2005 Efficient Service Location Design in Government Services a Decision Support System Framework

8/13/2019 2005 Efficient Service Location Design in Government Services a Decision Support System Framework

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Efficient service location design in government servicesA decision support system framework

Ram Narasimhana,*, Srinivas Talluria, Joseph Sarkisb, Anthony Rossa

aDepartment of Marketing and Supply Chain Management, Eli Broad College of Business, Michigan State University,

N370 North Business Complex, East Lansing, MI 48824, USAbGraduate School of Management, Clark University, 950 Main Street, Worcester, MA 01610-1477, USA

Received 30 October 2003; received in revised form 10 April 2004; accepted 15 July 2004

Available online 28 October 2004

Abstract

This paper presents a decision support system for efficient service location design for an agency in the State of Michigan. We

consider a total of 169 branch offices of the agency located in 79 counties that process a variety of transactions and provide

services including automobile registrations, issuance of driver licenses, recreational vehicle registration, and personal

identification registry. The proposed methodology and decision support system incorporate a number of factors such as branch

office efficiencies based on multiple measures, budget restrictions, capacity limitations for processing transactions, and demand

requirements in designing an efficient service system. Our approach employs data envelopment analysis (DEA) and mixed-

integer programming (MIP) models. A series of experiments are conducted with the proposed model by varying the levels of

system-wide efficiency, resource reallocation, and budget in generating a set of decisions that executive management of the

agency can implement. In addition, we investigate service channel management issues that the agency is currently facing in

providing the services by web, phone, facsimile, and mail in addition to branch offices. We discuss how the branch closures

influence channel management decisions.

# 2004 Elsevier B.V. All rights reserved.

Keywords: Data envelopment analysis; Mixed-integer programming; Service location design; Channel management

1. Introduction

Service design is an area in operations managementthat is receiving growing emphasis in the literature.

Design issues in new services or redesign in existing

services involve strategic, tactical, and operational

decisions that range from facility location or systemdesign to workforce training (Goldstein et al., 2002).

Effective system design for the service sector requires

careful consideration of a variety of factors that

include fixed and variable costs, service levels,

efficiency, sales, and profits (Shapiro and Haskett,

1985; Banker and Morey, 1993). While sales and

profits are not typically relevant factors in the design

www.elsevier.com/locate/dsw

Journal of Operations Management 23 (2005) 163178

* Corresponding author.

E-mail addresses: [email protected] (R. Narasimhan),

[email protected] (S. Talluri), [email protected] (J. Sarkis),

[email protected] (A. Ross).

0272-6963/$ see front matter # 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.jom.2004.07.004


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of service locations in government services, the other

three aforementioned factors are critical in the design

process. Davies and Rogers (1984) suggest that site

selectionand design decisions in services must considersize of the outlet (i.e., capacity levels), demographics,

and operational factors such as hours of operation at the

candidate site among other attributes.

This paper considers some of these issues in

addressing the problem of service location design for

a state government agency. Although the study was

carried out in a project setting in an agency of the State

of Michigan, the study exemplified difficult issues that

need to be satisfactorily addressed in developing viable

solutions to the problem of service location optimiza-

tion. The current domestic economic situation is

exerting extreme financial pressure on all states. The

State of Michigan is facing a budget shortfall approach-

ing $900 million. Executives in state government have

to deal with this exigency even as public pressure for

government services continues unabated. This combi-

nation of circumstances has renewed interest in

productivity and performance measurement as well

as fiscal resource optimization in service delivery on the

part of various government agencies and executives.

The problem addressed in this study brings to the

forefront the following research and practitioner

issues:

1. There are no formal methods for productivity

measurements that have simultaneously considered

both resources employed and multiple outcome

measures in evaluating the efficiencies of service

outlets in government services.

2. The budget situation faced by states necessitates

the consideration of service delivery office

closures. While such issues have been modeled

in the literature via location decision techniques,

the application in government services posesunique challenges because all decisions pertaining

to service delivery have to take into account

statutory mandates and political considerations.

3. The closures have to be defended in front of state

legislators. This justification requires support in the

form of objective and robust analysis of data.

4. The agency in our study was interested in

maintaining a high degree of customer satisfaction

and convenience by improving the efficiency of the

service delivery system.

5. It is not apparent how different policies would

affect service delivery operations.

It can be noted that these objectives and the co-mplexity of the problem context render conventional,

judgmental approaches less appropriate and provide

the impetus for the project that the research team

addressed. Unlike most for-profit organizations, ser-

vices performed by the governmental agency in this

study are driven by legal mandates. The implications

of this for optimizing service locations, as we disc-

overed, are multifarious.

The objectives of this paper are to (a) describe the

decision support system (DSS) framework that was

developed to address salient issues faced by the

executive management of the agency, (b) describe the

analytical models that were developed within the DSS

framework, (c) address issues pertaining to service

channel management, and (d) discuss the challenges in

developing and implementing analytical models

pertaining to service operations in the government

sector.

The remainder of this paper begins with a

discussion of the problem context in some detail.

This discussion is followed by a review of relevant

literature from operations management focusing on

facility location issues. The following sectiondescribes the DSS framework and the methodology.

Next, analyses results and managerial implications are

presented, which is followed by a discussion of service

channel management related issues. We conclude the

paper with some suggestions for additional work and

research.

2. Problem context

The state agency is responsible for processingtransactions and collecting fees associated with

obtaining personal identification cards, drivers

licenses, title issuance and transfers, and vehicle

registrations and renewals. These services are pro-

vided to the public through various service channels

including facsimile, phone, mail, and face-to-face

contact in a local branch office of the agency, and more

recently through the web. Data for the years 1999

2001 revealed that branch offices throughout the state

handled approximately 17 million out of 21 million

R. Narasimhan et al. / Journal of Operations Management 23 (2005) 163178164


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total transactions. This result shows that the branch

office channel is the channel of choice for service

delivery. State legislators have recognized the

importance of this service channel, and have thusmandated through state laws that at least one branch

office in each county must be available to constituents.

The agencys executive management (soon after the

elections in the year 2002) had engaged in a strategic

planning process prior to the start of this project and had

identified service delivery location optimizationas

an overarching strategic objective for the agency. Under

the rubric of service delivery location optimization, the

agency wished to pursue fiscal resource optimization

while maintaining customer satisfaction and con-

nectivity (i.e., availability of services through multiple

channels at different times and different locations). A

$900 million projected budget deficit faced by the State

of Michigan in the nextfiscal year provided a sense of

urgency to the project.

The problem context was complex in that political

realities had to be satisfactorily accounted for in

whatever decision environment that ensued. These

political concerns included potential negative reactions

to branch office closures from the citizens of the state,

state worker unions, branch administrators, and state

legislators. Operationally, the unpredictability of

demand (transactions) in each service channel, limitedability to divert demand from one service channel to

another, and the need to justify all decisions to the

stakeholders, especially the public, were of utmost

concern.

