ERP software selection with MCDM: application of TODIM method

Int. J. Business Information Systems, Vol. 13, No. 4, 2013 435

Copyright © 2013 Inderscience Enterprises Ltd.

ERP software selection with MCDM: application of TODIM method

Yigit Kazancoglu* Izmir University of Economics, Sakarya Cad. No: 156, 35330, Balcova, Izmır, Turkey E-mail: [email protected] *Corresponding author

Serhat Burmaoglu Turkish Army Academy (Harbiye), Dikmen Caddesi, Bakanliklar, 06654, Ankara, Turkey E-mail: [email protected] E-mail: [email protected]

Abstract: Any ERP software in the market cannot fully meet the needs and expectations of manufacturing companies, because each company, which looks for implementing ERP system, runs its business with different strategies and goals. Therefore, enterprise resource planning (ERP) software selection is an important and critical decision process. Another aspect of this problem is that multi-disciplinary content reveals the multi-criteria decision making as the appropriate field of study. In this study, by employing TODIM method, which allows the usage of both qualitative and quantitative data, an example, which involves ERP software selection process of a steel forming and hot dip-galvanising firm located in Izmir, Turkey, is provided by using a proposed framework. A new path toward ERP software selection is designed for the decision makers in various industries include manufacturing companies. In order to deal with the complex calculations in the decision making process, the proposed framework is formed as an applicable tool for all decision makers in various industries.

Keywords: ERP software selection; manufacturing; multi-criteria decision making; TODIM method.

Reference to this paper should be made as follows: Kazancoglu, Y. and Burmaoglu, S. (2013) ‘ERP software selection with MCDM: application of TODIM method’, Int. J. Business Information Systems, Vol. 13, No. 4, pp.435–452.

Biographical notes: Yigit Kazancoglu is an Assistant Professor in Izmir University of Economics, Department of Business Administration. He received his BS from Eastern Mediterranean University, Department of Industrial Engineering in 2002. He graduated from Coventry University, England, MBA and Izmir University of Economics, MBA programs in 2003 and 2004 respectively. He received his PhD in Production and Operations Management at Ege University in 2008. He studies on production planning and control, operations research, multicriteria decision making, fuzzy systems and total quality management.

436 Y. Kazancoglu and S. Burmaoglu

Serhat Burmaoglu is an Assistant Professor at the Turkish Army Academy Leadership R&D Center. His research interest is investigating the relationship between economic growth, productivity, competitiveness, innovation and knowledge economy in macro-economic level by using multivariate statistical analysis and data mining applications for extracting usable patterns to direct development policy.

1 Introduction

The organisations strive to reduce total cost through supply chain, production cycle, and inventory. Additionally, they request increasing diversity of product, more accurate delivery dates and coordinating the supply and production effectively (Liao et al., 2007). Enterprise resource planning (ERP) is increasingly important in modern business because of its ability to integrate the flow of material, finance, and information and to support organisational strategies (Yusuf et al., 2004; Yao and He, 2000). Increases of pressure on companies to lower total costs in the entire supply chain, shorten throughput times, drastically reduce inventories, expand product choice, provide more reliable delivery dates and better customer service, improve quality, and efficiently coordinate global demand, supply, and production (Shankarnarayanan, 2000). ERP is a system that integrates all of the business functions into a single system, designed to serve the needs of each different department within the enterprise. ERP is more of a methodology than a piece of software, although it incorporates several software applications, brought together under a single, integrated interface (Kahraman et al., 2010). Yazgan et al. (2009) defines ERP as an integrated, consulate enterprise wide information system (IS) that combines all necessary business functions like production planning, purchase, inventory control, sales, finance, human resource. Quality and cost do not suffice in competition and therefore new competition parameters are needed like delivery date in right time and customise product (Yusuf et al., 2006).

According to Umble et al. (2003) ERP provides two major benefits that do not exist in non-integrated departmental systems:

1 a unified enterprise view of the business that encompasses all functions and departments

2 an enterprise database where all business transactions are entered, recorded, processed, monitored, and reported.

Therefore ERP selection is an important decision making problem of organisation and affects directly the performance. The ERP selection is tiresome and time consuming in terms of complexity of business environment, resource shortages. There are a lot of ERP alternatives in market (Wei and Wang, 2004). The best suitable ERP system selection yields positive results like increasing productivity, timely delivery, reduction of setup time, reduction of purchasing cost. Owing to the complexity of the business environment, the limitations in available resources, and the diversity of ERP alternatives, ERP system selection is tedious and time consuming. However, given the considerable financial investment and potential risks and benefits, the importance of a pertinent ERP system selection cannot be overemphasised (Teltumbde, 2000).

ERP software selection with MCDM 437

Motwani et al. (2002) emphasised that ERP adoption involves initiating appropriate business process changes as well as information technology changes to significantly enhance performance, quality, costs, flexibility, and responsiveness. However, many companies install their ERP systems hurriedly without fully understanding the implications for their business or the need for compatibility with overall organisational goals and strategies (Hicks and Stecke, 1995). The result of this hasty approach is failed projects or weak systems whose logic conflicts with organisational goals. Wei et al. (2005) mentions that since the business environment are characterised by high uncertainty, the process of ERP system assessment involves numerous problems. Kumar et al. (2002) emphasised that installing an ERP system is much more than having another information technology tool; it is a decision on how to shape the organisational business. Ayağ and Özdemir (2007) state that one of the most critical issues in implementation of an ERP system is the selection of the appropriate software to be used. Because ERP software dramatically changes the structure of a company by integrating its business functions using database technology, this provides a greater opportunity to improve the effectiveness of its business activities, the selection process for determining the most satisfying ERP software among a set of possible alternatives in the market should be achieved using one of the proven MCDM methods with a team consisting of the company, the software developer, and the vendor or consultant firm.

