erp multicriteria decision making

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ORIGINAL PAPER

Strategic Enterprise Resource Planning in a Health-Care

System Using a Multicriteria Decision-Making Model

Chang Won Lee & N. K. Kwak

Received: 26 May 2009 /Accepted: 3 August 2009 /Published online: 10 September 2009# Springer Science + Business Media, LLC 2009

Abstract This paper deals with strategic enterprise re-

source planning (ERP) in a health-care system using amulticriteria decision-making (MCDM) model. The model

is developed and analyzed on the basis of the data obtained

from a leading patient-oriented provider of health-care

services in Korea. Goal criteria and priorities are identified

and established via the analytic hierarchy process (AHP).

Goal programming (GP) is utilized to derive satisfying

solutions for designing, evaluating, and implementing an

ERP. The model results are evaluated and sensitivity

analyses are conducted in an effort to enhance the model

applicability. The case study provides management with

valuable insights for planning and controlling health-care

activities and services.

Keywords Enterprise resource planning .

Health-care system . Multicriteria decision making

Introduction

In todays turbulent business environment, appropriate

enterprise resource planning (ERP) is required for more

efficient and strategic management decisions. The health-

care environment is no exception to this trend. Successful

ERP adoption planning and implementation may permit

decision-makers to overcome many of the challenges facedby health-care systems [1]. Such successful planning and

implementation can deliver unprecedented opportunities to

establish strategic ERP in health-care systems. Even though

significant differences exist between manufacturing and

health-care, ERP previously adopted and implemented for

manufacturing is attempted for the health-care setting [2, 3].

Due to technology and organizational paradigm shifts,

ERP in health-care settings may become more tightly

coupled with financing, manpower, capacity, revenue, and

admission resource functions. The successful linkages of

these complicated processes perform a critical function

affecting business performance in health-care settings [4, 5,

6]. A well-developed ERP in a health-care environment is a

growing requirement for improving both profitability and

productivity [7, 8, 9]. Although factors affecting business

performance in a health-care system have been widely

identified, monetary payoff and technical justifications are

overemphasized. Intangible attributes and operational ex-

cellence with customer intimacy should be considered in

the health-care ERP decision-making process.

When health-care management considers several

conflicting goals to be achieved, multicriteria decision-

making (MCDM) models enable effective results in the

strategic ERP process and other operational environments.

Subjective decision-making processes related to conflicting

health-care business problems with trade-off relationships

may produce the worst possible situation. Appropriate ERP

strategies must be established on a compromise-based and

objective decision-making process among diverse stake-

holders in the health-care system.

However, attempts to resolve such complicated and

multidimensional health-care managerial decision concerns

via an application of MCDM models have not been well

C. W. Lee

School of Business, Hanyang University,

Seoul 133-791, South Korea

e-mail: [email protected]

N. K. Kwak (*)

Department of Decision Sciences and ITM,

Saint Louis University,

St. Louis, MO 63108, USA

e-mail: [email protected]

J Med Syst (2011) 35:265275

DOI 10.1007/s10916-009-9362-x
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recognized as the best application for ERP in health-care

systems. In particular, the integrated approach of goal

programming (GP) and the analytic hierarchy process

(AHP) is rarely applied to the handling of an ERP adoption

process, considering admissions, capacity, financing, man-

power, and revenue planning as key ERP areas in a health-

care system.

The purposes of this study are as follows: (1) to developan integrated multicriteria decision-making model aimed at

designing, evaluating, and implementing a strategic ERP

for health-care planning, and (2) to provide strategic

managerial insights where the decision-making model can

successfully implement ERP process in health-care and

other similar settings.

The first section of this paper introduces current research

issues in both MCDM and strategic ERP process in a

health-care setting. The second section provides a review of

ERP and MCDM models. The third section describes the

background of the case study, along with the description of

data collected for the study. The fourth and fifth sectionspresents the application of the model to a real-world setting.

The sixth section presents analysis and discussion of the

model results and sensitivity analyses. The seventh section

provides the conclusions of the study.

Literature review

Enterprise resource planning

Enterprise resource planning (ERP) is defined as a business

philosophy to achieve effective business value creation and

enhance operational excellence with internal and external

customer intimacy via an integration of activities, processes

and functions. ERP is configured by a system that

integrates flows of information, materials, and monetary

transactions. ERP has evolved from material requirement

planning (MRP), followed by manufacturing resource

planning (MRP II). ERP has expanded to ERP II that

integrates supply chain management (SCM) and customer

relationship management. Recent ERP systems provide

management with tangible and intangible advantages and

strategic competitiveness, as well as new business values

via business process innovation [10, 11, 12].

ERP adoption strategy is identified as an extremely

complicated MCDM concern. It is complicated because

varied tangible and intangible attributes have to be

considered in the ERP adoption decision-making process

[13]. Since the ERP concern deals with practical applica-

tions, many researchers have applied diverse methodologies

to real-world ERP adoption situations.

