i.e., the approach assumes that if an agent with Skill 1 or 2 is available, then the ACD will deliver calls unconditionally to either Skill 1 or 2). This method cannot be used when agents have individualized skills, such as language, and are not uniformly trained to be in one of a few different skill sets.
Schedule All Agents, Then Simulate
If the above scenario will not work in your call center, then it is possible to schedule all agents against the combined requirements for all call types, then iteratively simulate, analyze and update schedules.
In order to do this accurately, schedules must be simulated with the same routing rules that will be executed in the ACD. After simulation, changes will need to be made to the schedules, since they were initially created without regard to individual call type requirements or agent skills. This process of adjusting schedules and simulating must be repeated until an effective set of schedules is created.
Once schedules are finalized, simulation must be used again to provide the data needed to accurately adjust agent requirements based on who is scheduled. This adjustment is essential to provide accurate agent requirements for management of schedules.
The only weakness in this approach is that the iterative process of manually simulating and then adjusting schedules is very time-consuming and subject to human error.
The Preferred Solution For Skills-Based Call Rong
When mathematical formulas alone cannot be used to model a complex system, simulation techniques can provide accurate modeling tools. Therefore, the preferred solution must include a simulator to accurately analyze a set of schedules. The simulator would calculate agent requirements by call type, including the economies of scale gained by multiskilled agents and would calculate agent availability by call type.
By itself, a simulator is impractical because of the time needed to analyze results, adjust schedules for a better solution and repeat the process multiple times until schedules are acceptable. Therefore, the preferred solution embeds a simulator into the scheduling program. This allows the scheduling program to automatically generate schedules, simulate network and ACD call routing, analyze the results, determine changes to schedules and adjust schedules to determine the best answer. The resulting output of this preferred solution is:
Agent requirements that account for economies of scale gained by using multiskilled agents,
Number of agents available by call type, and
Schedules refined to meet call center goals for service level and maximum occupancy.
This solution accommodates the many variables and the complexity associated with a multiskill environment and automatically creates accurate forecasts and effective working schedules.
Accuracy is the key to successfully forecasting and scheduling for skillsbased call routing. Accurate forecasting and scheduling is needed to consistently meet and exceed service level goals without significantly overstaffing. Without accurate scheduling for skillsbased routing, call centers will consistently miss service-level goals because of understaffing or exceed labor costs due to overstaffing.
To overcome these problems, call center managers can create accurate forecasts and schedules by including simulation in the workforce management process. There are varying levels of integration of simulation software into workforce management products. The most integrated and automated solution combines simulation within the scheduling software and allows the circular nature of this problem to be handled by automatically creating accurate forecasts and schedules.
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Paul Leamon is a senior systems engineer with IEX Corporation. Paul joined IEX in 1990 and has more than seven years’ experience guiding the TotalView Workforce Management product design at IEX. He manages the system engineering group that designs the new features in TotalView. His experience includes extensive work with customers to identify the system requirements needed to provide solutions that match customers’ needs.
Copyright Technology Marketing Corporation Mar 1999
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