In this study, as a multi-criteria decision making method TODIM is employed. The purpose of this study is to provide more applicable selection process for not only manufacturing companies but also the other companies that aim to implement ERP system. The major contribution of using TODIM has two aspects; first is to combine both qualitative and quantitative data in order to provide a new path toward most suitable ERP software selection for decision makers in companies and second, although there are many multi-criteria problems, they do not deal with risk, the TODIM method includes the same as the gain/loss function of prospect theory. Thus, the TODIM method is connected with a global multi-attribute value function. By using the TODIM method, the proposed framework provides a new aspect that involves psychological factors such as risk aversion or risk seeking in case of gain/loss function.

This study is structured as follows: in the second section, the methods used before are reviewed in literature. In the third section the TODIM method is explained. The proposed methodology explained in the fourth and the real-time application is presented in the fifth section. The final section presents the conclusions of the study.

2 ERP software selection

There are vast amount of researches on ERP and software selection (see also Nazemi et al., 2012). The selected papers are presented to demonstrate the methods and criteria, which were used before in this section. Wei et al. (2005) stated that ERP system selection decision, especially when some attributes are not readily quantifiable, as well as not too easy for managers to understand. Moreover, these methodologies focus too much on quantifiable calculations and look down upon the comprehensive selection framework of ERP system and the strategic considerations of a company. This is the first gap in the literature that this study tries to fill by combining both qualitative and quantitative data in the same MCDM technique, which is TODIM in the study. The failure in selection of ERP system leads to the failure of project or company performance will get weakened


(Liao et al., 2007). This is the second gap in the literature that this study tries to fill by hiring TODIM in which the risk concept is present in its structure making it different from other MCDM and supporting the need of its application in ERP software selection.

Stefanou (2001) argues that the framework of ERP systems evaluation and selection should involve not only the analysis of ERP product but also includes its additional applications throughout its life cycle. Tsai et al. (2009) point out the importance of ERP software selection criterion that is linked to software quality, information quality and ERP system success in the ERP implementation process. Companies should conduct a requirements analysis first to make sure what problems need to be solved and select ERP systems that best suit their requirements. Two main aspects should be taken into consideration when selecting software/hardware:

1 compatibility of the software/hardware with the company’s needs

2 ease of customisation (Zhang et al., 2003).

Liang and Lien (2007) proposed a procedure of ERP software selection that combines fuzzy analytic hierarchy process (FANP) and ISO 9126 standard.

In their recent research, Zielsdorff et al. (2010) made a research about decision making process, selection and implementation of a new system. The methodology used in that paper involved request for information (RFI), analytical hierarchical process (AHP), technology implementation envelope (TIE) and the technology acceptance model (TAM). RFI were sent to several vendors and six responses were received which were used to score each alternative in an AHP model. The result of the AHP model outputted the best alternative with respects to this company’s goals. Next, the different modules that make up the ERP system were assessed and TAM was used quantify the user’s perception and attitudes towards the system. With these two pieces of information, TIE was used in order to determine the best possible implementation plan for the system. That project’s end result will identify the best alternative to select and propose an ERP system for the medical device manufacturer.

Chang et al. (2008) proposed a conceptual model derived from the Triandis framework that is based on the importance of social factors on the adoption of the ERP system. Yang et al. (2007) represented a case study that involves the selection of ERP system suppliers and contract negotiation during ERP implementation of a local construction company in Taiwan. Lin et al. (2007) provide a comprehensive review of software selection applications. Karsak and Özoğul (2009) state that the methods, which have been applied to ERP or other IS selection, include scoring, mathematical programming, and multi-criteria decision analysis.

In his recent research, Sen and Baracli (2010) presents a fuzzy quality function deployment approach for determining which of the non-functional requirements reported by earlier studies are important to a company’s software selection decision based on and integrated with its functional requirements. The solution provided in this study not only assists decision makers in acquiring software requirements and defining selection criteria, but also supports determining the relative importance of these criteria. An actual case in audio electronics of Turkey’s electronic industry demonstrates the feasibility of applying the proposed framework in practice.

Ahn and Choi (2008) present a simulation-based AHP (SiAHP) method for group decision making and is applied to the real-world problem of selecting a suitable ERP system for a Korean homeshopping company. To enhance the fitness of a group AHP


method and to facilitate the ERP system selection process, this paper proposes a simulation-based approach for building a group consensus instead of forming point estimates that are aggregated from individual preference judgments. To be specific, the proposed method is based on observations from empirically observed frequency distributions and does not use aggregation procedures, compared to typical group AHP for obtaining a group solution. This approach, reflecting the diversification of group members’ opinions as they are, is conceived to be useful as a tool for obtaining insights into agreements and disagreements with respect to the alternatives among the individuals of a group.

Al-Mashari et al. (2008) presented several case studies of ERP software selection and determined different factors and criteria that influence the decision of choosing an ERP solution. They also provided an analysis of the critical success factors for a successful implementation in order to demonstrate their dependence upon the selection process criteria. That study was concluded with a proposed framework for the standard selection process of ERP systems, and attempted to streamline one of the most important decisions in the ERP implementation process and provided an alternative framework for the ERP systems selection process.

Teltumbde (2000) proposed a methodology based on the nominal group technique and the analytic hierarchy process (AHP) for evaluating ERP systems. In their recent work, Wei et al. (2005) used the AHP to systematically construct the objectives of ERP selection to support the business goals and strategies of an enterprise, identify the appropriate attributes, and set up a consistent evaluation standard for facilitating a group decision process.

