journal of strategy & perspective strategic performance...

43 Journal of Strategy and Performance Management, Volume 2, Issue 2, April 2014.

Muhammad Junaid Iqbal Center for Advanced Studies in Engineering, Islamabad

[email protected]

Problem Statement: In today's competitive atmosphere, firms need to assess and optimize their performances in diverse aspects. Strategic management of businesses is critical for the existence in the global market places. BSC is an important strategic management tool and is very effective multi-attribute evaluation framework for firms. The objective of this study is to construct a fuzzy based multiple criteria decision making (MCDM) model for balancing perspectives in BSC. In complex scenarios there are many strategic objectives that cannot be crisply assigned to one perspective. The proposed fuzzy model will balance the multi and cross perspective strategic objectives effectively and with a greater degree of success by determining the comprehensive set of evaluation criteria on the concept of BSC.

Methodology: The primary source is based upon a questionnaire and it is used to ask questions from balanced scorecard experts and their opinions are used to make comparisons matrices. A panel of 7 experts belonging to various sectors like manufacturing & services gave their expert opinions.

Results: Based upon the framework, the multi perspective strategic objectives are successfully balanced however; a more refined approach is needed for balancing cross perspective strategic objectives in BSC.

Originality/Value: This study will contribute in the following way: 1) it will provide the practitioners with a fuzzy way to approach the problem of balancing & addressing imprecision. 2) It will allow the managers to prioritize their resources/efforts/focus on the areas of interest in a better way when dealing with multi & cross perspective strategic objectives.

Keywords- multi criteria decision making, Fuzzy set theory, balanced scorecard, multi & cross perspective strategic objectives

INTRODUCTION

Balanced scorecard is considered as an important strategic management tool. Many organizations have successfully implemented it and have considered it to be an effective methodology if applied correctly. Multi criteria decision making is another important area of operation research and deals with intricate and complex engineering problems. Such problems bring very different challenges like; they are difficult to model using conventional mathematical techniques. Fuzzy logic provides a solution as it can mimic human reasoning. Using fuzzy set theory, one can model these problems and can determine the solutions in the form of linguistic terms that are subjective in nature. Most of the work regarding the BSC has been with respect to its application to a certain industry type. However, very little or no work has been found that particularly targets and discusses the objectives interaction with each other in BSC specifically in fuzzy sense. Multi perspective objectives are defined as those objectives that

Citation: Iqbal, M. J. (2014). Balancing Multi & Cross Perspective StrategicObjectives in a Balanced Scorecard - A Fuzzy MCDM approach, Journal of Strategy and Performance Management, 2(2), 43-66.

Balancing Multi & Cross Perspective Strategic Objectives in a Balanced Scorecard - A Fuzzy MCDM approach.

JOURNAL OF STRATEGY & PERFORMANCE MANAGEMENT

April 2014. Volume 2, Issue 2, 43-66. Article Type: Original Research

Journal of Strategy and Performance Management, Volume 2, Issue 2, April 2014. 44

exist in two or more perspectives simultaneously. Cross perspective objectives are defined as those objectives that can only exist in single perspective at an instant of time however they can move from one perspective to another owning to changes occurring in the criteria’s.

LITERATURE REVIEW

Decision scenarios may involve a large number of goals or criteria’s which may be unequal and in clash with each other (Weistroffer & Narula, 1989)]. An individual or group of people’s course of action in an unpredictable environment determines the outcomes. In complex scenarios where the availability of information is limited, vague, and inaccurate; will make it hard for the decision makers to take accurate decisions. Since a lot is at stake so one must ponder deeply and utilize all scientific approaches at his disposal to ensure that the path which his organization is going to take will bear fruit. Decision making depends on data or information so the quality of information is important indeed.

In decision scenarios the quality of information can significantly fluctuate from the scientifically reliable data to logical inferences and subjective interpretations; from accurate to uncertain outcomes characterized by probabilistic and fuzzy model. This variation in type and quality concerning a decision dilemma demands for distinct and exclusive methods that can support in information processing. Eventually, such approaches like MCDM may guide to an improved answer (Keeney & Raiffa, 1993).

