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Article Type: Editorial From: Journal of Enterprise Information Management, Volume 27, Issue 3.
It gives us great pleasure to welcome our readers to the third issue of the 27th volume of Journal Enterprise Information Management (JEIM), and express our appreciation for their continuous support during the past year. The continuous update of the journal's scope to promote theory and practice has led to an increase in submissions that has allowed us to further the quality of the journal. This special issue on “Uncertainty modelling in information management and decision making” incorporates excellent “quality” submissions that focus on providing a mixture of theoretical and practical contributions. The research work presented in these papers highlight that social media are playing a rapidly expanding role in both companies and in teaching and learning. The aim of this special issue is to provide a broad understanding on the significance that uncertainty modelling in the context of managing information resources and decision-making process plays. The techniques for uncertainty modelling and analysis in engineering presents appropriate analytical and useful tools to current and future analysts and practitioners to understand the fundamentals of knowledge, how to model and analyse uncertainty, and how to select these tools for particular problems.
Following rounds of extensive reviewing, this special issue offers six submissions that are representative of new and novel ideas in information management and decision-making research.
The third issue of volume 27 commences with a research paper by Ferhan Çebi, Bersam Bolat, Gül Tekin Temur and Írem Otay, entitled “A fuzzy integrated approach for project selection”. This paper aims to develop a systematic and comprehensive project selection model utilising fuzzy multi-objective linear programming that deals with the imprecise data in information systems projects, and uncertain judgment of decision makers. IS projects have intricate implementation processes (Umble et al., 2003), and complications that are caused from fast organisational expansions. To adapt effortlessly to the dynamic structure, organisations should enhance their decision-making mechanisms for overcoming complications resulted from varying conditions in the surroundings. Therefore, an integrated approach using multi-criteria decision-making techniques under fuzzy environments is proposed to support the decision makers during the IS project evaluation and selection process. In this approach, the authors employed:
a Fuzzy Analytical Hierarchical Process (FAHP) to determine the weights of projects to be used as “utilities” in the evaluation process; and
a Fuzzy Multi Objective Linear Programming (FMOLP) model is developed to complete the project selection process.
FMOLP model is formulated by including four different objectives such as maximisation of revenue, social benefits and utilities as well as minimisation of risk, and constraints in terms of resource limits. The results indicate that an integrated approach utilising FAHP and FMOLP together can be used as a supportive tool for project selection within an IS context.
Then Cagatay Iris and Ufuk Cebeci present their research, entitled “Analyzing relationship between ERP utilization and lean manufacturing maturity of Turkish SMEs”. This paper seeks to understand how effectively Turkish SMEs employ ERP systems in a modular aspect, and adherence to lean manufacturing requirements. This research is carried out in-line with the literature advocated by the authors, i.e. to ascertain the efficiency of systems in the organisations, there is a need to measure the utilisation of ERP systems through lean tools. Implementing ERP systems does not signify that these systems are generally accepted and utilised by users in the organisations. Thus, what is more important is to understand how effectively SMEs use such tools in improving organisational business functions and as a result of this, enhance overall system performance (e.g. see Yang and Su, 2009; Riezebos et al., 2009). Measuring and scaling these parameters are mostly based on perceived outcomes. To overcome such a problem, there is need to consider utilisation in terms of:
common master data;
degree of integration of business processes;
utilisation of the broad functionality; and
to what degree potential users use the system.
The authors further argue that enhancing efficiency is not sufficient to make an extrapolation about the relationship between ERP and lean practices. Therefore, a relational model is developed and proposed to analyse correlation between the use of ERP and lean manufacturing implementation in white goods manufacturing SMEs of Istanbul, Turkey. The empirical purpose of this study is to identify the dimensional structure underlying lean production applications and ERP utilisation, and to develop reliable and valid scales to represent the relationship between these tools. To achieve the above, the authors adopted a comprehensive multi-stage approach that starts with sample size validity analysis, thereafter; a questionnaire was constructed based on an extensive review of the literature in the areas of ERP and lean production implementation. This research puts forward numerous positive contributions, including among others are:
correlations between the lean measurement items indicate that the elements of the proposed model are complementing each other as a whole and that this model for measuring adherence to lean practices can be used for evaluating the leanness level of companies; and
this model would also serve to catalyse decision making as managers seek to match the ERP implementations to evolving lean needs.