Due to the paradigm differences between manufacturing

and health-care industries, a typical ERP used in manufac-

turing systems is not easily applicable to health-care

systems. Moreover, difficulty of clinical standardization

hinders the adoption of ERP for health-care business and

clinical system integration. However, ERP allows health-

care systems to integrate fully many business resource

activities and functions that are not necessarily connected

between decision processes and activities in clinical

resources. Recent ERP system in health-care settings ismore advanced to health/hospital information system

perspective. It is extended to integrate with customer

relationship management (CRM), supply chain manage-

ment (SCM), and clinical decision support system (CDSS).

ERP issues and applications are also treated in clinical

informatics [14], cultural issues [15], implementation [16,

17], and technology empowerment [18].

Multicriteria decision-making

The multicriteria decision-making (MCDM) model is

defined as a mathematical model of a decision process thatallows the decision-maker to assess a variety of competing

alternatives to achieve a set of goals. In MCDM, a decision-

maker must select the best overall decision among a

number of alternatives that are evaluated on the basis of

multiple criteria. Goal programming (GP) is one of the most

extensively utilized MCDM models [19, 20] . G P i s a

mathematical programming model which deals with multi-

ple conflicting and non-commensurate objective problems.

It is a mathematical model that establishes a specific

numeric goal for each of the objectives, formulating an

objective function along with goals, then seeking a solution

that minimizes the sum of the deviations of these goals.

Analytic hierarchy process (AHP) is a more generally

accepted remedy by which the priorities of preemptive

goals can be established. AHP utilizes hierarchical struc-

tures to represent a decision-making problem and then

develops priorities for the alternatives on the basis of

decision-makers judgments throughout the decision-

making process. The procedure requires the decision-

maker to judge the relative importance of each criterion

and specify a preference on each criterion for decision

alternatives based on pairwise comparisons for elements in

hierarchy using the pairwise comparison matrix. For

estimation of relative importance for the decision problems,

the decision-makers perform synthesization and compute

eigenvalues and eigenvectors that are used for measuring

consistency. The value of consistency in judgment is

determined by the smallest eigenvector. The result is that

the smaller the value of consistency, the smaller the value

of eigenvector. The value of the consistency ratio of 0.10 or

less is considered to be acceptable. AHP technique provides

a measure of consistency of comparisons by a consistency

ratio. The AHP results are a prioritized relative importance

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implying the overall preference for each decision alterna-

tive. (For a detailed description of the AHP technique

including eigenvalues and eigenvectors,1 see [21, 22].)

MCDM applications in health-care settings have spread

into various areas, including allocation of health resource

[23], business process reengineering [24], health policy

[25], medical assessment [26], medical decision [27],

regional resource [28], resource allocation [29], surgicalcase [30], and surgical waiting lines [31].

Problem statement

Data background

The Fatima Hospital for this study is a leading patient-

oriented health services provider in Korea. Its mission is to

support the institution by providing a financially sound

environment for health-care services. Its goal is to provide

high quality and cost-effective health services, whileenriching the organizations mission. The hospital has built

a new comprehensive building with intelligent functions.

The hospital system recently invested financial funds for

the construction of an ultra-modern building and estab-

lished another budget to adopt newly integrated ERP. The

hospital borrowed additional funds from a financial

institution.

As the dynamics of the demanding marketplace and the

requirements associated with competitive advantage have

changed, the need for strategic decision-making models for

ERP in the health-care system has been emphasized. The

health-care system has been faced with challenges in the

areas of financing, manpower, capacity, revenue, and

admission resource functions. Management wants to pro-

vide better services for patients in the health-care organi-

zation. Among 26 departments, 20 OB/GYN/pediatrics

departments, five surgery departments, and one internal

medicine department were selected for this study since they

are the most competitive areas in this organization.

Group decision-makers consisted of a chief of health

science center, chief information officer (CIO), and project

managers in the health-care system. A consulting firm also

participated in the overall review process. The associated

goals and criteria were created by the task force team

(TFT). Data templates relevant to the strategic ERP

proposal were derived. On the basis of the dataset, an

initial proposal of the ERP adoption was established. An

initially proposed ERP was re-evaluated in terms of

managerial and/or technical aspects of goals and criteria

establishments. It was validated and adopted by manage-

ment with minor modifications. Even though an ERP

system in the market and practice is currently consideredas a business information system itself, such as the one by

SAP or ORACLE, the real purpose of ERP is on effective

and efficient resource allocation. Ultimately, this will result

in improving patient safety and quality of care. Thus, this

study is focused on the context of effective and efficient

ERP resource allocation in health-care settings.

Model development

Goal prioritization

Establishing goal decomposition and prioritization is

completed for the MCDM model application in strategic

ERP. A synthesized priority is calculated for each goal in

order to obtain the overall relative importance of the five

goals using the AHP. Figure 1 shows individual criteria and

goals of the MCDM model. It presents the criteria to utilize

for prioritizing goals in this study. Four criteria are

considered for the strategic ERP in the health-care system.