MCDM helps in making preferred choices like evaluation, selection and prioritization over the given set of options and alternatives that are characterized by several conflicting criteria's (Hwang & Yoon, 1981). MCDM aims at identifying these conflicts, compares and evaluates these alternatives as per the diverse and unequal criteria’s; provides an approach to go to a best trade off result in a transparent process (Chen et al., 2009).

In MCDM, measurements are derived and interpreted subjectively as to indicate the strength of various preferences. Preferences differ from one decision maker to another, and final outcomes will depends upon the decision taking authority and his selection of goals and decision criteria’s (Saaty, 1980).

Subjectiveness is an important constituent of MCDM. A question may arise as to ask the degree of subjectiveness and its impact upon the findings and conclusions. This fact entirely remains at the mercy of the decision maker’s expertise. Normally the decision makers are specialist in their respective area with bundle of real world experience to back their judgments.

Many MCDM methods exist to date as discussed briefly in the previous chapter. One of common technique is Analytic hierarchy process (AHP) introduced by Thomas Saaty (Wind & Saaty, 1980), (Saaty & Erdener, 1979), (Saaty, 1986) to address the growing challenges dealing with multiple and conflicting criteria (Chen et al., 1992).

AHP has found applications in many areas like human resource (Kabak et al., 2012), (Bozbura et al., 2007), (Chou et al., 2012), supplier selection (Deng et al., 2014), (Peng, 2012), (Shaw et al., 2012), selection and evaluation of enterprise resource planning (ERP) systems (Cebeci, 2009), (Wei et al., 2005), site selection (Uyan, 2013), (Choudhary & Shankar, 2012), (Lai et al., 2011), services sectors (Büyüközkan & Çifçi, 2012), (Tsaur et al., 2002), and weapons selection (Dağdeviren et al., 2009), (Lee et al., 2010).

The AHP breaks down a complex system into a hierarchical structure of elements also called levels. At the top level is the objective or goal and subsequent levels consisting of different criteria's and sub criteria's and alternatives are at the bottom. Nominal scale is used to make comparisons among the pairs. Eigen values are computed for the quantified comparisons matrix which represents the relative


scores among different elements of the hierarchy. The figure 1 shows the hierarchical structure of analytic hierarchy process (AHP).

Figure 1: Hierarchical structure of AHP

In AHP, the linguistic pair-wise comparison is followed by a numerical pair wise comparison made with the help of a numerical scale in order to quantify the priority vectors. The validity thus depends upon the selection of numerical scale and design of the priority method to be used (Dong et al., 2008).

For priority vectors to be derived, linguistic comparisons must be represented by numerical one. In Saaty scale (Saaty, 1977) the linguistic terms are represented by numerical scale of 1-9. There are many other numerical scales and it is difficult to judge the effectiveness of the scale.

Computation of priorities is the linchpin of AHP method, without it rankings could not be established. A wide range of prioritization methods exits (Choo & Wedley, 2004), (Srdjevic, 2005). The eigen value method (EVM) (Saaty, 1977) and the logarithmic least squares method (LLSM) (Crawford & Williams, 1985)[52] are most common. There is debate on the superiority of one method over other.

RESEARCH METHODOLOGY

A Fuzzy AHP technique has been used for prioritizing the multiple & cross perspective objectives in BSC. Based upon the findings one can determine the level of emphasis required to be given to a certain objective in that particular perspective of balanced scorecard. A comprehensive set of evaluation criteria's and sub criteria’s were formed based on the expert opinion and literature review. A total of 10 multi perspective and 3 cross perspective objectives were identified after a study of 15 balanced scorecards and literature review. Strategic objectives belonging to respective perspectives were assigned related main and sub criteria’s out of the pool of sub criteria’s.

The primary source is based upon a questionnaire and it is used to ask questions from balanced scorecard experts and their opinions are used to make comparisons matrices. Experts belonging to various sectors like manufacturing & services gave their expert opinions. The experts were all balanced scorecard professionals and have relevant working experience in their respective domains. Different opinions help this study to diversify and remove any bias towards one particular sector and keep this study as generalized as possible. Out of our seven experts, four belonged to manufacturing sector and the rest to services sector. An evaluation framework was developed and used to compute the priority


weights for multi and cross perspective strategic objectives with respect relative criteria’s and sub criteria’s.

