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The companies are struggling to collect invoices due to the decrease in the economic growth. This global trend does not only affect undeveloped countries, but it also has a strong impact on the developed countries. Improving the debt collection process become a significant element to maintain financial stability. The institutions that are specialized on collecting payments, debt collection agencies and their call centers, with their expertise in the field can improve the payment process. Yet, managing evaluating the performance of debt collection agencies is a very hard process that involves uncertainty and imprecision. Performance measurement (PM) is a combination of numerically expressed characteristics which give insight about the success or degree of accomplishment of an activity. PM can be handled in various levels such as individual, team, department or company. The aim of this study is to present a systematic and objective PM method for call centers.

In this study, first an exploratory approach is used to understand the call center measurement problem. Several meetings are done with the representatives of both call center firms and the parent firms that outsource debt collection process. Simultaneously, a broad literature review is conducted. An iterative approach is selected to reach deeper knowledge on the process. New meetings are planned and scope of the literature review has changed based on this iterative approach. After these steps, the problem has been considered as the multi-criteria decision-making problem since more than one criteria should be considered for evaluating the performances of call centers. The result of the literature review and the meetings with experts show that defining the weights for the criteria is very crucial for evaluating the performances accurately. Collecting human judgment for defining the weights of call center criteria necessitates dealing with vagueness and uncertainty. The intuitionistic fuzzy sets excellent tools for representing uncertainty. Interval valued intuitionistic fuzzy sets can easily represent the human judgments. Thus, in this study, an intuitionistic fuzzy multi-criteria decision making approach is used to design the proposed methodology. Incomplete interval-valued intuitionistic preference relations are used to determine the weights of the indicators aggregating linguistic evaluations of the decision makers.

The proposed approach provides an objective calculation of performance measurement. In order to provide objectivity, indicator performance functions are proposed for the first time in this study. Nine different functions and related parameters are defined to objectively measure indicator performances.

The paper proposes an objective and easy-to-modify approach for call-center PM, which can be used by call center managers. It presents a new fuzzy multi-criteria decision-making (MCDM) method for call center performance evaluation, which can consider the multi-experts' judgments under vagueness and impreciseness, which may be conflicting and incomplete interval-valued intuitionistic fuzzy preference relations. Also nine new functions are defined for indicator performance.

With the emerging technologies, collecting and processing data about the behaviors of customers or employees in a specific location has become possible. The purpose of this paper is to evaluate existing data collection technologies.

Technology evaluation problem is handled as a multi-criteria decision-making (MCDM) problem. In this manner, a decision model containing four criteria and eight sub-criteria and four alternatives are formed. The problem is solved using hesitant analytic hierarchy process (AHP) and trapezoidal fuzzy numbers (TrFN).

The results show that the most important sub-criteria are: accuracy, quantity, ıntrospective and cost. Decision makers’ evaluate for alternatives, namely wireless fidelity (WiFi), camera, radio-frequency identification and Bluetooth. The best alternative is found as Bluetooth which is followed by WiFi and Camera.

Technology evaluation problem, just like many other MCDM problems are solved using expert evaluations. Thus, the generalizability of the findings is low.

In this paper, technology selection problem has been handled using hesitant AHP for the first time. In addition, the original methodology is extended by using TrFN to represent the expert evaluations in a better way.

Purchasing activities of a company form a very important part in the operation of the company. The quality of production and services mainly rely on the performance of the suppliers. In addition, a large amount of the product's total cost belongs to the purchasing cost of its materials. Therefore, selecting the most suitable suppliers is very important for the firm. Unfortunately, most of the time, suppliers cannot provide the highest quality parts on-time at the lowest costs in the market. In other words, quality, delivery and cost objectives conflict with each other. The purpose of this paper is to determine the best supplier for the firm according to these conflicting objectives.

In this study a fuzzy analytic hierarchy process (FAHP) approach is employed for the supplier selection problem of a textile company. First the selection criteria according to company's objectives are determined. Then the pair-wise comparisons are made on a fuzzy basis. Similarly, in the next step, the six alternative suppliers are compared by fuzzy means.

