Capacity 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 SofOptics Van Mieghem/Operations/Managing Services

Telemarketing 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. $1.5 billion sales (70% direct sales/catalogue) 4 call centers in Main Freeport. One is seasonal operating durng the busy time of the year- christmas. Max. handle 180,00 calls/day.. In 2005 14.5 million customer contacts. 4000 CSR’s employed during the peak time.. Even though they are concerned with their service to customers very much.. They exhibit certain problems at their call centers.. Here is a sneak preview of what is happening there. Van Mieghem/Operations/Managing Services

Some 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? Van Mieghem/Operations/Managing Services

Telemarketing: 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 Histogram Flow Time (minutes) Probability 0% 20% 40% 60% 80% 100% 15 30 45 60 75 90 105 120 135 150 165 180 195 8 Van Mieghem/Operations/Managing Services

Telemarketing with variability in arrival times + activity times 0% 5% 10% 15% 20% 25% 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 More Flow Time Histogram Probability 40% 60% 80% 100% 90% Cumulative Probability Flow Time (minutes) In reality inter-arrival times exhibit variability 0% 5% 10% 15% 20% 25% 30% 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 More Flow Time (minutes) Probability 40% 50% 60% 70% 80% 90% 100% Cumulative Probability In reality service times exhibit variability Van Mieghem/Operations/Managing Services

Telemarketing with variability: The effect of utilization Average service time = 9 minutes 9.5 minutes 0% 1% 2% 3% 4% 5% 6% 7% 8% 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 More Flow Time Probability 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 5% 10% 15% 20% 25% 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 More Flow Time Probability 30% 40% 50% 60% 70% 80% 90% 100% Van Mieghem/Operations/Managing Services

Inventory (# of calls in system) Why do queues form? 1 2 3 4 5 6 7 8 9 10 20 40 60 80 TIME Call # Inventory (# of calls in system) variability: arrival times service times processor availability Role of utilization: Impact of variability increases as utilization increases! (arrival throughput  or capacity ) Van Mieghem/Operations/Managing Services

Flow Times in White Collar Processes Van Mieghem/Operations/Managing Services

Queuing Systems to model Service Processes: A Simple Process Order Queue “buffer” size K Sales Reps processing calls Incoming calls Answered Calls Calls on Hold MBPF Inc. Call Center Blocked Calls (Busy signal) Abandoned Calls (Tired of waiting) Van Mieghem/Operations/Managing Services

What 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, priorities Van Mieghem/Operations/Managing Services

Performance Measures Sales Cost Customer service Throughput R Abandonment Ra Cost Server utilization r Inventory/WIP : # in queue Ii /system I Customer service Waiting/Flow Time: time spent in queue Ti /system T Probability of blocking Rb Van Mieghem/Operations/Managing Services

The 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 staffing Average Wait Time Variability 100% Utilization Process Capacity Van Mieghem/Operations/Managing Services

Levers to reduce waiting and increase QoS:  variability reduction + safety capacity 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 time Van Mieghem/Operations/Managing Services

Example 1: MBPF Calling Center with one server, unlimited buffer Example 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 one every 3 minutes. The CSR takes on average 2.5 minutes to complete the reservation. The time for service is also assumed to be exponentially distributed. The CSR is paid $20 per hour. It has been estimated that each minute that a customer spends in queue costs MBPF $2 due to customer dissatisfaction and loss of future business. Holding cost H = Average number waiting in buffer Ii = MBPF’s waiting cost = H  Ii = Van Mieghem/Operations/Managing Services

Example 2: MBPF Calling Center with limited buffer size Example 2: MBPF Calling Center with limited buffer size. Impact of blocking In reality only a limited number of people can be put on hold (this depends on the phone system in place) after which a caller receives busy signal. Assume that at most 5 people can be put on hold. Any caller receiving a busy signal simply calls a competitor resulting in a loss of $100 in revenue. # of servers c = buffer size K = What is the hourly loss because of callers not being able to get through? Van Mieghem/Operations/Managing Services

Putting Tech Support on The Fast Track THE BAT Case = Managing the operations of a customer service department Putting Tech Support on The Fast Track Handouts to be distributed in class Van Mieghem/Operations/Managing Services

Example 3: MBPF Calling Center with 1 or 2 queues Example 3: MBPF Calling Center with 1 or 2 queues. Impact of Resource Pooling 2 phone numbers MBPF hires a second CSR who is assigned a new telephone number. Customers are now free to call either of the two numbers. Once they are put on hold customers tend to stay on line since the other may be worse.. 1 phone number: pooling both CSRs share the same telephone number and the customers on hold are in a single queue Which system is “better?” In which sense? When? Why? Tp = 2.5min Server Queue 50% Ri = 1/3min Tp = 2.5min Tp = 2.5min Servers Queue Ri = 1/3min Van Mieghem/Operations/Managing Services