Ye

s

Figure 2: Evaluation Framework

1. Firstly, multi and cross perspective strategic objective were identified by literature review and expert opinions.

2. Sub criteria’s were identified for each perspective of balanced scorecard.


3. Based upon the expert’s opinion, a pair wise comparison matrices were constructed for balanced scorecard main perspectives and sub criteria’s of the individual perspectives.

4. Using the Chang’s extent analysis method, fuzzy importance weights were computed and assigned to main and sub criteria’s.

5. For multi perspective strategic objectives, the assessments were made based upon the priority weights assigned in previous.

6. For an objective to be cross perspective strategic objective, an initial value of 0.7 or 70 % priority weight was set so that the objective can be safely declared as occupant of that particular perspective. If an objective fails to achieve a value of 0.7 or 70%, it is declared as undetermined.

A recent research shows that approximately quarter of the overall published research articles regarding balanced scorecard relate to decision making (Hoque, 2013). So far little empirical work on the relationships and causality among balanced scorecard perspectives has been done. The intention is to contribute by bridging this gap; identifying multi and cross perspective objectives in BSC and applying fuzzy based AHP technique to balance these objectives. Extent analysis approach is used to prioritize the objectives based upon the main criteria’s and sub criteria’s.

Fuzzy Set Theory and Fuzzy Number

Lotfi A. Zadeh (Zadeh, 1965) introduced fuzzy set theory to address the ambiguity owning to vagueness and imprecision in the data set. In this study, triangular fuzzy numbers are used to represent the vagueness. TFN is also favored in this study because of its simplicity to use. A TFN is represented by three points l, m, u . Its membership function is represented as follows:

μ x M⁄

0, x ,x l m l⁄ , l x m,u x u m⁄ ,0,

m x u,x ,

…………… (1)

Figure 3: Membership Function of

Fuzzy AHP Method

There are many ranking methods which require complex mathematics. Different methods may produce in different ranking results. Different variations of fuzzy AHP method exist (Buckley, 1985), (Chang, 1992), (Chang, 1996), (Cheng, 1997), (Cheng et al., 1999), (Leung & Cao, 2000), (Mikhailov, 2004).

For this study, Cheng extent analysis method (Chang, 1992,1996) has been used because of the simplicity. Following are the steps of Chang extent analysis:


Let X x , x , … . . , x be an object set, and U u , u , …… . u be a goal set. According to the method of Chang (Chang, 1992,1996) extent analysis, each object is taken and extent analysis for each goal g is performed, respectively. Therefore, m extent analysis values for each object can be obtained, with the following signs:

, , …… . . , , 1,2, …… . . , ……………… (2)

Where all the M j 1,2, … . . , m are TFNs.

The steps of Chang’s extent analysis can be given as in the following:

Step 1: Value of fuzzy synthetic extent with respect to the ith object is defined as

S M M ……………… 3

To obtain∑ M , perform the fuzzy addition operation of m extent analysis values for a particular

matrix such that

M l , m , u …………… . 4

And to obtain ∑ ∑ M , perform the fuzzy addition operation of M (j=1,2,……,m) values

such that

M l , m , u …………… . . 5

And then compute the inverse of the vector in Eq. (6) such that

M 1

∑ u,

1∑ m

,1

∑ l………… . 6

Step 2: The degree of possibility of M l ,m , u M l ,m , u is defined as

V M M sup min μ x , μ y …………… 7

And can be equivalently expressed as follows:

V M M hgt M ∩M μ d


=

1, ifm m ,0, ifl u ,

otherwise,………… (8)

Where d is the ordinate of the highest intersection point D between μ and μ (see fig. 4). To compare M andM , we need both the values of V M M and V M M .

