[communications in computer and information science] digital enterprise and information systems...

E. Ariwa and E. El-Qawasmeh (Eds.): DEIS 2011, CCIS 194, pp. 724–738, 2011. © Springer-Verlag Berlin Heidelberg 2011

Maturity Assessment of Business/IT Alignment Using Fuzzy Expert System

Ahmad Nadali1, Sanaz Pourdarab1, Aliakbar Mazloumi2, and Hamid Eslami Nosratabadi3,*

1 Department of Information Technology Management, Science and Research Branch, Islamic Azad University

2 Department of Mechanical and Manufacturing Engineering, University Putra Malaysia43400 UPM, Serdang, Malaysia

3 Young Researchers Club, Science and Research Branch, Islamic Azad University [email protected]

Abstract. Business/IT alignment means the degree to which the information technology mission, objectives, and plans are supported by the business mission, objectives, and plans. The aim of this research is the maturity assessment of Business/IT alignment by an intelligent system. Here, a Fuzzy Expert System has been designed in which main effective variables on Business and IT alignment have been considered as Inputs and level of maturity as output. Then, system rules have been extracted by the IT experts and the system has been developed with the use of FIS tool of MATLAB software. Finally, the presented steps have been run in an Iranian Bank as empirical study.

Keywords: Business IT Alignment, Strategic Alignment, Business Strategy, IT Strategy, Fuzzy Expert System.

1 Introduction

Although alignment is a top management concern, no comprehensive model of the construct is commonly used. A number of models of strategic business/IT alignment have been proposed till now from which the most well known are [1], [2]. The alignment of business and IT strategies has been utilized by organizations to create and improve efficiencies, reduce costs, create barriers to entry, improve customer and buyer/supplier relationships, and to create new products and business solutions. In the research of [3] concerning the alignment of business and IT strategies, they have found that there were at least four common themes which were repeated by respondents of a survey of the companies who were more aligned at all levels of IT/business strategy: Clear direction, Commitment, Communication and Cross-functional Integration. These themes are called “The four C’s,” and the alignment results are in cross functional integration. In fact the managers from organization that have aligned IT with business strategies argue that the integration was crucial to the organization’s survival and its success and the IT units have added value to an * Corresponding author.

Maturity Assessment of Business/IT Alignment Using Fuzzy Expert System 725

organization’s effectiveness by acting as change agents, focusing on business imperatives, and helping to achieve effectiveness and efficiency. Another alignment model has been introduced to investigate the evidence of alignment between business and IT [4]. They expanded a model with two new components: the relationship management as an antecedent of alignment, and the balanced scorecard as the mechanism of alignment [5].While most of the literature has focused on the fit between a firms’s IT and business strategy, the alignment issues need to be addressed at Strategic, Tactical, and operational levels. Strategic alignment helps meet an organizations’ future IT needs; tactical alignment allows the organization to allocate its IT resources effectively; operational alignment ensures effectiveness and efficiency of IT in supporting the organization’s operations on a daily basis. Organizations that have a higher level of alignment are more likely to achieve higher levels of performance and perceived business value from IT. The five factors that affected the social dimension of IT alignment identified by [5] included shared domain knowledge between business and IT executives, IT implementation success, communication between IT and business executives, connections between business and IT planning processes, and strategic business plans. A research of 256 UK manufacturing firms found that the major factors that influenced alignment were IT maturity, technical IT sophistication, and the CEO’s software knowledge [6]. By applying the theory of knowledge integration, it provided a comprehensive framework that examined the relationship among integration behavior, alignment, IT planning and projects, and business impact of IT [7]. Their study asserted that business–IT alignment was influenced by IT managers’ participation in business planning and business managers’ participation in strategic IT planning. Furthermore, alignment was found to be positively associated with the success of IT planning and projects, which led to a positive business impact of IT. A framework called strategic alignment maturity (SAM) has been proposed to assess the extent to which business and IT functions align [8]. SAM provides organizations with a tool to evaluate the maturity of their strategic choices and alignment activities and identify areas in which they can achieve a higher level of alignment. Subsequent research included a benchmark study by [8] that used SAM at 50 Global 2000 companies to create a benchmark for business–IT alignment and identify specific recommendations for improving alignment. In another Paper, an operational model of strategic alignment is proposed and empirically validated through a mail survey of 110 small firms [9]. The purpose of this research is designing a fuzzy expert system which is able to assess the maturity level of business/IT alignment. The reminder of this paper is organized as follows. In Section 2, we review some key investigations in the area of Business/ IT alignment. In Section 3, the explanation of Fuzzy Expert System is presented. We detail, in Section 4, the system and present the results achieved using the system. Finally, we conclude the paper conveying final considerations and perspectives for future work in Section 5.

