Zsuzsanna Eszter Tóth

Course evaluation by importance‐performance analysis and improving actions at the Budapest University of Technology and Economics

Purpose – The Department of Management and Corporate Economics as the flagship of teaching and researching quality management at the Budapest University of Technology and Economics has conducted an extended survey among students in five different business courses in order to get deeper knowledge about the factors influencing student (dis)satisfaction. The papers aims to discuss this.Design/methodology/approach – Initially students were asked to fill out a course evaluation questionnaire of 11 questions. After processing the questionnaires, problem solving techniques with strong student involvement were applied in order to lay the foundation for long‐term course improvement actions. The main objective was to identify the factors that were given the greatest importance by the students, but where the performance of the course was low in order to reveal the potentials of development.Findings – Improving student satisfaction is a must at all courses as the financial issues of the faculty and the department are...

Clustering Empirical Failure Rate Curves for Reliability Prediction Purposes in the Case of Consumer Electronic Products

In this paper, a methodology based on the combination of time series modeling and soft computational methods is presented to model and forecast bathtub-shaped failure rate data of newly marketed consumer electronics. The time-dependent functions of historical failure rates are typified by parameters of an analytic model that grabs the most important characteristics of these curves. The proposed approach is also verified by the presentation of an industrial application brought along at an electrical repair service provider company. The prediction capability of the introduced methodology is compared with moving average-based and exponential smoothing-based forecasting methods. According to the results of comparison, the presented method can be considered as a viable alternative reliability prediction technique. Copyright

A Pliant Arithmetic-Based Fuzzy Time Series Model

In this study, a fuzzy arithmetics-based fuzzy time series modeling method is introduced. After input data normalization, the fuzzy c-means clustering algorithm is used for fuzzification and establishment of antecedents of the fuzzy rules. Here, each rule consequent is treated as a fuzzy number composed of a left and a right hand side fuzzy set, each of which is given by a sigmoid membership function. The novelty of the proposed method lies in the application of pliant arithmetics to aggregate separately the left and the right hand sides of the individual fuzzy consequents, taking the activation levels of the corresponding antecedents into account. Here, Dombi’s conjunction operator is applied to form the fuzzy output from the aggregates of the left and right hand side sigmoid functions. The introduced defuzzification method does not require any numerical integration and runs in constant time. The output of the pliant arithmetic based fuzzy time series model is obtained by denormalizing the crisp output produced by the fuzzy inference. Lastly, the modeling capability of the introduced methodology was tested on empirical data. Based on these results, our method may be viewed as a viable alternative prediction technique.

Modeling failure rate time series by a fuzzy arithmetic-based inference system

In this study, a fuzzy arithmetic based inference system is introduced to model and forecast linear trends of empirical failure rate time series. Here, a simple heuristic is introduced to form the membership functions of the fuzzy rule antecedents, while each rule consequent is treated as a fuzzy number composed of a left hand side and a right hand side fuzzy set, each of which is given by a sigmoid membership function. The novelty of the proposed method lies in the application of pliant arithmetics to aggregate separately the left hand sides and the right hand sides of the individual fuzzy consequents, taking the activation levels of the corresponding antecedents into account. Here, Dombis conjunction operator is applied to form the fuzzy output from the aggregates of the left hand side and right hand side sigmoid functions. The introduced defuzzification method does not require any numerical integration and its speed is independent of the number of fuzzy rules. The output of the pliant arithmetic based fuzzy inference system is used to predict linear trends of failure rate time series. Next, the modeling capability of the introduced methodology is compared to that of an Adaptive Neuro-Fuzzy Inference System. Based on the results, our method may be viewed as a viable alternative modeling and prediction technique.

Forecasting Short-Term Demand for Electronic Assemblies by Using Soft-Rules

Organizations competing in the electronic industry, where product life-cycles are getting shorter and shorter, need to understand the short-term behavior of customer demand to make the right decisions in time. This work presents a new way of modeling and forecasting customer demand for electronic assemblies in the electronic manufacturing services industry, where demand patterns are more volatile. The objective of the paper is to propose a new short-term forecasting technique that is based on learning soft-rules in demand time series of electronic assemblies. The approach presented is also verified by empirical results. The prediction capability of the introduced methodology is compared to moving average, ARIMA, and exponential smoothing based forecasting methods. According to the results the presented forecasting method can be considered as a viable alternative customer demand prediction technique.

A Fuzzy Time Series Model with Customized Membership Functions

In this study, a fuzzy time series modeling method that utilizes a class of customized and flexible parametric membership functions in the fuzzy rule consequents is introduced. The novelty of the proposed methodology lies in the flexibility of this membership function, which we call the composite kappa membership function, and its curve may take various shapes, such as a symmetric or asymmetric bell, triangular, or quasi trapezoid. In our approach, the fuzzy c-means clustering algorithm is used for fuzzification and for the establishment of fuzzy rule antecedents and a heuristic is introduced for identifying the quasi optimal number of clusters to be formed. The proposed technique does not require any preliminary parameter setting, hence it is easy-to-use in practice. In a real-life example, the modeling capability of the proposed method was compared to those of Winters’ method, the Autoregressive Integrated Moving Average technique and Adaptive Neuro-Fuzzy Inference System. Based on the empirical results, the proposed method may be viewed as a viable time series modeling technique.

A knowledge discovery based approach to long-term forecasting of demand for electronic spare parts

This paper deals with modeling and predicting purchase life-cycles of electronic spare parts that are supplied to the repair vendors by companies which provide the so-called spare parts logistics service. We introduce a soft computational method that can be used to discover the typical purchase life-cycles of end-of-life spare parts that belong to the same commodity class. In our approach, the discovered knowledge is embodied by typical demand models which can be utilized to forecast the demand for active spare parts, that is, for components for which there are current demands. We apply a fuzzy similarity based approach to generate the forecast from the typical demand models. The introduced forecasting method is advantageous in long-term prediction, it can be especially useful in supporting purchase planning decisions in the ramp-up and declining phases of purchase life-cycles. The application of our method is demonstrated through real-life examples.