Step 3: the degree possibility for a convex fuzzy number to be greater than k convex fuzzy number M i 1,2, … . . , k can be defined by

V M M ,M ,…… ,M V M M and

M M and… . and M M minV M M

i 1,2, ……… . , k. …………………. (9)

Assume that

d A min V S S ………………… (10)

For k 1,2, … . , n; k i.Then the weight vector is given by

W d A , d A ,……… . , d A ,…………… . 11

Figure 4: Intersection between

Step 4: The output weight vectors from step 3 are normalized.

, , ……… . , ,…………… . 12

The W represents a non fuzzy value.

Table 1 shows the linguistic scale being represented by TFNs.


Linguistic Scale Triangular Fuzzy Scale

Triangular Fuzzy Reciprocal Scale

Equally Important (EI) [1,1,1] [1,1,1]

Moderately important (MI) [1/2,1,3/2] [2/3,1,2]

Strongly Important (SI) [1,3/2,2] [1/2,2/3,1]

Strongly Plus important (SPI) [3/2,2,5/2] [2/5,1/2,2/3]

Absolutely important (AI) [2,5/2,3] [1/3,2/5,1/2]

Table 1: Triangular Fuzzy Scale

Figure 5 shows the main criteria’s and sub criteria's as identified by literature review and experts. A total of 25 sub criteria's were determined. The sub criteria’s have been made as generic as possible.

MAIN CRITERIA’S

Customer (C) Organization Capacity (O)

SU

B C

RIT

ER

IA'S

1. Increase customer satisfaction / confidence

1. Highly motivated & performing staff

2. Lowest costs 2. Improve use of IT

3. Increase customer retention 3. Increase capacity to respond

4. Quality of new products 4. Effective management of knowledge

5. Quality of product/services 5. High performance organizational culture

6. Infrastructure

Internal Processes (P) Constituent/Stakeholder (S)

SU

B C

RIT

ER

IA'S

1. Excellent/world class/effective service quality 1. Improve awareness & accessibility

2. Reduce cycle time 2. Improve program outcomes

3. Effective/efficient Processes 3. Improve quality of life

4. Effective strategy management & alignment

4. Win & maintain stakeholder support

5. Effective regulations policies & plans. 5. Key planning player

6. Continual monitoring of quality systems 6. Sustainable solutions

7. Cost Effectiveness of Processes

8. Optimization of Processes

Table 2: Sub Criteria’s with respect to Main Criteria’s


Figure 5: FAHP membership functions

MULTI & CROSS PERSPECTIVE OBJECTIVES WITH CRITERIA’S & SUB CRITERIA’S

From the study of 15 balanced scorecards of various organizations, 10 multi-perspective & 3 cross perspective objectives were identified. In the figures 6-17, the top level represents the objectives, the second level represents the main criteria’s which are the balanced scorecard perspectives and the bottom level represents the sub criteria’s for individual perspectives.

MULTI PERSPECTIVE OBJECTIVES

1. Contribution to knowledge related capacity of HR - Organizational Capacity & Internal Processes Perspectives.

1.Contribution to knowledge related capacity of HR - Organizational Capacity & Internal Processes Perspectives.

Organization Capacity

(O)

Internal Processes

(P)

2.Improve use of IT 3.Increase capacity to respond4.Effective management of knowledge

1.Excellent/world class/effective service quality5.Effective regulations policies & plans.

Figure 6: Hierarchy of Objective 1


2. Contribution to tangible infrastructural capacity of organization - Organizational Capacity & Internal Processes Perspectives.

2.Contribution to tangible infrastructural capacity of organization - Organizational Capacity & Internal Processes Perspectives.


(O)

Internal Processes(P)

2.Improve use of IT 6.Infrastructure

6.Continual monitoring of quality systems7.Cost Effectiveness of Processes8.Optimization of Processes


3. Contribution to Increase in IT capacity (Hardware) - Organizational Capacity & Internal Processes Perspectives.

3. Contribution to Increase in IT capacity (Hardware) - Organizational Capacity & Internal Processes Perspectives.


(O)


1.Highly motivated & performing staff2.Improve use of IT 6.Infrastructure

2.Reduce cycle time3.Effective/efficient Processes6.Continual monitoring of quality systems7.Cost Effectiveness of Processes


4. Contribution to increase in IT capacity (Software) - Both 'Organizational Capacity' & 'Internal Processes' Perspectives depending on whether it is operations/use of software to improve processes or acquisition of software.


