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Operations Management in Service Systems: Call Centers – Health Care Systems Oualid Jouini Assistant Laboratoire Génie Industriel Ecole Centrale Paris. 05 mai 2009. Research activities. Optimization of manufacturing and service systems. Thème 2. Operations Management. Service
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Operations Management in Service Systems: Call Centers – Health Care Systems Oualid Jouini Assistant Laboratoire Génie Industriel Ecole Centrale Paris 05 mai 2009
Research activities Optimization of manufacturing and service systems Thème 2 Operations Management Service Operations Management Supply Chain Management (SCM)
Research activities • Research areas • Optimization and operations management in call centers • Optimization and operations management in health care systems • Stochastic modeling in general (Markov chains) • Tools (Operations research) • Stochastic processes, Markov chains, Queueing theory, Mathematical programming, Discrete-events simulation, …
CTI Call centers Lost demand Abandonment Dedicated agents Identification Generalists Customers calls Customer representatives Abandonment Lost demand
Call centers • Developping new models of call centers (better human resource management, more flexibility, …) • Staffing and shift-scheduling • Real-time problems of call centers (announcing anticipated delays to customers, dynamic routing of customers, …)
Customers Customers Customers CR Team 1 CR Team 1 CR Team 1 Portfolio 1 Portfolio 1 Portfolio 1 Customers Customers Customers CR Team 2 CR Team 2 CR Team 2 Call Center Call Center Customer Customer Portfolio 2 Portfolio 2 Portfolio 2 Customers Customers Representatives Representatives Customers Customers Customers CR Team n CR Team n CR Team n Portfolio n Portfolio n Portfolio n Team-Based Organizations Pooled organization Team-based organization - More variability - Performances deterioration for equal parameters • However + Agents are more responsible + Easier management of teams + Creation of Competition between teams of employees + Less call backs (customer satisfaction) • Qualitative and quantitative improvements have not that high requirements
Real-TimeScheduling Policies m lA s lB Motivation • VIP and regular customers (types A and B) • Customers may abandon while waiting in queue • Fractions of abandoning customers: QA and QB • Strict priority policy (customers A over B): most of the QoS goes to type A • In addition: • Workload underestimated: all the QoS goes to A, and QB is close to 100% • Workload overestimated: QA is close to 0, and QB is not that good The system is highly unbalanced for both customer types