Instructions for AssignmentRead the unit overviews, the associated chapters in the book, participate in the online discussions until week 6, read related research, the web pages related to this assign

doi: 10.1016/j.protcy.2012.03.032 The 2012 Iberoamerican Conference on Electronics Engineering and Computer Science An empirical study in selecting Enterprise Resource Planning Systems: The relation between some of the variables involve on it. Size and Investment Augusto A Pacheco-Comer *, Juan C González-Castolo † CUCEA-Universidad de Guadalajara, Periferico Norte 799 Module L 305, Zapopan 45100, México Abstract Enterprise Resource Planning (ERP) system is one of the most important projects on business optimization than an enterprise could attempt. The rate of failure on ERP implementations keeps high. Selection process is a critical success factor. The paper presents the first results from empirical study where we found that there is a relation between size of the company and the amount of investment. Other enterprise systems that can be seen as important to include on the ERP are Business Intelligence and Customer Relationship Management. Evolutionary Computation, Multi Agent Systems and Petri Nets can be used as computational intelligence technics to model the ERP selection process.

Keywords: Enterprise Resource Planning (ERP); selection; critical success factor (CSF); Business Intelligence(BI); Customer Relationship Management (CRM); Multi Agent Systems (MAS); Evolutionary Computation (EC); Petri Nets (PN); *Corresponding author. Tel.: +52-333-770-3430; fax: +52-333-770-3353. E-mail address: [email protected]. † Corresponging author. Tel.: +52-333-770-3430; fax: +52-333-770-3353. E-mail address: [email protected]. Available online at www.sciencedirect.com 293 Augusto A Pacheco-Comer and Juan C González-Castolo / Procedia Technology 3 ( 2012 ) 292 – 303 1. Introduction The process of selecting an Enterprise Resource Planning (ERP) system is a complex problem which involves multiple actors and variables, since it is a decision-making process which is characterized as unstructured type [1, 2]. An Enterprise Resource Planning (ERP) is an information system, which integrates most of the data than an organization can process and use in their operations [3], Fig. 1.

Its implementation requires: Money, time, and a great amount of people effort; and, as an Enterprise System (ES), enforce a change in the organizational culture [4].

ERP systems are increasingly important in today businesses, as they have the ability to support organizational strategies, integrate the flow of information and enhance competitive advantage and individual performance [5-8]. It has a central database that contains all of the transactions that an organization could register; depending on its set of functional modules. Those functional modules could be: material management, production, sales, marketing, distribution, financial services, human resources, reports, etc. [9, 10]. As its impact affect the whole organization, the ERP system implemented should be the right one [11, 12].

Implementing an ERP system is not an inexpensive or risk-free venture. An estimated 40-70% of ERP implementations experienced some degree of failure [13]. That is why an organization should select the most appropriated ERP systems for their business needs [7].

Fig.1. ERP Anatomy (Davenport, 1998) Our research hypothesis is: “An Enterprise Resource Planning system selection process based on a computational intelligence improves success on implementation, compared with the use of empirical selection processes”. Directing us to the following research methodology [14, 15]:

x Identify, know and analyze research literature regarding:

○ Critical success Factors on ERP implementation, ○ Effect between ERP selection process and successful implementation and 294 Augusto A Pacheco-Comer and Juan C González-Castolo / Procedia Technology 3 ( 2012 ) 292 – 303 ○ Meaning of ERP system successful implementation.

x Understand and explaining ERP selection process in a sample of businesses in Metropolitan Zone of Guadalajara Mexico by applying an empirical study to: ○ Classified critical variables used, ○ Classified and explaining relationship between critical variables and ○ Find a relation between selection and ERP delivery success.

x Study of computational intelligence techniques that haven´t been used at literature for the selection process.

These research elements are linked to the following research questions:

1. How is selection process affecting the success of implementation of ERP systems?

2. What are the variables involve in an ERP selection process? 3. What are the interactions between those variables?

4. How they can be included in a computational model for ERP selection? 5. How a computational intelligence can be used to model the ERP selection process?

In this paper, we are going to present in section 2 answers to question 1. In section 3 we present multi- agent model proposed, as computational intelligence technic, as a way to improve ERP selection process to a first attempt to answer question 4 and 5. In section 4 the first results of the empirical study that has been done to a sample of companies located in the Metropolitan Zone of Guadalajara (ZMG) Mexico to partially answer question 2 and 3. In section 5 the conclusions of our findings and thoughts, beside future work that could be done.