In addition to branch offices as the primary channel

for service delivery, the agency perceived its ability to

divert demand among channels to be limited in the

near term. Consequently, it was apparent that any

reorganization of service delivery must look for

opportunities in the branch office service locations. An

analysis of cost data showed that the unit cost ofprocessing a transaction in the branch offices for the

year 2002 did not differ significantly, although there

was a slight indication that the larger offices were

somewhat more efficient. For the purpose of our

analysis and model development, the project team

decided to treat the unit cost of transaction to be the

same across the branch offices.

There were several issues and constraints that also

needed recognition. The agency had undergone an

extensive re-engineering of its processes a few years

earlier. Consequently, in our analysis, we assumed that

the processes were given and unchangeable. We also

assumed that statutory mandates had to be complied

with. Although it would be possible to study the effectsof statutory changes, agency executive management

stated that changing the pertinent laws was a lengthy

process and that they were to be treated as inviolable.

The scope of the problem to be studied took into

account these constraints as well as other operational

constraints. For example, not all transactions could be

completed by all channels. Branch offices were unique

in this regard in that the branch offices could process

all transactions. Title transactions, for example, could

not be processed via phone or fax. Issuance of driver

licenses cannot happen via the web or mail. Each

service channel differed in the type of transaction it

could handle, unit cost of processing a transaction and

processing capacity. The DSS approach presented in

this paper thus focuses on the branch office delivery

channel, but consideration of additional channel

design may also be targeted at a future date.

3. Literature review

In this section, we discuss literature relating to

location design in service operations management. Wepresent a variety of models and methodologies that

have been utilized for location decisions in services.

3.1. Location design in services

Location decisions arise in a variety of public and

private sectors (Brandeau and Chiu, 1989; Francis

et al., 1983; Daskin, 1995; Ogryczak, 1997; Wright and

Mechling, 2002). The success or failure of both public

and private sector firms depends on the locations

chosen for their network of facilities. In the context ofpublic service facilities, many of the models developed

are covering models (Balas and Ho, 1980; Etchberry,

1977). A complete review of covering problems in

facility location can be found inSchilling et al. (1993).

In general, mathematical location (or covering) models

are designed to address a number of questions

including: number of facilities, location of these

facilities, type of such facilities, and allocation of

demand to the facilities. Our study focuses on similar

issues within a DSS framework in the public sector.

R. Narasimhan et al. / Journal of Operations Management 23 (2005) 163178 165


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Shapiro and Haskett (1985) discussed the important

design attributes that need to be considered in the

location of retail service outlets. They stress the

importance of considering costs (fixed and variable),efficiency, service levels, and demand and capacity

issues, in addition to profits in retail outlet location

decisions. Davies and Rogers (1984)suggest a set of

factors to consider in evaluating candidate sites for

location in the service industries, which include size of

the outlet, information technology and other infra-

structure issues, and local demand level. In general,

many site location models in service industries have

effectively considered site characteristics, demo-

graphics, and local competition (Ryans and Weinberg,

1979, 1987).

A great deal of location research has been carried

out to develop a variety of models applied to, e.g., bill

delivery services (Lin et al., 2002), regional social

services center (Patel, 1979), ambulatory and health-

care services (Cromley and Shannon, 1986; Rahman

and Smith, 2000), design of cellular networks (Dutta

and Hsu, 2002), and public road network districting for

salt-spreading operations (Muyldermans et al., 2002).

Typically, the overarching issue has been to locate

facilities so as to provide appropriate response times or

service levels to all neighborhoods within a defined

municipality. The problem considered in our studydiffered in the following respects: the need to consider

constraints imposed by statutory laws, political

realities, and limited ability to manage demand.

Most common solution approaches to this class of

problems are either exact heuristic procedures of

Lagrangian relaxation (LR) or meta-heuristics (MH)

(Erlenkotter, 1978; Aikens, 1985; Robinson and

Swink, 1994; Pirkul and Jayaraman, 1996; Revelle

and Laporte, 1996; Lim and Kim, 1999; Ross, 2000).

Meta-heuristics such as simulated annealing, genetic

algorithms, and Tabu search have been widelyapplied to facility location problems with mixed

results. They offer great promise to this class of

problem (Feterolf and Anandanlingham, 1991;

Vernekar et al., 1990).

Banker and Morey (1993)have proposed a model

based on allocative DEA and mixed-integer, nonlinear

programming for integrated system design for service

outlets by considering demand forecasts, efficiency

factors, demographics, and other interdependencies.

While they consider strategic issues in their study, their

work is directed more at the operational level of

designing service outlets. Korpela et al. (2001)

proposed an approach based on analytic hierarchy

process (AHP) and mixed-integer program (MIP) forincorporating service elements and a companys own

strategies into traditional cost-based design of a supply

network. The primary goal of the model is to optimize a

companys supply network based on customer service

requirements within the constraints of the supply chain.

What remains absent from the facility location

literature is not the development of new computational

algorithms, but rather the examination of perfor-

mance-based configuration and resource allocation

issues facing executive managers in the public sector.

The unique environmental and public policy issues

facing these managers offer a new domain in which to

develop a novel solution framework to the well-known

problem, which is the focus of this paper.

4. Methodology

4.1. DSS framework for service channel

management

The DSS framework for service channel manage-

ment depicted inFig. 1illustrates the sequence of stepsand the decision process that management can utilize

for effective design and operation of the state agency

services. The core service delivery location model, i.e.,

the MIP model, is developed by considering efficiency

of branch offices identified from DEA, and other

constraints that include demand requirements, capacity

limitations, and budgetary restrictions. The managerial

restrictions imposed on the model include the

minimum efficiency that needs to be maintained at

both system and county levels and the amount of

demand reallocation that can be performed at eachbranch office. The solution to the MIP model identifies

which branch offices need to be kept open and

corresponding capacity allocations, which involves

resource deployment decisions. The branch closures

resulting from the MIP model may have channel

management implications, i.e., to divert some of the

demand to other channels. In order to perform this

diversion effectively, channel transition probabilities

need to be estimated based on customer response,

policy decisions, and available channels. A simple



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Markov analysis can be undertaken to estimate the

steady-state demands for each of the channels because

non-zero transition probabilities can occur out of every

state, which allows for the identification of appropriate

resource levels that must be dedicated to each of the

channels in the long run. In light of these results, the

MIP model needs to be rerun in identifying potential

branch closures/openings or capacity reallocations. We

now discuss the DEA methodology and the MIP model

used in our framework.