4.Contribution to increase in IT capacity (Software) - Both 'Organizational Capacity' & 'Internal Processes' Perspectives depending on whether it is operations/use of software to improve processes or acquisition of software.


(O)


1.Highly motivated & performing staff2.Improve use of IT 3.Increase capacity to respond4.Effective management of knowledge

1.Excellent/world class/effective service quality3.Effective/efficient Processes7.Cost Effectiveness of Processes8.Optimization of Processes


5. Enhancement of suppliers'/partners'/vendors' ability to assist your business - Organizational Capacity & Internal Processes Perspectives.

5. Enhancement of suppliers'/partners'/vendors' ability to assist your business - Organizational Capacity & Internal Processes Perspectives.


(O)


1.Highly motivated & performing staff3.Increase capacity to respond6.Infrastructure

1.Excellent/world class/effective service quality2.Reduce cycle time3.Effective/efficient Processes7.Cost Effectiveness of Processes8.Optimization of Processes


6. Contribution to Leadership's ability to understand strategy & decision making - Organizational Capacity & Internal Processes Perspectives.


6. Contribution to Leadership's ability to understand strategy & decision making - Organizational Capacity & Internal Processes Perspectives.


(O)


1.Highly motivated & performing staff3.Increase capacity to respond5.High performance organizational culture

4.Effective strategy management & alignment5.Effective regulations policies & plans.


7. Contribution to improving organizational culture - Organizational Capacity & Internal Processes Perspectives.

7. Contribution to improving organizational culture - Organizational Capacity & Internal Processes Perspectives.


(O)


1.Highly motivated & performing staff2.Improve use of IT 3.Increase capacity to respond6.Infrastructure

3.Effective/efficient Processes4.Effective strategy management & alignment5.Effective regulations policies & plans.


8. Contribution to reducing time taken by a process - Organizational Capacity & Internal Processes Perspectives.


8. Contribution to reducing time taken by a process - Organizational Capacity & Internal Processes Perspectives.


(O)


2.Improve use of IT 6.Infrastructure

2.Reduce cycle time3.Effective/efficient Processes7.Cost Effectiveness of Processes8.Optimization of Processes


9. Contribution to reducing cost of doing business - Internal Processes & Customer perspectives.

9. Contribution to reducing cost of doing business - Internal Processes & Customer perspectives.

Customer (C)


1.Increase customer satisfaction / confidence2.Lowest costs3.Increase customer retention

2.Reduce cycle time3.Effective/efficient Processes7.Cost Effectiveness of Processes8.Optimization of Processes



10. Contribution to reducing manpower required to run a business process - Internal Processes & Organizational Capacity perspectives.

10. Contribution to reducing manpower required to run a business process - Internal Processes & Organizational Capacity perspectives.


(O)Internal Processes

(P)

1.Highly motivated & performing staff2.Improve use of IT 3.Increase capacity to respond

1.Excellent/world class/effective service quality2.Reduce cycle time3.Effective/efficient Processes8.Optimization of Processes


CROSS PERSPECTIVE OBJECTIVES

11. Contribution o improvement of communication of policy & procedures - Internal Processes & Organizational Capacity perspectives.



12. Contribution to improvement of stakeholder’s confidence - Internal Processes & Constituent/Stakeholder perspectives.


13. Contribution to improvement of efficiency & effectiveness - Internal Processes & Organizational Capacity perspectives.



RESULTS

The tables 3-15 shows the individual priority weights of main criteria’s and sub criteria’s. The overall global weights are produced by multiplying the individual weights of sub criteria’s with weight of main criteria.

Table 3: weights for objective 1 - Contribution to knowledge related capacity of HR - Organizational Capacity & Internal Processes Perspectives

BSC Perspectives

Inter-dependency weights

Sub Criteria's Individual Weights

Global Weights

P 0.3158 Excellent/world class/effective service quality 0.6842 0.2161

Effective regulations policies & plans. 0.3158 0.0997

O 0.6842 Improve use of IT 0.2731 0.1869

Increase capacity to respond 0.4301 0.2943

Effective management of knowledge 0.2968 0.2031

Explanation: The result as per table 3 for objective 1- contribution to knowledge related capacity of HR - Organizational Capacity & Internal Processes Perspectives indicate that 31.58 % of effort or resources should be directed to internal processes perspective. More emphasis should be given (68.42 %) to organizational capacity.