2 Business/IT Alignment

There is a definition of alignment as “the degree to which the information technology mission, objectives, and plans are supported by the business mission, objectives, and

726 A. Nadali, S. Pourdarab, and A. Mazloumi

plans” [10].Another one define alignment as the “fit between an organization and its strategy, structure, processes, technology and environment” [11]. It has been stated that in business/IT alignment, IT is applied in an appropriate and timely way, in harmony and collaboration with business needs, goals, and strategies [12]. The importance of alignment is that it can help organizations in three different ways. The first is by maximizing the return on IT investment; the second is by enabling organizations to achieve competitive advantage through IS; and thirdly, it facilitates them to respond to new opportunities by providing direction and flexibility [13].To improve alignment, a research identified that having clear organizational goals, understanding the relationship between IT and business priorities, closing the gap between IT and business strategy, communication between IT staff and line manager, support from senior managers, strong management, IT control, trust, understanding the business environment are all enablers of alignment[14]. IT, when accompanied with BPR efforts, can provide business with a number of benefits, such as cost reduction, time elimination, and error minimization. However, there are other benefits that are mostly related to IT-enabled process orientation. These can be summarized in the following: Enabling parallelism. Moving from a sequential structure of processes into a parallel one reduces the processes’ cycle time, problems resulting from delays, process disruptions, and handoffs. Facilitating integration. Moving from the division of labor approach into the “case management” approach eliminates unnecessary tasks and improves communication and quality of services. Enhancing decision making. Reducing the number of levels in an organization’s hierarchies enhances the decision-making process. Minimizing points of contact. BPR, when combined with IT, eliminates intermediaries at different levels and reduces time and distance in the exchange of information required in any process [15]. MIT research on the utilization of IT within the enterprise context led to the development of a ‘strategic alignment model’. With some changes of the terminology used, this model was published later by Henderson and Venkatraman and is shown in figure 1[1]. The model distinguishes between business and IT (columns) and the external versus internal focus (rows).Here, four cells or areas of attention are defined that are considered important for obtaining alignment. The unity between business and IT strategy is called ‘functional integration’ and that between the external and internal perspective the ‘strategic integration’. For overall integration, multiple alignment perspectives concurrently play a role, as indicated by the arrows between the four areas of attention. Within these four areas, some sub-domains are indicated for which mutual alignment is considered important. Hence, one might refer to a multivariable, co-alignment perspective. The multiple facets are an indication of the difficulty of operationalizing the alignment concept practically, at least by means of these concepts [16].

Within the strategic alignment model, the process of alignment is understood as using a certain pattern to bring into unity the relationships between (remarkably only) three of the four areas of attention (alignment as state). Four patterns are distinguished, depending on the chosen starting point. That starting point is called the ‘dominant alignment perspective’. The four alignment patterns are shown in figure 2. With the first pattern, the dominant alignment perspective is called strategic execution. The starting point is the business strategy, which subsequently defines the organizational infrastructure and processes that must be supported by the IT


Fig. 1. Strategic alignment model [1]

infrastructure and processes. Notably an explicit IT strategy is not addressed within this dominant alignment perspective. The IT function is seen merely as a service and cost center. Possibilities and opportunities offered by IT for arranging the organizational infrastructure and processes differently are thus not considered within this perspective. The second dominant alignment perspective, and associated pattern, is labeled technology potential. Here too the business strategy is the starting point, but is used to formulate the IT strategy that subsequently defines the IT infrastructure and processes. Within this perspective the central issue concerns how to use technology for supporting the business optimally. The competitive potential is the third dominant perspective. In this case the IT strategy is the starting point, where the renewing possibilities and opportunities that IT can offer are utilized for defining an innovative and competitive business strategy. Subsequently, the business strategy defines the organizational structures and processes. Finally, the fourth dominant alignment perspective is labeled service level. Again, the IT strategy is the starting point, but unlike the third perspective, the focus lies with arranging the IT infrastructure and processes such that IT services can be delivered effectively and efficiently. One can also label this the IT supplier perspective, since the business strategy does not play a primary role. It is emphasized that the four perspectives (and associated alignment patterns) are dominant, but not necessarily exclusive. Given a certain dominant perspective, the other perspectives might play a role [16].