4.2. Data envelopment analysis for branch

efficiency evaluation

DEA is a nonparametric multi-factor productivity

analysis model that evaluates the relative efficiencies

of a homogenous set of decision-making units in the

presence of multiple input and output factors. A unit

with an efficiency score of 1 (slack = 0) is considered

to be efficient and a score less than 1 indicates that the

unit is inefficient. Model (A1) shows the CCR

(Charnes, Cooper, and Rhodes) model (Charnes et

al., 1978). The model is run n times, where n

represents the number of decision-making units, in

determining the efficiency scores of all the units. Each

unit is allowed to select optimal weights that

maximize its efficiency (ratio of weighted output to

weighted input), but at the same time the efficiencies

of all the units in the set when evaluated with these

weights is prevented from exceeding a value of 1.

Model (A1):

maxXs

k1

vkykp

s:t:Xm

j1

ujxjp 1

Xs

k1

vkykiXm

j1

ujxji 0 8 i

vk; uj 0 8 k;j


Fig. 1. A DSS framework for service channel management.


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wherep is the branch office being evaluated,s repre-

sents the number of outputs,m represents the number

of inputs, yki the amount of output k provided by

branch office i, xji the amount of input j used bybranch office i, and vk and uj are the weights given

to output kand input j, respectively.

For every inefficient unit, DEA identifies a set of

efficient units that can be utilized as benchmarks for

improvement, which can more easily be obtained by

utilizing the envelopment side (the dual formulation)

of model (A1), shown as model (A2) below. Also, in

situations where the number of decision-making units

is very high, model (A2) may prove to be

computationally easier to solve. For more details on

model formulations and related theoretical develop-

ments, seeCharnes et al. (1978).

Model (A2):

min u

s:t:X

i

lixji uxjp 8j

X

i

liyki ykp 8 k

li 0 8 iwhere urepresents the efficiency score of branch office

pand l represents the dual variables that identify the

benchmarks for inefficient units.

4.3. MIP model for branch closures and resource

reallocation

In order to assist management with a decision

model for branch closures and resource reallocation,

we developed the following mixed-integer program-

ming (MIP) model, which effectively considers

efficiency scores, capacities, demand requirements,

and budget constraints. The objective function of the

model, shown as (1), minimizes the number of

branch offices in the state. Expression (2) ensuresthat the average efficiency score of the open branch

offices meets a target value set by the executive

management of the state agency. Expression (3)

maintains a management specified average effi-

ciency score for open branches within a county.

Expression (4) captures the budget restriction, and

expression (5) ensures that the branch offices that

remain open in a county satisfy demand for service

expressed in number of transactions in that county.

Expression (6) involves a linking (logical) constraint

that ensures that a branch office can only process

transactions if it is open, and also imposes a lower

bound on the amount of capacity that needs to be

allocated if a branch is open. Expression (7)represents the capacity at a branch offic e a s a

soft constraint that allows for over- and under-

utilization. This allows for the reallocation of

capacity from closed branches to open branches

within a county. Expression (8) prevents the capacity

reallocation at a branch office from exceeding a

value set by the executive management of the state

agency. Expression (9) represents the non-negative

and binary restrictions in the model.

minX

k

X

lk

Yklk (1)

subject to:X

k

X

lk

uklkYklk uminX

k

X

lk

Yklk 0 (2)

X

lk

uklkYklk fminX

lk

Yklk 0 8 k (3)

X

k

X

lk

bklkYklkB (4)

X

lk

XklkDk 8 k (5)

gCklkYklkXklkMYklk 8 klk (6)

Xklk Sklk Sklk Cklk 8 klk (7)

Sklk lCklk0 8 klk (8)

Xklk;Sklk

; Sklk 0 8 klk; Yklk2 f0; 1g (9)

where Xklkis the number of transactions assigned to

branch office lk in county k, Dk the demand of

transactions in county k, B the overall operating

budget from the state, bklk the operating costs atbranch office lk in county k, Cklk the capacity at

branch office lkin countyk,Yklkis 0 or 1, 1 if branch

office lkin county kis open and 0 otherwise, Mis a

large positive number, uklk the efficiency of branch

office lk in county k, umin the average system effi-

ciency score that needs to be maintained for open

branches, fmin the average county efficiency score

that needs to be maintained for open branches within

a county,SklkandSklk

are slack and surplus variables,

lthe proportion of capacity that can be reallocated to



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a branch office lk in county k, and g the minimum

proportion of capacity that must be allocated to

branch office lkin county kif it is open.

5. Results and managerial implications

5.1. Efficiency evaluation of branch offices

Branch office ef ficiencies were evaluated by

utilizing two inputs and three outputs. Based on

managements participation, the inputs considered

were number of hours worked and total costs

(including salaries, rent, and other operating

expenses) at each of the branch offices. The outputs

used were number of personal identification, driver

license, and registration transactions performed at

each of the branch offices. The mean and standard

deviation values for the inputs, i.e., hours worked

and total costs are (9248.6, 5115.3) and ($322,498.5,

$178,221.4), respectively. For the outputs, i.e.,

personal identification, driver license, and registra-

tion transactions, the mean and standard deviation

values are (1556.5, 1815.1), (13,111.4, 8095.4), and

(41,848.0, 21,693.4), respectively. The branch office

data were obtained from the state agency for the year

2001. We have utilized the ratio DEA model,proposed byCharnes et al. (1978), shown as Model

(A1), in evaluating the relative efficiency scores of

the branch offices. The relative efficiency results

arranged by branch offices by county are shown in

Table 1. Based on DEA evaluations, a total of 19

branches across the state were identified to be

efficient with a relative efficiency score of 1.0 (with

slack of zero), and the remaining 149 branches

were inefficient with scores ranging from 0.997 to

0.567. Within the inefficient units, 65 branches

achieved an efficiency score between 0.999 and0.900, 60 branches had a score between 0.899 and

0.800, 17 branches had efficiency scores between

0.799 and 0.700, 6 branches had a score between

0.699 and 0.600, and 1 branch had a score of 0.567.

These results provide valuable information for

decision-makers regarding the productivity of

branches, and have major implications for policy

decisions regarding branch closures and resource

reallocations, which we consider in the following

section.