The ‘effective service quality’ sub criteria’s in the internal processes perspective is more dominant when compared with ‘effective regulations policies & plans’. Similarly, in organizational capacity sub criteria’s ‘improve use of IT’ and ‘effective management of knowledge’ is almost equally important with ‘increase capacity to respond’ the dominant one. Out of the entire sub criteria’s ‘increase capacity to respond’ is the most important with global weight of 0.2943 or 29.43 %.

Table 4: weights for objective 2 - Contribution to tangible infrastructural capacity of organization - Organizational Capacity & Internal Processes Perspectives

BSC Perspectives

Inter‐dependency weights


Global Weights




Infrastructure 0.5 0.3421


Explanation: The results as per table 4 indicate that ‘improve use of IT’ (34.21 %) & ‘infrastructure’ (34.21 %) being the most dominant sub criteria’s.

Table 5: weights for objective 3 - Contribution to Increase in IT capacity (Hardware) - Organizational Capacity & Internal Processes Perspectives

BSC Perspectives



Global Weights

O 0.6842 Highly motivated & performing staff 0.4522 0.3094

Improve use of IT 0.1302 0.0891


P 0.3158 Continual monitoring of quality systems 0.3815 0.1205

Cost Effectiveness of Processes 0.2371 0.0749

Optimization of Processes 0.3815 0.1205

Explanation: The results as per table 5 indicate that ‘highly motivated & performing staff’ (30.94 %) being the most dominant sub criteria and ‘cost effectiveness’ (7.49 %) being the least important sub criteria.

Table 6: weights for objective 4 - Contribution to increase in IT capacity (Software) - Both 'Organizational Capacity' & 'Internal Processes' Perspectives depending on whether it is

operations/use of software to improve processes or acquisition of software

BSC Perspectives



Global Weights






Effective/efficient Processes 0.2866 0.0905



Explanation: The results as per table 6 indicate that ‘highly motivated & performing staff’ (24.19 %) being the most important sub criteria and ‘cost effectiveness of processes’ (2.24 %) being the least important sub criteria.


Table 7: weights for objective 5 - Enhancement of suppliers'/partners'/vendors' ability to assist your business - Organizational Capacity & Internal Processes Perspectives

BSC Perspectives



Global Weights





Reduce cycle time 0.1949 0.0615




Explanation: The results as per table 7 indicate that ‘highly motivated & performing staff’ (25.42 %) being the most important sub criteria and ‘cost effectiveness of processes’ (1.65 %) being the least important sub criteria.

Table 8: weights for objective 6 - Contribution to Leadership's ability to understand strategy & decision making - Organizational Capacity & Internal Processes Perspectives

BSC Perspectives



Global Weights



High performance organizational culture 0.0000 0.0000

P 0.3158 Effective strategy management & alignment 1.0000 0.3158


Explanation: The results as per table 8 indicate that ‘highly motivated & performing staff’ (68.42 %) & ‘effective strategy management & alignment’ (31.58 %) are the only criteria’s that impact the objective while the rest of sub criteria’s show do not affect the multi perspective objective.

Table 9: weights for objective 7 - Contribution to improving organizational culture - Organizational Capacity & Internal Processes Perspectives


BSC Perspectives



Global Weights





P 0.3158 Effective/efficient Processes 0.1766 0.0558

Effective strategy management & alignment 0.8234 0.2600


Explanation: The results as per table 9 indicate that ‘highly motivated & performing staff’ (26.23 %) being the most important sub criteria and ‘cost effectiveness of processes’ (5.58 %) being the least important sub criteria. ‘Effective regulations policies & plans’ do not impact the objective and hence no emphasis should be given to this sub criteria.