2. Selection and project management are critical success factors for ERP systems implementations.

The main goals of an ERP system are to automate business processes [16], to improve interactions and communications inside and outside organization [17] and to eliminate patch work to legacy systems [18]. Its implementation success is affected by CSFs [7, 8]. “CSF is defined as the limited number of areas in which results, if satisfactory, will ensure successful competitive performance for the organization” [19].

Economic research data show that the average mean investment for an ERP implementation could be between fifty thousand dollars to several millions, by ERP implementation attempt [20], in Small and Medium Enterprises (SME). The estimated of wasted money could be of several millions dollars, so the importance of a good selection process could be evident.

In a search in EBSCO, Springer, ACM, AIS and IEEE repositories using Enterprise Resource Planning, Selection and Critical Success Factors, we found 97 references regarding these keywords. After a classification of the references, we identified that 11 were regarding ERP definitions, 30 presented compilations of CSFs on ERP projects, 29 presented compilations of ERP implementations model phases, 39 mentioned ERP selection models as a way to improve ERP delivery and finally two presented what to take care regarding post-implementation of ERP solution.

295 Augusto A Pacheco-Comer and Juan C González-Castolo / Procedia Technology 3 ( 2012 ) 292 – 303 In this set of papers we found that selecting the appropriate system, apply project management and follow an implementation methodology are CSFs. As claimed by different authors [7, 8, 19, 21-33].

As mentioned selection is a CSF, so an appropriate selection methodology is needed to improve success of ERP delivery, but: What are the variables associated to the selection process and how those variables are related? This question is what we are going to try to identify applying an empirical study to a sample of companies from the Metropolitan Zone of Guadalajara Mexico (ZMG) since implementation of this kind of systems has a great deal with social behaviors inside each business and the best way to evaluate this behaviors is with an empirical survey [15].

3. Computational Intelligence Technique proposal for ERP selection process Different research groups proposed different selection evaluations models to improve selection process, including WSM, Weighted Scoring Method; AHP, Analytic Hierarchy Process; FL, Fussy Logic; ANP, Analytic Network Process; SHERPA, Systematic Help ERP Acquisition; PM, Priority Matrix; PROMETHEE; TOPSIS, Technique for Order Preference by Similarity to Ideal Solution; DEA, Data Envelopment Analysis [6, 16, 31, 33-45] as presented on Table 1. Table 1. ERP selection evaluation models WSM AHP FL ANP SHERPA PM PRO METHEE TOPSIS DEA Haghighi Karaarslan Ayag Ayag Burqués Dimitrova Razmi Razmi Llal Chiesa Perera Kahraman Dimitrova Pastor Jadhav Muñiz Kecek Ya-Yueh Fan Reuther Nikoukaran Cebeci Yazgan Ahituv Carvallo Bueno Carvallo Ya-Yueh Jadhav Cebeci Bernroider Lv Neves Agent-based computing is one of the computers intelligences technique to find solutions to complex problems [46]. Multi Agents Systems (MAS) seek to address the trends in computer science such as ubiquity, interconnectedness and intelligence [47]. An agent is a computer system capable of undertaking activities independently to benefit its owner or user. A multi agent system consists of a set of agents that interact within an environment, which act on behalf of the objectives and motivations of their owners. Agents have the ability to cooperate, coordinate and negotiate looking at all times comply with the purposes for which they were created [47, 48]. Agents can be used to simulate administrative and economic problems [49]. MAS can be used to identify the standards used by flows of processes regarding an already implemented ERP system [50].

On the analysis and design of organizations, MAS can be used to describe any type of organization using only three elements: agents, roles and groups [51-53]. Our proposed architecture MAS model is intended exclusive for the evaluation process stage, which is where the selection process and decision making is 296 Augusto A Pacheco-Comer and Juan C González-Castolo / Procedia Technology 3 ( 2012 ) 292 – 303 modeled [1, 2, 8, 54]. The categories to be evaluated in selection process include: functional, operational, technical and economic aspects. These categories are the easiest to represent in a model and simulation tool, since these categories could be translated to quantitative rules easily.