Other methods employing non-linear programming models and zero–one goal programming models are also proposed for the selection of a suitable IS. Santhanam and Kyparisis (1995, 1996) proposed non-linear zero–one programming models for IS project selection. Santhanam and Kyparisis (1995) presented a multi-criteria decision model for IS project selection which utilises non-linear zero–one goal programming. Santhanam and Kyparisis (1996) developed a non-linear zero–one programming model which considered technical interdependencies among IS projects. Lee and Kim (2000) claimed that Santhanam and Kyparisis’ model dealt with IS selection problems with limited criteria. They combined the ANP and a 0–1 goal-programming model to select an IS project. Badri et al. (2001) presented a 0–1 goal programming model to select an IS Project considering multiple criteria including benefits, hardware, software and other costs, risk factors, preferences of decision makers and users, completion time, and training time constraints.

Wei and Wang (2004) suggested a hierarchical attribute structure model to evaluate the ERP alternatives systematically. They proposed a framework employing an integration model that uses the fuzzy average method and fuzzy integral value ranking for ERP system selection combining data obtained from professional studies with that surveyed from interviews with vendors.

Performance-based applications are another group for ERP selection. Fisher et al. (2004) used DEA to analyse and compare the performance of ERP packages. Lall and Teyarachakul (2006) provided a case study on how DEA can be applied for ERP performance evaluation based on the real corporate data reflecting the organisation’s needs and requirements. In a recent study, Bernroider and Stix (2006) combined the utility ranking method and the DEA to overcome the limitations of DEA in software selection. Cebeci (2009) represented an approach based on a fuzzy extension of the


multi-criteria decision making techniques AHP that was used to compare ERP systems for textile industry. Kalbande and Thampi (2010) proposed a framework based on Neuro-Analytical Hierarchy Process (NAHP) in order to select and evaluate ERP software.

From the criteria perspective, Umble et al. (2003) reviewed a list of critical success factors of IT/IS, ERP software implementation. ERP selection process begins with planning, followed by information search, selection, evaluation, negotiation and ends up with choice (Verville and Halingten, 2002). Also, Kamhawi and Gunasekaran (2009) used a field study to compare between the perceptions of the IS managers and non-IS managers who work in Bahraini enterprises using ERP systems concerning implementation success and the predicators for such systems. The study results showed that the meaning of success of ERP systems did not seem to differ between IS and non-IS managers. They both adopt user satisfaction and business value metrics to measure success. Moreover, both groups perceived ‘resistance’ and ‘organisational fit’ as the prime factors for ERP success. However, their perceptions about the rest of the success factors investigated in this study differed, as IS managers selected ‘ease of use’, ‘training’ and ‘technical fit’ as success predicators while non-IS managers selected ‘competitive pressures’, ‘strategic fit’ and ‘business process reengineering’ factors. In the end, implications concerning the differences between the perceptions of the two main stakeholders have been put forward. Benefits associated with a strategic IT/IS investment like ERP often consisting of significant intangible non-financial benefits (Hall et al., 1997). Karaarslan and Gundogar (2009) suggested that, therefore experts started to propose some non-financial techniques to include the intangible costs and benefits into account. Another approach is off-the-shelf option (OTSO) is a method oriented to the search, evaluation and selection of reusable in a six-phase process proposed by Kontio (1995).

Benlian and Hess (2011) focused on another issue. They compared the relative importance of evaluation criteria in proprietary and open-source enterprise application software selection in their study. They found that the relative importance of evaluation criteria significantly varies between proprietary and opensource ERP systems. Implementation factors such as ease of implementation and support are much more crucial in the evaluation of open-source than of proprietary ERP systems, which is generally due to IS managers’ risk mitigation behaviour. Interestingly, there are no major differences in the ranking of evaluation criteria between proprietary and open-source Office systems.

3 The TODIM method

The TODIM method (an acronym in Portuguese of interactive and multi-criteria decision making), conceived in its current form at the beginning of the nineties by Gomes and Lima (1992b), is a discrete multi-criteria method based on prospect theory (Kahneman and Tversky, 1979). This means that underlying the method is a psychological theory, which was the subject of the Nobel Prize for Economics awarded in 2002. Thus, while practically all other multi-criteria methods start from the premise that the decision maker always looks for the solution corresponding to the maximum of some global measure of value – for example, the highest possible value of a multi-attribute utility function, in the


case of MAUT (Keeney and Raiffa, 1993) – the TODIM method makes use of a global measurement of value calculable by the application of the paradigm of prospect theory. In this way, the method is based on a description, proved by empirical evidence, of how people effectively make decisions in the face of risk. Although not all multi-criteria problems deal with risk, the shape of the value function of the TODIM method is the same as the gain/loss function of prospect theory. The use of TODIM relies on a global multi-attribute value function. This function is built in parts, with their mathematical descriptions reproducing the gain/loss function of prospect theory. The global multi-attribute value function of TODIM then aggregates all measures of gains and losses over all criteria.

In its calculations the TODIM method must test specific forms of the losses and gains functions. Each one of the forms depends on the value of one single parameter. The forms, once validated empirically, serve to construct the additive difference function of the method. This notion of an additive utility function is taken from Tversky (1969). The additive difference function is indeed a global multi-attribute value function and reflects the dominance measurements of each alternative over each other alternative. In this sense, TODIM maintains a similarity with outranking methods, such as PROMÉTHÉE (Brans and Mareschal, 1990), because the global value of each alternative is relative to its dominance over other alternatives in the set. Although it appears complicated to have to test the validity of the application of the paradigm to the database, which may on occasions oblige the decision analyst to use other forms of the losses and gains functions, in fact it is not so. Since the first practical uses of the TODIM method, back in the 90s, the same two mathematical forms have been used successfully, and have been validated empirically in different applications (Gomes and Lima, 1992a, 1992b; Trotta et al., 1999).

From the construction of the aforementioned TODIM additive difference function, which functions as a multi-attribute value function and, as such, must also have its use validated by the verification of the condition of mutual preferential independence (Keeney and Raiffa, 1993; Clemen and Reilly, 2001), the method leads to a global ordering of the alternatives. It can be observed that the construction of the multi-attribute value function, or additive difference function, of the TODIM method is based on a projection of the differences between the values of any two alternatives (perceived in relation to each criterion) to a referential criterion or reference criterion.