Thereafter, Fahimeh Ramezani and Jie Lu present their work entitled “An intelligent group decision support system and its application for project performance evaluation”. The authors argue that for any organisation, there are essential goals and there are a number of projects primarily designed and developed to achieve the vital goals. Concurrently, it is vital for an organisation to recognise the significance of their individual projects and how these projects affect the achievement of the stated goals. Identifying the most influential and effective projects to achieve the company's goal(s) is a complex problem that requires consideration of conditions from multiple perspectives. The inevitability to take into consideration several decision parameters, besides economic ones, for instance socio-political, technical, institutional and environmental, lead to the use of multi-criteria decision methods (Goletsis et al., 2003; Lu et al., 2007). In line with this literature critique, the authors propose:
a new Fuzzy Multiple Attribute-Based Group Decision Support System (FMAGDSS) model to evaluate projects’ performance in promoting the goals, as such a selection may involve both quantitative and qualitative assessment attributes.
The proposed FMAGDSS model has the ability to choose the most appropriate fuzzy ranking method to solve given FMADM problems. The authors assert that this system contains a sensitivity analysis functions, which provide an opportunity to analyse the impacts of “attributes’ weights” and “projects’ performance” on evaluating projects in achieving the organisations’ goals, and to assess the reliability of the decision-making process. Furthermore, a software prototype has been developed on the basis of the proposed FMAGDSS model, which can be applied to solve every FMADM problem that needs to rank alternatives according to certain attributes. The authors claim that the proposed model simplifies and accelerates the evaluation process and by applying this model not only supports organisations to select the most efficient projects for sustainable growth but also helps them to evaluate the reliability of the decision-making process.
Following the above research, we have Pınar Mızrak Özfırat, Gökçeçiçek Tuna Tasoglu, and Gonca Tunçel Memis, who present their research paper, entitled “A Fuzzy Analytic Hierarchy Process methodology for the supplier selection problem”. This research aims to provide a comprehensive methodology for the prioritisation of preference intensities by dealing with subjective and imprecise assessments. Also, ensuring alignment between performance improvements in supplier selection and the operational requirements of the firm. In so doing, the authors aim to answer the following the question of:
“Who to buy from and how much to buy?” " The authors assert that this is simply the supplier selection problem.
A number of research studies have successfully applied the analytic hierarchy process (AHP) technique to solve the multi-criterion decision-making problems (such as Saaty, 1980; Salmeron and Herrero, 2005; Kamal and Alsudairi, 2009) including supplier selection and resource allocation (such as Barbarosoglu and Yazgaç, 1997), its ability is limited in handling the uncertainties and vagueness inherent in real life decision-making environment. Consequently, in recent times, there has been an increasing attention towards assimilating AHP and fuzzy set theory to extend the ability to catch decision maker's uncertain preferences. In this paper, a fuzzy AHP approach (Barbarosoglu and Yazgaç, 1997) is used 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. 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.
The above research is followed by an intelligent data-driven based research by Ming Ren, Qiang Wie, Shuangjie Li, Guoqing Chen, entitled “Providing an effective group-buying aggregation service: a data driven approach”. Customer related data are a vital organisational resource contributing towards making appropriate decisions and devising suitable strategies for retaining existing customers. According to Kahraman et al. (2011) data are aggregated on a daily basis, and in this context an instinctive and intelligent approach is strongly desired to develop a fine schema, so as to enhance aggregation performance and support desirable functions (e.g. semantic search, personalised recommendation and advertising). The authors assert that to-date limited issues related to online group buying have attracted the attention of academia, e.g. the price discovery mechanism (Anand and Aron, 2003), factors that influence group-buying decision (Erdogmus and Çiçek, 2011), customer behaviours discovered in group-buying data (Liao et al., 2012), while the data aggregation of group buying remains unexplored yet. This paper attempts to improve the data aggregation performance from a logic perspective, by developing a compact view of the data and eliminating the redundancy and noise in the data. The purpose is to exhibit an intelligent data-driven framework that provides an effective group-buying aggregation service and thus offers a new opportunity for personalised services in recommendation and advertisement. The empirical work presented in the paper evaluates the aggregated group-buying data in China and develops a compact view of the data that 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 valued, as noise and exception is inevitable in the mass data. The proposed intelligent framework improves the aggregation performance and forms the basis of personalised services.