In this proposed architecture there are five different kinds of agents (Fig. 2). Those agents could be composed by two databases, one for knowledge and behaviors and one to archive characteristics. Fig. 2. Multi Agent System architecture for ERP selection process The five agents modeled are: Evaluate Characteristics Agent (ACE), Top Management Agent (AAD), Functional Manager Agent (AGF), ERP System Agent (ASERP) and Monitoring and Control Company Agent (AMCE).

ACE agents will be responsible to contain criteria information that must be met for each ERP, the characteristics to evaluate and the rules that permit an interaction between those characteristics. AAD agents will contain features and personal profiles of top management behaviors, presented in accordance with data obtained from the ACE and ASERP agents, decision rules based on profiles of AAD, ACE and ASERP agents.

AGF agents will contain features and personal profiles of functional management and behaviors, decision rules based on profiles of AGF, ACE and ASERP agents.

ASERP agents will contain the information and characteristics of ERP systems, which are linked to the criteria that must be met for each of the characteristics evaluated. AMCE agent will be responsible to initiate the evaluation process. AMCE agent will contain the necessary knowledge to issue a suggested ERP system solution based on the information contained at ASERP agents, based on evaluations submitted by the AAD and AGF agents, combined with the levels of influence on decisions of the mentioned agents.

Each of the agents can be placed on different computers which will connect to the central agent that will allow agents to communicate between them in order to achieve the purpose for which they were created. This architecture needs the variables and the relationships than could be found in the empirical study. 297 Augusto A Pacheco-Comer and Juan C González-Castolo / Procedia Technology 3 ( 2012 ) 292 – 303 Also, in order to understand the data obtained through the application of the empirical survey mentioned, we think that data mining techniques could provide knowledge, as data mining help the process of identifying novel correlations and patterns that are valid, understandable and functional. The application of data mining techniques is a step in the process of knowledge discovery in databases (KDD) [55]. This process includes pre-processing, sorting, cleaning and the results of the interpretation of the application of data mining.

Verifying that the knowledge gained from the data is useful[55].

The analysis of the literature using the keywords: "Enterprise resource planning", "Data mining" and "Selection process" found no studies related to the use of these techniques. This may correspond to the absence of sufficient archived data of the selection process conducted by the thousands of enterprises that have already selected and implemented this type of system. So the application of this technic to the empirical surveyed data could help us to understand the selection process and try to identify a model or patterns behaviors inside that data.

Also, evolutionary computation is part of the computational intelligence algorithms using heuristics to better understand the process of creation and development of artificial intelligence, using computational tools.

The evolutionary computing elements include learning, adaptation and optimization of the proce ss of obtaining the desired results, which correspond to identifying the best solution to the problem [56, 57], in our case, the best process for selecting an ERP system.

According to Koza [56], the most important steps in the process of preparation for the use of conventional genetic algorithm into a fixed chain are: (1) Determine the schema to represent, (2) Determine the extent of fitness, (3) Determine the parameters and variables that control the algorithm and (4) Determine how the nomination of completion criteria and results.

This model requires the definition of a gene or chromosome chain that uses the various elements identified as best practices in the selection process and implementation of ERP systems. The measure of fitness of individuals corresponds to the method of evaluation of each of the registered genes that has a favorable impact on the success of the installation and use of ERP system.

Additional work to do correspond to the identification of the fitness function and if the elements referred to as genes are the only or should include more, including evaluating whether any of the above items can be conjoined, such as project plan which consists of various plans [58], in a unique gene. The empirical survey could help to identify each of the genes that could be used on the chain.

4. Empirical study to understand ERP selection process Our hypothesis to question two and three at the end of section 1 are: “There is a set of variables that can be identified through the application of an empirical study to a sample of companies located in the Metropolitan Zone of Guadalajara (ZMG, ZMG is conformed by the following municipalities: Guadalajara, Zapopan, Tlaquepaque, Tonala and Tlajomulco), México. And those variables can be modeled in a computational intelligence to improve ERP delivery success.” For this purpose the elements that we have to introduce in an empirical survey are: x Evaluation criteria and environmental elements of the decision, 298 Augusto A Pacheco-Comer and Juan C González-Castolo / Procedia Technology 3 ( 2012 ) 292 – 303 x Identification of decision takers and stakeholders, x Selection methodology and elements followed on past decisions and x Definition of success for ERP delivery The empirical study (survey) was elaborated following these characteristics [14, 15]:

x Divided survey by research topic, x Use of closed questions using five Linkert points scale, x Use of ordinal variables with the following general numeration: Null importance, Low importance, Medium importance, High importance, Very high importance The survey has 21 questions: Six demographics, one to identify project investment, one to identify position of person inside the project, one to identify reason of implementation, one to identify the most important planning aspect of the project, two to identify experience on ERP implementation, four to evaluate implementation experience, one to identify selection criteria, one to identify who take part on the decision, one to identify what define success, one to identify evaluation criteria weights and two to identify essential ERP modules (The format survey can be asked to the authors).