The TODIM method makes use of pair comparisons between the decision criteria, using technically simple resources to eliminate occasional inconsistencies arising from these comparisons. It also allows value judgments to be carried out in a verbal scale, using a criteria hierarchy, fuzzy value judgments and making use of interdependence relationships among the alternatives. It is a non-compensatory method in the sense that tradeoffs do not occur (Bouyssou, 1986).

Roy and Bouyssou (1993), talking about the TODIM method, state that it is: “...a method based on the French School and the American School. It combines aspects of the Multi-attribute Utility Theory, of the AHP method and the ELECTRE methods”.

The concept of introducing expressions of losses and gains in the same multi-attribute function, present in the formulation of the TODIM method, gives this method some similarity to the PROMÉTHÉE methods, which make use of the notion of net outranking flow. Barba-Romero and Pomerol (2000) have stated the following in respect of the


TODIM method: “it is based on a Notion extremely similar to a net flow, in the PROMÉTHÉE sense”.

Consider a set of n alternatives to be ordered in the presence of m quantitative or qualitative criteria, and assume that one of these criteria can be considered as the reference criterion. After the definition of these elements, experts are asked to estimate, for each one of the qualitative criteria c, the contribution of each alternative i to the objective associated with the criterion. This method requires the values of the evaluation, of the alternatives in relation to the criteria, to be numerical and to be normalised; consequently the qualitative criteria evaluated in a verbal scale are transformed into a cardinal scale. The evaluations of the quantitative criteria are obtained from the performance of the alternatives in relation to the criteria, such as, for example, the level of noise measured in decibels, the power of an engine measured in horsepower or a student’s mark in a subject, etc.

TODIM can therefore be used for qualitative as well as quantitative criteria. Verbal scales of qualitative criteria are converted to cardinal ones and both types of scales are normalised. The relative measure of dominance of one alternative over another is found for each pair of alternatives. This measure is computed as the sum over all criteria of both relative gain/loss values for these alternatives. The parts in this sum will be gains, losses, or zeros, depending on the performance of each alternative with respect to every criterion.

The evaluation of the alternatives in relation to all the criteria produces the matrix of evaluation, where the values are all numerical. Their normalisation is then performed, using, for each criterion, the division of the value of one alternative by the sum of all the alternatives. This normalisation is carried out for each criterion, thus obtaining a matrix, where all the values are between zero and one. It is called the matrix of normalised alternatives’ scores against criteria. P = [Pnm], with n indicating the number of alternatives and m the number of criteria, as shown in Table 1.

After the attribution of the weights of the criteria and their normalisation, the partial matrices of dominance and the final matrix of dominance must be calculated. The decision makers must indicate which criterion r is to be chosen as the reference criterion for the calculations according to the relative importance assigned to each criterion. In this way, the criterion with the highest value accorded to its importance will usually be chosen as the reference criterion. The weight of each criterion is determined by the decision makers on a numerical scale (e.g., from 1 to 5) and is then normalised. Thus, wrc is the weight of criterion c divided by the weight of the reference criterion r. Using wrc allows all pairs of differences between performance measurements to be translated into the same dimension, i.e., that of the reference criterion. The measurement of dominance of each alternative Ai over each alternative Aj, now incorporated to prospect theory, is given by the mathematical expression

( ) ( )1

, ,m

i j c i jc

δ A A A A=

= Φ∑ (1)

( , ).i j∀

when


( ) ( ) ( )

1

, if 0,rc ic jcc i j ic jcm

rcc

w P PA A P P

w=

−Φ = − >

∑ (2)

( )0 if 0,ic jcP P= − = (3)

( )( )11 if 0,

m

rc jc icc

ic jcrc

w P PP P

θ w=

⎛ ⎞−⎜ ⎟⎜ ⎟− ⎝ ⎠= − <

∑ (4)

Table 1 Matrix of normalised alternatives’ scores against criteria

Criteria Alternatives

C1 C2 … Cj … Cm

A1 P11 P12 … P1j … P1m

A2 P21 P22 … P2j … P2m

… … … … … … … Ai Pi1 Pi2 … Pij … Pim

… … … … … … … An Pn1 Pn2 … Pnj … Pnm

Thus, δ(Ai, Aj) represents the measurement of dominance of alternative Ai over alternative Aj; m is the number of criteria; c is any criterion, for c = 1,…, m; wrc is equal to wc divided by wr, where r is the reference criterion; Pic and Pjc are, respectively, the performances of the alternatives Ai and Aj in relation to c; θ is the attenuation factor of the losses; different choices of θ lead to different shapes of the prospect theoretical value function in the negative quadrant.

The expression Φc(Ai, Aj) represents the parcel of the contribution of criterion c to function δ(Ai, Aj), when comparing alternative i with alternative j. If the value of Pic – Pjc is positive, it will represent a gain for the function δ(Ai, Aj) and, therefore the expression Φc(Ai, Aj) will be used, corresponding, that is, to equation (2). If Pic – Pjc is nil, the value zero will be assigned to Φc(Ai, Aj) by applying equation (3). If Pic – Pjc is negative, Φc(Ai, Aj) will be represented by equation (4). The construction of function Φc(Ai, Aj) in fact permits an adjustment of the data of the problem to the value function of prospect theory, thus explaining the aversion and the propensity to risk. This function has the shape of an ‘S’, represented in Figure 1. Above the horizontal axis, considered as a reference for this analysis, there is a concave curve representing the gains, and, below the horizontal axis, there is a convex curve representing the losses. The concave part reflects the aversion to risk in the face of gains and the convex part, in turn, symbolises the propensity to risk when dealing with losses.

