Industrial Engineering & Management

We first present a brief review of the essentials fuzzy system models: Namely (1) Zadeh’s rule base model, (2) Takagi and Surgeon’s model which is partly a rule base and partly a regression function and (3) Türkşen fuzzy regression functions where fuzzy regression functions correspond to each fuzzy rule. Next we review the well-known FCM algorithm which lets one to extract Type 1 membership values from a given data set for the development of Type 1 fuzzy system models as a foundation for the development of Full Type 2 fuzzy system models. For this purpose, we provide an algorithm which lets one to generate Full Type 2 membership value distributions for a development of second order fuzzy system models with our proposed second order data analysis. If required one can generate Full Type 3. Full Type n fuzzy system models with an iterative execution of our algorithm. We present our application results graphically for TD_Stockprice data with respect to two validity indeces, namely: 1) Çelikyılmaz-Türkşen and 2) Bezdek indeces.

Our paper presents an approach of how RFID technology can be used to simplify operations and improve the effectiveness and efficiency of inventory management. The goal of our research is to design system architecture for identifying and monitoring movement of monitored items. The basic requirement is to create a modular system and application of this system for real hospital laundry management application. We analysed possible solutions and available technologies. We also analysed a specific use-case of laundry management process for designed laundry monitoring system based on the IoT platform principles. The development is related to the hot topic of the Internet of Things and the utilization of RFID technology as a key technology for detection and identification of monitored objects.

This research investigates the factors that influence the IT adoption in Small and Medium-sized Enterprises (SME), lists the benefits that SMEs see by adopting commonly used business applications, arrives at a potential framework/ method for SMEs to enhance IT adoption and also suggests on ways of using analytics for business growth. Though SMEs in India is main focus of this work, the literature study included and takes cognizant of the facts that helped or helping SMEs in other countries to adopt IT to remain competitive and enhance efficiency that are also applicable in Indian context

For an effective CO₂ absorption by amines, the flue gas should contain at least 10% (mol.) of CO₂. Moreover, in order to avoid technical problems related to the oxidative degradation of amines, the flue gas should also contain less than 5% (mol.) of O₂. This paper presents preliminary calculations and simulation of the effects of Flue gas recirculation (FGR) ratios and excess air (EA) on the temperature and the concentration of CO₂ and O₂ in the exhaust gas of a natural-gas fired turbine. The results of the methodology utilized (preliminary calculations and simulation) indicate that, for a gas turbine that limits the temperature of the exhaust gas leaving the combustor at 1035°C, an excess air (EA) of 200% and a Flue Gas Recirculation (FGR) ratio of 0.65 are needed to fit the requirements of an effective absorption process by amines. For a turbine that allows temperatures as high as 1480°C, the operating parameters (EA=100% and FGR ratio of 0.4) will be selected.

In this research some techniques for process statistical control were applied, such as frequency histograms, Pareto diagrams, process capability analysis and control charts. The purpose of this investigation was to reduce the variability of the canned tomato paste filling process coming from a tomato processing food industry that has problems with the net weight of their processed product. The results of the process capability analysis showed that 35.52% of the observations were out of the specifications during the months in study, which generates a real capability of the process (C_pk) of 0.124, and it indicates that the process does not have enough ability to fulfill the required specifications by the firm. The process potential capability (C_p) is 0.676. Given that C_pk < C_p, then it is concluded that the process is not centered, indicating that the measurement of the filling process is away from the center of the specifications. The process fits 0.371 sigma between the process mean and the nearest specification limit. It was estimated that 40.42% of the cans produced during February did not meet the specifications required, and neither did a 16.84% during March. In consequence of this, it was necessary to identify the potential causes of the can excess fillings. Afterwards, it was proposed an orthogonal array L9 to adequate the process to the company filling specifications. The optimum operating conditions achieved a 5.28 sigma process quality. In addition, it was noted that the process had only 77.49 parts per million (ppm) out of specification. This situation places the tomato paste filling process as a world class quality process.

Today large and mid-size companies have more complex operations which involve different types of risk and are difficult to identify the risk. To extract these risks and analyze it scientifically, a professional risk assessment tool namely ANN is most appropriate and flexible network technology. This paper briefly presents Artificial Neural Networks (ANNs), universal and highly flexible functions, approximations for any data as an application to assess risk in cement industries for instance HOLCIM CEMENT BANGLADESH LIMITED has been selected to assess risks. To assess the risks a threelayer feed forward architecture of 10 risk factors which are independent and their strength of relationship was used as relative weight of input variables. Six neurons of hidden layer and two neurons of output layer with back propagation algorithm were designed using Neural Network Toolbox of MATLAB. The input data normalized to the range 0–0.9 and initial weights were randomly selected. The algorithm propagates the weights backwards and then controls the weights. The factors were inputted to a MATLAB-based application program and the numbers of iterations were set 9000. After implementation the overall process the adjustment weights were trained and by using adjusted weights actual output was found 0.63 in which predicted value was 0.66with 95.45% prediction success, the results very promising.