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Implementation of Enterprise Resource Planning systems (ERPs) is a complex and costly process that is usually accompanied with serious risks. Numerous research projects have been conducted to illuminate ERP Critical Success Factors (CSFs) so as to identify the main factors in enhancing success rate. Although project managers' skills of ERP system implementation projects are viewed as one of the most effective factors in the success of such projects, scant attention has been paid to them and their unique aspects have not been sufficiently discussed in the extant literature. Hence, this article aims at identifying the most relevant skills of ERP project managers and proposing a classification scheme. Based on the results of the robust Exploratory Factor Analysis (EFA), 16 identified skills were grouped into four distinct categories: “managerial,” “project management,” “human resource,” and “technical.” The results of this article can help scholars and managers to grasp an in-depth understanding of the skills required for project managers and the challenges they have to mitigate while implementing ERP projects.

Selecting the appropriate IT outsourcing (ITO) strategy is important for firms in general and banking institutions in particular to achieve expected benefits, but it remains complex, risky and lengthy process for clients to select the appropriate ITO strategy. Previous academic efforts to support ITO decision incorporated a limited number of factors, which is over-simplification of multi-faceted real world problems. In this paper, after doing in-depth literature review for identifying influencing factors on such ITO decisions, a model with an application of Fuzzy TOPSIS based approach is proposed to rank and prioritize the alternatives. Throughout this study, the authors use data from a real banking case to run the model for supporting outsourcing decision for five ATM, POS, tele-banking, mobile, and internet-banking services. The proposed model can help ITO decision makers advance their decision making process, especially where parameters involve uncertainties and hardly can be assessed by human judgment.

At the present time, Business Process Management software (BPMS) implementation is on focus of organizations. To implement a BPMS in a firm successfully, selecting a suitable BPMS is critical. Evaluation and selection of the BPMS software is complex and a time consuming decision-making process. This paper propounds an approach for dealing with such a decision. This approach introduces functional, non-functional and fuzzy evaluation method for BPMS evaluation. The presented BPM lifecycle based approach breaks down BPMS evaluation and selection criteria into two broad categories namely functional and non-functional requirements including totally 48 selection criteria. A facile Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) is customized for BPMS selection based on identified criteria. The provided approach is applied to a port and maritime organization in order to evaluate and acquire a BPMS and the provided numerical example illustrates the applicability of the appro...

this research presents a novel asymmetrical four degree of freedom parallel kinematic machine tool with rotational capability around one of its horizontal axis, developed for milling processes. Mobility analysis is carried out via screw theory and kinematic analyses for position and velocity are done. Form obtained kinematic relationships, the Jacobian matrix is introduced and singularity analysis is carried out and global condition index indicating the dexterity of mechanism is obtained. Finally, the workspace is simulated considering the physical and kinematical constraints. Results, demonstrated the high capability of the proposed mechanism in dexterity and non-singular workspace, which makes the novel mechanism a fitting candidate for milling processes.

Support vector machine is a well-known and computationally powerful machine learning technique for pattern classification and regression problems, which has been successfully applied to solve many practical problems in a wide variety of fields. Nonparallel Support Vector Machine (NPSVM) which is an extension of Twin-SVMs, is proved to be theoretically and practically more flexible and superior than TWSVMs and also it overcomes several drawbacks of the existing typical SVMs in order to be applicable in large-scale data sets. However, one of the difficulties in successful implementation of NPSVM is its different parameters, which should be well adjusted during the training process. In fact, the generalization power, robustness and sparsity of NPSVM are extremely depended on well setting of its parameters. In this paper, we propose a hybrid approach for parameter determination of the NPSVM by Particle Swarm Optimization techniques. Furthermore, in order to increase the sparsity of NPSVM and to reduce the training time, we take into account the number of support vectors (SVs) along with classification accuracy as a weighted objective function. Our experiments on several public datasets show that the proposed method can achieve better classification accuracy compare to that of TWSVM and NPSVM with less computational time.