Table 1

Relative efficiency scores of branch offices

County/

branch

Relative

efficiency

County/

branch

Relative

efficiency

1/1 0.797 53/1 0.915

2/1 0.695 54/1 0.873

3/1 0.949 55/1 0.925

4/1 1.000 55/2 0.904

5/1 0.781 55/3 0.827

6/1 0.939 56/1 0.953

7/1 0.690 56/2 0.847

8/1 1.000 57/1 0.781

9/1 0.823 58/1 0.782

10/1 0.974 58/2 0.867

11/1 0.797 58/3 0.954

11/2 0.825 59/1 0.932

11/3 0.763 60/1 0.952

11/4 0.829 60/2 0.86612/1 0.806 60/3 0.951

13/1 0.727 60/4 1.000

13/2 0.778 60/5 1.000

13/3 0.996 60/6 0.925

13/4 0.893 60/7 0.950

14/1 0.836 60/8 0.843

15/1 0.961 60/9 0.989

16/1 0.919 60/10 0.811

17/1 0.893 60/11 0.718

18/1 0.890 60/12 0.911

19/1 0.977 60/13 0.882

20/1 0.827 61/1 0.929

21/1 0.944 62/1 0.932

22/1 0.889 63/1 0.567

23/1 0.899 64/1 0.923

24/1 1.000 65/1 0.847

24/2 0.896 66/1 1.000

24/3 1.000 66/2 0.921

24/4 0.623 66/3 0.995

24/5 0.862 67/1 0.795

24/6 0.940 68/1 0.689

25/1 0.915 68/2 0.877

26/1 1.000 69/1 0.819

27/1 0.858 69/2 0.954

28/1 0.916 69/3 0.814

29/1 0.860 69/4 0.948

30/1 0.845 70/1 1.00031/1 0.980 70/2 0.946

32/1 0.810 70/3 0.973

32/2 0.824 71/1 0.722

32/3 1.000 72/1 0.972

32/4 1.000 73/1 0.954

33/1 0.889 73/2 0.942

34/1 0.916 73/3 0.891

35/1 0.832 73/4 0.824

36/1 0.920 74/1 0.800

37/1 0.978 74/2 0.905

37/2 0.880 75/1 0.920

38/1 0.771 75/2 0.873


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5.2. Experiments with the MIP model

The capacity, demand, and annual budget utilized

in solving the MIP model were obtained from

executive management of the state agency. The

average efficiency restrictions (umin, fmin) and limits

in capacity reallocation (l,g) were judgmental inputs.For the base case of the model, we have considered

umin= 0.8, l= 0.3, B= $55 million, g= 0.5, and

fmin= 0.6 in consultation with management. For

these parameters, the MIP model resulted in selecting

142 branch offices for operation and 26 branch offices

for closure. In order to investigate the sensitivity of

these parameter values on branch closure and resource

reallocation decisions, we performed illustrative

sensitivity analyses with the MIP model. Following

model experiments were conducted in our analysis:

Varying the average efficiency bound (umin); Varying the proportion of capacity that can be

reallocated (l);

Varying overall operating budget of the state (B).

The model did not require extensive computational

resources with optimal answers achieved in fractions

of a second on commercially available software pa-

ckages. But due to the large number of integer vari-

ables there is potential for a significantly time-

consuming solution process with tighter constraints

occurring. There is room for more investigation of

efficient solution procedures if computational limita-

tions exist. We now discuss the model results for the

above three scenarios, and derive managerial impli-

cations for each of three cases.

5.2.1. Varying the average efficiency bound (umin)

The umin value was varied from 0 to 0.89, while

maintaining the other parameter values at the base

case level. The results of this analysis are shown in

Table 2. It must be noted that Table 2 only shows

counties with multiple branches, since single branches

in counties have to be kept open and assigned the

respective demands. In the notation of thefirst column

ofTable 2, thefirst two values after the X variable (the

assigned transactions) represent the county numberand the next two values represent the branch number.

Thus, X1101 represents the assigned transactions

value for County 11, Branch 1. Foruminranges from 0

to 0.86, the solution did not change with 142 branch

offices open and corresponding capacity reallocations

all similar. For values of 0.87, 0.88, and 0.89, as the

constraint tightened, we have observed changes in

both branch selections and capacity reallocations as

shown inTable 2.For example, branch 4 in county 11

(X1104) is selected for the range 00.88, but is closed

at 0.89. Also, note the changes in capacity realloca-tions for this case. Other results can be interpreted in a

similar manner. Forumin values over 0.89, the problem

was infeasible while maintaining the other base case

parameters.

There are some managerial implications from

sensitivity analysis of the minimal acceptable effi-

ciency score. Essentially, reasonable minimum effi-

ciency levels, a managerial decision, must be set for

feasible solutions to occur. It is also important that

management be aware of the dynamics that occur with


Table 1 (Continued)