Finally, the two suppliers which achieved the highest performance are advised to the company and this study is the first one which employs FAHP using fuzzy prioritization method in supplier selection problem.

There are many different applications of FAHP in literature and some of these have found applications in supplier selection problem. However, to the best of the authors’ knowledge this study is the first one which employs FAHP using fuzzy prioritization method in supplier selection problem.

The purpose of this paper is to develop a systematic and comprehensive project selection model utilizing fuzzy multi-objective linear programming (FMOLP) that deals with the imprecise data in IS projects and uncertain judgment of decision makers.

First, projects are prioritized by considering both quantitative and qualitative factors. A fuzzy analytical hierarchical process (FAHP) is used in order to obtain weights of each project that indicates their priorities. At the second step, project selection decision is completed by using FMOLP. Then, the sensitivity analysis is performed to evaluate the robustness of the proposed integrated model.

The result of this study indicates that an integrated approach utilizing FAHP and FMOLP can be used as a supportive tool for project selection in IS context. It decreases the uncertainty caused from uncertain judgment of decision makers.

Future studies are suggested to design models having fuzzy constraints such as budget and resources. Moreover, for future studies, non-linear membership functions can be used.

Actual projects are provided from the Turkish IS company for prioritizing process and a hypothetical mathematical model is demonstrated using illustrative data.

This study contributes to the relevant literature by proposing a comprehensive model considering many conflicting ideas of decision makers on quantitative and qualitative criteria, and evaluating projects in an integrated way including FAHP and FMOLP.

The purpose of this study is to develop a fuzzy expert system to design robust forecast of return quantity in order to handle uncertainties from the return process in reverse logistic network.

The most important factors which have impact on return of products are defined. Then the factors which have collinearity with others are eliminated by using dimension redundancy analysis. By training data of selected factors with fuzzy expert system, the return amounts of alternative cities are forecasted.

The performance metrics of the proposed model are found as satisfactory. That means the result of this study indicates that fuzzy expert systems can be used as a supportive tool for forecasting return quantity of alternative areas.

In the future, the proposed model can be used for forecasting other uncertain parameters such as return quality and return time. Other fuzzy systems such as type-2 fuzzy sets can be used, or other expert systems such as artificial neural networks can be integrated into fuzzy systems.

An application at an e-recycling facility is conducted for clarifying how the method is used in a real decision process.

It is the first study which aims to model an alternative forecasting by utilizing fuzzy expert system. Furthermore, a comprehensive factor list which includes predictors of the system is defined. Then, a dimension redundancy analysis is developed to reveal factors having significant impact on the return process and eliminate the rest.

The purpose of this paper is to understand how effective Turkish small and medium size enterprises (SMEs) use enterprise resource planning (ERP) systems in module aspect and to assess the adherence to lean manufacturing requirements. Obtaining each efficiency result separately is not adequate to make an inference about relationship between ERP and lean practices. Hence, a relational model is developed to analyze correlation between use of ERP and lean manufacturing implementation in white goods manufacturing SMEs of Istanbul, Turkey.

The examination of the proposed conceptual framework is made by statistical analyses. In parallel, leveling efficiency of each enterprise requires an expert analysis in order to obtain a fair comparison. Hence, authors have visited each facility to evaluate performance of ERP efficiency and lean manufacturing applications. In Istanbul region, 53 of total listed SMEs are visited. The questionnaire survey is designed by considering different scales of performance. Exploratory factor analyses and various correlation analyses are applied to figure out relationship between ERP use and lean manufacturing.

Interaction with rapid development of information technology (IT) and progress in modern production management strategies have emerged. Obtained results show that Turkish SMEs have widely initiated lean production practices. However, applications are in initial level in the most cases. In respect to ERP systems, enterprises are more active, some basic modules are extensively used. Another finding of this study implies that effective usage of specific ERP modules can contribute toward applying lean principles, vice versa.

The whole sample space of white goods SMEs could not be covered due to the fact that, each facility is visited and field studies are done. However, sample size calculations prove that attained feedback is statistically significant (adequate) to reflect sample space.