Table 10: weights for objective 8 - Contribution to reducing time taken by a process - Organizational Capacity & Internal Processes Perspectives

BSC Perspectives



Global Weights



P 0.3158 Reduce cycle time 0.3133 0.0989




Explanation: The results as per table 10 indicate that ‘improve use of IT’ & ‘Infrastructure’ (34.21 %) being the most important sub criteria’s and ‘cost effectiveness of processes’ (4.95 %) being the least important sub criteria.

Table 11: weights for objective 9 - Contribution to reducing cost of doing business - Internal Processes & Customer perspectives

BSC Perspectives



Global Weights

C 1.0000 Increase customer satisfaction / confidence

0.5000 0.5000

Lowest costs 0.0000 0.0000

Increase customer retention 0.5000 0.5000






Explanation: The results as per table 11 indicate that the initially identified multi perspective strategic objective 9 only belongs to customer perspective hence the as per the experts opinions it is not a multi perspective objective.

Table 12: weights for objective 10 - Contribution to reducing manpower required to run a business process - Internal Processes & Organizational Capacity perspectives

BSC Perspectives



Global Weights

O 0.6842 Highly motivated & performing staff

0.7279 0.4980


Increase capacity to respond

0.2721 0.1862

P 0.3158 Excellent/world class /effective service quality

0.3988 0.1259

Reduce cycle time 0.2488 0.0786



Explanation: The results as per table 12 indicate that ‘highly motivated & performing staff’ (49.80 %) being the most important sub criteria and ‘optimization of processes’ (4.79 %) being the least important sub criteria.

Table 13: weights for objective 11 - Contribution o improvement of communication of policy & procedures - Internal Processes & Organizational Capacity perspectives

BSC Perspectives



Global Weights







Explanation: The results as per table 13 indicate that ‘highly motivated & performing staff’ (72.79 %) being the most important sub criteria in the organizational capacity perspective while ‘effective strategy management & alignment’ (100 %) being the on sub criteria in the internal processes perspective.

Since the objective 11 is a cross perspective strategic objective, hence as per the evaluation framework, the objective could not be placed successfully in either perspective. As an initial starting point a value of 0.7 or 70% was set to validate an objective as a cross perspective objective. However, a value of 0.6842 or 68.42 % could be achieved.

Table 14: weights for objective 12 - Contribution to improvement of stakeholder’s confidence - Internal Processes & Constituent/Stakeholder perspectives

BSC Perspectives



Global Weights

S 0.6842 Improve awareness & accessibility 0.3158 0.2161

Improve program outcomes 0.6842 0.4681




Explanation: The results as per table 14 indicate that ‘Improve program outcomes’ (46.81 %) being the most important sub criteria and ‘effective/efficient process’ (5.58 %) being the least important sub criteria. ‘Effective regulations policies & plan’ do impact the objective

Table 15: weights for objective 13 - Contribution to improvement of efficiency & effectiveness - Internal Processes & Organizational Capacity perspectives

BSC Perspectives



Global Weights












Explanation: The results as per table 15 indicate that ‘highly motivated and performing staff’ (24.19 %) being the most important sub criteria when compared with all other sub criteria’s.

CONCLUSION

Using the different perspectives of BSC, a fuzzy set theory based model was proposed and implemented. A generic Chang’s extent analysis algorithm was developed in Matlab. The four perspectives were taken as the main criteria’s while sub criteria's were also evaluated. Based upon the expert’s opinions, the comparison matrices were assigned triangular fuzzy values.

Our findings indicate that multi perspective objectives were successfully balanced however, a more refined approach is needed for cross perspective strategic objectives. Perhaps, a more comprehensive set of evaluation criterion may resolve the problem. As per our model, a cross perspective objective resides in that particular perspective if a priority weight of 0.7 or 70% is achieved. However, the maximum score was 0.6842 or 68.42 %. This indicates that the proposed model can effectively work if the provided sets of criteria’s are more specific.

This proposed model provides the practitioners with a fuzzy way to approach the problem of balancing. Imprecision can be addressed. Furthermore this study will help to explore this topic further and other MADM techniques could be applied to solve this problem. Separate studies could be done to explore the individual sub criteria’s and industry specific research could also be looked into in the future.

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