Goals are listed in order of priority.

Financial resource goal

Table 1 presents an operational measurement matrix for the

financial resource goal (G1). This resource goal has the

following two sub-goals: (1) prepare a proper fund for

service expenditure and (2) supply an appropriate budget

for information facilities.

1 Note: Eigenvalues and eigenvectors are derived from the German word

eigen which means proper orcharacteristics. In matrix algebra, an

eigenvalue of a scalar matrix is a scalar that is usually represented by the

Greek letter (lambda). An eigenvector is a non-zero vector, commonly

denoted by the smaller letterx. All eigenvalues and eigenvectors satisfy

the equation Ax=x for a given square matrix A. Definition: Consider

the square matrix A. It is called that is an eigenvalue of A if there

exists a non-zero vector x such that Ax=x. In this case, x is called an

eigenvector (corresponding ), and the pair (, x) is called an eigenpair

for A.

Criteria Goals

FinancingResource

ManpowerResource

RevenueResource

CapacityResource

AdmissionResource

Cost

Quality

Flexibility

Delivery

ERP

Fig. 1 Individual criteria and goals for ERP

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Manpower resource goal

Manpower resource goal (G2) has two sub-goals to achieve:

(1) optimize the human resource utilization and (2) honor the

payroll increase agreement. Table 2 exhibits the salary levels

in various human resources types and total manpower level.

Revenue resource goal

Revenue resource goal (G3) has two sub-goals to achieve:

(1) limit the increase in total revenue and (2) achieve the

profitability requirement.

Capacity resource goal

Capacity resource goal (G4) is related to hospital utilization

in each departmental level as follows: (1) minimize the

under-achievement of the accommodation goal; (2) opti-

mize hospital utilization with three department levels; and

(3) optimize the hospital capacity for new patients. Table 3

presents the related capacity resources.

Admissions resource goal

Admission resource goal (G5) is also exhibited in Table 3.

There are three sub-goals to achieve in the admission

resource maximization as follows: (1) minimize the under-

achievement of resident goal; (2) minimize the over-

achievement of the admissions goal of non-resident

patients; and (3) attain the admissions goal of first-visit

patients. Characteristics of patients are divided by residency

status (resident in the city or non-resident in the city) and

visit type (first visits or revisits). Identifying these charac-

teristics is very important to estimate the potential profit-

ability of the hospital system. Three major divisions have

an annual admissions goal of 15,000 patients per month.

For example, 70% of 15,000 patients are expected as first

visit residents. This estimation is important for planning

utilization of the system.

Normalized eigenvectors

Table 4 illustrates the relative importance with normalized

eigenvectors with respect to each criterion that the task

force team developed. It also illustrates the final prioritiza-

tion for goals of health-care strategic ERP using the AHP.

This table presents the relative priority (RP) and the order

of prioritization. Decision-makers have justified the syn-

thesized prioritization of the overall goals for the strategic

ERP in the health-care system under consideration. Syn-

thesized detail results by AHP are provided in the

Appendix. The output of Appendix provides the decision-maker with a prioritized ranking indicating the overall

preference for each of the decision alternatives. It enables

the decision-maker to handle problems in which the

subjective judgment of individual decision-maker consti-

tutes an important role of the decision-making process.

Based on the above data, the goal priorities and the

relevant information on ERP are established as follows:

priority 1 (P1)financial goal (G1), priority 2 (P2)

manpower goal (G2), priority 3 (P3)revenue goal (G3),

priority 4 (P4)capacity goal (G4), and priority 5 (P5)

admissions goal (G5). In general, MCDM models for

health-care management are limited to addressing financial

goals, rather than other strategic policies of an organization.

In this paper, an MCDM model is formulated based on the

following information.

MCDM problem formulation

MCDM is appropriate for situations in which the decision-

maker must consider multiple criteria in arriving at the best

overall decision. The MCDM model can be expressed in

the following generalized form for preemptive goal

programming:

Minimize: Z XK

k1

Xm

i1

wi Pk di d

i

subject to:Xm

i1

aijXj di d

i bi; j 1; 2; . . . ; n

Xj; di ; d

i ! 0; i 1; 2; . . . ; m; j 1; 2; . . . ; n

Table 2 Human resources types and salary levels

Human resources type Base salary level Total manpower

Physician group 4,053 37

Nurse group 11,645 166

Senior technician 914 10

Technician 1,460 39

Line management 1,313 53

Senior management 1,082 13

Total 20,467 318

Table 1 Financial measurement matrix

Operational matrix Values ($000)

Total service revenue 31,124

Total service expenditure 30,252

Information facilities budget 2,088

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where:

Z the sum of the weighted deviational variables,

w a weight assigned to a priority,

Pk a preemptive priority for the kth P of m goals (k=

1, 2,, K),

di ; di negative and positive deviational variablesdescribing under- and over-achievement of the ith

goal,

aij technical coefficients for the decision variable Xj,

bi the right-hand-side (RHS) value for the ith goal

constraint.

