Slide 1Service Operations

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

Slide 2Service Operations

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.

Slide 3Service Operations

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?

Slide 4Service Operations

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

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Slide 5Service Operations

Telemarketing with variability inarrival times + activity times

In reality service times– exhibit variability

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In reality inter-arrival times– exhibit variability

Slide 6Service Operations

Telemarketing with variability: The effect of utilization

Average service time = – 9 minutes

Average service time =– 9.5 minutes

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Slide 7Service Operations

Why 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 )

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Slide 8Service Operations

Industry Process AverageFlow Time

TheoreticalFlow Time

Flow TimeEfficiency

Life Insurance New PolicyApplication

72 hrs. 7 min. 0.16%

ConsumerPackaging

NewGraphicDesign

18 days 2 hrs. 0.14%

CommercialBank

ConsumerLoan

24 hrs. 34 min. 2.36%

Hospital PatientBilling

10 days 3 hrs. 3.75%

AutomobileManufacture

FinancialClosing

11 days 5 hrs 5.60%

Flow Times in White Collar Processes

Slide 9Service Operations

Queuing Systems to model Service Processes: A Simple Process

Sales Repsprocessing

calls

Incoming callsCalls

on Hold

Answered Calls

MBPF Inc. Call Center

Blocked Calls(Busy signal)

Abandoned Calls(Tired of waiting)

Order Queue“buffer” size K

Slide 10Service Operations

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

Slide 11Service Operations

Performance 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 Rb

Slide 12Service Operations

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

Variability

AverageWait Time

Utilization 100%

ProcessCapacity

Slide 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 time

Levers to reduce waiting and increase QoS: variability reduction + safety capacity

Slide 14Service Operations

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 =

Slide 15Service Operations

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?

Example 2: MBPF Calling Center with limited buffer size. Impact of blocking

Slide 16Service Operations

THE BAT Case = Managing the operations of a customer service department

Handouts to be distributed in class

Putting Tech Support on The Fast Track

Slide 17Service Operations

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?

Servers

Queue

ServerQueue

ServerQueue

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50%

Tp = 2.5min

Tp = 2.5min

Tp = 2.5min

Ri = 1/3min

Ri = 1/3min

Slide 18Service Operations

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?

ServerQueue ServerQueue

Tp = 2.5min

Ri = 1/3min

Tp = 2.5min

ServersQueue

Tp = 5min

Ri = 1/3min

Slide 19Service Operations

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

Slide 20Service Operations

How increase quality of service with stochastic variability 2. reducing utilization is your only option

How reduce utilization?

1. Reduce throughput– Not typically desired b/c of social, ethical, or financial concerns …

2. 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

Slide 21Service Operations

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

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Source: McKinseyQuarterly 2001

Slide 22Service Operations

Smart Staffing/Capacity Management at Sof-Optics

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Optimized Supply w/o demand mgt or capital investment

Slide 23Service Operations

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

Slide 24Service Operations

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

Slide 25Service Operations

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

Slide 26Service Operations

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