The term ‘‘IT alignment’’ refers to the coordination of a firm’s IT strategy with its business strategy. This high level coordination has received extensive coverage in contemporary MIS literature. A research characterized business-IT alignment as applying IT in an appropriate and timely way, which is in harmony with business strategies, goals, and needs [2]. Another one stated that alignment is the degree of fit and integration among business strategies, IT strategies, business infrastructures, and


Fig. 2. Alignment perspectives[16]

IT infrastructures [1]. In an extensive review of alignment research, classified business-IT alignment along four dimensions, namely strategic/intellectual, structural, social, and cultural[17]. The strategic/intellectual alignment dimension offers organizations an opportunity to enhance its competitive capability. A survey asserted intellectual alignment as the state in which a set of high-quality interrelated business and IS plans exist [5]. This state emerges from internal consistencies and external validity between the outputs of business and IT planning. [18] Refers to structural alignment as the degree of structural fit between IT and business. Factors influencing structural fit include IS decision making rights, reporting relationships, the centralization and/or decentralization of IS services and infrastructure, the deployment of IS personnel, and the alignment of IT structures with competitive strategy. The social alignment dimension is the state in which IS and business executives understand, and are committed to the business and IS mission, objectives, and plans. The authors examined factors derived from the political behavior model, the resource dependency theory and the social construction theory that either enable or inhibit alignment. The cultural alignment dimension can be viewed through several lenses [19]. Successful planning between business and IT is a precondition of cultural fit and a strong company culture is a precondition to the type of informal structure that fosters alignment [19].

3 Fuzzy Expert System

A fuzzy expert system basically uses a group of fuzzy rules to solve problems. A fuzzy rule consists of two parts: an IF part (antecedent) and a THEN part (consequent). After defining fuzzy rules, the fuzzy inference procedure is then applied to get the fuzzy output. However, in the real word, we need a crisp value to solve the problems. Therefore, in most of cases, we need to transfer the fuzzy output to a crisp value. This is called ‘‘defuzzification procedure”. There are two main defuzzification methods used in fuzzy expert systems: the Mean of Maximum (MOM) method and the Center of Area (COA) method. Briefly, the procedure of a fuzzy expert system is described as follows:


1. Define problems with fuzzy variables and membership functions. 2. Generate fuzzy rules. 3. Input crisp values and transfer them to matching degrees using appropriate

membership functions. 4. Conduct fuzzy inference and produce fuzzy output. 5. Employ the defuzzification procedure to get a crisp value based on the fuzzy

output [20].

Fuzzy expert systems deal with phenomena that are uncertain and nonlinear in nature. Fuzzy logic provides the appropriate mathematics for sound reasoning under these conditions, and does not introduce unwarranted precision into the analysis.

Hence, a fuzzy-logic based expert system can overcome the shortcomings of traditional linear modeling. Moreover, the fuzzy expert system can interpret, and encompass as rules, the valuable project experiences gained by the system test team (Fig. 3).

Fig. 3. Fuzzy expert system structure [22]

The fuzzy inference system is a popular computing framework based on the concepts of fuzzy set theory and fuzzy rules such as (x is A) AND (y is B) then (z is C), where the antecedent A(x),B(y), and the consequent C(z) can be fuzzy sets. The inference rule is a relation R(x, y, z) whose membership function is defined as:

µR (x,y,z)=µA (x)× µB(x)×µC(x) . (1)

Where µA (x), µB (x), µC(x) are membership functions or characteristic functions of fuzzy sets A, B, and C. There are different manifestations of the conjunction operation ×. Mamdani proposed using the minimum operator for conjunction [21], and Larsen used the algebraic product operator for conjunction. Execution of a rule is based on composition where the antecedent A’ is a fuzzy match of inputs with A, B’ is a fuzzy match of inputs with B, and the inferred consequent is,

C’ =(A’ ∩ B’ ) o R (2)


Where R is the fuzzy relation between A, B and C. There are different approaches to implement Eq. (2). Mamdani proposed a min–max method [21].When rules are organized in a hierarchical manner, the inference engine determines how pieces of knowledge are related together and matches them against relevant data. The inference procedures are based upon the modus ponens rule. For a given rule A→B, whenever the premise of the rule is true, the rule fires and its conclusion also becomes true. A chain of rules can be fired in sequence as x1→xn .There are three different inference methods: (1) forward chaining, (2) backward chaining, and (3) direct chaining. A chain that is searched or traversed from a problem to its solution is called a forward chain. A backward chain goes from a solution to the problem (hypothesis). Forward chaining can also be described as reasoning from facts to the conclusions that follow from the facts, while backward chaining can also be described as obtaining sub goals through the goal. The direct chaining inference procedure differs from the first two methods in that it uses a bit matrix, or a relational list, to infer xn from any xi Thus, the direct chaining inference process can begin at any point between the solution and problem.