Decision variables

There are four different types of decision variables

embracing 28 decision variables in this study. They are:

XAi numbers of admissions in patient group i (i=1, 2,, 12),

XBi financing resource levels for services expenditure (i=1)

and for information facilities (i=2),

XHi human resource levels in different types of work

(i=1, 2,, 6),

XRi amounts of health services revenue in health services

type i (i=1 and 2),

XSi salarylevel based on health services type i (i=1, 2,, 6),

where XAi , XBi , X

Ri , X

Ri , and X

Si ! 0.

Constraints

The MCDM model in this study has 12 system constraints

and 24 goal constraints. Since the system constraints do nothave deviational variables, these variables will not appear

in the objective function.

System constraints System constraints (112): The number

of various group patients cannot exceed the maximum level

of accommodation in each patient category (see Table 3).

That is:

XA1 bi; i 1; 2; . . . ; 12 112

Thus, XA1 1; 800; XA2 900; X

A3 850; X

A4 5; 700;

XA

5

1; 900; XA6

2; 100; XA7

1; 500; XA8

400;XA9 550; X

A10 2; 500; X

A11 800; and X

A12 1; 200.

Goal constraints

Priority 1. (P1): Financial resource goal (G1) has two

sub-goals.

Sub-goal 1: Prepare proper budgets for service expen-

diture considering economic trends. This

Table 4 Relative importance (normalized eigenvectors)

COST QUAL FLEX DELI RP Rank

Financing (G1) 0.254 0.398 0.280 0.235 0.300 1

Manpower (G2) 0.183 0.241 0.362 0.159 0.269 2

Capacity (G4) 0.278 0.100 0.162 0.117 0.151 4

Revenue (G3) 0.209 0.171 0.088 0.368 0.178 3

Admissions (G5) 0.076 0.090 0.108 0.121 0.102 5

CRP 0.121 0.269 0.417 0.193 1.000

COSTcost criteria, QUAL quality criteria, FLEX flexibility criteria, DELI delivery criteria, RP relativity priority, CRP criteria relative priority

Table 3 Maximum number of monthly admissions and its capacity

Patient type OB/GYN/pediatrics Surgery Internal medicine Total demand Patient ratioa

FVRP 1,800 900 850 3,550 0.7

RVRP 5,700 1,900 2,100 9,700 0.7

FNRP 1,500 400 550 2,450 0.4

RNRP 2,500 800 1,200 4,500 0.5

20,200

Total capacity 9,000 3,500 4,000 16,500

FVRP first-visit residential patient, RVRP revisit residential patient, FNRP first-visit non-residential patient, RNRP revisit non-residential patientaEach ratio is independent probabilities

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right-hand-side (RHS) value is an in-

creased amount over total service expen-

ditures of 30,252 (see Table 1).

XB1 d1 d

1 2; 520 13

Sub-goal 2: Supply an appropriate budget for infor-mation facilities (see Table 1).

XB2 d2 d

2 2; 088 14

Priority 2. (P2): Manpower resource goal (G2) has two

sub-goals.

Sub-goal 1: Meet the effective utilization of the

required human resource level (see

Table 2).

XH

1

d

3

d

3

37 15

XH2 d4 d

4 166 16

XH3 d5 d

5 10 17

XH4 d6 d

6 39 18

X

H

5 d

7 d

7 53 19

XH6 d8 d

8 13 20

Sub-goal 2: Achieve the payroll increase agreement

by certain percentage points required

from the current salary level (see Table 2).

That is, the RHS values are the sum of

the current salary amount plus the salary

increase proportion.

XS1d9 d9 4; 053 21

XS2d10 d

10 11; 645 22

XS3d11 d

11 914 23

XS4d12 d

12 1; 460 24

XS5d13 d

13 1; 313 25

XS6d14 d

14 1; 082 26

Priority 3. (P3): Revenue resource goal (G3) has two

sub-goals.

Sub-goal 1: Achieve total revenue increase from the

current level in terms of profitability and

sustainability in the health-care system.

The RHS value is an increased amount

over total service revenue amounts of

31,124 (see Table 1).

XR1 d15 d

15 2; 860 27

Sub-goal 2: Achieve the increased profitability level of

340. This amount is the difference betweenthe expected increase in revenue (2,860) and

the expected increase in expenditure

(2,520).

XR2 d16 d

16 340 28

Priority 4. (P4): Capacity resource goal (G4) has three

sub-goals.

Sub-goal 1: Meet the current capacity of 16,500 (see

Table 3).

