Quantitative issues in contact centers

Ger Koole

Vrije Universiteit

seminar E-commerce & OR

18 January 2001

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What is a contact center?

Central place for all customer contacts

Typically:

• Different types of contacts (information, sales, after sales, etc.)

• Different channels (telephone, email, fax, regular mail, internet)

Why contact centers?

• Improves customer contacts

• ICT enabled it

• Contacts over different channels in one hand

Grown from call centers

Math issues in contact centers

• Planning:– Need for “agents” and their training– Types of contracts

• Scheduling:– Construction of agent rosters

• Operational control:– Matching customers to agents

Quantitative management: objective

• Satisfy service level constraints

• Minimize (personnel) costs

Service level

Service level depends on channel

Typically:

• Telephone: 80% within 20 seconds (max. 3% abandonments)

• Email: within 4 hours

• Fax: within 1 day

• “Call me” button: between 1 and 2 minutes

Presentation overview

• Show current scheduling practice

• Identify problems

• Suggest possible solutions:– Flexibility in staffing and task assignment– Relate to multi-channel contact center

Current scheduling practice

• Step 1: Forecasting traffic load

• Step 2: Determining staffing levels

• Step 3: Making schedules

Forecasting: traffic model

• Customer contacts arrive by piece-wise constant inhomogeneous Poisson process

• Handling times (incl. wrap-up time) depend on channel-skill combination

• Arrival rates depend on day of week, time of day, and many other factors

Forecasting: current practice

• Standard statistical methods with explanatory variables

• Sometimes stand-alone software, sometimes part of workforce management package

Staffing levels: model

• Per interval with constant arrival rate

• Arrival rate and average handling time (both in same time unit)

• Load a = * (unitless, Erlang)

• Suppose we schedule s dedicated agents

• Productivity = a / s

• Overcapacity = s - a

Staffing levels: current practice

• “Low” service level requirements: take s=a

• “High” service level requirements (calls): have to take random variations in arrival process and service times into account schedule just enough overcapacity to satisfy service level using Erlang formula

Staffing levels: Erlang formula

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Demonstration

Making schedules: model

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Making schedules: current practice

Workforce management software:

• Formulate as mathematical programming problem

• Solve it using CSP / simulated annealing / genetic programming

Still often by hand!

Forecasting: problems

• Too many explanatory variables

• Non-predictable events (e.g., weather)

Point estimate does not work

Solution:

Give confidence interval for arrival rate Interval for staffing level!

Staffing: problems

Staffing reflects operational control

By staffing separately we need more capacity:

• economies of scale (demonstration)

• low service level classes can be used to fill random fluctuations in load (e.g., the 4th agent becoming available handles an email); important in case of long holding times!

Scheduling: problems

• Incompatibility shifts and staffing levels

• Shortening shifts means more overhead

• Unpredictable events: meetings, absence

The flexible contact center

• Flexibility in staffing– Flexible contracts– Non-contact center personnel on stand-by

• Flexibility in task assigment– Cross-skill training– Multiple channels

The benefits of flexibility

• Flexibility in staffing can help solve– Variations in load– Unpredictable absence

• Cross-skill training gives– Advantages of scales

• Switching between channels helps solving– High load problems (switch to calls)– Unproductivity due to random variations– Staffing peaks over the day

Conclusions

• Contact centers desirable from a math perspective

• Stimulate shift from high to low service level channels

• Advanced models partly implemented

• Based on joint work with Erik van der Sluis, Sandjai Bhulai, and Geurt Jongbloed