Quantitative issues in contact centers Ger Koole Vrije Universiteit seminar E-commerce & OR 18...
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![Page 1: Quantitative issues in contact centers Ger Koole Vrije Universiteit seminar E-commerce & OR 18 January 2001 Lunteren.](https://reader030.fdocuments.in/reader030/viewer/2022032710/56649d555503460f94a32b3f/html5/thumbnails/1.jpg)
Quantitative issues in contact centers
Ger Koole
Vrije Universiteit
seminar E-commerce & OR
18 January 2001
Lunteren
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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)
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Why contact centers?
• Improves customer contacts
• ICT enabled it
• Contacts over different channels in one hand
Grown from call centers
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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
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Quantitative management: objective
• Satisfy service level constraints
• Minimize (personnel) costs
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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
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Presentation overview
• Show current scheduling practice
• Identify problems
• Suggest possible solutions:– Flexibility in staffing and task assignment– Relate to multi-channel contact center
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Current scheduling practice
• Step 1: Forecasting traffic load
• Step 2: Determining staffing levels
• Step 3: Making schedules
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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
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Forecasting: current practice
• Standard statistical methods with explanatory variables
• Sometimes stand-alone software, sometimes part of workforce management package
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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
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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
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Staffing levels: Erlang formula
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Demonstration
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Making schedules: model
time
shifts
t
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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!
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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!
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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!
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Scheduling: problems
• Incompatibility shifts and staffing levels
• Shortening shifts means more overhead
• Unpredictable events: meetings, absence
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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
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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
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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
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