Vol. 5, No. 5 May 2014 ISSN Journal of Emerging Trends in Computing and Information Sciences CIS Journal. All rights reserved.

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1 ERP Software Selection using IFS and GRA Methods 1 Yucel Ozturkoglu, 2 Ebru Esendemir 1 International Logistics Management Department, Yasar University, İzmir, Turkey 2 International Trade and Finance Department, Yasar University, İzmir, Turkey ABSTRACT Enterprise resource planning (ERP) is like a modular, generic software package integrating the flow of information between departments in all logistics company. Furthermore extended ERP applications provide web-based connections between interdependent organizations like supply chain partners. In order to meet ever-changing needs of the logistics companies competing in dynamic environments, ERP vendors, regularly offer new business solutions or make modifications to the existing ones. Therefore companies have to decide on the suitable ERP software package meeting their acquirements from a diverse range of choices in the ERP market. Due to the failures resulting from misfit problems, ERP package selection is considered to be a strategic decision which has a significant effect on the company performance throughout the implementation stage. In this paper, we combine grey relational analysis (GRA) with an intuitionistic fuzzy set multi-criteria method (IFS) for selecting the ERP software selection process. First we obtain the weights with using IFS method and then rank and select the alternatives with using GRA. The real case study shows that proposed model significantly increase the efficiency of decision making procedure for the decision makers. Keywords: ERP, Intuitionistic fuzzy set, Grey relational analysis, Decision models 1. INTRODUCTION ERP is a modular, standard software package integrating business functions while offering a variety of business solutions for companies competing in dynamic markets (Brown and Vessey 1999). The common theme in explaining the evolution of ERP systems is that they are derived from material requirements planning / MRP and manufacturing resources planning / MRP II (Klaus 2000, Başoğlu 2007, Kumar and Hillegersberg 2003, Umble et al 2003, Al Mashari et al 2003, Jacobs and Weston 2007, Muscatello et al, Ngai et al 2008) software packages designed to increase efficiency in production companies. Subsequently the development of computer integrated manufacturing (CIM) providing integration for manufacturing functions (Klaus 2000) is followed by the breakthrough of ERP designed to meet the organizationwide information system needs of the companies. The purpose of ERP implementations is to control the information within the whole enterprise and even in the whole supply chain to obtain a competitive advantage (Genoulaz et al 2005). To serve this purpose ERP allows an expeditious information flow between departments through one single database providing real time access and integration (Nah et al 2001). Due to the availability of timely and appropriate information (Gupta 2000), successful ERP implementations lead to improvements in managing financial and physical resources including inventories and assets while helping managers in decision making process (Davenport 1998, Hawking et al 2004). Some of the expected benefits resulting from automation and integration (Nah et al 2001) are reductions in cycle time, supply chain and order management as well as capacity and inventory planning (Davenport 1998). To illustrate the inventory levels are reduced from 15 to 35 percent (Gupta 2000). In addition the internet based technologies allow suppliers to provide the needs of customers effectively leading to satisfaction and customer loyalty (Rao 2000). On the whole, ERP applications have an impact on productivity, quality of outputs and customer satisfaction which all contribute to organization s competitiveness (Stefanou 2001). New versions of ERP packages are being introduced by vendors regularly due to the changing needs of the companies (Kumar 2000). For instance, the recent ERP packages offer web based integration with suppliers (supply chain management SCM), customers (customer relationship management CRM) and businesses (Business to Business B2B) (Stefanou 2001, Gupta 2000, Başoğlu 2007). As the requirements of the companies and the solutions provided by vendors get more complex, the risk of mismatch increases due to the varying needs, limited resources of the organizations (Wei 2005) and the generic nature of ERP. Mismatches between ERP packages and companies require complicated adaptations at organization and system level (Kumar 2000). An increase in customization resulting from adaptations, yield to higher implementation costs and longer implementation periods (Bingi 1999). The 2013 ERP Report by Panorama Consulting concludes that approximately while 61% of ERP projects exceeded their forecasted time periods while 53% of respondent organizations have to overspend (Panorama Consulting Solutions 2013). For instance in 2011 New York City Time reported hundreds of millions of cost overruns in payroll system project implemented by SAIC. There are global (SAP, Oracle and Microsoft) and local vendors offering a variety of products with a number of modules. Every ERP package has different attributes. In this respect, for successful ERP implementations companies have to choose the one which provides the best solutions according to the needs of the company (Brown and Vessey 1999, Somers and Nelson 2003). The differences in the company size, industry, business processes, customer profile etc. have an 363