According to the economic census done by the National Institute of Statistics of Mexico (INEGI) in 2 004, there were 127,730 economic units in ZMG, Table 2. In the Information System of Mexican Business (SIEM) there are 87,408 economic units registered in ZMG. This corresponds to approximately the 68.43% of the INEGI census, becoming a good reference of the population to study, since the data contained in the SIEM database are the most suitable because it has detailed information regarding email contact, it is more actualized and accessible.

Table 2 Comparison of economic units by municipality and data source Municipality INEGI A SIEM B % C = B / A SENT D % E = (D / B COMPLETED F % G = F / D % H = F / B Guadalajara 77,003 49,907 64.81% 3,170 6.35% 85 2.68% 0.17% Zapopan 24,964 24,698 98.93% 1,715 6.94% 45 2.62% 0.18% Tlaquepaque 12,838 5,573 43.41% 348 6.24% 14 4.02% 0.25% Tonala 9,307 2,763 29.68% 89 3.22% 0 0.00% 0.00% Tlajomulco 3,618 4,468 123.49% 201 4.49% 8 3.98% 0.17% Total 127,730 87,409 68.43% 5,523 6.31% 152 2.75% 0.17% Column D present the total population used to the application of the survey and represent the 6.31% of SIEM population, economic units that had registered an email address. Accordingly with the kind of municipality, Tonala and Tlajomulco shows a lower percentage of email addresses registered as this municipalities correspond to a more rural than urban municipality. The total completed survey were 152 surveys, 2.75% of the D column.

Using data from columns A, B, D for a multi-sample comparison analysis, we found that since the P-value of 0.248044 on Levene's variance check is greater than or equal to 0.05, there is not a statistically significant difference amongst the standard deviations at the 95.0% confidence level. So we can say that the results are a representation of the population as the verification of the ANOVA table showed, because the F-ratio was 299 Augusto A Pacheco-Comer and Juan C González-Castolo / Procedia Technology 3 ( 2012 ) 292 – 303 2.42319. As the P-value of the F-test was 0.1036 and is greater than or equal to 0.05, we conclude that there is not a statistically significant difference between the means of the 4 variables at the 95.0% confidence level.

The samples come from normal distributions as the standardized skewness and standardized Kurtosis values comply with those needed for normal distributions as show on table 3. Confirming that survey results comes from a representation of the population.

Table 3 Normal distribution tests by samples Minimum Maximum Range Stnd. skewness Kurtosis Stnd. kurtosis Sum Sum of squares INEGI 3618.0 77003.0 73385.0 1.71424 3.617490 1.65115000 127730.0. 6.81719E9 SIEM 2763.0 49907.0 47144.0 1.26479 1.047900 0.4782990 87409.0 3.15936E9 SENT 89.0 3170.0 3081.0 1.10651 0.186179 0.0849786 5523.0 1.31596E7 COMPLETED 0.0 85.0 85.0 1.08714 0.459513 0.2097380 152.0 951.0E1 4.1. Survey results The survey was applied between 19th September and 6th November 2011 by electronic via. It was viewed 781 times, 216 surveys were started and 180 surveys were completed. From the completed surveys, there were 152 valid surveys, since they were answered by companies in the study area.

There are 117 variables from the survey results: Seven variables are nominal and 110 variables are ordinal.

All of them can be defined as random discrete variables, as their numeral of different values is finite [59].

Statistical processes can be used to analyze this kind of variables.