County/

branch

Relative

efficiency

County/

branch

Relative

efficiency

38/2 0.912 76/1 0.88538/3 0.889 76/2 0.817

39/1 0.908 77/1 1.000

40/1 0.937 77/2 0.900

40/2 0.824 77/3 0.904

40/3 1.000 78/1 0.844

40/4 0.953 78/2 0.848

40/5 0.838 78/3 0.755

41/1 0.879 78/4 0.877

42/1 0.965 78/5 0.944

43/1 0.921 78/6 0.871

44/1 0.997 78/7 0.834

44/2 1.000 78/8 0.742

45/1 0.653 78/9 0.958

46/1 0.658 78/10 0.952

47/1 0.821 78/11 1.000

47/2 0.928 78/12 0.831

47/3 0.900 78/13 0.831

47/4 0.922 78/14 0.855

47/5 0.905 78/15 0.938

47/6 0.733 78/16 0.828

47/7 0.814 78/17 0.943

47/8 0.768 78/18 0.830

47/9 1.000 78/19 0.837

48/1 0.958 78/20 1.000

49/1 0.853 78/21 0.841

49/2 0.943 78/22 0.759

50/1 0.975 78/23 1.00051/1 0.897 78/24 0.839

51/2 0.939 78/25 1.000

52/1 0.917 79/1 0.908


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Table 2

Sensitivity analysis results on branch closures and capacity alloca-

tions for ranges of average efficiency bound (umin) parameter

umin

0.000.86 0.87 0.88 0.89

X1101 67745.4 68283.8 61314.6 68283.8

X1102 88415.6 81446.4 88415.6 88415.6

X1103 0 0 0 20897.6

X1104 21436 27866.8 27866.8 0

X1301 102674 102674 65282.7 73954

X1302 58010.5 58010.5 95401.8 73386

X1303 79605.5 79605.5 79605.5 79605.5

X1304 0 0 0 13344.5

X2401 0 0 0 67553.2

X2402 87808.5 87808.5 67545 67746.9

X2403 110362.2 59831.2 110362.2 110362.2

X2404 98022.6 98022.6 98022.6 0

X2405 56128.8 56128.8 56128.8 56128.8

X2406 41291.9 91822.9 61555.4 91822.9

X3201 0 0 0 0

X3202 137935.2 137935.2 106104 137935.2

X3203 54981.3 54981.3 93044.9 93044.9

X3204 92085.5 92085.5 85853.1 54021.9

X3701 41468.8 41468.8 41468.8 41468.8

X3702 106982.2 106982.2 106982.2 106982.2

X3801 0 0 0 0

X3802 97119.7 97119.7 97119.7 97119.7

X3803 129065.3 129065.3 129065.3 129065.3

X4001 0 0 0 0

X4002 162571 162571 168376 168376

X4003 0 0 0 61896.9X4004 179722.4 179722.4 179722.4 112020.5

X4005 181664.6 181664.6 175859.6 181664.6

X4401 69544 69544 90407.2 69544

X4402 88227 88227 67363.8 88227

X4701 174392.4 174392.4 174392.4 174392.4

X4702 134308.2 134308.2 134308.2 134308.2

X4703 0 0 0 81586.7

X4704 176842.9 176842.9 176842.9 172175.3

X4705 0 81352.7 80647.79 81352.7

X4706 93026.7 93026.7 93026.7 93026.7

X4707 80647.79 0 0 0

X4708 126139 125434.1 126139 48515

X4709 0 0 0 0

Theta 0.000.86 0.87 0.88 0.89X4901 57157.5 57157.5 57157.5 57157.5

X4902 11867.5 11867.5 11867.5 11867.5

X5101 41269 41269 35579.5 50751.5

X5102 18965 18965 24654.5 9482.5

X5501 46941 46941 67425.8 51866

X5502 124377.5 124377.5 65848.7 81408.5

X5503 23777.5 23777.5 61821.5 61821.5

X5601 50593 62877 43222.6 62877

X5602 24568 12284 31938.4 12284

X5801 67761 67761 84046.3 84046.3

X5802 111280 111280 94994.7 80599.7

Table 2 (Continued)

umin

0.000.86 0.87 0.88 0.89

X5803 0 0 0 14395X6001 0 0 0 0

X6002 129049.7 111008.5 129049.7 99269

X6003 94733.6 94733.6 94733.6 94733.6

X6004 0 0 0 67512.9

X6005 107853.2 107853.2 89811.99 107853.2

X6006 130631.8 130631.8 130631.8 130631.8

X6007 130362.7 130362.7 130362.7 130362.7

X6008 141242.4 141242.4 141242.4 85468.99

X6009 87669.59 105710.8 105710.8 105710.8

X6010 98945.6 98945.6 98945.6 98945.6

X6011 0 0 0 0

X6012 0 0 0 0

X6013 114800.4 114800.4 114800.4 114800.4

X6601 56737.3 43338.5 68454.1 112680.1X6602 129738.7 124914.6 99799 55573

X6603 60743 78965.9 78965.9 78965.9

X6801 13166 17115.8 17115.8 13166

X6802 22317 18367.2 18367.2 22317

X6901 98814.3 83978.4 98814.3 98814.3

X6902 50339.9 50339.9 0 50339.9

X6903 42921.8 57757.7 57757.7 42921.8

X6904 0 0 35504 0

X7001 18289 18289 18289 23775.7

X7002 32962.8 32962.8 32962.8 25356

X7003 15213.2 15213.2 15213.2 17333.3

X7301 113474.4 110854.1 113474.4 110854.1

X7302 0 0 0 0

Theta 0.000.86 0.87 0.88 0.89

X7303 108706 108706 108706 108706

X7304 56299.6 58919.9 56299.6 58919.9

X7401 21401.4 21401.4 46125.3 35481

X7402 61011.6 61011.6 36287.7 46932

X7501 28295 36783.5 18162.5 28295

X7502 33775 25286.5 43907.5 33775

X7601 60838.7 60838.7 35457.5 46799

X7602 23765.3 23765.3 49146.5 37805

X7701 127302.5 114276.1 114276.1 114276.1

X7702 0 0 0 0

X7703 124075.5 137101.9 137101.9 137101.9

X7801 97211.4 97211.4 97211.4 97211.4

X7802 0 0 0 65019.5X7803 0 0 0 0

X7804 66391 66391 66391 0

X7805 125752.9 125752.9 125752.9 125752.9

X7806 75946 75946 75946 0

X7807 87426.09 112984.3 112984.3 112984.3

X7808 93745.6 93745.6 93745.6 93745.6

X7809 79905.8 79905.8 79905.8 79905.8

X7810 104845 104845 79286.79 93838.98

X7811 125511.1 125511.1 125511.1 125511.1

X7812 0 0 0 62765.3

X7813 90922 90922 90922 90922


10/16

the appropriate threshold selection, the demand shifts

are not monotonically consistent. For example, we see

that branch X5101 (first branch of the 51st county)

decreases in demand allocation when umingoes from

0.87 to 0.88, but increases when it goes to 0.89. This

and other results show that the solution can be very

sensitive and care must be taken to set an appropriate

and realistic efficiency threshold. Also, these sensi-

tivity results can be used by decision-makers for

closures (or maintaining locations). If under various

levels of minimum efficiency thresholds, somelocations are targeted for closure, this would help

prioritize these locations for closure. Managers should

also examine the solutions carefully since a small

change in the required threshold efficiency might lead

to a branch office closure causing difficulties for the

constituents in that county leading to political

repercussions. Consequently, the threshold values

must be chosen with care. This implication is true

for all the parameter sensitivities tested, and is a

reason for sensitivity analysis.

5.2.2. Varying the proportion of capacity that can

be reallocated (l)

In investigating the effects of varyinglvalues on

the solution, we have identified several significant

changes. These results are depicted inTable 3. Just as

in the earlier case,Table 3only shows counties with

multiple branches. It is evident that as the l value

increased from 0 to 1, the number of branch offices

open decreased from 168 to 110. Also, significant

differences exist with respect to capacity realloca-

tions. The important managerial implications are that

the state agency must identify the appropriate level of

l based on these results for possible consolidations

and reallocations. This value identification would befor current slack capacities associated with the branch

offices. While we have utilized the same l value for all

the branch offices, additional analysis can be

performed by varying these values by branch office

based on individual branch office volume flexibility

characteristics. Management can also consider assign-

ing higher l values for more efficient branches. This

analysis helps determine where additional investments

in branch capacity may be most appropriate. For

example, some of the locations may have larger

volume flexibilities and can handle the additional

demands, while others may not be able to. These

results can help the agency determine whether

additional capacity would allow for more or less

closures in a county. We can see for county 49 (X4901

and X4902), any additional percentage of reallocation

capacity, volume flexibility, over 50% (l > 0.5) would

not cause the solution to change for that county. Thus,

management may wish to increase the capacity of that

facility by only 50%. Yet, this result is not necessarily

true for all counties and branches, and the sensitivity

analysis shows this result.