0:7XA1 0:7XA2 0:7X

A3 0:8X

A4 0:8X

A5

0:8XA6 0:4XA7 0:4X

A8 0:4X

A9

0:5XA100:5XA110:5X

A12d

17

d17 16; 500

29

Sub-goal 2: Meet the hospital resource utilization

capacity to handle a total capacity of

9,000 patients in OB/GYN/Pediatrics,

3,500 in surgery, and 4,000 in internalmedicine (see Table 3).

XA1 XA4 X

A7 X

A10d

18

d18 9; 000

30

XA2 XA5 X

A8 X

A11d

19

d19 3; 500

31

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XA3 XA6 X

A9 X

A12d

20

d20 4; 000

32

Sub-goal 3: Meet the hospital admission capacity goal

of 15,000 expected new patients in three

divisions.

XA1 XA2 X

A3 X

A4 X

A5 X

A6 X

A7 X

A8

XA9 XA10X

A11X

A12d

21 d

21

15; 000

33

Priority 5. (P5): Admissions resource goal (G5) has

three sub-goals.

Sub-goal 1: Minimize the under-achievement of the

goal of 70% admission for residential

patients (FVRP and RVRP) in totalvisits.

0:3XA1 0:3XA2 0:3X

A3 0:3X

A4 0:3X

A5

0:3XA6 0:7XA7 0:7X

A8 0:7X

A9

0:7XA10 0:7XA11 0:7X

A12d

22 0

34

Sub-goal 2: Minimize the over-achievement of the

30% admission goal for non-residential

patients (FNRP and RNRP) in totalvisits.

0:3XA1 0:3XA2 0:3X

A3 0:3X

A4

0:3XA5 0:3XA6 0:7X

A7 0:7X

A8

0:7XA9 0:7XA100:7X

A110:7X

A12

d23 0

35

Sub-goal 3: Meet the 60% goal for revisit patients

(RVRP and RNRP) in total visits.

0:6XA1 0:6XA2 0:6X

A3 0:4X

A4

0:4XA5 0:4XA6 0:6X

A7 0:6X

A8

0:6XA9 0:4XA100:4X

A110:4X

A12

d24 d24 0

36

Objective function

Minimize : Z P1X2

i1

di di

P2

X14

i3

di di

P3X16

i15

di di

P4

X21

i17

di di

P5 d22d

23d

24d

24

Therefore, the integrated MCDM model for strategic

ERP in the health-care system is to minimize the value of

the objective function subject to goal constraints 13, 14, 15,

16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,

32, 33, 34, 35, and 36, satisfying the preemptive priority

rules.

Table 5 Analysis of decision variables and its solutions

Decision variables Solution values

XA1 1,800

XA2 900

XA3 850

XA4 5,700

XA5 1,900

XA6 2,100

XA7 1,500

XA8 400

XA9 550

XA10 2,500

XA11 800

XA12 1,200

XB1 2,520

XB2 2,088

XH1 37

XH2 166

XH3 10

XH4 39

XH

553

XH6 13

XS1 4,053

XS2 11,645

XS3 914

XS4 1,460

XS5 1,313

XS6 1,082

XR1 2,860

XR2 340

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Analyses and discussion

Model results

The proposed model is solved by optimization-basedsoftware packages, AB:QM [32] and Management Scientist

[33] with minor modifications (e.g., salary amounts,

capacity, and admissions demand numbers are adjusted for

the software format) to satisfy software requirements. The

solution is derived after 43 iterations. The possible solutions

are enumerated at the first goal priority level and reduced at

each subsequent goal priority level until overall goal

satisfaction is no longer achieved. Tables 5 and 6 illustrate

the computer solution of the model results.

The financial planning goal (G1) is the most important

goal in this MCDM model for strategic ERP in the health-

care system. Priority 1 (P1) has two sub-goals: prepare

appropriate funds for both service expenditure (d1 and d1 )

and information facilities d2 and d2 ). This priority is fully

satisfied, since P1=0. All related deviational variables are

zero (d1 , d1 , d

2 , and d

2 0).

Priority 2 (P2) with manpower planning goal (G2) has

two sub-goals: manpower utilization and payroll increase

agreement. This goal is fully satisfied, since P2=0. All

positive and negative deviational variables d3 ; d3 ; d

4 ;

d4 ; . . . ; d14; d

14 are zero.

Priority 3 (P3) is the revenue planning goal (G3) with

two sub-goals of total revenue increase rate and profitability

fulfillment. This priority is fully satisfied, since P3=0. The

related deviational variables (d15, d15, d

16, and d

16) are

zero.

Priority 4 (P4) is the capacity planning goal (G4) with

three sub-goals: the accommodation level, hospital utiliza-

tion, and hospital admissions level. This priority is not fullysatisfied, since P4=11,925. The related deviational varia-

bles are not zero (d17 3; 025, d18 2; 500, d

19 500,

d20 700, and d21 5; 200).