Fig. 4. The Mamdani fuzzy inference system using the product-sum method[22]

Defuzzification is a mapping from a space of fuzzy output into a space of single output. Once the inference engine performs the reasoning, an obtained fuzzy output, needs to be transformed into a single value representing the results of inference. There are various standard techniques available for this mapping process, including the max criterion, mean of maximum, and the center of area. The latter is the most common method, and is based on finding the center of gravity of the solution fuzzy sets as shown in Eq. (3). A discrete fuzzy set with N members yields a single result,

ZCOA, which is computed as:

= ∑∑ . (3)

Where di is the value from the set that has a membership valueµi. Unfortunately, there

is no systematic procedure for choosing a defuzzification strategy. And because some information is likely to be lost during defuzzification, additional research work is


needed on how to use the information available in the solution fuzzy sets. The most important component of fuzzy expert systems is the set of rules and how the rules interact with each other to generate useful results. The rules are typically obtained from the domain experts so that the expert system can emulate the reasoning and inference process of a human expert. There is always an argument about using an expert’s domain knowledge as a guideline for an expert system. Given the value of expert input, it is practical to start with an expert’s guideline. Of course, a necessary feedback mechanism is needed for experts to adjust their judgment as the testing moves on [22].

4 The Proposed Fuzzy Expert System

This expert system is designed for assessing the maturity of IT Business alignment based on Luftman’s criteria. Maturity categories were included: Communications, Competency/value measurements, Governance, Partnership, Technology scope, Skills [23]. Communications: The effectiveness of leveraging information for mutual understanding and knowledge sharing. This category evaluates such issues as whether business and IT understand each other’s operating environment, whether a liaison is used to facilitate knowledge transfer between them, and whether there are rigid protocols that impede discussion and sharing of ideas. Competency/value measurements: The management decisions and strategic choices that an organization makes when determining the value and contribution of IT to the firm. This category evaluates such issues as whether an organization uses technical or business metrics for measuring IT success, whether the organization has formal benchmarking practices, and whether IT has contributed to the achievement of the organization’s strategic goals. Governance: The choices organizations make when allocating decision rights for IT activities. This evaluates issues such as how IT projects are prioritized and how IT budgets are controlled. Partnership: Pertains to how IT and the business perceive each other’s contribution. This evaluates issues such as IT’s role in strategic business planning and how risk and rewards are shared by IT and business functions. Technology scope: The management decisions and strategic choices an organization makes when allocating resources toward its IT infrastructure. This evaluates whether the primary systems of the organization enable business strategy, whether business or IT changes are transparent across the organization, and whether the IT architecture is flexible in accommodating business and technology changes. Skills: The organization’s cultural climate toward change and innovation. This evaluates issues such as the organization’s ability to change, whether career crossover opportunities among IT and business professionals exist, and whether the organization has the ability to attract and retain the best business and technical professionals[23].The criteria used in the model are the most effective factors on maturity of IT/Business alignment including six major factors and thirty-eight attributes which being selected according to the background research that have been introduced by Luftman and presented as follows[2,24]:

- Communications (Com): Understanding of Business by IT, Understanding of IT by Business, Inter-/Intra-organizational leading, protocol rigidity, Knowledge sharing, Liaison(s) effectiveness.


- Value Measurement (VM): IT metrics, Business metrics, Balanced metrics, Service level agreements, Benchmarking, Formal assessment/reviews, Continuous improvement.

- Governance (Gov): Business strategic planning, IT strategic planning, Reporting/Organization structure, Budgetary control, IT investment Management, Steering committee(s), Prioritizing process.

- Partnership (Par): Business perception of IT value, Role of IT in strategic Business planning, Shared goals, Risk, Rewards/Penalties, IT program Management, Relationship/Trust style, Business Sponsor/Champion.

- Scope & Architecture (SA): Traditional, Enabler/Driver, External, Standards Articulation, Architectural integration, Functional, Enterprise, Inter-enterprise, Architectural transparency, Flexibility.

- Skills (Sk): Innovation and Entrepreneurship, Locus of power, Management Style, Change readiness, Career crossover, Education and cross-training, Social-political and trusting environment.

Here, the purpose is the maturity assessment of IT/Business alignment in Infotex1 Bank according to situation of these main six factors. Since the obtained Ideas by the experts, managers and IT consultants, about the relation between the maturity level and each criterion, are not precise and have ambiguity, evaluation is done by linguistic variables. To do this, a Mamdani's Fuzzy Expert system has been designed. In this system, six main criteria of maturity have been considered as Inputs and maturity level as output. In Table 1 & 2, the Inputs and Output of the designed fuzzy expert system have been presented.

