Total quality management (TQM) is a unified management approach that concentrates on the consecutive improvement of processes, products, and methods to surpass client expectations. This paper aims to highlight the advantages of implementing TQM in the airline industry by examining the key enablers of TQM. This study was based on a research model including six factors that were found to have a profound influence in implementing TQM. The data needed for the study was collected with the aid of questionnaires and secondary sources. The study discovered that procedures, policies, and a culture of knowledge transfer were the most significant enablers of successful TQM initiatives in Royal Jet Airways. In order to effectively implement TQM, the study suggests that management should offer appropriate recognition and rewards to employees. This will motivate them to adopt and practice TQM initiatives. Apart from that, management should also arrange seminars or workshops to accustom the employees to the basic procedures and objectives of effective TQM.
We show that some results introduced in Girish and John (2011) are incorrect. Moreover, a counterexample is given to confirm our claim. Furthermore, the correction form of the incorrect results in Girish and John (2011) is presented.
Multicriteria models have been proposed for inventory classification in previous studies. However, it is important to make a decision when a particular multicriteria inventory classification model should be preferred over other models and also if the highest performing model remains the highest performing at all times. Companies always look for ways to improve customer order fulfillment process. This paper shows how better inventory classification can improve customer order fill rate in variable settings. The method to compare the inventory classification models with regard to improving customer order fill rate is proposed. The cut-off point is calculated which indicates when a model currently in use should be dropped in favor of another model to increase revenue by filling more orders. Sensitivity analysis is also performed to determine how holding cost and demand uncertainty affect the performance metric. Finally, regression analysis and hypothesis testing inform the decision-maker of how a model’s performance differs from other models at various values of holding cost and standard deviation of demand.
One of the key questions in credit dependence modelling is the specfication of the copula function linking the marginals of default variables. Copulae functions are important because they allow to decouple statistical inference into two parts: inference of the marginals and inference of the dependence. This is particularly important in the area of credit risk where information on dependence is scant. Whereas the techniques to estimate the parameters of the copula function seem to be fairly well established, the choice of the copula function is still an open problem. We find out by simulation that the t-copula naturally arises from a structural model of credit risk, proposed by Cossin and Schellhorn (2007). If revenues are linked by a Gaussian copula, we demonstrate that the t-copula provides a better fit to simulations than does a Gaussian copula. This is done under various specfications of the marginals and various configurations of the network. Beyond its quantitative importance, this result is qualitatively intriguing. Student's t-copulae induce fatter (joint) tails than Gaussian copulae ceteris paribus. On the other hand observed credit spreads have generally fatter joint tails than the ones implied by the Gaussian distribution. We thus provide a new statistical explanation why (i) credit spreads have fat joint tails, and (ii) financial crises are amplified by network effects.
This paper gives an empirical investigation of some tests of goodness of fit for the inverse Gaussian distribution.
Usually, traditional methods for investment project appraisal such as the net present value (hereinafter NPV) do not incorporate in their values the operational flexibility offered by including a real option included in the project. In this paper, real options, and more specifically the option to abandon, are analysed as a complement to cash flow sequence which quantifies the project. In this way, by considering the existing analogy with financial options, a mathematical expression is derived by using the binomial options pricing model. This methodology provides the value of the option to abandon the project within one, two, and in general n periods. Therefore, this paper aims to be a useful tool in determining the value of the option to abandon according to its residual value, thus making easier the control of the uncertainty element within the project.
The paper compares numerically the results from two real option valuation methods, the Datar-Mathews method and the fuzzy pay-off method. Datar-Mathews method is based on using Monte Carlo simulation within a probabilistic valuation framework, while the fuzzy pay-off method relies on modeling the real option valuation by using fuzzy numbers in a possibilistic space. The results show that real option valuation results from the two methods seem to be consistent with each other. The fuzzy pay-off method is more robust and is also usable when not enough information is available for a construction of a simulation model.
This paper aims to cluster entities which are described by a data matrix. Under the assumption of normality of observations contained in each table, each entity is represented by samples from Gaussian distribution, that is, a number of measurements in the data matrix, the sample mean vector, and the sample covariance. We propose a new distance based on Mahalanobis’s discriminant score to measure the similarity between objects. The present study is thought to be an important and interesting topic of research not only in the quest for an adequate model of the data representation but also in the choice of the distance index between entities that would allow justifying the homogeneity of any observed classes.
