A Fuzzy Inference System Approach for Knowledge Management Tools Evaluation

Transcription

1 th International Conference on Computer Modelling and Simulation A Fuzzy Inference System Approach for Knowledge Management Tools Evaluation Ferdinand Murni H School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia (ferdinandmurni@gmail.com) Rahmat Budiarto School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia (rahmat@cs.usm.my) Abstract Knowledge management has become a very important role in enterprises business process. Regarding to the effective and efficiency of knowledge management system (KMS) implementation, information technology (IT) or tools to support KMS can be a successful factor. One of the challenges that will be faced by enterprises in KMS implementation is the evaluation and selection an appropriate knowledge management tools (KMT). KMT evaluation is decision making process that consists with the multiple-criteria. The prior methods have been developed based on multiple-criteria. However, these methods are still lack capable to deal with the human judgment, uncertainty and different purchasing policy in every enterprise. This paper proposed fuzzy inference system (FIS) approach to solve the problem in evaluating knowledge management tools. Our aim is to produce useful decision model such as capable to be applied in many different environments of enterprise, reduce time-consuming calculations and be able to handle multiple conflicting criteria. We believe that the system shown can deal with the problem faced by enterprise in knowledge management tools evaluation before implementing the KMS. Keywords: Knowledge management tools; decision support system; fuzzy inference system I. INTRODUCTION There is no doubt that knowledge management (KM) has came to play an important role in enterprises. KM refers to the set of processes or practice of developing the ability to create, acquire, capture, store, maintain and disseminate the enterprise s knowledge. Even though managers knew how important KM, it was very difficult to implement it successfully[1]. One of the main important things that will be faced by manager in enterprises before implementing the knowledge management system is evaluation an appropriate knowledge management tools (KMT). An appropriate choice of KMT is significant expected in establishing KMS to facilitate KM activities[2] besides the other factors such as human aspect, and organizational aspect. Therefore, it is necessary to selecting the right tool that suitable with the enterprise circumstances to support KMS implementation. The prior researches of KMT evaluation have created versatile methods which can effectively deal with the KMT evaluation problem. Ngai [3] applied an analytic hierarchy process (AHP), Erensal [4] used fuzzy linear programming, Yu-Rong [5] integrated modified Delphi, fuzzy comprehensive evaluation and grey relational analysis. Those models can handle both qualitative and quantitative multi-criteria problems. However, the problem is not as simple as it seems. This is due to the fact that uncertainty character always present in decision making and enterprises have different business circumstances each one; it means that enterprise always different in IT purchasing policy. Furthermore, the knowledge management tools evaluation is time consuming task and filled with the multiple conflicting criteria that must be interrelation among each factor. Hence, the purpose of this paper is to improve conceptual model of KMT evaluation by applying fuzzy inference system. Over the years, Artificial Intelligence (AI) techniques such as Artificial Neural Network (ANN), Genetic Algorithm (GA), and Fuzzy Logic (FL) have been studied and employed in decision making. FL has been widely used because of its obvious advantages of effectively dealing with uncertainty and capturing experts knowledge on a specific problem and using this knowledge to make decisions. In this paper, FL has been applied to deal with the knowledge management tools evaluation problem. The evaluation decision can be effectively made based on the criteria and knowledge base which have been constructed by experts of a specific domain. Additionally, the knowledge management tools evaluation criteria and rules used in making decision can be adapted to the changing environment of enterprise. The rest of this paper is organized as follows; we begin with the literature KM system and tools, and KMT evaluation criteria in section 2. Section 3 discusses about FIS, section 4 presents the details of the proposed FIS for the KMT evaluation and to complete the FIS work, we present the simulation through section 5. Finally, we discuss the conclusion in section 6. II. LITERATURE REVIEW A. Knowledge management system and tools Knowledge management systems (KMS) are the ITbased systems developed to support and enhance the /10 $ IEEE DOI /UKSIM /UKSIM /UKSIM