After the diverse partial matrices of dominance have been calculated, one for each criterion, the final dominance matrix of the general element δ(Ai, Aj) is obtained, through the sum of the elements of the diverse matrices.

Equation (5) is used to determine the overall value of alternative i through normalisation of the corresponding dominance measurements. The rank of every alternative originates from the ordering of their respective values.


Figure 1 Value function of the TODIM method

Source: Gomes and Lima (1992b)

( ) ( )

( ) ( )1 1

1 1

, min ,.

max , min ,

n ni j i jj j

i n ni j i jj j

δ A A δ A Aξ

δ A A δ A A

= =

= =

−=

−

∑ ∑∑ ∑

(5)

Therefore, the global measures obtained computed by (5) permit the complete rank ordering of all alternatives. A sensitivity analysis should then be applied to verify the stability of the results based on the decision makers’ preferences. The sensitivity analysis should therefore be carried out on θ as well as on the criteria weights, the choice of the reference criterion, and performance evaluations.

4 Proposed methodology

The TODIM is used in the application phase of the ERP software problem. The main reasons of choosing TODIM are; first is to combine both qualitative and quantitative data in order to provide a new path toward most suitable ERP software selection for decision makers in companies and second, although there are many multi-criteria techniques, they do not deal with risk, whereas the TODIM method includes the same as the gain/loss function of prospect theory.


In this study, the criteria proposed by Teltumbde (2000) are used. Teltumbde (2000) proposed a framework evaluating ERP project based on the nominal group technique and AHP. In his paper, he proposed ten criteria on this consideration are (C1) strategy-fit, (C2) cost, (C3) change management, (C4) implementability, (C5) risk, (C6) business functionality, (C7) vendor credentials, (C8) flexibility, (C9) technology, (C10) benefits so m = 1,…, 10. By using the proposed methodology, the set of the criteria can be reformed according to the different needs and expectations of the company. Thus, this methodology can be implemented in various companies and also industries. The selected criteria for a steel forming and hot dip galvanising firm are described below.

C1 Strategy-fit

By rapidly changing in business and technological environment and increasing competition in the industry, ERP projects have become strategically imperative. In fact, there are many projects, which are the direct results of business strategy in the industry. Moreover, there are many strategic drivers for ERP projects such as reducing product development cycle time and response, increasing the IT intensity in organisations, etc. As a result, best-suited ERP software may serve the specific needs pf the companies.

C2 Technology

Because of the rapid changes in technology, ERP has become one of the major IT investments for man companies. Furthermore, the technology has an essential role to determine the longevity of the product. These rapid changes in technology can cause obsolescence that affects IT applications. Therefore, if the ERP products are more technology oriented, it may cause the potential risk of obsolescence. In order to deal with this risk, the ERP software design is critical to avoid the crucial technologies such as hardware, operating systems, networks, etc. The technology is also important role for the flexibility and scalability dimensions of the projects. Therefore, it directly affects the total cost of the project.

C3 Change management

The characteristics of ERP products are various and need their specific business model. ERP implementation is predominantly a change management project (Kay, 1998). Therefore, ERP products have the ability to enable the regulated change constitutes an important parameter in project evaluation. In order to take an advantage in using ERP software, companies need to adopt a more dynamic approach to conducting business and should change themselves.

C4 Risk

Risk is a measure of the degree of possible variation in the outcome or benefits of the project (Fitzgerald, 1998). The risk depends on the size of investment and the complexity of the enterprise. The project management also related risks; ERP projects have more serious risks in ERP projects, which relate to technology and process. Therefore, ERP projects may cause the substantial risk.


C5 Implementability

Implementability is related with the degree of mismatch between the available technical infrastructure and the product requirements. Various ERP products put various demands on technical architecture, especially in terms of capacities of communication links. Thus the assessment of implementability of ERP projects is an important subject for the companies.

C6 Business functionality

In the selection process of ERP systems, companies expect to select ERP products that have the best functional fit according to their business processes. All major ERP products offer a broad support across industries; in fact, it is almost impossible for these ERP products to it totally. The generic functionality of ERP products is unlikely to meet all the industry-specific functionalities. Therefore, business functionality is another important subject for the selection of ERP software.

C7 Vendor credentials

Besides the expected longevity of ERP products, the vendor’s commitment to the product, the vendor’s capability to support it and the vendor’s support infrastructure constitute significant parameters. The commitment may depend on the relative importance of the product in the vendor’s product portfolio. The capability may be assessed other basis of the help of certain surrogates like the earning profile of the vendor, the market share of the vendor and the general situation of vendor’s balance sheet.

C8 Flexibility

Flexibility represents the capability of the system to fulfil the needs of the business over its lifetime. It also relates to provide various options to configure or improve business processes with relative case. The lack of flexibility may be the result of rendering the system suboptimal or even obsolete. During the life cycle of the product, the flexibility may be utilised to predict large-scale changes.

C9 Cost

ERP products have a wide range of costs. Therefore, some ERP products considered more expensive than the others. The cost relates to the total cost. Although the implementation costs change significantly for different ERP software, it is not easy to obtain the cost data on ERP projects.

C10 Benefits

Benefits are also same as costs and directly related to various ERP products. Although some business benefits can be easily quantifiable, many important benefits cannot be quantifiable. Therefore, it is not easy to estimate total benefits from ERP projects.


The criteria listed above are used in the MCDM of the study. Therefore the methodology of the study can be listed as following:

Step 1 listing the criteria to be used and ERP software alternatives to be compared

Step 2 deciding on the composition of the group of experts to be included in ERP selection

Step 3 evaluating the importance of the criteria with pairwise comparison

Step 4 calculate a weight for each criterion

Step 5 let experts to assess alternatives with various scales, including linguistic variables, numeric values and even 0–1 type values.