Priority 5 (P5) is on admissions for residential patients

d22

, admissions for non-residential patients d23

, and

admissions for revisit patients (d24 and d24). This priority

is not fully satisfied, since P5=3,860. The related devia-

tional variables are not zero (d22 890, d23 890, and

d24 2; 080).

Sensitivity analysis

Sensitivity analysis is an evaluation method that is used

once a satisfying solution has been found. This analysis

provides management with potential alternatives based on

how the acceptable result is affected by changes in the input

data. Two aspects are highlighted in this study: (1) analysis

of the goal conflict and (2) changes in priority level. This

analysis can be utilized to resolve complicated problems at

less cost. The elements in this problem are approximated at

best, which makes it necessary to evaluate more than one

business scenario.

Table 7 illustrates goal conflicts among the selected goal

priorities. Priorities P4 and P5 are relatively not sensitive

due to the ranges of 890 and 2,967 in d 22 and 2,080 and

3,467 in d24. Thus management may not need to be

concerned about a goal conflict between two goals in this

study situation.

Table 8 shows the change in the objective function. In

the original goal settings, each sub-goal is equally impor-

tant within a certain priority level. For the sensitivity

analysis perspective, the strategic task force team agrees to

make different priorities to each sub-goal. Based on the

Table 6 Analysis of the objective function for strategic ERP

Goal

priority

Output

values

Goal

achievement

Deviational

variablesa

P1 0 Fully achieved d17 3; 025

P2 0 Fully achieved d18 2; 500

P3 0 Fully achieved d19 500

P4 11,925 Not achieved d

20 700P5 3,860 Not achieved d

21 5; 200

d22 890

d23 890

d24 2; 080

aAll other deviational variables are zeros

Table 7 Analysis of goal conflicts

Goal conflicts Related variables Allowable increase Allowable decrease Marginal substitution rate

P4 vs. P5 d22 vs: X

A1 890.00 2,966.66 (0.2, 1.2)

P4 vs. P5 d22 vs: X

A2 890.00 2,966.66 (0.2, 1.2)

P4 vs. P5 d22 vs: X

A3 890.00 2,966.66 (0.2, 1.2)

P4 vs. P5 d24 vs: X

A4 2,080.00 3,466.66 (1.2, 1.3)

P4 vs. P5 d24 vs: X

A5 2,080.00 3,466.66 (1.2, 1.3)

P4 vs. P5 d24 vs: X

A6 2,080.00 3,466.66 (1.2, 1.3)

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new scenario, goals have a total of 12 priorities. Among the12 priorities, the first six goals are fully achieved, since P1to P6 are all zeros.

In this decision-making model, a non-dominated solution

has been sought. A non-dominated solution is defined in the

following manner: a feasible solution to a multicriteria

decision-making problem is non-inferior, if no other feasible

solutions derive an improvement in one objective without

creating a trade-off in another objective. Regardless of the

weighting structures and the goals, this model can lead to

inferior, sub-optimal solutions. These solutions are not

necessarily the optimal ones available to the decision-maker.

Opportunity costs are given as well as the increases and

decreases in the values of the coefficients and the right-hand-

side elements. Management can determine in advance what

will happen if the outcome deviates from the overall

objectives. In this case study, management can use the

information from the solutions to alter their decision variables

as any plan can come up with the new satisfying solution.

The MCDM model for this case study gives decision-

makers the ability to improve business performance and

productivity through appropriate decision-making techni-

ques. More appropriate MCDM models can be established

by decision-making groups with diverse organizational

views in decision-making processes. With this perspective,

the MCDM model can be one of the most promising

options, increasing core business competition in the new

market environment. The MCDM model in a health-care

system is presented and analyzed to aid total ERP scheme.

The health-care system in this study considers the proposed

planning as a potential business strategy.

Since patient safety and quality of care are primary

indicators in health-care settings, patient care indicators and

management indicators are usually considered as a trade-off

relationship. Thus, most health-care managements give the

patient care indicators preemptive goals, placing less

emphasis on effective and efficient resource allocation.

However, proper management of admissions, capacity,

financing, manpower, and revenue will enhance the level

of patient satisfactionthe most important key perfor-

mance indicator of the todays health-care setting.

Concluding remarks

Health-care business environments are rapidly changing,

and increasingly involve global, multifarious, and complex

decision-making problems. The emerging health-care envi-

ronment in Korea provides new business markets to

management. The recent application of the MCDM model

in health-care organizations and other managerial areas take

advantage of opportunities to establish a strategic plan and

to take action in real-world settings.

The MCDM model, in particular the combination of GPand AHP, may certainly represent one of the most useful

planning tools in aiding healt h-care decision-making

processes for enterprise resource planning (ERP) adoption

strategy under multiple-criteria decision-making situations.

The reason for this is that satisfying behavior makes sense

when an organization can pursue sufficiently satisfying

profits to overcome potential competition to stave off

possible regulations, or to thwart pressures from the

demands for higher wages. Thus, the satisfying principle

is substantially meaningful in analyzing competitive and

other environmental situations faced by the system.