2 enterprise processes of knowledge creation, storage/retrieval, transfer, and application [6]. IT plays an important role in determining the success or failure of the implementation of KMS [7]. Hence, during the development of KMS, attention should be paid to various issues and challenges related to using IT to support KM [8]. Ruggles [9] defined KM tools as technologies to enhance and enable the generation, codification and transfer of knowledge. The most frequently utilized types of technology in KM tools are: Intranets, content management systems, document management systems, relational and object databases, groupware and workflow systems, data warehousing systems and data mining systems [10]. B. Knowledge management tools evaluation criteria KMT evaluation is a multi-criteria evaluation model; therefore it is necessary to identify the factors that influence KM practitioner s choice of KMT. E.W.T Ngai and E.W.C Chan [3] identified three evaluation criteria to be used in evaluating the best of KM tools : costs, functionality, and vendors with sub-criteria and their attributes. 1. Costs In purchasing activity, costs are the biggest influencing factor because directly relate to the product purchase. There are two major criteria, namely are capital expenditures and operating expenditures. Capital expenditures are the nonrecurring costs involved in setting up the KMS. They include product costs (the basic cost of the KM tool), license costs and training costs. Operating expenditures are the recurring costs involved in operating the KMS, which include maintenance costs and software subscription costs 2. Functionality Since the objectives of KM can vary in different enterprises, different functions provided by the KM tool can help the enterprise to achieve their KM goals. There are seven key functional elements of a KM tool such as document management, collaboration for generating and sharing knowledge, communication, measurement, workflow management, scalability and security. 3. Vendors The quality of vendor support and its characteristics are major importance in the selection of software[11]. It is also critical for the successful installation and maintenance of the software. The important factors affecting the decision to select a KM tool are vendor reputation, the training provided, the implementation partner, KM consulting services and support, maintenance, upgrades and integration. III. FUZZY INFERENCE SYSTEM Because fuzziness and vagueness are common characteristics in many decision-making problems, good decision-making models should be able to tolerate vagueness or ambiguity [12]. Therefore, involving the fuzziness in human decision making is necessary to avoid misleading of uncertainty in models by using fuzzy set and fuzzy logic. A fuzzy set is a collection of object is X denoted generally by x is a set of ordered pairs, where µ A ( x ) is the membership function of x in A that maps X to the membership grade between 0 and 1 [13]. A = {( x, µ A ( x )) x X } Fuzzy inference system (FIS) is a computing framework based on the concept of fuzzy set theory, fuzzy rules, and fuzzy reasoning [14]. FIS works by mapping from a given input to an output using fuzzy logic as illustrated in Figure 1. There are two well-known fuzzy inference system, the Mamdani fuzzy model and the Sugeno fuzzy model. In this paper we used the Mamdani Fuzzy inference system because the output membership functions of a Sugeno fuzzy model can only be either linear or constant [13]. The Mamdani Fuzzy Inference System works by using the fuzzy operation of min and max to determine output. A fuzzy rule in a Mamdani fuzzy model has the form [14]. If x 1 is A 1 and.and x n is A n then y is B where A and B are the linguistic variable defined by fuzzy sets of the universe of discourse X and Y respectively. The if-part of the rule x is A is the set of facts called antecedent or promise, while the then-part of the rule y is B is a set of action called consequent or conclusion [14]. Figure 1. Fuzzy Inference System In general, Mamdani FIS has four factors to produce the outputs as follows: 1. Fuzzification Fuzzification is the process converting a crisp input variable into fuzzy membership functions. 2. Knowledge base The knowledge base is the collection of fuzzy if-then rules and facts. The knowledge bases typically consist of a database and a rule-base. The basic function of the database is to provide the necessary information and known facts to be used in fuzzy reasoning. 3. Fuzzy inference engine The fuzzy inference engine aims to generating fuzzy conclusions from the knowledge base. The engine will take the conclusion since the fuzzified inputs have been determined by rule evaluation. Then, one fuzzy output distribution will be properly produced by combining all conclusions of each rule. 4. Defuzzification