5.2.3. Varying overall operating budget of the

state (B)

For budget levels of $47 million or higher (in $1

million increments), there was no change in the branch

offices selected and the resource reallocations. The

optimal solution resulted in 142 open branch offices

and the results are identical to that of umin= 0.8 in

Table 2. For budget values of less than $47 million the

problem was infeasible, or only non-integer solutions

were found, while maintaining the base case levels ofother parameters. This implies that other parameters

and policy constraints will need to be adjusted if the

budget falls below $47 million. A valuable managerial

implication with this result is that the budget from the

legislature (assuming the selected managerial para-

meter values are appropriate) should be a minimum of

$47 million.

Similar sensitivity analysis can be undertaken with

fmin, which represents the minimum threshold value

for average efficiency of open branches in a county.


Table 2 (Continued)

umin

0.000.86 0.87 0.88 0.89

X7814 143279.5 143279.5 143279.5 143279.5X7815 0 0 0 0

X7816 0 0 0 0

X7817 96622.5 96622.5 96622.5 96622.5

X7818 0 0 0 0

X7819 0 0 0 0

X7820 113466.6 113466.6 113466.6 113466.6

X7821 126454.9 116771.5 126454.9 126454.9

X7822 0 0 0 0

X7823 62452 62452 62452 62452

X7824 91657.8 91657.8 91657.8 91657.8

X7825 68790.8 52916 68790.8 68790.8


11/16


Table 3

Sensitivity analysis results on branch closures and capacity allocations for varying the proportion of capacity that can be reallocated ( l)

parameter

l

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

X1101 52526 57778.6 53235 67745.4 73536.4 75579 84041.6 89294.2 55175.4 48374.2 105052

X1102 68012 74813.2 81614.4 88415.6 74050.2 102018 93555.4 88302.8 122421.6 129222.8 72545

X1103 35623 21425.6 42747.6 0 0 0 0 0 0 0 0

X1104 21436 23579.6 0 21436 30010.4 0 0 0 0 0 0

X1301 78980 62849 78744.8 102674 110572 0 126368 115533.8 0 0 0

X1302 73386 80724.6 88063.2 58010.5 92353.4 108404 113922 124756.2 130067 123943.5 117820

X1303 61235 67358.5 73482 79605.5 0 91852.5 0 0 110223 116346.5 122470

X1304 26689 29357.9 0 0 37364.6 40033.5 0 0 0 0 0

X2401 51964 57160.4 62356.8 0 0 77946 0 0 0 0 0

X2402 67545 74299.5 81054 87808.5 94563 96615.5 108072 0 0 128335.5 135090

X2403 84894 54022 74961.2 110362.2 118851.6 0 0 123766.5 152809.2 0 169788

X2404 75402 82942.2 90482.4 98022.6 105562.8 113103 120643.2 128183.4 135723.6 143263.8 88736

X2405 43176 47493.6 0 56128.8 0 0 69081.6 21588 0 0 0

X2406 70633 77696.3 84759.6 41291.9 74636.59 105949.5 95817.2 120076.1 105081.2 122014.7 0

X3201 36490 40139 0 0 37287.4 36490 18245 0 0 0 0

X3202 106104 106104 127324.8 137935.2 148545.6 159156 169766.4 180376.8 190987.2 201597.6 212208

X3203 71573 60840.5 72675.2 54981.3 0 0 96990.59 0 0 0 0

X3204 70835 77918.5 85002 92085.5 99169 89356 0 104625.2 94014.8 83404.41 72794

X3701 66157 72772.7 49698.2 41468.8 92619.8 66157 33078.5 66157 33078.5 0 0

X3702 82294 75678.3 98752.8 106982.2 55831.2 82294 115372.5 82294 115372.5 148451 148451

X3801 40284 44312.4 48340.8 0 0 0 0 68482.8 72511.2 76539.59 52945

X3802 86620 82591.6 58707 97119.7 87191.59 77263.5 126904 0 153673.8 149645.4 173240

X3803 99281 99281 119137.2 129065.3 138993.4 148921.5 99281 157702.2 0 0 0

X4001 68835 75718.5 0 0 0 0 95529.2 0 0 0 0

X4002 129520 142450.5 155424 162571 181328 194280 207232 207994.3 0 0 259040

X4003 47613 0 47613 0 0 0 0 80942.09 47613 0 0X4004 138248 152072.8 165897.6 179722.4 193547.2 207372 221196.8 235021.6 248846.4 262671.2 0

X4005 139742 153716.2 155023.4 181664.6 149082.8 122306 0 0 227498.6 261286.8 264918

X4401 69544 69544 69544 69544 34772 69544 69544 69544 0 0 0

X4402 88227 88227 88227 88227 122999 88227 88227 88227 157771 157771 157771

X4701 134148 134148 157733 174392.4 187807.2 201222 214636.8 213516.1 241466.4 254881.2 268296

X4702 103314 113645.4 123976.8 134308.2 144639.6 154971 165302.4 175633.8 185965.2 167237.6 0

X4703 62759 69034.9 75310.8 0 0 0 0 0 0 0 0

X4704 136033 149636.3 163239.6 176842.9 139682.6 204049.5 161937.8 231256.1 244859.4 258462.7 212711

X4705 62579 64375.5 0 0 0 0 0 0 0 0 0

X4706 71559 78714.9 85870.8 93026.7 100182.6 79569.5 0 0 0 0 0

X4707 62790 69069 62790 80647.79 0 0 0 0 0 0 0

X4708 97030 106733 116436 126139 135842 145545 155248 164951 113066 0 194060

X4709 55145 0 0 0 77203 0 88232 0 0 104775.5 110290

X4901 45290 45290 54348 57157.5 57157.5 45290 69025 69025 69025 69025 69025X4902 23735 23735 14677 11867.5 11867.5 23735 0 0 0 0 0

X5101 41269 41269 41269 41269 41269 60234 60234 60234 60234 60234 60234

X5102 18965 18965 18965 18965 18965 0 0 0 0 0 0

X5501 51866 51866 62239.2 46941 0 0 0 0 0 0 0

X5502 95675 105242.5 95675 124377.5 133945 143512.5 153080 114252.5 171318.5 147541 171318.5

X5503 47555 37987.5 37181.8 23777.5 61151 51583.5 42016 80843.5 23777.5 47555 23777.5

X5601 50593 50593 50593 50593 62877 75161 75161 75161 75161 75161 75161

X5602 24568 24568 24568 24568 12284 0 0 0 0 0 0

X5801 64651 70486 76321 67761 59201 50641 0 0 0 0 0

X5802 85600 94160 102720 111280 119840 128400 136960 130098 150251 124340 164646

X5803 28790 14395 0 0 0 0 42081 48943 28790 54701 14395


12/16


Table 3 (Continued)

l

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

X6001 68258 75083.8 81909.6 0 0 0 0 0 0 0 0X6002 99269 109195.9 119122.8 129049.7 138976.6 148903.5 158830.4 168757.3 170056 188611.1 198538