Table 1. The inputs of fuzzy expert system

Sign Inputs Interval Type of

membership function

Linguistic terms

Com Communications [0 1] Gbell VeryLow(VL),

Low(L), Medium(M), High(H), VeryHigh(VH)

VM Value Measurement [0 1] Gaussian Low(L) Medium(M)

High(H)

Gov Governance [0 1] Gaussian2 Low(L), Medium(M),

High(H)

Par Partnership [0 1] Gaussian VeryLow(VL),

Low(L), Medium(M), High(H), VeryHigh(VH)

SA Scope & Architecture [0 1] Gbell Low(L) Medium(M)

High(H)

Sk Skills [0 1] Gbell Low(L), Medium(M),

High(H)

1 Since the information of considered bank are confidential, The Authors have not been

authorized to present The name of considered Band. So The name of the Bank is changed to protect its anonymity.


Table 2. The output of fuzzy expert system

Sign Output Interval Type of

membership function

Linguistic terms

ML Maturity Level [0 1] Gaussian2 VeryLow(VL),Low(L), Medium(M) High(H),

VeryHigh(VH)

This system according to the obtained rules from IT/IS experts about the relation between Input variables and Output, has been designed via MATLAB software. Table3 shows the obtained rules.

Table 3. The obtained rules from the experts for designing Fuzzy expert system

Com VM Gov Par SA Sk ML

1 VH L H H H H H 2 M H H M H M M 3 L M L VL M H VL 4 H M M H L L L 5 M H H L M M M 6 VH M H VH H M VH 7 H M L H M L H 8 M L H H M M M 9 VL H M M L H M 10 M L M H L M L

After specifying Input and Output variables, membership functions by the experts have been defined for the variables which are shown in Fig 5 to Fig 11.

Fig. 5. Five Gbell Membership function for Communications


Fig. 6. Three Gaussian Membership function for Value Measurement

Fig. 7. Three Gaussian2 Membership function for Governance

Fig. 8. Five Gaussian Membership function for Partnership


Fig. 9. Three Gbell Membership function for Scope & Architecture

Fig. 10. Three Gbell Membership function for Skills

Fig. 11. Five Gaussian2 Membership function for Maturity Level


Here, Fuzzy Inference System (FIS) in MATLAB software has been used and some useful MATLAB commands to work with the designed FIS have been presented. To create a FIS, MATLAB fuzzy logic toolbox provides a user friendly interface in which they can choose the intended specification from drop-down menus.

>>fis = readfis (‘BusinessITAlignment’) fis = name: ‘BusinessITAlignment’ type: ‘mamdani’ andMethod: ‘min’ orMethod: ‘max’ defuzzsMethod: ‘centroid’ impMethod: ‘min’ aggMethod:’max’ input: [1*6 struct] output: [1*1 struct] rule: [1*10struct]

This system is able to determine the maturity level of IT/Business alignment based on the effective criteria. Regarding to the proposed fuzzy expert system, we have evaluated the maturity level of IT/Business alignment in the considered bank as Fig 12.

Fig. 12. Assessed maturity level of IT Business alignment by designed fuzzy expert system

According to the experts’ opinions as the inputs, the following results have been identified: Communications (Com): 0.41 Value Measurement (VM):0.33 Governance (Gov): 0.77 Partnership (Par): 0.57 Scope & Architecture (SA): 0.43 Skills (Sk): 0.45 As a result, maturity level (ML) of IT/Business alignment would be 0.694 out of 1. The final objective of this study was to present a Fuzzy Expert system to evaluate the maturity level of IT/Business alignment for companies or banks and as an empirical


study, we have examined the current state of business–IT alignment of the Bank in IRAN. The findings of this study could serve as the starting point for developing a benchmark for companies/banks.

5 Conclusions

Assessing the maturity level of IT/Business alignment based on effective criteria is the major fundamental in this paper. To reach this goal, a Mamdani's Fuzzy expert system has been designed with considering the situation of six main effective factors on maturity level of IT/Business alignment as the Inputs and the maturity level as the output and Membership functions have been defined for the variables. Then, according to the rules obtained from consultants and IT managers as the experts, the fuzzy expert system has been designed. This system has the ability to determine the maturity level based on criteria levels as an evaluator system. The most important advantage for this Fuzzy Expert system is predicting the maturity level of IT/Business alignment. Finally, managers will be able to plan for future works with considering the obtained results of the proposed system to improve the maturity level of the IT/Business alignment.

Acknowledgement

Here, we appreciate from the IT Experts of the considered bank for their cooperation and sharing their knowledge and the data with us as the researchers.

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