3 Deffuzification is the reverse operation of a fuzzification process [15]. The output of the fuzzy inference system must be crisp output because the value of fuzzy inference engine result is fuzzy. Hence, the aim of defuzzification process is transforms a fuzzy value into a crisp value. There are popular methods in defuzzification process such as centroid of area (COA) and Mean of Maximum (MOM). In this paper we use the Mean of Maximum because COA is computationally quite complex and slow; being that involves finding the center of gravity, known as the integration process [15]. The mean of maximum (MOM) handles the calculation of the average of all output values that have the highest degrees [13]. The mean of maximum (Z MOM ) of the area A is computed by equation (1); where Z MOM is the defuzzified output, z is the maximizing z at which the membership function reach its maximum [14]. Z MOM = Z ' IV. FIS FOR KNOWLEDGE MANAGEMENT TOOLS EVALUATION In this paper, we build the system to assist the user in making suitable decision through use Fuzzy Logic Toolbox built in MATLAB software. Generally, these processes are as follows: 1. Creating fuzzy modules In this system, the KMT evaluation criteria s discussed in section IIB are taken into consideration for building the system. At this point, six modules are created with the intention to determining a final decision for knowledge management technology evaluation as illustrated in Figure 2. Firstly, two fuzzy modules are used in finding the output of the cost factor, which consists of capital expenditure and operating expenditure. Then, two other fuzzy modules are employed to each of the two main factors such as Functionality and Vendor to generate the output for each one of them. Last, one fuzzy module is applied for makes final judgement by considering the three outputs of the three main of fuzzy modules. Z ' zdz dz Figure 2. Fuzzy modules (1) 2. Defining membership functions All membership functions (MF) of the inputs and the outputs of the Fuzzy modules for KMT evaluation have the shape of triangular MF as shown in Figure 4 as example. All the outputs of each fuzzy module are compound into nine levels of membership. Furthermore, the MATLAB codes that present the membership are provided in Appendix. Figure 4. Membership Function Editor of KMT Evaluation 3. Constructing fuzzy inference rules Rules are constructed in the term of conditional if-then statements as shown in Figure 3. Here, the rules are created based on experts knowledge and literature review. For example, there are 12 rules of the total of 523 rules shown below. The rules can be adjusted based on the suitability of a particular purchasing policy and deal with the enterprise circumstances. These rules are as follows: a. Fuzzy module costs - If Capital Expenditures are Low and Operating Expenditures are Medium Then Costs are Moderatly Low - If Capital Expenditures are Medium and Operating Expenditures are High Then Costs are Moderatly High - If Capital Expenditures are Very High and Operating Expenditures are Medium Then Costs are High b. Fuzzy module functionality - If Document Management is Poor and Communication is Poor and Measurement is Poor and Workflow Management is Poor and Scalability is Difficult and Security is Poor Then Functionality is Poorest - If Document Management is Poor and Communication is Poor and Measurement is High and Workflow Management is Average and Scalability is Easy and Security is Good Then Functionality is Moderatly Poor - If Document Management is Poor and Communication is Poor and Measurement is High and Workflow Management is Good and Scalability is Easy and Security is Average Then Functionality is Average