The general results obtained from 152 complete surveys shows that 31.6% belongs to micro companies (1 to 10 employees), 23.7% to small (11 to 50 employees), 9.2% to medium (51 to 100 employees), 15.1% to large (101 to 250 employees) and 20.4% to big companies (more than 250 employees). 4.1.1. Relation between size and investment Cross tabulating Investment and Size variables to validate the hypothesis that “There is a direct relation between the size of the company and the amount of money that it can invest on an ERP system”. We found that the square root-Y squared-X model (1) is a more adequate correlation model as its results gave us a correlation coefficient of 0.9124 and a R-squared of 83.25%, after eliminating 13 unusual data that is not appropriate to the model and can be explained as opinions from the persons that had answered the survey but are not related to the size of the companies in where they are employed. Y = (a + b * X 2)2 (1) The equation model obtained to describe the relationship between Investment and Size is: Investment = (0.943869 + 0.0550766 Size 2)2 (2) The standard error of the estimate shows that the standard deviation of the residuals was 0.228549. The mean absolute error (MAE) was 0.17126. We conclude that there is not any significant correlation based on 300 Augusto A Pacheco-Comer and Juan C González-Castolo / Procedia Technology 3 ( 2012 ) 292 – 303 the order of the data and there is no indication of serial autocorrelation in the residuals at the 95.0% confidence level. So we conclude that this equation can be used to predict future behavior.

4.1.2. System that can be include it on ERP implementation Ordinal elements are qualification of the importance of the evaluated variables, these values correspond to a numeric data between 1 and 6, where 1 correspond to a not known answer and is treated as a missing value for statistics processes. 2 correspond to a null importance meaning that this variable is not important or criti cal to the evaluated topic. 3 correspond to a low importance meaning that this variable is important but not critical to the evaluated topic. 4 correspond to a medium importance meaning that this variable is moderate important and critical to the evaluated topic. 5 correspond to a high importance meaning that this variable is important and critical to the evaluated topic. And 6 correspond to a very high importance that means that this variable is extremely critical and important to the evaluated topic.

We applied a “t student” test to identify if the mean of the values obtained for the variables on table 4 are significant equal or greater than value 5 (High importance). If the mean of the variable is equal or greater than value 5, we can consider the variable as a “System that would be included in ERP system as a module”. H0 = the mean of the variable is less than 5 (The variable is not important and critical). And Ha = the mean of the variable is equal or greater than 5 (The variable is critical and important).

Table 4 One sample test “t student” 95% confidence interval of the difference to test value = 5 N Mean Std. Deviation Std. Error Mean t df Sig. (2-tailed) Mean Difference Lower Upper Accepted 10 ERP would include PMO system integration 137 4.86 .994 .085 -1.633 136 .105 -.139 -.31 .03 H0 10 ERP would include HRP system integration 138 4.72 .957 .081 -3.379 137 .001 -.275 -.44 -.11 H0 10 ERP would include CRP system integration 139 4.58 .924 .078 -5.325 138 .000 -.417 -.57 -.26 H0 10 ERP would include MRP system integration 135 5.04 1.025 .088 .420 134 .675 .037 -.14 .21 H0 10 ERP would include BSC system integration 130 5.00 .932 .082 .000 129 1.000 .000 -.16 .16 H0 10 ERP would include BI system integration 133 5.19 .889 .077 2.439 132 .016 .188 .04 .34 Ha 10 ERP would include KM system integration 130 4.85 .968 .085 -1.812 129 .072 -.154 -.32 .01 H0 10 ERP would include SCM system integration 135 5.05 .972 .084 .620 134 .536 .052 -.11 .22 H0 10 ERP would include CRM system integration 137 5.28 .795 .068 4.193 136 .000 .285 .15 .42 Ha 301 Augusto A Pacheco-Comer and Juan C González-Castolo / Procedia Technology 3 ( 2012 ) 292 – 303 Only on the rows that the mean difference is greater than zero and the value is inside the range formed by the lower and upper limits of the confidence interval columns the Ha is accepted.

5. Conclusions The first conclusion that can be deduced from the empirical study is that there is a direct relation between size of the company and the amount that it can invest on an ERP implementation project. This relation can be modeled with the equation (2) of this paper, where size of the company as to be defined as 1 for a company from 1 up to 10 employees, 2 for a company from 11 up to 50 employees, 3 for a company from 51 up to 100 employees, 4 for a company from 101 up to 250 employees and 5 for a company wit h more than 250 employees; And investment result is in the range of 1 to 7 where 1 correspond to an amount investment up to 100,000 MXP, 2 an investment up to 500,000 MXP, 3 an investment up to 1’000,000 MXP, 4 an investment up to 2’000,000 MXP, 5 an investment up to 5’000,000 MXP, 6 an investment up to 10’000,000 MXP and 7 an investment greater than 10’000,000 MXP.