Nowadays, there is an increasing number of Voices over IP (VoIP) services offered in telecommunication networks with specific quality requirements. Such requirements impact upon both the objective quality of service (QoS) of the end-to end connection as well as the subjective quality of experience (QoE) as perceived by the end user. In this study, we have proposed an integrated QoS and QoE evaluation system based on the combination of fuzzy inference systems and fuzzy evidence theory. For quality criteria, we have used QoS as a technical source from the viewpoint of service provider and QoE as the end user approach in front of service. We have divided the positive and negative variables of QoE into two distinct systems. At the same time, a parameter has been proposed as quality, which is the total quality of a service according to the objective and subjective viewpoints. To calculate the parameters mentioned above, a fuzzy inference system has been utilized. In addition, to obtain all data sources as an evidence of quality, Dempster---Shafer evidence theory has been employed. Finally, the proposed approach evaluated through the quality of VoIP services in three real cases and the results are discussed.

Constructability is a concept with relative and not absolute value to increase optimization capacity of resources such as workforce, time, cost, quality, and working environment conditions. Given the growing complexity of projects and the increased number of failed and abandoned projects, the necessity to implement constructability in projects has become more tangible. Although the effects of lack of quantitative definition of constructability role in the traditional construction approaches are evident and have led to lack of coordination in performance of construction projects, so far no comprehensive quantitative approach has been considered to analyze the obstacles to constructability implementation. This study aims to identify and categorize the obstacles to facilitating the presence of contractors in the early stages of planning and design to implement constructability. In this study a comprehensive list of obstacles to constructability implementation is developed as a questionnaire. This questionnaire was presented to the experts, active in the field of construction. Finally, its results were analyzed using exploratory factor analysis method. Totally, 63 obstacles were questioned, then they were categorized by some of the experts of this industry into five categories of macro factors, including contractual, environmental, managerial, technical, and organizational. The significance of this study is due to this fact that identifying and categorizing the key obstacles to constructability implementation provides a useful reference for managers and owners of the construction industry to identify and develop solutions to resolve them. Identifying the obstacles to the presence of contractors in the planning and design stage and having a quantitative view toward this issue affects project implementability. In this regard, one can present more effective solutions to facilitate the presence of contractors in the early stages of design and also improves the effectiveness of constructability. Constructability is a concept with relative and not absolute value to increase optimization capacity of resources such as workforce, time, cost, quality, and working environment conditions. Given the growing complexity of projects and the increased number of failed and abandoned projects, the necessity to implement constructability in projects has become more tangible. Although the effects of lack of quantitative definition of constructability role in the traditional construction approaches are evident and have led to lack of coordination in performance of construction projects, so far no comprehensive quantitative approach has been considered to analyze the obstacles to constructability implementation. This study aims to identify and categorize the obstacles to facilitating the presence of contractors in the early stages of planning and design to implement constructability. In this study a comprehensive list of obstacles to constructability implementation is developed as a questionnaire. This questionnaire was presented to the experts, active in the field of construction. Finally, its results were analyzed using exploratory factor analysis method. Totally, 63 obstacles were questioned, then they were categorized by some of the experts of this industry into five categories of macro factors, including contractual, environmental, managerial, technical, and organizational. The significance of this study is due to this fact that identifying and categorizing the key obstacles to constructability implementation provides a useful reference for managers and owners of the construction industry to identify and develop solutions to resolve them. Identifying the obstacles to the presence of contractors in the planning and design stage and having a quantitative view toward this issue affects project implementability. In this regard, one can present more effective solutions to facilitate the presence of contractors in the early stages of design and also improves the effectiveness of constructability.

E-government has provided the state-run sector with numerous opportunities to offer a variety of online services to citizens and businesses through websites. These websites have been launched in order to provide easy access to government services, boost the efficacy of the government-run sector and reduce operational costs. Given the point that many of these websites have gradually been developed and have scarcely been standardized to meet the requirements of site visitors, it is necessary to develop models for quality assessment of such websites. In this study, an information systems quality assessment (ISQA) based method along with fuzzy analytic network process (ANP) has been exploited to assess the quality of governmental websites. In order to validate the proposed model, it is applied to portals in six Iranian free trade zones. Finally, the assessment results are discussed.