4 - If Document Management is Average and Collaboration is Low and Communication is Poor and Measurement is High and Workflow Management is Good and Scalability is Easy and Security is Good Then Functionality is ModeratlyGood - If Document Management is Good and Collaboration is High and Communication is Good and Measurement is Low and Workflow Management is Good and Scalability is Easy and Security is Good Then Functionality is Good c. Fuzzy module vendor - If reputation is Average and Training provided is Low and Consulting services is Average and Implementation Partner is Good and Maintenance is Poor and Upgrades is Difficult and Integration is Poor Then Vendor is Moderately Poor - If reputation is Average and Training provided is Low and Consulting services is Average and Implementation Partner is Poor and Maintenance is Average and Upgrades is Easy and Integration is Average Then Vendor is Average - If reputation is Average and Training provided is High and Consulting services is Average and Implementation Partner is Poor and Maintenance is Average and Upgrades is Easy and Integration is Average Then Vendor is Moderately Good - If reputation is Good and Training provided is High and Consulting services is Good and Implementation Partner is Average and Maintenance is Poor and Upgrades is Easy and Integration is Good Then Vendor is Very Good 4. The user interface After three main processes have been done, it is important to create the user interface with the purpose for KMT evaluation can be tested and used in assisting the user for selecting the best KMT. Here, Graphical User Interface Development Environment (GUIDE) on MATLAB is used for creating a Graphical User Interface (GUI). Four screens for KMT evaluation are proposed including one main screen and three sub-screens. The user interface work as follows: 1. The sub-screen of the considered factor will appear when once the selected button has been pushed. Subscreen will provide sliders to the user. The number of slider depends on how many sub-factors in each main factor. 2. The user can input the data by moving the slide bar along the way regarding to the linguistic variable above the slide bar. 3. Once the slider bar has been moved, all the current values of the slider displayed next to the slider will be automatically sent to the particular fuzzy module and converted into the fuzzified inputs. The fuzzy inference engine will generate the output by considering the inputs based on the collection of rules. 4. The user will choose Back to Main Page button for display the final result of the particular main factor on the main screen. 5. After all, the user will push the Evaluate button. Then, the fuzzy module 6 will be executed in order to give the final output as the KMT evaluation on the main screen demonstrated in Figure 5. If the user wants to evaluate another KMT, user just needs to push Reset button and all information will blank as early. V. TEST AND RESULTS The test for the fuzzy logic program based on the four screens created in the user interface as discussed in section IV is done through simulation. The input of this simulation attempts to deal with the different value according to a particular enterprise circumstance that might happen in actual practice. The results that have been obtained in this simulation of each main parameter are presented in linguistic and numerical formats. Many different inputs can be given to test the model. In this paper, only 9 typical examples are represented for each fuzzy module. For instance, the user evaluates vendor characteristic factor by push vendor button in main screen as shown in Figure 5, then the vendor sub screen will be appear as shown in Figure 6. The user input the information about the vendor characteristic respective to the FIS by moving the slider of vendor sub factor. Figure 6 (a) exhibits case 8 which the inputs are given as Reputation is Good (88.5), Implementation Partner is Poor (26.7), KM Consulting Services is Best (96.7), Training Provided is High (83.6), Maintenance is Good (77.8), Upgrades is Easy (14.4), Figure 3. Functionality rule editor