X6003 72872 80159.2 87446.4 94733.6 0 0 0 0 0 0 0

X6004 51933 0 0 0 0 0 0 0 0 0 0

X6005 82964 91260.4 99556.8 107853.2 116149.6 124446 132742.4 0 149335.2 152826.4 0

X6006 100486 110534.6 120583.2 130631.8 140680.4 150729 160777.6 170826.2 180874.8 190923.4 200972

X6007 100279 110306.9 120334.8 130362.7 140390.6 150418.5 160446.4 170474.3 180502.2 190530.1 200558

X6008 108648 119512.8 111452.2 141242.4 108648 0 173836.8 184701.6 195566.4 0 217296

X6009 81316 89143.8 97579.2 87669.59 113842.4 121974 107362.6 138237.2 0 0 0

X6010 76112 83723.2 91334.4 98945.6 94298.99 114168 0 0 0 144612.8 0

X6011 41908 0 0 0 58671.2 0 0 52168.81 0 0 83816

X6012 62936 69229.6 0 0 0 92188 0 0 0 0 0

X6013 88308 97138.8 105969.6 114800.4 123631.2 132462 141292.8 150123.6 158954.4 167785.2 134109

X6601 86677 95344.7 104012.4 56737.3 107500.4 97520.5 0 143955.9 137881.6 131807.3 125733

X6602 99799 109778.9 99799 129738.7 139718.6 149698.5 150030.2 0 0 0 0X6603 60743 42095.4 43407.6 60743 0 0 97188.8 103263.1 109337.4 115411.7 121486

X6801 13166 14482.6 13166 13166 13166 13166 0 0 0 0 0

X6802 22317 21000.4 22317 22317 22317 22317 35483 35483 35483 35483 35483

X6901 76011 83612.1 75837.1 98814.3 91785.6 76063 120989.6 116546.7 122374.6 144420.9 152022

X6902 38723 42595.3 46467.6 50339.9 54212.2 0 0 0 69701.4 0 0

X6903 44429 44429 53314.8 42921.8 0 66643.5 71086.4 75529.3 0 0 0

X6904 32913 21439.6 16456.5 0 46078.2 49369.5 0 0 0 47655.1 40054

X7001 18289 20117.9 18289 18289 0 0 0 27671 32920.2 34749.1 36578

X7002 25356 27891.6 25356 32962.8 35498.4 38034 40569.6 0 0 0 0

X7003 22820 18455.5 22820 15213.2 30966.6 28431 25895.4 38794 33544.8 31715.9 29887

X7301 87288 96016.8 103437.2 113474.4 122203.2 85065.5 72171.2 136326 127964 119602 0

X7302 62249 68473.9 74698.8 0 0 0 0 0 0 0 111240

X7303 83620 83620 100344 108706 117068 125430 133792 142154 150516 158878 167240

X7304 45323 30369.3 0 56299.6 39208.8 67984.5 72516.8 0 0 0 0

X7401 35481 30787.8 26094.6 21401.4 17740.5 35481 35481 35481 0 0 0

X7402 46932 51625.2 56318.4 61011.6 64672.5 46932 46932 46932 82413 82413 82413

X7501 28295 28295 33954 28295 28295 14147.5 14147.5 45182.5 14147.5 0 0

X7502 33775 33775 28116 33775 33775 47922.5 47922.5 16887.5 47922.5 62070 62070

X7601 46799 51478.9 46799 60838.7 65518.6 46799 24116 65701.5 23399.5 84604 84604

X7602 37805 33125.1 37805 23765.3 19085.4 37805 60488 18902.5 61204.5 0 0

X7701 97925 107717.5 97925 127302.5 103729.8 146887.5 82637.2 145915 164996 0 0

X7702 47990 52789 57588 0 0 0 0 0 86382 91181 95980

X7703 105463 90871.5 95865 124075.5 147648.2 104490.5 168740.8 105463 0 160197 155398

X7801 74778 82255.8 89733.6 97211.4 104689.2 112167 119644.8 127122.6 134600.4 142078.2 149556

X7802 50015 55016.5 50015 0 0 0 0 0 0 0 0

X7803 49182 54100.2 0 0 0 0 0 0 0 0 0

X7804 51070 56177 61284 66391 0 0 0 0 0 0 0X7805 96733 106406.3 116079.6 125752.9 135426.2 145099.5 154772.8 164446.1 174119.4 183792.7 193466

X7806 58420 64262 70104 75946 81788 87630 0 0 0 0 0

X7807 86911 76354.7 95549.2 87426.09 105952.8 119815.5 111249.8 147748.7 156439.8 165130.9 173822

X7808 72112 79323.2 86534.4 93745.6 100956.8 108168 115379.2 122590.4 129801.6 137012.8 0

X7809 61466 67612.6 73759.2 79905.8 86052.4 0 0 0 0 0 120371

X7810 80650 88715 96780 104845 112910 120975 129040 137105 145170 153235 161300

X7811 96547 106201.7 115856.4 125511.1 135165.8 144820.5 154475.2 164129.9 118968 183439.3 193094

X7812 48281 53109.1 0 0 0 0 0 0 0 0 0

X7813 69940 76934 83928 90922 97916 104910 111904 118898 0 0 0

X7814 110215 121236.5 132258 143279.5 154301 165322.5 176344 187365.5 198387 209408.5 0

X7815 50195 0 60234 0 0 0 0 0 0 0 0


13/16

6. Channel management issues and managerial

implications

Profit and non-profit companies are increasingly

relying on multiple channels to make their services

available to their customers (Easingwood and Coelho,

2003). The pressure on organizations to offer services

in alternate channels and specifically via electronic

channel systems is escalating rapidly (Langdon and

Shaw, 2002). In a recent study conducted byDurkin

et al. (2003)in the area of service in retail banking, the

authors concluded that despite the increase in remote

banking (Web, phone, ATM, etc.), customers are

placing significantly greater emphasis on personaltransactions with bank employees. According to

Devraj et al. (2002), the factors that influence B2C

(business to customer) electronic channel satisfaction

and preference include perceived ease of use,

usefulness, and service quality. Thus, it is important

for organizations to educate their customers on the

tangible service benefits that they would receive if

they were to utilize alternative channels. This may

entail providing incentives and disincentives for

making customers switch to alternative channels in

meeting their service requirements.In the context of the agency operations, the model

proposed in this paper addresses the issue of service

location design of branch offices and the service

delivery system. The model identifies an optimal

solution for branch closures and resource reallocation.

This may mean that some customers in a specific

county may have to travel longer distances to transact

business in the open branch offices in the county. This

change can be expected to decrease customer

satisfaction. In order to overcome this effect on

customer convenience and satisfaction and divert

demand to other service channels, the state agency

needs to promote some of their alternative service

channels, which include web, phone, mail, and

facsimile. An investigation of the costs that agency

incurs per transaction by channel revealed consider-

able differences in unit transaction costs. Facsimile is

the most expensive and mail is the least expensive.