Data mining has a tremendous contribution for researchers to extract the hidden knowledge and information which have been inherited in the raw data. This study has proposed a brand new and practical fuzzy analytic network process (FANP) based weighted RFM (Recency, Frequency, Monetary value) model for application in K-means algorithm for auto insurance customers' segmentation. The developed methodology has been implemented for a private auto insurance company in Iran which classified customers into four “best†, “new†, “risky†, and “uncertain†patterns. Then, association rules among auto insurance services in two most valuable customer segments including “best†and “risky†patterns are discovered and proposed. Finally, some marketing strategies based on the research results are proposed. The authors believe the result of this paper can provide a noticeable capability to the insurer company in order to assess its customers' loyalty in marketing strategy.

One of the most widely used algorithms to solve clustering problems is the K-means. Despite of the algorithm's timely performance to find a fairly good solution, it shows some drawbacks like its dependence on initial conditions and trapping in local minima. This paper proposes a novel hybrid algorithm, comprised of K-means and a variation operator inspired by mutation in evolutionary algorithms, called Noisy K-means Algorithm (NKA). Previous research used K-means as one of the genetic operators in Genetic Algorithms. However, the proposed NKA is a kind of individual based algorithm that combines advantages of both K-means and mutation. As a result, proposed NKA algorithm has the advantage of faster convergence time, while escaping from local optima. In this algorithm, a probability function is utilized which adaptively tunes the rate of mutation. Furthermore, a special mutation operator is used to guide the search process according to the algorithm performance. Finally, the proposed algorithm is compared with the classical K-means, SOM Neural Network, Tabu Search and Genetic Algorithm in a given set of data. Simulation results statistically demonstrate that NKA out-performs all others and it is prominently prone to real time clustering.

Nowadays, many organizations take Business Intelligence (BI) systems to improve their decision-making processes. Although many organizations have adopted BI systems, not all of these implementations have been successful. This paper seeks to identify critical success factors (CSFs) that impact on successful implementation of BI systems in organizations. So, at first, through literature review, 26 CSFs were identified. Following that, a questionnaire was developed and then filled out by domain experts who had at least three years of experience in BI implementation projects in Iran. Robust Exploratory Factor Analysis (EFA) was run for data analysis, which finally classified 26 CSFs into four distinct groups termed as “organizational”, “human”, “project management”, and “technical”. The results of this study provide a very useful reference for scholars and managers to identify the relevant issues of BI projects in Iran.

Knowledge management (KM) has been regarded as a critical issue for the practitioners and academicians in these years. Recently, knowledge management strategies have been highly considered in organizations. The article tries to explore knowledge management strategies from different aspects and explains the most important factors in its success. Here, nine main factors are considered for the success of the knowledge management strategies and it has been tried to present a new framework for knowledge management strategies based on experts’ opinions and exploring two case studies. The presented strategies for knowledge management include two relation and substitution strategies. Also, characteristics of each strategy are investigated and the differences with current strategies in the field are discussed.

Implementing an enterprise resource planning system is a sophisticated, lengthy, and costly process which tends to face serious failure. Thus, it is essential to perform a success assessment at the post-implementation stage of an ERP project to evaluate how much the system has succeeded in achieving its predetermined objectives. This paper proposes a practical framework for assessing a firm's ERP post-implementation success utilising current models through a fuzzy analytic network process. The construct of ERP success is broken down into three main parts, including managerial success, organisational success, and individual success. Using this framework, the firm's ERP system success can be determined and the required improvement projects can be proposed to promote the success level. The proposed framework has been applied to a real international company, in the field of manufacturing and supplying turbines, to measure the firm's ERP post-implementation success. Finally, the advantages of the model are ill...

