Slide 1Telemarketing atSome Questions to discuss:Telemarketing: deterministic analysisTelemarketing with variability in arrival times + activity timesTelemarketing with variability: The effect of utilizationWhy do queues form?Flow Times in White Collar ProcessesQueuing Systems to model Service Processes: A Simple ProcessWhat to manage in such a process?Performance MeasuresThe drivers of waiting: How reduce waiting?Levers to reduce waiting and increase QoS:  variability reduction + safety capacityExample 1: MBPF Calling Center with one server, unlimited buffer. The basics of QoSExample 2: MBPF Calling Center with limited buffer size. Impact of blockingTHE BAT Case = Managing the operations of a customer service departmentExample 3: MBPF Calling Center with 1 or 2 queues. Impact of Resource PoolingExample 4: MBPF Calling Center with 2 service tasks. The impact of process structure & resource capabilities: Specialization Vs. FlexibilityIncrease quality of service: 1. reduce variabilityHow increase quality of service with stochastic variability 2. reducing utilization is your only optionIncrease quality of service: anticipate predictable variability + build safety-capacity for stochastic variability. e.g. smart staffingSmart Staffing/Capacity Management at Sof-OpticsCall CentersFramework for Analysis and Improvement of Service SystemsHow do these insights related to our earlier “Levers for Reducing Flow Time?”Learning objectives: General Service Process ManagementSlide 1Service OperationsCapacity Management in Services ModuleWhy do queues build up?Process attributes and Performance measures of queuing processesSafety Capacity Its effect on customer servicePooling of capacityQueuing Processes with Limited BufferOptimal investmentSpecialists versus generalistsManaging Customer ServiceSofOpticsSlide 2Service OperationsTelemarketing atDuring some half hours, 80% of calls dialed received a busy signal.Customers getting through had to wait on average 10 minutes for an available agent. Extra telephone expense per day for waiting was $25,000.For calls abandoned because of long delays, L.L.Bean still paid for the queue time connect charges.L.L.Bean conservatively estimated that it lost $10 million of profit because of sub-optimal allocation of telemarketing resources.Slide 3Service OperationsSome Questions to discuss:Why did they loose money?What are the performance measures for a call center?How model this as a process?What decisions must managers make?Slide 4Service OperationsTelemarketing: deterministic analysisit takes 8 minutes to serve a customer6 customers call per hour –one customer every 10 minutesFlow Time = 8 min–same for every customer–histogram: →Flow Time HistogramFlow Time (minutes)Probability0%20%40%60%80%100%01530456075901051201351501651801958Slide 5Service OperationsTelemarketing with variability inarrival times + activity times In reality service times–exhibit variability0%5%10%15%20%25%30%0102030405060708090100110120130140150160170180190MoreFlow Time (minutes)Probability0%10%20%30%40%50%60%70%80%90%100%Cumulative Probability0%5%10%15%20%25%0102030405060708090100110120130140150160170180190MoreFlow Time HistogramProbability0%20%40%60%80%100%90%Cumulative ProbabilityFlow Time (minutes)In reality inter-arrival times–exhibit variabilitySlide 6Service OperationsTelemarketing with variability: The effect of utilizationAverage service time = –9 minutesAverage service time =–9.5 minutes 0%1%2%3%4%5%6%7%8%0102030405060708090100110120130140150160170180190MoreFlow TimeProbability0%10%20%30%40%50%60%70%80%90%100%0%5%10%15%20%25%0102030405060708090100110120130140150160170180190MoreFlow TimeProbability0%10%20%30%40%50%60%70%80%90%100%Slide 7Service OperationsWhy do queues form?1. variability: –arrival times–service times–processor availabilityRole of utilization: –Impact of variability increases as utilization increases! (arrival throughput  or capacity )0123456789100 20 40 60 80 100 TIME01234 50 20 40 60 80 100 TIMECall #Inventory (# of calls in system)Slide 8Service OperationsIndustry Process AverageFlow TimeTheoreticalFlow TimeFlow TimeEfficiencyLife Insurance New PolicyApplication72 hrs. 7 min. 0.16%ConsumerPackagingNewGraphicDesign18 days 2 hrs. 0.14%CommercialBankConsumerLoan24 hrs. 34 min. 2.36%Hospital PatientBilling10 days 3 hrs. 3.75%AutomobileManufactureFinancialClosing11 days 5 hrs 5.60%Flow Times in White Collar ProcessesSlide 9Service OperationsQueuing Systems to model Service Processes: A Simple ProcessSales RepsprocessingcallsIncoming callsCalls on HoldAnswered CallsMBPF Inc. Call CenterBlocked Calls(Busy signal)Abandoned Calls(Tired of waiting)Order Queue“buffer” size KSlide 10Service OperationsWhat to manage in such a process?Inputs–InterArrival times/distribution–Service times/distributionSystem structure–Number of servers–Number of queues–Maximum queue length/buffer sizeOperating control policies –Queue discipline, prioritiesSlide 11Service OperationsPerformance Measures Sales–Throughput R–Abandonment RaCost–Server utilization –Inventory/WIP : # in queue Ii /system ICustomer service–Waiting/Flow Time: time spent in queue Ti /system T–Probability of blocking RbSlide 12Service OperationsThe drivers of waiting:How reduce waiting?Queuing theory shows that waiting increases with:–variabilityArrival timesService times–length of avg. service time–Arrival throughputNonlinearly: “it blows up!”Hence: reduce waiting by:–Reduction of variability–Reduction of arrivals/throughput–Add “safety” capacityReduce length of serviceIncrease staffingVariabilityAverageWait TimeUtilization 100%ProcessCapacitySlide 13Service OperationsHow reduce system variability?Safety Capacity = capacity carried in excess of expected demand to cover for system variability–it provides a safety net against higher than expected arrivals or services and reduces waiting timeLevers to reduce waiting and increase QoS:  variability reduction + safety capacitySlide 14Service OperationsExample 1: MBPF Calling Center with one server, unlimited buffer. The basics of QoSConsider MBPF Inc. that has a customer service representative (CSR) taking calls. When the CSR is busy, the caller is put on hold. The calls are taken in the order received. Assume that calls arrive exponentially at the rate of