2 influence on the characteristics of ERP packages. Company s business needs and capabilities are evaluated in relation to ERP software specifications while the costs and benefits are estimated including both financial and non-financial measures for operational performance and strategic position of the organization (Stefanou 2001). For instance small and medium-sized organizations have different requirements than large companies (Buonanno et al 2005). While most ERP solutions have similarities they also have differences (Umble et al 2003). Every package has different strengths and weaknesses compared to individual business requirements. Some packages have more functional modules while some packages are specialised in certain industries so the optimal software and the modules is selected according to requirements and constraints of the company. (Stefanou 2001). Prior research activities related to critical success factors in ERP implementation include package selection as one of the key determinants (Al Mashari et al 2003, Somers and Nelson 2004, Brown and Vessey 1999, Somers and Nelson , Finney and Corbett 2007, Ngai and Law 2008, Umble et al 2003, Dezdar and Sulaiman 2009, King and Burgess 2006). ERP implementation requires organization-wide process reengineering, change management and high resource commitment (Kumar 2003). The company applying ERP has to reengineer the business processes due to the standard nature of ERP. Therefore it is very difficult and expensive to undo the changes resulting from ERP applications (Bingi 1999). Hence selecting a suitable ERP package that fits with the needs of the company is an important strategic decision. For instance the respondents of a survey indicated that best fit with current business procedures is the most important criterion in selection of information technologies while functionality and quality are the most important criteria for vendor selection (Van Everdingen et al 2000). The failures in some companies show that ERP companies may not meet the companies requirements (Davenport 1998). According to the results of 2013 ERP Report 56 % of respondent organizations have received 50 % of the expected benefits from ERP implementations (Panorama Consulting, 2013). Hence, every year a number of companies file lawsuits against vendors due to failures in ERP Projects. A decision making model will help companies to choose the best alternative for their needs, will lower the misfit risks, save time and resources. This study proposes a systematic ERP selection model (decision making model) to gain from benefits of ERP. Stefanou (2001) classifies the factors that need to be considered in the evaluation process as strategic level factors and operational level factors. He proposes a conceptual framework of ERP software ex-ante evaluation. In addition, a thirteen step selection process is developed by Umble (2003). Baki and Çakar (2005) proposed a total of 17 main selection criteria through literature review and interviews with managers while Rao (2000) identified five criteria for selection of ERP for SMEs. Further Mexas et al (2012) proposed five main criteria and 45 sub-criteria to support ERP system selection. Lien and Chen (2007) have chosen ISO 9126 software quality characteristics for ERP product aspect in their fuzzy analytic hierarchy process (FAHP) model. Franch and Carvallo (2003) propose a structured quality model which relies on ISO 9126 quality standard. Kararslan and Gundogar (2009) used AHP method to select the appropriate ERP software while analysing the modules needed by the company. Perera et al (2008) proposed model based on AHP using seven key criteria and related sub-criteria to be considered in the selection of ERP software. Yazgan et al (2009) introduces applications of ANP and ANN methods in ERP selection. Ptak (2000) used scoring method for ERP selection. Badri and Davis (2001) adopted 0-1 goal programming for package system. Bernroider and Stix (2006) applied utility ranking and DEA methods. AHP is another commonly used method for ERP selection, Teltumbde (2000), Wei et. al (2005) used that method in a different perspective. Ravi et al (2005) developed ANP model and Ayag and Ozdemir (2007) adopted ANP with fuzzy sets. Karsak and Ozogul (2009) developed a novel decision framework for ERP software selection based on quality function deployment (QFD), fuzzy linear regression and zero-one goal programming. Up to now, researchers have applied various techniques to select ERP package. To best of our knowledge, this is the first study to combine IFS and GRA methods for ERP System selection. Proposed method is just used for personnel selection by Zhang and Liu (2011). 2. METHODOLOGY Zadeh (1965) introduced fuzzy sets concepts in the research area. After this date, many researchers have focused on this issue. One of the most important developments of the concept, Intuitionistic fuzzy set (IFS), Atanassov (1986) found by generalizing the concept of fuzzy set. Deschrijver and Kerre (2003) defined that fuzzy sets give the degree of membership of an element in a given set; IFS s give both a degree of membership and a degree of non-membership. Deng (1989) developed grey relational analysis (GRA) method to solve multi criteria decision problems. Zhang and Liu (2011) mentioned that GRA method can measured the degree of similarity and difference between two sequences based on the relation. So GRA method has applied different type of multiple attribute decision making (MADM) problems such as supplier selection, personnel section, warehouse location selection and ERP package selection. In this paper we combine two methods for taking into account a number of criteria; the selection will be the best ERP package system among multiple alternatives. 364