The analytic hierarchy process (AHP) has advantages that the whole number of comparisons can be reduced via a hierarchy structure and the consistency of responses verified via a consistency ratio. However, at the same time, the AHP has disadvantages that values vary according to the form of hierarchy structure and it is difficult to maintain consistency itself among responses. If the number of comparisons can be reduced, a comparison within a single level is optimal, and if comparison can be made while the priority among entities is maintained, consistency may be automatically maintained. Thus, in this study, we propose a method of assigning weights, which applies hierarchy structure of AHP and pairwise comparison but complements the disadvantages of AHP. This method has advantages that the number of comparisons can be reduced and also consistency is automatically maintained via determination of priorities first on multiple entities and subsequent comparisons between entities with adjoined priorities.
The Greenhouse Gas (GHG) emissions due to transport operations have drastically increased in recent years. The sea transport in particular contributes 2.7 to 3 percent of CO 2 , a major component of GHG emissions globally. Numerous measures have been undertaken locally and internationally to alleviate the sea transport share of Greenhouse Gases. However, most of these measures will be fruitful if ship investors (e.g., ship owners and operators) would fully employ the GHG emission reduction strategies. Due to the scarcity of the statistical data in this respect, this study therefore presents a rough set synthetic assessment (RSSA) model to GHG emission abatement strategies in the Tanzanian shipping sector. The results of the assessment reveal that the Tanzanian shipping companies engaged in Cabotage trade are aware of the abatement strategies and moderately apply them.
This paper proposes a new hybrid multiattribute decision making (MADM) model which deals with the interactions that usually exist between hostel attributes in the process of measuring the students’ satisfaction towards a set of hostels and identifying the optimal strategies for enhancing their satisfaction. The model uses systematic random stratified sampling approach for data collection purpose as students dwelling in hostels are “naturally” clustered by block and gender, factor analysis for extracting large set of hostel attributes into fewer independent factors, λ-measure for characterizing the interactions shared by the attributes within each factor, Choquet integral for aggregating the interactive performance scores within each factor, Mikhailov’s fuzzy analytical hierarchy process (MFAHP) for determining the weights of independent factors, and simple weighted average (SWA) operator to measure the overall satisfaction score of each hostel. A real evaluation involving fourteen Universiti Utara Malaysia (UUM) hostels was carried out in order to demonstrate the model’s feasibility. The same evaluation was performed using an additive aggregation model in order to illustrate the effects of ignoring the interactions shared by attributes in hostel satisfaction analysis.
The main purpose of this paper is to improve on the conceptual as well as the methodological aspects of BSC as a quantitative model by combining elements from traditional balanced scorecard (BSC) thinking with the Systems Thinking. This is done by combining short and long term aspects of measurements. The result is then used to build and construct a balanced scorecard model for strategic learning with the specific aim to maintain satisfied customers and motivated employees. Strategic planning, operational execution, feedback, and learning are some of the most important key features of any performance measurement model. This paper aims to address not only the conceptual domain related to BSC, that is, learning and system dynamics causality and feedback, but also the methodological domain concept of precision solved by differential equations. Our results show how a potential move from a static strategic vision map to a linked and dynamic understanding may be not fully realistic but very useful for learning purposes. The new knowledge obtained from the learning feedbacks fertilizes both decision discussion and decision-making and what may be required in order to move to the next level of BSC and system dynamics integration.
We study asset pricing dynamics in artificial financial markets model. The financial market is populated with agents following two heterogeneous trading beliefs, the technical and the fundamental prediction rules. Agents switch between trading rules with respect to their past performance. The agents are loss averse over asset price fluctuations. Loss aversion behaviour depends on the past performance of the trading strategies in terms of an evolutionary fitness measure. We propose a novel application of the prospect theory to agent-based modelling, and by simulation, the effect of evolutionary fitness measure on adaptive belief system is investigated. For comparison, we study pricing dynamics of a financial market populated with chartists perceive losses and gains symmetrically. One of our contributions is validating the agent-based models using real financial data of the Egyptian Stock Exchange. We find that our framework can explain important stylized facts in financial time series, such as random walk price behaviour, bubbles and crashes, fat-tailed return distributions, power-law tails in the distribution of returns, excess volatility, volatility clustering, the absence of autocorrelation in raw returns, and the power-law autocorrelations in absolute returns. In addition to this, we find that loss aversion improves market quality and market stability.