The principal contributions of this case study in academia

and in practice are as follows: (1) the MCDM model improves

a practical way for strategic ERP adoption decision, consider-

ing both financial and non-financial business factors, (2) health-

care ERP planning studies enhance long-term organization-

wide issues including admissions, capacity, financing, and

manpower resources, which can be applied in a limited fashion

to the previous studies, and (3) the study proposes an integrated

MCDM model that most previous ERP studies in health-care

settings have not explored. However, some limitations should

be realized. The commercialized software employed in this

case study may not always fit into all health-care business

settings. Some of the data employed in this study had been

slightly modified to meet a software system requirement, even

though the modified data did not degrade the overall solution.

The decision-making groups in the health-care organiza-

tion accepted the final results as both valid and feasible for the

implementation of strategic resource planning in their real-

world situation. The health-care organization embarked on its

strategic ERP adoption plan with its ongoing base. The effects

and outcomes from this model will be scrutinized over the

next two or three fiscal years. The future ERP agenda will be

Table 8 Analysis of effect by changes in objective function for

strategic ERP

Revised priority Goal achievement

P1=0 Fully achieved

P2=0 Fully achieved

P3=0 Fully achieved

P4=0 Fully achievedP5=0 Fully achieved

P6=0 Fully achieved

P7=12,095 Partially achieved



P10=890 Partially achieved

P11=890 Partially achieved


J Med Syst (2011) 35:265275 273


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reassessed in order to compare it with the proposed ERP

decision-making model. The strategic plan predicated on the

proposed MCDM model will provide the management with

significant insights by which appropriate process planning

will be established. It will also enhance the level of patient

satisfaction and other stake-holders needs (considering

patient safety and quality of care), leveraging up competitive

advantages of this health-care organization. Thus, the organi-

zation currently reviews all these proposals as valid alternative

strategies.

Appendix

References

1. van Merode, G., Groothuis, S., and Hasman, A., Enterprise resource

planning for hospitals. Int. J. Med. Inf . 73(6):493501, 2004.

2. Grimson, J., Delivering the electronic healthcare record for the

21st century. Int. J. Med. Inf . 64(23):111127, 2001.

3. Pedersen, M. K., and Larsen, M. H., Distributed knowledge

management based on product state models: The case of decision

support in health care administration. Decis. Support Syst. 31

(1):139158, 2001.

4. Collen, M. F., A vision of health care and information in 2008. J.

Am. Med. Inf. Assoc. 6(1):15, 1999.

5. Li, L. X., Benton, W. C., and Leong, G. K., The impact of

strategic operations management decisions on community hospital

performance. J. Oper. Manag. 20(4):389408, 2002.

6. Oddoye, J. P., Jones, D. F., Tamiz, M., and Schmidt, P.,

Combining simulation and goal programming for healthcare

Table 9 Excel-based AHP results of criteria and resources in ERP strategy

FIN MAN CAP REV ADM RP CRP max CR

COST 0.121 5.068 0.015

FIN 1 2 1/2 2 3 0.254

MAN 1/2 1 1/2 2 2 0.183

CAP 2 2 1 1/2 4 0.278

REV 1/2 1/2 2 1 2 0.209

ADM 1/3 1/2 1/4 1/2 1 0.076

QUAL 0.269 5.056 0.012

FIN 1 2 4 2 4 0.399

MAN 1/2 1 2 2 3 0.241

CAP 1/4 1/2 1 1/2 1 0.100

REV 1/2 1/2 2 1 2 0.171

ADM 1/4 1/3 1 1/2 1 0.090

FLEX 0.417 5.161 0.036

FIN 1 1/2 2 4 3 0.280

MAN 2 1 2 3 2 0.362

CAP 1/2 1/2 1 2 2 0.162

REV 1/4 1/3 1/2 1 1 0.088

ADM 1/3 1/2 1/2 1 1 0.108DELI 0.193 5.067 0.015

FIN 1 2 2 1/3 1/3 0.235

MAN 1/2 1 2 1/3 1 0.159

CAP 1/2 1/2 1 1/5 2 0.117

REV 3 3 5 1 2 0.369

ADM 3 1 1/2 1/2 1 0.121

CR 0.300 0.269 0.151 0.179 0.102

Rank 1 2 4 3 5

FIN financing resource, COSTcost criteria, MAN manpower resource, QUAL quality criteria, CAP capability resource, FLEX flexibility criteria,

REV revenue resource, DELI delivery criteria, ADM admission resource, RP relative priority, CRP criteria relative priority, max

the smallest

eigenvector of a matrix A, CR consistency ratio

274 J Med Syst (2011) 35:265275


11/12

planning in a medical assessment unit. Eur. J. Oper. Res. 193

(1):250261, 2009.