5 Integration is Good (87.6), so that the interface display the result of Vendor Characteristics are Best. Otherwise, by the similar simulation as in case 1 the result for case 9 is obtained as shown in Figure 6 (b) which is the result of Vendor Characteristics are Poor. Through giving various character input simultaneously based on the assumed situation, the result of vendor characteristic obtained from the nine test run are shown in Table 1. The testing process is also same for the two other factors before accomplishing all evaluation of the KMT, it is shown in figure 5 (a) that gives the result of KMT evaluation is Very Good from input values: Costs Low (31), Functionality High (75), Vendor Best (94.8) while figure 5 (b) shown the result is Poor. Even though what is representing in this paper is just provide the recommendation to assist any user in evaluating KMT by using FIS and not absolute the correct one. From the result, it can be seen that the FIS for KMT evaluation work properly to suggest the suitable results in any purchasing policy by the parameter that interrelated each factor to the enterprise. Table 1. Result for vendor characteristic simulation Figure 5. Main screen of KMT evaluation Figure 6. Sub Screen of Vendor Characteristic Evaluation

6 VI. CONCLUSSION Based on the FIS implementation in this paper, we conclude the following. First, managers in enterprise know how important KM was; but it was very difficult to implement it successfully, especially how to choose the appropriate tool to support knowledge management system. Second, the knowledge management tools evaluation is filled with multiple conflicting criteria as well as incomplete information. Third, applying the fuzzy inference system Mamdani is an important part of the intelligent decision making because of its ability to deal with the uncertainty that always occurs in decision making process. In addition, through a graphical user interface (GUI) the interaction between a user and fuzzy logic system make KMT evaluation is simply, fast and efficient. ACKNOWLEDGMENT First author would like to thank PT.Antam,Tbk for the conference fund. Appendix Membership code of Fuzzy KMT evaluation module [System] Name='KMTVendors' AndMethod='min' Type='mamdani' OrMethod='max' Version=2.0 ImpMethod='min' NumInputs=5 AggMethod='max' NumOutputs=1 DefuzzMethod='mom' NumRules=252 [Input1] Name='Reputation' MF3='Good':'trimf',[ ] [Input2] Name='Training&Consulting' MF3='Good':'trimf',[ ] [Input3] Name='ImplementationPartner' MF3='High':'trimf',[ ] [Input4] Name='Maintenance' MF3='High':'trimf',[ ] [Input5] Name='Upgrades&Integration' MF3='High':'trimf',[ ] [Output1] Name='Vendors' NumMFs=9 MF1='VeryPoor':'trimf',[ e ] MF2='Poor':'trimf',[ ] MF3='ModeratlyPoor':'trimf',[ ] MF4='Average':'trimf',[ ] MF5='ModeratlyGood':'trimf',[ ] MF6='Good':'trimf',[ ] MF7='VeryGood':'trimf',[ ] MF8='Outstanding':'trimf',[ ] MF9='VeryOutstanding':'trimf',[ ] References: [1] Bradley, J.H., R. Paul, and E. Seeman, Analyzing the structure of expert knowledge. Information & Management, (1): p [2] Alavi, M. and D.E. Leidner, Review: Knowledge management and knowledge management: Conceptual foundations and researchchissues. MIS Quarterly, (1): p [3] Ngai, E.W.T. and E.W.C. Chan, Evaluation of knowledge management tools using AHP. Expert Systems with Applications, (4): p [4] Erensal, Y.C. and Y.E. Albayrak. A Problem Solving Perspective on Evaluating Knowledge Management Technologies: Using Fuzzy Linear Programming Technique for Multiattribute Group Decision Making with Fuzzy Decision Variables. in Technology Management for the Global Future, PICMET [5] Yu-Rong, Z., et al. A new model for evaluating knowledge management tools. in Machine Learning and Cybernetics, 2008 International Conference on [6] Alavi, M. and D.E. Leidner., Review: Knowledge management and knowledge management:conceptual foundations and research issues. MIS Quartely, (1): p [7] Johannessen, J.-A., J. Olaisen, and B. Olsen, Mismanagement of tacit knowledge: the importance of tacit knowledge, the danger of information technology, and what to do about it. International Journal of Information Management, (1): p [8] Hahn, J. and M.R. Subramani, A framework of knowledge management systems: Issues and challenges for theory and practice. Proceedings of the twenty first international conference on information systems, 2000: p [9] Ruggles, R., Knowledge Management Tools. Oxford :Butterworth-Heinemann, [10] Lee, S.M. and S. Hong, An enterprise-wide knowledge management system infrastructure. Industrial Management & Data Sytems, (1): p [11] Byun, D.-H., The AHP approach for selecting an automobile purchase model. Information & Management, (5): p [12] Yu, C.-S., A GP-AHP method for solving group decisionmaking fuzzy AHP problems. Computers & Operations Research, (14): p [13] Jang, J.-S.R.a.G., Ned, Fuzzy Logic Toolbox User's Guide 1997: The MathWorks, Inc. [14] Jang, J.-S.R., Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Inteligence". 1997, Prentice Hall: Upper Sadle River. [15] Buckland, M., Programming Game AI by Example. 2005: Wordware Publishing