Step 6 hiring the TODIM method

Step 7 ranking the alternatives.

5 Application

The application is conducted in a steel forming and hot dip galvanising firm located in Izmir, Turkey with an initiative to install ERP system but have a serious problem of which software to use a common question for this type of companies which has intent, however lack in the selection phase because of not know how to proceed.

The application is conducted with the aid of criteria presented in Section 4, with five experts with the following composition; the general manager of the firm, the IT manager, the operations manager, the consultant of the firm and a member of the firm’s executive board. There are six ERP software alternatives that are compared so that n = 1,…, 6.

The pairwise comparison of the criteria is conducted with the following scales:

• very important (VI)

• important (I)

• equal (E)

• unimportant (U)

• very unimportant (VU) with values of 5, 4, 3, 2, 1 respectively.

The assessment of the alternatives is conducted with the following scale:

• very good (VG)

• good (G)

• fair (F)

• poor (P)

• very poor (VP) with values of 5, 4, 3, 2, 1 respectively.

The attenuation factor of the losses is taken as 1. In order to calculate the weights of each criterion, each decision maker is asked to

evaluate the importance of each criterion in pairwise comparison. The weights represent


the importance of the criteria and are calculated by pairwise comparison with evaluation of five experts and the weights are shown in Table 2. Table 2 Weights of the criteria

wrc

C1 0.202 1.000 C2 0.169 0.835 C3 0.129 0.637 C4 0.118 0.586 C5 0.114 0.566 C6 0.075 0.373 C7 0.062 0.308 C8 0.054 0.269 C9 0.038 0.186 C10 0.038 0.186 Sum 1.000 4.947

After calculating the weights of each criterion, each decision maker is asked to estimate, for each one of the qualitative criteria C, the contribution of each alternative i to the objective associated with the criterion by using the scale given above (VG, G, F, P, VP with values of 5, 4, 3, 2, 1 respectively). The measurement of dominance of alternative A1 over all other alternatives is shown in Table 3. Table 3 The measurement of dominance of alternative A1 to other alternatives

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10

A1–A1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 A1–A2 –0.390 –0.228 0.103 –0.535 –1.707 0.067 –0.455 0.059 –1.243 –0.764 A1–A3 0.047 –0.332 0.092 –0.465 –1.707 0.047 0.046 0.043 0.031 0.050 A1–A4 0.071 –0.501 0.076 –0.478 0.000 0.047 0.019 0.077 –0.897 0.044 A1–A5 0.133 –0.228 0.074 0.063 –1.707 –1.082 0.046 0.072 –1.215 0.029 A1–A6 0.120 –0.420 0.039 –0.280 0.000 0.047 0.046 –0.549 –1.089 –1.005

In order to form the final dominance matrix, the dominance of each alternative is used. In this step, the TODIM method allows us to see the relationship among each alternative. The final dominance matrix is shown in Table 4. Table 4 The final dominance matrix

A1 A2 A3 A4 A5 A6

A1 0.000 –2.282 –4.956 –4.289 –4.226 –2.052 A2 –5.092 0.000 –4.438 –6.010 –4.307 –4.052 A3 –2.147 –1.665 0.000 –2.908 –2.365 –2.333 A4 –1.542 –1.115 –3.096 0.000 –1.993 –1.588 A5 –3.814 –1.564 –3.917 –5.005 0.000 –3.119 A6 –3.091 –3.112 –4.551 –3.954 –4.100 0.000


After forming of the final dominance matrix by using the calculated dominance of each alternative, the global values for each alternative are calculated. In order to find the most suitable choice for the company according to the selected criteria, each ERP software alternative is ranked by using their global values and shown in Table 5. Table 5 Ranking of alternatives according to global values

Global value (ξi)

A2 1.000

A6 0.726

A1 0.521

A5 0.416

A3 0.097

According to the global values of each alternative, the global value of A2 is greater than the rest of the other alternatives and it can be considered as the best choice that satisfies the necessary conditions for the optimal ERP software for the company. As a second choice, A6 is considered. The other alternatives, A1 and A5 are ranked respectively according to their global values. On the other hand, because of its lowest value, which is 0.097, A3 is considered as the least suitable choice on the list according to the selected criteria for the ERP software selection.

6 Conclusions

The MCDM methods support the decisions given in the business life, which contains many criteria waiting to be satisfied. The uniqueness of this study for the ERP software selection problem is that, the qualitative and quantitative factors with different scales are combined in the same technique, also the risk concept is inserted in the analysis and last, TODIM method is applied to ERP software selection for the first time. The advantages of hiring TODIM method can be listed as; using various type of scales in assessing criteria weights and especially in alternative evaluations by the help of group of decision makers; using various types of scales enriches the flexibility of decision makers and doing so enables to be more in line with the real life conditions; both qualitative and quantitative as well as both cost or benefit criteria can be used. Furthermore, by employing the TODIM method, the psychological factors such as risk aversion and risk seeking are considered in this study. The managerial point of the study is to propose a road map for managers in ERP software selection. In spite of the different backgrounds of the managers, this study also provides the managers to analyse the ERP software selection problem within a common procedure. Although there are many decision-making methods that based on complex calculations, the proposed framework can be applicable easily by the companies in various industries. In order to collect more accurate data from different individuals and also help each decision maker to express their opinions efficiently in the decision making process, future studies may concentrate on the implementation of fuzzy logic to TODIM method. By doing this, the uncertainty and ambiguity in the decision making process can be reduced by using fuzzy logic.


References Ahn, B.S. and Choi, S.H. (2008) ‘ERP system selection using a simulation-based AHP approach: a

case of Korean home shopping company’, Journal of the Operational Research Society, Vol. 59, No. 3, pp.322–330.