In the case of the systems that would be included in ERP system as a module we can said that BI and CRM would be included because the empirical survey gave us statistical evidence that support this idea.

There are still ordinal variables to analyze from the empirical study. The most relevant variables could be used to identify the model to use to correlate those variables. That model could be used on a computational intelligence technic as MAS, evolutionary computation, neural networks or Petri Nets.

We could also introduce the surveys data into a data mining tools, as R or WEKA, to analyze if there are other not evident relations.

The empirical survey applied; help us in the identification of the need of a new survey that clarify the elements used on the selection and implementation processes and how those elements are related to the success an ERP delivery. By example, including specific questions regarding the percentage of satisfaction that the companies have on their actual ERP system, the percentage of time and investment done against the planned, if the .mandatory and wishes requirements were realized and in what percentage. With these results it could be possible to define a better model to know the element that should be included to do a better selection and implementation, based on implementations done by the responders to the surveys. Future research on this topic correspond to the identification of the model and its use on a computational intelligence technique as an genetic algorithm or a Petri Net since each of the elements defined as helpful on fulfill satisfaction on the use of an ERP system can be introduced on a chromosome of genes and the model obtained between the relation of elements and satisfaction can be translated to an equation or aptitude function to evaluate each generation. Also those elements can be seen as state that can be used to model a Petri Net that shoot a state of satisfaction.

Acknowledgements CONACYT, http://www.conacyt.gob.mx CUCEA, Universidad de Guadalajara, http://www.cucea.udg.mx 302 Augusto A Pacheco-Comer and Juan C González-Castolo / Procedia Technology 3 ( 2012 ) 292 – 303 References [1] E. Turban and J. Aronson, Decision Support Systems and Intelligent Systems: Prentice Hall, 1998.

[2] H. A. Simon, The New Science of Management Decision: Prentice Hall PTR, 1977.

[3] T. H. Davenport, "Putting the enterprise into the enterprise system," Harvard Business Review, vol. 76, pp. 121-+, Jul-Aug 1998.

[4] T. H. Davenport, "The Future of Enterprise System-Enabled Organizations," Information Systems Frontiers, vol. 2, pp. 163-180, 2000.

[5] V. Botta-Genoulaz, R. A. Millet, and B. Grabot, "A survey on the recent research literature on ERP systems," Computers in Industry, vol. 56, pp. 510-522, Aug 2005.

[6] J. Razmi and M. S. Sangari, "A hybrid multi-criteria decision making model for ERP system selection," in Information and Automation for Sustainability, 2008. ICIAFS 2008. 4th International Conference on, 2008, pp. 489-495.

[7] D. Aloini, R. Dulmin, and V. Mininno, "Risk management in ERP project introduction: Review of the literature," Information & Management, pp. 547-567, SEP 2007 2007.

[8] S. Hoermann, H. Kienegger, M. Langermeier, M. Mayer, and H. Krcmar, "Comparing Risk and Success Factors in ERP Projects: A Literature Review," in Americas Conference on Information Systems, Detroit, Michigan, USA, 2011, p. 241.

[9] H. B. Ali and H. B. Saad, "Enterprise resource planning: a review and a STOPE view," International Journal of Network Management, vol. 15, pp. 363-370, 2005.

[10] E. Bravo and M. Santana, "Impacto de la implementación de los sistemas de planeamiento de recursos empresariales ERP en el desempeño individual," in Americas Conference on Informations Systems, Lima, Perú, 2010, p. 265.

[11] J. Karimi, T. M. Somers, and A. Bhattacherjee, "The Role of Information Systems Resources in ERP Capability Building and Business Process Outcomes," Journal of Management Information Systems, vol. 24, pp. 221-260, Fall2007 2007.

[12] T. M. Somers and K. G. Nelson, "A taxonomy of players and activities across the ERP project life cycle," Information & Management, vol. 41, pp. 257-278, 2004.

[13] J. Frano, "ERP System Acquisition Project Planning," in ERP vol. 2010, I. Toolkit, Ed., ed: IT Toolkit, 2008, pp. Our topic. Projects to implement ERP tend to be difficult, expensive and drawn-out. They are often full of painful surprises; they often overrun budgets and schedules that were extravagant in the first place.