The literature studies reveal that there is an obvious lack of studies which focus on relationship between ERP use and lean manufacturing. Finding such a correlation helps IT-practitioners to design ERP modules by assessing related requirements of lean philosophy.

Customer relationship management (CRM) projects have a low success rate, which can be solved by better measurement of CRM process. The purpose of this paper is to define the CRM processes within a company and propose a tool for CRM measurement.

An empirical study is conducted in industrial organizations in Turkey. The research is designed according to scale development literature. The responses from various industries (manufacturing, information technologies, tourism, service, retail, finance, logistics) were collected. Using exploratory factor analysis, 167 valid responses are analyzed.

The paper provides a general understanding of CRM processes in customer‐oriented perspective and proposes a measurement tool that addresses seven main processes which are: targeting management, customer information management, production/service customization, expansion management, referrals management, termination management and win back.

The results of the analysis provides useful information for managers to define, measure and improve CRM process.

The paper provides an enhanced review about CRM measurement literature. Based on this review, the paper defines seven CRM processes and maintains a tool for evaluation of these processes within a company.

The purpose of this paper is to present an intelligent data-driven framework which provides an effective group-buying aggregation service and thus offers a new opportunity for personalized services in recommendation and advertisement.

The work presented in the paper analyzes the aggregated group-buying data and creates a compact view of the data which eliminates the potential redundancy and noise. In doing this, the dependencies are discovered from the data in a reverse engineering way. A noise-tolerant method is appreciated, as noise and exception is inevitable in massive data.

The paper finds that, through the implementation of the intelligent framework, the aggregator will provide a compact view of the group-buying data to customers. According to the empirical study, a 38 percent average decrease of redundancy and noise in the searching results is achieved through the newly built views and corresponding data.

The paper presents the innovative process of discovering the dependencies and creating views in a data-driven and noise-tolerant way. The proposed intelligent framework improves the aggregation performance and forms the basis of personalized services.

In any organization there are main goals, with lots of projects designed to achieve these goals. It is important for any organization to determine how much these projects affect the achievement of these goals. The purpose of this paper is to develop a fuzzy multiple attribute-based group decision-support system (FMAGDSS) to evaluate projects’ performance in promoting the organization's goals utilizing simple additive weighting (SAW) algorithm and technique for order of preference by similarity to ideal solution (TOPSIS) algorithm. The proposed FMAGDSS deals with choosing the most appropriate fuzzy ranking algorithm for solving a given fuzzy multi attribute decision making (FMADM) problem with both qualitative and quantitative criteria (attributes), and uncertain judgments of decision makers.

In this paper, a FMAGDSS model is designed to determine scores and ranks of every project in promoting the organization's goals. In the first step of FMAGDSS model, all projects are assessed by experts based on evaluation criteria and the organization's goals. The proposed FMAGDSS model will then choose the most appropriate fuzzy ranking method to solve the given FMADM problem. Finally, a sensitivity analysis system is developed to assess the reliability of the decision-making process and provide an opportunity to analyze the impacts of “criteria weights” and “projects” performance’ on evaluating projects in achieving the organizations’ goals, and to assess the reliability of the decision-making process. In addition, a software prototype has been developed on the basis of FMAGDSS model that can be applied to solve every FMADM problem that needs to rank alternatives according to certain attributes.

The result of this study simplifies and accelerates the evaluation process. The proposed system not only helps organizations to choose the most efficient projects for sustainable development, but also helps them to assess the reliability of the decision-making process, and decrease the uncertainty in final decision caused by uncertain judgment of decision makers.

Future studies are suggested to expand this system to evaluate and rank the project proposals. To achieve this goal, the efficiency of the projects in line with organization's goals, should be predicted.

This study contributes to the relevant literature by proposing a FMAGDSS model to evaluate projects in promoting organization's goals. The proposed FMAGDSS has ability to choose the most appropriate fuzzy ranking algorithm to solve a given FMADM problem based on the type and the number of attributes and alternatives, considering the least computation and time consumption for ranking alternatives.