Al-Mashari, M., Ghani, S.K. and Al-Braithen, M. (2008) ‘The enterprise resource planning (ERP) selection process: case analysis and proposed framework’, International Journal of Business Information Systems, Vol. 3, No. 2, pp.120–139.

Ayağ, Z. and Özdemir, R.G. (2007) ‘An intelligent approach to ERP software selection through fuzzy ANP’, International Journal of Production Research, Vol. 45, No. 10, pp.2169–2194.

Badri, M.A., Davis, D. and Davis, D. (2001) ‘A comprehensive 0–1 goal programming model for project selection’, International Journal of Project Management, Vol. 19, No. 4, pp.243–252.

Barba-Romero, S. and Pomerol, J.C. (2000) Multicriterion Decision in Management: Principles and Practice, Kluwer Academic Publishers, Boston.

Benlian A. and Hess T. (2011) ‘Comparing the relative importance of evaluation criteria in proprietary and open-source enterprise application software selection – a conjoint study of ERP and Office systems’, Info Systems J., Vol. 21, No. 6, pp.503–525.

Bernroider, E.W.N. and Stix, V. (2006) ‘Profile distance method – a multiattribute decision making approach for information system investments’, Decision Support Systems, Vol. 42, No. 2, pp.988–998.

Bouyssou, D. (1986) ‘Some remarks on the notion of compensation in MCDM’, European Journal of Operational Research, Vol. 26, No. 1, pp.150–160.

Brans, J.P. and Mareschal, B. (1990) ‘The PROMETHEE methods for MCDM, the PROMCALC GAIA and BANDADVISER software’, in Bana e Costa, C.A. (Ed.): Readings in Multiple Criteria Decision Aid, Springer Verlag, Berlin.

Cebeci, U. (2009) ‘Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard’, Expert Systems with Applications, Vol. 36, No. 5, pp.8900–8909.

Chang, M-K., Cheung, W., Cheng, C-H. and Yeung, J.H.Y. (2008) ‘Understanding ERP system adoption from the user’s perspective’, International Journal of Production Economics, Vol. 113, No. 2, pp.928–942.

Clemen, R.T. and Reilly, T. (2001) Making Hard Decisions with Decision Tools, Pacific Grove, Duxbury.

Fisher, D.M., Kiang, M.Y., Fisher, S.A. and Chi, R.T. (2004) ‘Evaluating mid-level ERP software’, Journal of Computer Information Systems, Vol. 45, No. 1, pp.38–45.

Fitzgerald, G. (1998) ‘Evaluating information systems projects: a multi-dimensional approach’, Journal of Information Technology, Vol. 13, No. 1, pp.15–27.

Gomes, L.F.A.M. and Lima, M.M.P.P. (1992a) ‘From modeling individual preferences to multicriteria ranking of discrete alternatives: a look at prospect theory and the additive difference model’, Foundations of Computing and Decision Sciences, Vol. 17, No. 3, pp.171–184.

Gomes, L.F.A.M. and Lima, M.M.P.P. (1992b) ‘TODIM: basics and application to multicriteria ranking of projects with environmental impacts’, Foundations of Computing and Decision Sciences, Vol. 16, No. 4, pp.113–127.

Hall, Z., Ezingeard, J.N. and Grieve, R.J. (1997) ‘Integrating the cost of a manufacturing IT/IS infrastructure into the investment decision-making process’, Technovation, Vol. 17, Nos. 11–12, pp.695–706.

Hicks, D.A. and Stecke, K.E. (1995) ‘The ERP maze: enterprise resource planning and other production and inventory control software’, IIE Solutions, Vol. 27, No. 8, pp.12–16.

Kahneman, D. and Tversky, A. (1979) ‘Prospect theory: an analysis of decision under risk’, Econometrica, Vol. 47, No. 2, pp.263–292.


Kahraman, C., Beskese, A. and Kaya, I. (2010) ‘Selection among ERP outsourcing alternatives using a fuzzy multi-criteria decision making methodology’, International Journal of Production Research, Vol. 48, No. 2, pp.547–566.

Kalbande, D.R. and Thampi, G.T. (2010) ‘Neuro-Analytical Hierarchy Process (NAHP) approach for selecting ERP technologies in a ongoing project settings’, Proceedings of 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), Vol. 9, pp.274–279, doi: 10.1109/ICCSIT.2010.5564949.

Kamhawi, E.M. and Gunasekaran, A. (2009) ‘ERP systems implementation success factors: IS and non-IS managers’ perceptions’, International Journal of Business Information Systems, Vol. 4, No. 6, pp.688–704.

Karaarslan, N. and Gundogar, E. (2009) ‘An application for modular capability-based ERP software selection using AHP method’, Int. J. Adv. Manuf. Technol., Vol. 42, Nos. 9–10, pp.1025–1033.

Karsak, E.E. and Özoğul, C.O. (2009) ‘An integrated decision making approach for ERP system selection’, Expert Systems with Applications, Vol. 36, No. 1, pp.660–667.

Kay, E. (1998) Going Global with ERP, available at http://itmanagement.earthweb.com/entdev/article.php/603341/Going-global-with-ERP.htm (accessed on 21 July 2011).

Keeney, R.L. and Raiffa, H. (1993) Decisions with Multiple Objectives: Preferences and Value Tradeoffs, Cambridge University Press, Cambridge.

Kontio, J. (1995) ‘OTSO: ‘a systematic process for reusable software component selection’’, Univ. of Maryland Research Report, CS-TR-3478 UMIASC-TR-95-63.

Kumar, V., Maheshwari, B. and Kumar, U. (2002) ‘Enterprise resource planning systems adoption process: a survey of Canadian organizations’, International Journal of Production Research, Vol. 40, No. 3, pp.509–523.

Lall, V. and Teyarachakul, S. (2006) ‘Enterprise resource planning (ERP) system selection: a data envelopment analysis (DEA) approach’, Journal of Computer Information Systems, Vol. 47, No. 1, pp.123–127.