[14] M. N. Namakforoosh, Metodología de la Investigación, 2da ed. MEXICO: LIMUSA, 2010.

[15] R. Hernández Sampieri, C. Fernández Collado, and M. d. P. Baptista Lucio, Metodología de la Investigación, 2010 ed. MEXICO:

McGraw Hill, 2010.

[16] N. Ahituv, S. Neumann, and M. Zviran, "A system of development methodology for ERP systems," Journal of Computer Information Systems, vol. 42, p. 56, Spring2002 2002.

[17] Momoh, Roy, and Shehab, "Challenges in enterprise resource planning implementation: state-of-the-art," Business Process Management Journal, vol. 16, pp. 537 - 565, 2010.

[18] L. Lau, "Developing a successful implementation plan for ERP: Issues and challenges," presented at the International Associat ion for Computer Information Systems, Las Vegas, Nevada, USA, 2003.

[19] N. Garcia-Sanchez and L. E. Perez-Bernal, "Determination of critical success factors in implementing an ERP system: A field study in Mexican enterprises," Information Technology for Development, vol. 13, p. 293, 2007.

[20] C. Jutras and N. Castellina, "ERP Plus in process industries Managing Compliance in the Pursuit Profits," 2010.

[21] J. Esteves and V. Bohorquez, "An updated ERP systems annotaded bibliography: 2001-2005," Communications of AIS, vol. 2007, pp. 386-446, 2007.

[22] F. Chiesa, "Metodología para selección de sistemas ERP," Reportes técnicos en ingenieria de software, vol. 6, p. 17, 2004.

[23] H. Kyung-Kwon and K. Young-Gul, "The critial success factors for ERP implemetation: an organizational fit perspective," Information and management, vol. 40, pp. 25-40, 2002.

[24] F. Adam and D. Sammon, The Enterprise Resource Planning Decade: Lessons Learned and Issues for the Future. London: Idea Group Publishing, 2004.

[25] L. Muñiz, ERP Guía práctica para la selección e implantación. España: Ediciones Gestión 2000, 2004.

[26] P. Upadhyay and P. K. Dan, "ERP in Indian SME's: A Post Implementation Study of the Underlying Critical Success Factors," International Journal of Management Innovation Systems, vol. 1, pp. 1-10, 2009.

[27] G. Tomb, "Implementing Enterprise Resource Planning: Lessons Learned from the Front," vol. January, 2006.

[28] C. Stefanou, "The Selection Process of Enterprise Resource Planning (ERP) Systems," 2000.

[29] I. H. Vilpola, "A method for improving ERP implementation success by the principles and process of user-centred design," Enterprise Information Systems, vol. 2, pp. 47-76, 2008.

[30] M. Supramaniam and M. Kuppusamy, "Investigating the Critical Factors in Implementing Enterprise Resource Planning system in Malaysian Business Firms," Proceedings of World Academy of Science: Engineering & Technology, vol. 57, pp. 332-341, 2009.

[31] C. Kahraman, A. Beskese, and I. Kaya, "Selection among ERP outsourcing alternatives using a fuzzy multi -criteria decision making methodology," International Journal of Production Research, vol. 48, pp. 547-566, 2010. 303 Augusto A Pacheco-Comer and Juan C González-Castolo / Procedia Technology 3 ( 2012 ) 292 – 303 [32] P. Iskanius, "Risk Management in ERP Project in the Context of SMEs," Engineering Letters, vol. 17, pp. 266-273, 2009.

[33] H. Haghighi and O. Mafi, "Towards a Systematic, Cost-Effective Approach for ERP Selection," Proceedings of World Academy of Science: Engineering & Technology, vol. 61, pp. 231-237, 2010.

[34] L. Fan and C. Jinliang, "Influencing factors on ERP system selection," in Software Engineering and Service Sciences (ICSESS), 2010 IEEE International Conference on, 2010, pp. 671-673.

[35] S. Ya-Yueh, "A Study of ERP Systems Selection via Fuzzy AHP Method," in Information Engineering and Electronic Commerce (IEEC), 2010 2nd International Symposium on, 2010, pp. 1-4.

[36] U. Cebeci, "Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard," Expert Systems with Applications, vol. 36, pp. 8900-8909, Jul 2009.

[37] N. Karaarslan and E. Gundogar, "An application for modular capability-based ERP software selection using AHP method," The International Journal of Advanced Manufacturing Technology, vol. 42, pp. 1025-1033, 2009.