7. Kursters, R. J., and Groot, P. M. A., Modeling resource

availability in general hospitals design and implementation of a

decision support model. Eur. J. Oper. Res. 88(3):428445, 1996.

8. Jenkins, E. K., and Christenson, E., ERP (enterprise resource

planning) systems can streamline healthcare business functions.

Healthc. Financ. Manage. 55(5):4852, 2001.

9. Newman, K., Towards a new health care paradigm, patient-

focused care: The case of Kingston hospital trust. J. Manage.Med. 11(56):357371, 1997.

10. Davenport, T., Putting enterprise into the enterprise system.

Harvard Bus. Rev. 76(4):121131, 1998.

11. Klaus, H., Rosemann, M., and Gable, G. G., What is ERP? Inf.

Syst. Front. 2(2):141162, 2000.

12. Motwani, J., Mirchandani, D., Madan, M., and Gunasekaran, A.,

Successful implementation of ERP projects: Evidence from two

case studies. Int. J. Prod. Econ. 75 (12):8396, 2002.

13. Teltumbde, A., A framework for evaluating ERP projects. Int. J.

Prod. Res. 38(17):45074520, 2000.

14. Siau, K., Health care informatics. IEEE Trans. Inf. Technol.

Biomed. 7(1):17, 2003.

15. Soh, C., Kien, S. S., and Tay-Yap, J., Enterprise resource

planning: Cultural fits and misfits: Is ERP a universal solution?

Commun. ACM. 43(4):4751, 2000.16. Botta-Genoulaz,V.,and Millet, P.-A., An investigationinto the use of

ERP systems in the service sector. Int. J. Prod Econ. 99(12):202

221, 2006.

17. Trimmer, K. J., Pumphrey, L. D., and Wiggins, C., ERP implemen-

tation in rural health care. J. Manage. Med. 16(2/3):113132, 2002.

18. Sia, S. K., Tang, M., Soh, C., and Boh, W. F., Enterprise resource

planning (ERP) systems as a technology of power: Empowerment or

panoptic control? Database Adv. Inform. Syst. 33(1):2337, 2002.

19. Charnes, A., and Cooper, W. W., Management models and the

industrial applications of linear programming, vols. 1-2. Wiley,

New York, 1961.

20. Onut, S., Kara, S. S., and Isik, E., Long term supplier selection using

a combined fuzzy MCDM approach: A case study for a telecommu-

nication company. Expert Syst. Appl. 36(2):38873895, 2009.

21. Saaty, T. L., The analytical hierarchy process. McGraw-Hill, New

York, 1980.

22. Saaty, T. L., Decision making for leaders: The analytical hierarchy

process for decisions in a complex world, 3rd edition. RWS,

Pittsburgh, 1999.

23. Earnshaw, S. R., and Dennett, S. L., Integer/linear mathematical

programming models: A tool for allocating healthcare resources.

PharmacoEconomics. 21(12):839851, 2003.

24. Kwak, N. K., and Lee, C. W., Business process reengineering for

health-care system using multicriteria mathematical programming.Eur. J. Oper. Res. 140(2):447458, 2002.

25. Epstein, D. M., Chalabi, Z., Claxton, K., and Sculpher, M.,

Efficiency, equity, and budgetary policies: Informing decisions

using mathematical programming. Med. Decis. Mak. 27(2):128

137, 2007.

26. Oddoye, J. P., Yaghoobi, M. A., Tamiz, M., Jones, D. F., and

Schmidt, P., A multi-objective model to determine efficient

resource levels in a medical assessment unit. J. Oper. Res. Soc.

57(10):11731179, 2006.

27. Liberatore, M. J., and Nydick, R. L., The analytic hierarchy

process in medical and health care decision making: A literature

review. Eur. J. Oper. Res. 189(1):194207, 2008.

28. Wilson, R. M., and Gibberd, R. W., Combining multiple criteria

for regional resource allocation in health care systems. Math.

Comput. Model. 13(8):1527, 1990.29. Flessa, S., Priorities and allocation of health care resources in

developing countries: A case-study from the Mtwara region-

Tanzana. Eur. J. Oper. Res. 150(1):6780, 2003.

30. Cardoen, B., Demeulemeester, E., and Belien, J., Sequencing

surgical cases in a day-care environment: An exact branch-

and-price approach. Comput. Oper. Res. 36(9):26602669,

2009.

31. Arenas, M., Bilbao, A., Caballero, R., Gmez, T., Rodrguez, M.

V., and Ruiz, F., Analysis via goal programming of the minimum

achievable stay in surgical waiting lists. J. Oper. Res. Soc. 53

(4):387396, 2002.

32. Lee, S. M., AB:QM. Allyn & Bacon, Boston, 1996.

33. Anderson, D. R., Sweeney, D. J., and Williams, T. A., The

management scientist V 6.0. West Publication, St. Paul, 2004.

J Med Syst (2011) 35:265275 275


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