Lee, J.W. and Kim, S.H. (2000) ‘Using analytic network process and goal programming for interdependent information system project selection’, Computers, Operations Research, Vol. 27, No. 4, pp.367–382.

Liang, S-K. and Lien, C-T. (2007) ‘Selecting the optimal ERP software by combining the ISO 9126 standard and fuzzy AHP approach’, Contemporary Management Research, Vol. 3 No. 1, pp.23–44.

Liao, X., Li, Y. and Lu, B. (2007) ‘A model for selecting an ERP system based on linguistic information processing’, Information System, Vol. 32, No. 7, pp.1005–1017.

Lin, H-Y., Hsu, P-Y. and She”en, G-J. (2007) ‘A fuzzy-based decision making procedure for data warehouse system selection’, Expert Systems with Applications, Vol. 32, No. 3, pp.939–953.

Motwani, J., Mirchandani, D., Madan, M. and Gunasekaran, A. (2002) ‘Successful implementation of ERP projects: evidence from two case studies’, International Journal of Production Economics, Vol. 75, Nos. 1–2, pp.83–96.

Nazemi, E., Tarokh, M.J. and Djavanshir, G.R. (2012) ‘ERP: a literature survey’, Int. J. Adv. Manuf. Technol., Vol. 61, Nos. 9,–12, pp.999–1018.

Roy, B. and Bouyssou, D. (1993) Aide multicrite`re a` la de´csion: Me´thodes et cas. Economica, Gestion, Paris.

Santhanam, R. and Kyparisis, G.J. (1996) ‘A decision model for interdependent information system project selection’, European Journal of Operational Research, Vol. 89, No. 2, pp.380–399.

Santhanam, R. and Kyparisis, J. (1995) ‘A multiple criteria decision model for information system project selection’, Computers and Operations Research, Vol. 22, No. 8, pp.807–818.

Sen, C.G. and Baracli, H. (2010) ‘Fuzzy quality function deployment based methodology for acquiring enterprise software selection requirements’, Expert Systems with Applications, Vol. 37, No. 4, pp.3415–3426.


Shankarnarayanan, S. (2000) ERP Systems – Using IT to Gain a Competitive Advantage, available at http://www.angelfire.com/co/troyc/advant.html (accessed on 21 July 2011).

Stefanou, C.J. (2001) ‘A framework for the ex-ante evaluation of ERP software’, European Journal of Information Systems, Vol. 10, No. 4, pp.204–215.

Teltumbde, A. (2000) ‘A framework for evaluating ERP projects’, International Journal of Production Research, Vol. 38, No. 17, pp.4507–4520.

Trotta, L.T.F., Nobre, F.F. and Gomes, L.F.A.M. (1999) ‘Multi-criteria decision making – an approach to setting priorities in health care’, Statistics in Medicine, Vol. 18, No. 23, pp.3345–3354.

Tsai, W-H., Lee, P-L., Shen, Y-S. and Yang, C-C. (2009) ‘The relationship between ERP software selection criteria and ERP success’, Proceedings of International Conference on Industrial Engineering and Engineering Management (IEEM), pp.2222–2226, doi: 10.1109/IEEM.2009.5373085.

Tversky, A. (1969) ‘Intransitivity of preferences’, Psychological Review, Vol. 76, No. 1, pp.31–48. Umble, E.J., Hafti, R.R. and Umble, M.M. (2003) ‘Enterprise resource planning: implementation

procedures and critical success factors’, Eur. J. Oper. Res., Vol. 146, No. 2, pp.241–257. Verville, J. and Halingten, A. (2002) ‘An investigation of the decision process for selecting an ERP

software: the case of ESC’, Management Decision, Vol. 40, No. 3, pp.206–216. Wei, C-C. and Wang, M-J.J. (2004) ‘A comprehensive framework for selecting an ERP system’,

International Journal of Project Management, Vol. 22, No. 2, pp.161–169. Wei, C-C., Chien, C-F. and Wang, M-J.J. (2005) ‘An AHP-based approach to ERP system

selection’, International Journal of Production Economics, Vol. 96, No. 1, pp.47–62. Yang, J-B., Wu, C-T. and Tsai, C-H. (2007) ‘Selection of an ERP system for a construction firm in

Taiwan: a case study’, Automation in Construction, Vol. 16, No. 6, pp.787–796. Yao, Y. and He, H.C. (2000) ‘Data warehousing and the internet’s impact on ERP’, IT

Professional, Vol. 2, No. 2, pp.37–41. Yazgan, H.R., Boran, S. and Goztepe, K. (2009) ‘An ERP software selection process with using

artificial neural network based on analytic network process approach’, Expert Systems with Applications, Vol. 36, No. 5, pp.9214–9222.

Yusuf, Y., Gunasekaran, A. and Abthorpe, M.S. (2004) ‘Enterprise information systems project implementation: a case study of ERP in Rolls-Royce’, Journal of Production Economics, Vol. 87, No. 3, pp.251–266.

Yusuf, Y., Gunasekaran, A. and Wu, C. (2006) ‘Implementation of enterprise resource planning in China’, Technovation, Vol. 26, No. 12, pp.1324–1336.

Zhang, L., Lee, M.K.O., Zhang, Z. and Banerjee, P. (2003) ‘Critical success factors of enterprise resource planning systems implementation success in China’, Proceedings of the 36th Hawaii International Conference on System Sciences (HICSS’03), Vol. 8, pp.236–245.

Zielsdorff, C., McGinnis, C., Daim, T. and Basoglu, N. (2010) ‘Enterprise resource planning (ERP) selection for a medical devices manufacturing company’, International Journal of Business Information Systems, Vol. 6, No. 2, pp.265–293.

Documents

ERP software selection with MCDM: application of TODIM method