Example 4: MBPF Calling Center with 2 service tasks Example 4: MBPF Calling Center with 2 service tasks. The impact of process structure & resource capabilities: Specialization Vs. Flexibility A second service task is added. Two possibilities to structure the process: Specialization Each service task is performed by a specialized agent Average flow time T = Flexibility The entire service is performed by one of two flexible agents = generalists. Agerage flow time T = Which system is “better?” In which sense? When? Why? Tp = 2.5min Tp = 2.5min Ri = 1/3min Server Queue Servers Queue Tp = 5min Ri = 1/3min Van Mieghem/Operations/Managing Services

Increase quality of service: 1. reduce variability Two types of variability: Predictable Unpredictable = “Stochastic” Two sources of variability: Arrivals Length of service Predictable variability is reduced by: Proper triage: differentiated treatment Proper scheduling & appointments Standardization of service (not always an option) Key = synchronize arrivals with end of service Van Mieghem/Operations/Managing Services

How increase quality of service with stochastic variability. 2 How increase quality of service with stochastic variability 2. reducing utilization is your only option How reduce utilization? Reduce throughput Not typically desired b/c of social, ethical, or financial concerns … Increase capacity Recall section 3!: bottleneck management Key: when one cannot perfectly synchronize flows so that there is remaining, irreducible stochastic process variability then one must build in a capacity cushion. One cannot provide high quality of service at high utilization Van Mieghem/Operations/Managing Services

Increase quality of service: anticipate predictable variability + build safety-capacity for stochastic variability. e.g. smart staffing Average walk-ins often are fairly predictable Keep data (use IT!): find average trend (predictable) + stochastic variations Staff accordingly: use time-buckets + build safety-capacity staffing Number of patients 4 8 12 16 5AM 11AM 5PM 11PM 4AM Time of day Maximum # Patient arrivals/hr Average # +1 s -1 s Source: McKinsey Quarterly 2001 Van Mieghem/Operations/Managing Services

Smart Staffing/Capacity Management at Sof-Optics 10 20 30 40 50 60 70 80 90 100 6:30 7:30 8:30 9:30 10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30 Demand (# Calls/30min) Current Supply/Capacity (# Calls/30min) Optimized Supply w/o demand mgt or capital investment Van Mieghem/Operations/Managing Services

Call Centers In U.S.: $10B, > 70,000 centers, > 3M people (>3% of workforce) Most cost-effective channel to serve customers Strategic Alignment accounting: 90% are cost centers, 10% are revenue centers role: 60% are viewed as cost, 40% as revenue generators staffing: 60% are generalists, 40% specialists Trend: more towards profit centers & revenue generators Trade-off: low cost (service) vs. high revenue (sales) Source: O. Zeynep Aksin 1997 Van Mieghem/Operations/Managing Services

Framework for Analysis and Improvement of Service Systems Divide day into blocks based on arrival rates: Separate “peaks” from “valleys” For each block evaluate performance measures given current staffing Quantify financial impact of each action Workforce training: reduces mean and variability of service time Work flexibility from workforce: pools available capacity Time flexibility from workforce: better synchronization Retain experienced employees: increased safety capacity Additional workforce: Increases safety capacity Improved Scheduling: better synchronization Incentives to affect arrival patterns: better synchronization Reservation mgt, pre-sell, Disney’s FastPass Decrease product variety: reduces variability of service time Increase maximum queue capacity Consignment program, fax, e-mail etc. Supply mgt Demand mgt Van Mieghem/Operations/Managing Services

How do these insights related to our earlier “Levers for Reducing Flow Time?” “is to decrease the work content of (only ?) critical activities”, and/or move it to non critical activities. Reduce waiting time: reduce variability arrivals & service requests synchronize flows within the process increase safety capacity lower utilization Pooling Match resource availability with flows in and out of process Van Mieghem/Operations/Managing Services

Learning objectives: General Service Process Management Queues build up due to variability. Reducing variability improves performance. If service cannot be provided from stock, safety capacity must be provided to cover for variability. Tradeoff is between cost of waiting, lost sales, and cost of capacity. Improving Performance Reduce variability Increase safety capacity Pooling servers/capacity Increase synchronization between demand (arrivals) and service Manage demand Synchronize supply: resource availability Van Mieghem/Operations/Managing Services