[38] H. R. Yazgan, S. Boran, and K. Goztepe, "An ERP software selection process with using artificial neural network based on analytic network process approach," Expert Systems with Applications, vol. 36, pp. 9214-9222, Jul 2009.

[39] S. Bueno and J. L. Salmeron, "Fuzzy modeling Enterprise Resource Planning tool selection," Computer Standards & Interfaces, vol.

30, pp. 137-147, Mar 2008.

[40] V. A. Dimitrova, "Application of the Analytic Network Process (ANP) in a framework of ERP systems implementation success," in Intelligent Systems, 2008. IS '08. 4th International IEEE Conference, 2008, pp. 20-19-20-24.

[41] Z. Ayag and R. G. Özdemir, "An intelligent approach to ERP software selection through fuzzy ANP," vol. 45, 2007.

[42] V. Lall and S. Teyarachakul, "ENTERPRISE RESOURCE PLANNING (ERP) SYSTEM SELECTION: A DATA ENVELOPMENT ANALYSIS (DEA) APPROACH," Journal of Computer Information Systems, vol. 47, pp. 123-127, Fall2006 2006.

[43] H. S. C. Perera and W. K. R. Costa, "ANALYTIC HIERARCHY PROCESS FOR SELECTION OF ERP SOFTWARE FOR MANUFACTURING COMPANIES," Vision (09722629), vol. 12, pp. 1-11, 2008.

[44] A. Jadhav and R. Sonar, "A Hybrid System for Selection of the Software Packages," in Emerging Trends in Engineering and Technology, 2008. ICETET '08. First International Conference on, 2008, pp. 337-342.

[45] E. W. N. Bernroider, F. Sudzina, and A. Pucihar, "European criteria for assessing enterprise resource planning (ERP) systems:

Preliminary results from multiple empirical studies " in Bled, Bled, Slovenia, 2009, p. 19.

[46] L. Zhang and Y. Zhang, "Research on Hierarchical Distributed Coordination Control in Process Industry Based on Multi-agent System," in Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on, 2010, pp. 96-100.

[47] M. Wooldridge, An introduction to multi agents systems. Sussez, UK: John Wiley & Sons Ltd, 2002.

[48] D. Kehagias, A. L. Symeonidis, K. C. Chatzidimitriou, and P. A. Mitkas, "Information agents cooperating with heterogenous data sources for customer-order management," presented at the Proceedings of the 2004 ACM symposium on Applied computing, Nicosia, Cyprus, 2004.

[49] A. R. Chaturvedi and S. R. Mehta, "Simulations in economics and management," Commun. ACM, vol. 42, pp. 60-61, 1999.

[50] Y. Jingrong, "Research on Reengineering of ERP System Based on Data Mining and MAS," 2008, pp. 180-184.

[51] J. Ferber, "A Meta-Model for the Analysis and Design of Organizations in Multi-Agent Systems," 1998, pp. 128-128.

[52] R. A. Flores-Mendez, "Towards a standardization of multi-agent system framework," Crossroads, vol. 5, pp. 18-24, 1999.

[53] E. A. Kendall, P. V. M. Krishna, C. V. Pathak, and C. B. Suresh, "Patterns of intelligent and mobile agents," presented at the Proceedings of the second international conference on Autonomous agents, Minneapolis, Minnesota, United States, 1998.

[54] L. Lechtchinskaia, J. Uffen, and M. H. Breitner, "CRITICAL SUCCESS FACTORS FOR ADOPTION OFINTEGRATED INFORMATION SYSTEMS IN HIGHER EDUCATION INSTITUTIONS – A META-ANALYSIS," in Americas Conference on Information Systems, Detroit, Michigan, USA, 2011, p. 53.

[55] J. Jackson, "Data Mining; A Conceptual Overview," Communications of the Association for Information Systems, vol. 8, p. 32, 2002.

[56] J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection 6th ed.: Massachusetts Institute of Technology, 1998.

[57] A. P. Engelbrecht, Computational Intelligence An Introduction Wiley, 2002.

[58] PMI, A guide to the project management body of knowledge, 4th ed. Newton Square, EUA: Project Management Institute, 2008.

[59] W. Mendenhall, Introducción a la probabilidad y la estadistica. Florida: Wadsworth Internacional/Iberoamérica, 1982.