Finally, we have Gül Tekin Temur, Muhammet Balcilar and Bersam Bolat, who present their research paper, entitled “A fuzzy expert system design for forecasting return quantity in reverse logistics network”. The purpose of the last paper is to develop a fuzzy expert system to design robust forecast of return quantity to handle uncertainties come from the return process in reverse logistic network. Dowlatshahi (2010) states that with the support reverse logistic, business organisations collect used or scrapped products from consumers for recovering them in order to increase sustainability, total profit and productivity. In the real-world, reverse logistic implementation has a hard nature because there are many unknown parameters such as product return quantity, quality and time in the process. Such uncertain parameters increase the complexity of material requirement planning which focuses on the flow of secondary materials from recovery activities into manufacturing (Gomez et al., 2002). Based on the literature analysis, the authors argue that in order to accomplish difficulties resulted from uncertainty; an effective forecasting method is required to use. Thus, this research aims to decrease the complexity of product return processes in reverse logistic and develop more successful forecasting system at obtaining more realistic return quantity values. Compared to other researches, this study contributes to the product return literature by providing two new solutions: proposed system increases the robustness by eliminating insufficient predictors with dimension redundancy analysis; the study proposes a fuzzy expert system methodology that is preferable for problems containing less data and fuzzy structure copes with uncertainty.
We hope that this issue will provide a useful resource of ideas, techniques, and methods for additional research on uncertainty modelling in information management and decision making. We are grateful to the referees whose valuable and highly appreciated works contributed to select the high quality of papers published in this issue.
Zahir Irani, Muhammad Kamal, Cengiz Kahraman, Basar Öztaysi, Özgür Kabak and Irem Uçal Sar;
Anand, K.S. and Aron, R. (2003), “Group buying on the web: a comparison of price-discovery mechanisms”, Management Science, Vol. 49 No. 11, pp. 1546-1562
Barbarosoglu, G. and Yazgaç, T. (1997), “An application of the analytic hierarchy process to the supplier selection problem”, Production and Inventory Management Journal, Vol. 38 No. 1, pp. 14-21
Dowlatshahi, S. (2010), “A cost-benefit analysis for the design and implementation of reverse logistics systems: case studies approach”, International Journal of Production Research, Vol. 48 No. 5, pp. 1361-1380
Erdogmus, I.E. and Çiçek, M. (2011), “Online group buying: what is there for the consumers?”, Procedia Social and Behavioral Sciences, Vol. 24 No. 15, pp. 308-361
Goletsis, Y., Psarras, J. and Samouilidis, J.-E. (2003), “Project ranking in the Armenian energy sector using a multicriteria method for groups”, Annals of Operations Research, Vol. 120 Nos 1/4, pp. 135-157
Gomez, J.M., Rautenstrauch, C., NÜrnberger, A. and Kruse, R. (2002), “Neuro-fuzzy approach to forecast returns of scrapped products to recycling and remanufacturing”, Knowledge-based Systems, Vol. 15 Nos 1/2, pp. 119-128
Kahraman, C., Kaya, I. and Çevikcan, E. (2011), “Intelligence decision systems in enterprise information management”, Journal of Enterprise Information Management, Vol. 24 No. 4, pp. 360-379
Kamal, M.M. and Alsudairi, M. (2009), “Investigating the importance of factors influencing integration technologies adoption in local government authorities”, Transforming Government: People, Process and Policy, Vol. 3 No. 3, pp. 302-331
Liao, S., Chu, P., Chen, Y. and Chang, C. (2012), “Mining customer knowledge for exploring online group buying behavior”, Expert Systems with Applications, Vol. 39 No. 3, pp. 3708-3716
Lu, J., Zhang, G., Ruan, D. and Wu, F. (2007), Multi-Objective Group Decision Making: Methods, Software and Applications with Fuzzy Set Techniques, World Scientific, Imperial College Press, London
Riezebos, J., Klingenberg, W. and Hicks, C. (2009), “Lean production and information technology: connection or contradiction?”, Computers in Industry, Vol. 60 No. 4, pp. 237-247
Saaty, T.L. (1980), The Analytic Hierarchy Process: Planning, Priority Setting: Resource Allocation, McGraw-Hill, New York, NY
Salmeron, J.L. and Herrero, I. (2005), “An AHP-based methodology to rank critical success factors of executive information systems”, Computer Standards & Interfaces, Vol. 28 No. 1, pp. 1-12
Umble, J.E., Haft, R.R. and Umble, M.M. (2003), “Enterprise resource planning: implementation procedures and critical success factors”, European Journal of Operational Research, Vol. 146 No. 2, pp. 241-257
Yang, C., and Su, Y. (2009), “The relationship between benefits of ERP systems implementation and its impacts on firm performance of SCM”, Journal of Enterprise Information Management, Vol. 22 No. 6, pp. 722-752