Organizations typically employ the ABC inventory classification technique to have an efficient control on a huge amount of inventory items. The ABC inventory classification problem is classification of a large amount of items into three groups: A, very important; B, moderately important; and C, relatively unimportant. The traditional ABC classification only accounts for one criterion, namely, the annual dollar usage of the items. But, there are other important criteria in real world which strongly affect the ABC classification. This paper proposes a novel methodology based on a common weight linear optimization model to solve the multiple criteria inventory classification problem. The proposed methodology enables the classification of inventory items via a set of common weights which is very essential in a fair classification. It has a remarkable computational saving when compared with the existing approaches and at the same time it needs no subjective information. Furthermore, it is easy enough to apply for managers. The proposed model is applied on an illustrative example and a case study taken from the literature. Both numerical results and qualitative comparisons with the existing methods reveal several merits of the proposed approach for ABC analysis.
Challenges for text processing in ancient document images are mainly due to the high degree of variations in foreground and background. Image binarization is an image segmentation technique used to separate the image into text and background components. Although several techniques for binarizing text documents have been proposed, the performance of these techniques varies and depends on the image characteristics. Therefore, selecting binarization techniques can be a key idea to achieve improved results. This paper proposes a framework for selecting binarizing techniques of palm leaf manuscripts using Support Vector Machines (SVMs). The overall process is divided into three steps: (i) feature extraction: feature patterns are extracted from grayscale images based on global intensity, local contrast, and intensity; (ii) treatment of imbalanced data: imbalanced dataset is balanced by using Synthetic Minority Oversampling Technique as to improve the performance of prediction; and (iii) selection: SVM is applied in order to select the appropriate binarization techniques. The proposed framework has been evaluated with palm leaf manuscript images and benchmarking dataset from DIBCO series and compared the performance of prediction between imbalanced and balanced datasets. Experimental results showed that the proposed framework can be used as an integral part of an automatic selection process.
Almost all of the today’s modern daily life conditions of humankind depend on the electricity. The countries either by only themselves or sometimes with some international intuitions and/or organizations have been trying to find the best methods, ways, and projects to supply the electricity to their societies. One of the important tools for the countries to increase the amount and quality of the electricity generation is to activate/ignite/initiate the private investment capabilities/opportunities. The electricity generation market in Turkey is a free/open market for both the foreign and domestic private investors. Hence, both the foreign and domestic private investors have been looking for the most suitable electricity generation plant projects. Small hydropower plant (SHPP) investments (SHPPIs) are one of the alternatives in the Turkish electricity generation market especially for the private investors searching for the renewable energy investments. This experimental research study investigates the possibility of using the ELECTRE III/IV, Shannon’s Entropy, and Saaty’s Analytic Hierarchy Process (AHP) subjective weighting (for criteria) methods for the solution of this problem. In the experimental case study, the most appropriate SHPPIs amongst five alternative SHPPIs at the SHPPIs’ predevelopment investment stages in Turkey were evaluated and ranked in order.
Previous studies reported mixed and ambiguous results of the relationship between TQM practices and performances. This study investigated impacts of TQM practices on various performance measures as well as the reasons and the barriers of the TQM practices of firms in Turkey. We used a cross-sectional survey methodology in this study, and the unit of the sample was at the plant level. The sample was selected from the member firms to Turkish Quality Association and the firms located in the Kocaeli-Gebze Organized Industrial Zone. We obtained 242 usable questionnaires, with a satisfactory response rate of 48.4 percent. We conducted exploratory factor analysis and multiple regression analysis. This study has shown that different TQM practices significantly affect different performance outcomes. Results revealed that primary obstacles that the firms in Turkey face were lack of employee involvement, awareness and commitment of the employees, inappropriate firm structure, and lack of the resources. It is recommended that firms should continue implement TQM with all variables to improve performance. Firms should improve employees’ involvement/commitment/awareness to TQM, enhance firm structure, and provide resources to overcome the barriers that prevent effective implementation of TQM practices.
We deal with the conditions which ensure exact penalization in stochastic programming problems under finite discrete distributions. We give several sufficient conditions for problem calmness including graph calmness, existence of an error bound, and generalized Mangasarian-Fromowitz constraint qualification. We propose a new version of the theorem on asymptotic equivalence of local minimizers of chance constrained problems and problems with exact penalty objective. We apply the theory to a problem with a stochastic vanishing constraint.
We consider in this paper an M/G/1 type queueing system with the following extensions. First, the server is unreliable and is subject to random breakdowns. Second, the server also implements the well-known N-policy. Third, instead of a Bernoulli vacation schedule, the more general notion of binomial schedule with K vacations is applied. A cost function with two decision variables is developed. A numerical example shows the effect of the system parameters on the optimal management policy.