The objective of this research is to help customers in purchasing perfumes, with the aid of the expert system program developed by using artificial neural networks. The expert system's role is in the preparation to capture the data from the customer's requirements and predict appropriate perfume. For this end, factors of perfume costumers' decision were recognized using Fuzzy Delphi method and a back propagation neural network classification model was developed and trained with 2303 data of customers. In addition, to validate the approach, the expert system program has been tested with 583 data of customers. The model demonstrates the usefulness of 70.33% classification rate in classifying consumers' styles that looks satisfying.

As traditional concept in management, decision support had a remarkable\ud role in competitiveness or survival of organisations and following, as modern impression,\ud nowadays business intelligence (BI) has various applications in achieving desirable\ud decision supports. Consequently, assessing BI competencies of enterprise systems can\ud enable decision support in firms. This paper presents a practical framework for\ud assessing the business intelligence capabilities of enterprise systems based on a set of\ud novel factors and utilising fuzzy analytic network process (FANP). Through this, the\ud construct of BI competency is decomposed into three main competency parts including\ud ‘managerial’, ‘technical’ and ‘system enabler’ sub-goals, five main factors and 26\ud criteria. Using this framework, the BI competency level of enterprise systems can be\ud determined which can help the decision makers to select the enterprise system that best\ud suits organisations’ intelligence decision support needs. In order to validate the proposed\ud model, it is applied to a real Iranian international offshore engineering and construction\ud company in the oil industry to select and acquire ERP system. This research provides a\ud complete frame (factors, criteria and procedures) for firms to assess their proposed\ud software and systems in the field of BI competencies and functions.

The Enterprise Resource Planning system (ERP) has been pointed out as a new information systems paradigm. However, achieving a proper level of ERP success relies on a variety of factors that are related to an organization or project environment. In this paper, the idea of predicting ERP post-implementation success based on organizational profiles has been discussed. As with the need to create the expectations of organizations of ERP, an expert system was developed by exploiting the Artificial Neural Network (ANN) method to articulate the relationships between some organizational factors and ERP success. The expert system role is in preparation to obtain data from the new enterprises that wish to implement ERP, and to predict the probable system success level. To this end, factors of organizational profiles are recognized and an ANN model is developed. Then, they are validated with 171 surveyed data obtained from Middle East-located enterprises that experienced ERP. The trained expert system predicts, with an average correlation coefficient of 0.744, which is respectively high, and supports the idea of dependency of ERP success on organizational profiles. Besides, a total correct classification rate of 0.685 indicates good prediction power, which can help firms predict ERP success before system implementation.

Most organisations still experience a lack of business intelligence BI in their decision-making processes when implementing enterprise systems. The current state-of-the-art in decision support takes the intelligence requirements of enterprise systems as important quality aspects into consideration, along with their functional and non-functional needs, but the literature lacks studies on the evaluation of these intelligence requirements. This paper proposes a fuzzy, multi-criteria decision-making procedure and a multi-objective programming model to evaluate and make final decisions about the selection of enterprise systems that also include the requirements of business intelligence in addition to their other goals and requirements. In order to validate the model with a real application, all phases of the approach were applied in the evaluation of the enterprise systems of a company in the oil industry. Companies can use this model to evaluate, select and implement enterprise software and systems that will provide better decision support for their organisational environment.

Nowadays, there is a growing interest in Business Process Management Suites (BPMSs) implementation in organizations. In order to implement a BPMS in an organization successfully, it is essential to select a suitable BPMS. Evaluation and selection of the BPMS packages is complicated and time consuming decision making process. This paper presents an approach for dealing with such a problem. This approach introduces functional, non-functional and fuzzy evaluation method for BPMS selection. The presented BPM lifecycle based approach breaks down BPMS selection criteria into two broad categories namely functional (process strategy development, process discovery, process modeling, process design, process deployment, process operation and analysis) and non-functional requirements (quality, technical, vendor, implementation) including totally 48 selection criteria. A facile Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) is customized for BPMS selection based on identified criteria. The proposed approach is applied to a local Iranian company in oil industry in order to select and acquire a BPMS and the provided numerical example illustrates the applicability of the approach for BPMS selection. The approach can help practitioners assess BPMSs more properly and have a better software acquisition decision.