3 2.1 Basic Definitions We first define the basic concept of fuzzy sets and some properties of the IFSs measures. Definition 1: Let X be a universe of discourse, then assign each element x in the interval [0, 1]. Definition 2: A triangular fuzzy number can be defined by a triplet ( ). denotes the degree of membership of the element x to the set A, denotes the degree of non-membership of the element x to the set A and called the degree of indeterminacy of x to A. Definition 3: A fuzzy set A defined in space X is a set of pairs; Table1: Linguistic Variables and IFNs for the importance of Decision Maker Linguistic Variables IFNs Extreme low (0.05, 0.95, 0.00) Very low (0.15, 0.80, 0.05) Low (0.25, 0.65, 0.10) Medium low (0.35, 0.55, 0.10) Medium (0.50, 0.40, 0.10) High (0.75, 0.15, 0.10) Very high (0.85, 0.10, 0.05) Extreme high (0.95, 0.05, 0.00) The second stage begins with the appointment of the importance levels of the each decision makers. To translate the importance with the linguistic variables, Table 2 can be used. Definition 4: For each fuzzy set, A in X if; Definition 5: Let and be two IFNs, then distance between and is defined as follows; 2.2 Steps of the Proposed Method Wei (2010) applied the same method to select best investment option for their company. But in mentioned study, they ignored the entropy weights of the criteria. In proposed method, we also include entropy weights of the each criterion. The stepwise representation of the proposed method is given below; In the first stage intuitionistic fuzzy decision making matrix is built. All kind of decision problems can be expressed in matrix form which columns represent criteria (n) and the rows represent decision alternatives (m). In that step, each decision makers use Table 1 to rate the each alternative with respect to criteria. Table2: Linguistic Variables for the importance of Decision Maker Linguistic Variables IFNs very importance (0.90, 0.05, 0.05) Importance (0.75, 0.20, 0.05) Medium (0.50, 0.40, 0.10) Unimportance (0.25, 0.60, 0.15) Very unimportance (0.10, 0.80, 0.10) After using Table 2 to convert the linguistic variables into IFNs, determine the weights of each decision makers with using equation which is proposed by Boran et. al (2009); The third stage is constructing an aggregated intuitionistic fuzzy decision matrix according to the opinions of decision makers. Xu (2007) presented an equation to obtain the matrix; The following stage is determining the entropy weights of the criteria. Firstly, calculate the intuitionistic fuzzy entropy with using Vlachos and Sergiadis (2007) s equation; 365

4 = and then obtain the weights of the each criteria by using given equation; = To calculate the grey relational coefficient, the reference sequence is needed. The reference sequence is defined as, so the maximum value should be used as the reference value. Afterward, entire grey relational coefficient of each alternative can be calculated by using the following equation; The priorities of the alternatives can be determined based on the grey relational grade which is given below; In the last stage, rank all the alternatives; the highest value of grey relational grades is the most important alternative. 3. CASE STUDY In this section, we face with the real industrial application to select the ERP software system in a logistics company. The logistics service provider is located in the biggest city of the Turkey, Istanbul. The firm offers transportation, warehousing, packaging and custom services to their customers. A firm wants to select suitable ERP package for the organization. The firm teaming up with four decision makers (d 1, d 2, d 3, d 4 ) to select the most appropriate ERP package. Decision makers obtained five criteria from Mexas et al. (2012) study s; financial (c 1 ), business (c 2 ), software (c 3 ), technological (c 4 ) and vendor (c 5 ). This is a kind of multi criteria decision making problem and we applied proposed method which is given in the previous section. At the first stage of problem, each decision maker uses the Table 1 variables and determines rate the each alternative with respect to criteria. ERP package selection is related with lots of department. So decision makers composed of employees of different positions of the company. The severity ratings are as follows according to their position; d 1 : very important, d 2 : important, d 3 : important, d 4 : medium. We translated this information into linguistic variables with using Table 2. Afterward, we calculated the decision maker s weights as listed in Table 3. Table 3: Weights of decision makers weights of decision makers λ 1 0,3074 λ 2 0,2561 λ 3 0,2561 λ 4 0,1802 To bring close together all individuals opinion, we used Xu (2007) equation s to obtain the group opinion decision matrix as listed in Table 4. Table 4: Aggregated decision Matrix Alter. Criteria ,735 0,544 0,079 0,729 0,552 0,072 0,900 0,089 0,039 0,711 0,294 0,085 0,899 0,279 0,032 0,580 0,351 0,100 0,497 0,391 0,083 0,591 0,268 0,100 0,789 0,273 0,075 0,639 0,649 0,091 0,607 0,578 0,045 0,294 0,846 0,050 0,802 0,184 0,075 0,541 0,368 0,100 0,588 0,328 0,087 0,428 0,704 0,070 0,662 0,533 0,100 0,813 0,176 0,072 0,624 0,249 0,070 0,744 0,607 0,045 Assuming that all of the criteria set by the decision-makers might not be of equal importance, thus we found the weights of each criterion which is given by in Table 5 & 6. Table 5: Intuitionistic fuzzy entropy intuitionistic fuzzy entropy H 1 0,690 H 2 0,664 H 3 0,537 H 4 0,692 H 5 0,

5 Table 6: Weights of each criterion entropy weights of the criteria w 1 0,181 w 2 0,196 w 3 0,271 w 4 0,180 w 5 0,173 The values of the grey relational coefficient are given in Table 7 Table 7: Grey relational coefficient ξ = ,444 0,448 0,114 0,334 0,206 0,435 0,488 0,388 0,280 0,550 0,508 0,801 0,228 0,463 0,414 0,673 0,486 0,218 0,348 0,454 The relative relational degree of each alternative is given in Table 8. Table 8: Grey relational grade grey relational grade ɣ 1 0,181 ɣ 2 0,196 ɣ 3 0,271 ɣ 4 0,180 ɣ 5 0,173 Figure1: Ranking the alternatives based on relational degree In the illustrative example, there are five different ERP packages alternatives. After the calculations, based on the grey relational grades, alternative 3 is the best ERP software system for the company. 367

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