Phone, web, and branch office costs fell in a range

between facsimile and mail service channels with

branch office costs slightly higher than phone and web.

Based on this data, the agency can promote web and

phone channels by educating customers on their

advantages such as increased transaction efficiency interms of reduced processing times, quality of

transaction in the case of web, ease of use and

availability, and high degree of responsiveness. This

promotion needs to be made in conjunction with the

branch closures by offering incentives and disin-

centives to customers. Of course, consumers need to

incur the actual and service costs associated with web

and phone access.

It would be possible to undertake a simple Markov

chain approach for predicting the steady-state demand

for each of the channels based on the current demandand estimated transition probabilities from one service

channel to another service channel. The transition

probabilities would depend on policy alternatives that

the agency chooses to pursue. At present, the agency is

considering the following options: offering incentives

for using certain service channels (e.g. telephone

transactions), establishing disincentives (e.g., fax

transactions) or pursuing a combination of both.

The transition probabilities would depend on the

magnitude of the incentives and disincentives. The


Table 3 (Continued)

l

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

X7816 49586 54544.6 59503.2 0 0 0 0 0 0 0 0X7817 74325 81757.5 89190 96622.5 104055 111487.5 118920 47371.11 133785 141217.5 148650

X7818 39725 0 0 0 0 0 0 0 0 0 0

X7819 31596 34755.6 0 0 0 47394 50553.6 0 0 0 0

X7820 87282 96010.2 104738.4 113466.6 122194.8 130923 139651.2 148379.4 157107.6 150247.4 174564

X7821 97273 107000.3 116727.6 126454.9 136182.2 145909.5 155636.8 165364.1 175091.4 184818.7 194546

X7822 42617 0 0 0 0 0 0 0 0 0 0

X7823 48040 52844 0 62452 0 0 0 0 0 0 0

X7824 70506 77556.6 84607.2 91657.8 98708.4 105759 112809.6 119860.2 126910.8 0 141012

X7825 52916 58207.6 63499.2 68790.8 74082.4 0 0 0 0 0 0


14/16

agency is currently in the process of estimating these

transition probabilities through market survey and

experiments. While we could not undertake this

exercise due to lack of data, it is something that wouldbe highly beneficial for the agency because they can

plan and implement policy changes based on the

steady-state demands for each of the channels,

integrating thesefindings with additional MIP execu-

tions.

7. Conclusions and extensions

Service channel location design and optimization is

not only critical for private-for-profit organizations,

but also for non-profits and government agencies.

Governments are currently facing pressures where

consumers of government services are seeking

continued service, while these governments are under

increased budgetary pressures. There is significant

room and opportunity for operations models to be

applied to these public agencies such that these

pressures are mitigated with little investment. One

step in this direction is the development and

applications of these tools to show that they are

effective means for governmental and public organi-

zational decision-makers to help further manage theirservice delivery mechanisms.

The proposed DSS is expected to guide the

decisions and policy choices of executive management

in the agency. As future work, the steady-state

distribution of transactions across driver licenses,

registrations, personal identifications, etc., developed

from the Markov analysis would enable the executive

management of the agency to make near-optimal

resource deployment decisions (i.e., investments in

technology, transaction capacities of channels, etc.).

Service channel management-related decisions asdiscussed will influence and lead to a reconfiguration

of service delivery system of branch offices. As

conditions change, agency will be able to use the DSS

for optimizing service delivery encompassing such

objectives as fiscal resource optimization, customer

satisfaction, customer care and connectivity, and

highly efficient processes. Efforts are under way to

move the project in this direction.

We would also like to offer some perspectives

regarding the use of mathematical models and DSS in

optimizing service delivery location in governmental

operations. The authors were involved in this project

from the outset and had a unique opportunity to make

observations in the sense of a longitudinal study. Theexecutive management of the agency had to be

convinced about the usefulness of formal methods for

performance assessment of branch offices. One of the

authors spent a considerable amount of time

explaining the DEA methodology to the agency

executive management, illustrating its application in

non-profit organizations. Once the methodological

ideas were understood by management, they were

engaged in identifying a correct set of inputs and

outputs to be used in the DEA evaluations. In the

second stage of the project, in focus-group sessions

essential issues to be addressed by the MIP model

were identified. Next, the issues surrounding service

channel management were identified by the manage-

ment team. The researchers then evolved the

approach to be taken for the remainder of the

project. This sequence of events suggests the

following in implementing quantitative models to

improve service delivery:

It is incumbent on the research team to sell ideasfirst not methodology or specific solutions. It is

essential for managers to understand the conceptualideas of a methodology such as DEA, so that they

can participate effectively in performance assess-

ment.

It is necessary to sell the ideas and methodologynext to the operating level managers and super-

visors. Without their buy-in it would be difficult or

impossible to implement the decisions stemming

from the model.

Divide the longitudinal study into independentphases so that managers can develop a sense of

comfort with the results of each phase beforeproceeding to the next phase.

Optimizing service delivery locations in govern-ment operations is at once a service operations

problem as well as a political problem. The political

realities have to be handled with care to success-

fully secure the buy-in from management. For

example, our MIP model had to take into

consideration the statutory mandate that each

county must have a branch office regardless of

productivity and performance considerations.



15/16

Continue to involve the affected parties (i.e.,operating level supervisors and managers) as the

project involves and new methodologies are

introduced.

We were able to achieve considerable success in the

course of this project using these guidelines.

From a service operations research perspective, our

study shows the usefulness of adopting a DSS

framework as opposed to the use of prescriptive

models to address service channel management issues

in the government sector. The general validity of this

as a hypothesis can be empirically tested. The study

also suggests that a specific sequence of implementa-

tion steps should be followed to successfully imple-ment strategies leading to the optimal use of multiple

service channels. The approach of unfreezing,

changing and refreezing for change management

pertains to internal organizational change. However,

in service delivery systems customers are an integral

part of change management. What should be the

approach taken to this problem in service channel

management? What approaches might prove useful to

managers? This study has suggested the usefulness of

modeling this problem as a Markov chain. Can this be

useful in a for-profit context? This issue deserves

further investigation. Firms in industries such as

airlines and retailers face similar issues in introducing

technology-based initiatives. It would be useful to

undertake a comparative study of what techniques are

efficacious in inducing customers to switch among

channels of service delivery. It would also be useful to

study how the proposed DSS framework can be

adapted to for-profit organizations (e.g., banks and

retailers) in designing their service delivery network.

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2005 Efficient Service Location Design in Government Services a Decision Support System Framework

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