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This paper focuses on organizational change through the business process management approach. While “business process modeling” permits understanding process activities and their activities with other participants, “current reality tree (CRT)” technique promotes the identification of process constraints. The purpose of this study is to compare the results from applying both diagnostic techniques, process modeling, using the business process modeling notation, and root cause analysis, using the CRT.

The comparison is made using a pre-experiment in which two teams conducted diagnoses concomitantly in the information technology management (ITM) process of one unit of the biggest and prestigious higher education institution (HEI) in Brazil.

The modeling technique and the CRT should be considered complementary techniques, since applying one does not diminish or exclude the importance of using the other. Results were compared analyzing which dimensions of the process each technique highlighted: strategy, organization, activity/information and resources.

A possible limitation of this research is that the experiment was conducted in a single process and the result cannot be generalized to other processes.

It may be noted that the main contribution of this study is the presentation of the steps of two techniques for process diagnosis. It is expected that with the reports on diagnoses outcomes, team's assessment and the perception of the managers presented here other improvement teams may use the results of this research as an inspiration to perform process diagnosis, and as basis for decision making to define which technique to use according to the specific needs of process improvement.

The paper stands out the comparison of the technique application's outcomes. This study offers valuable insights to the organizations that are interested in restructuring their processes. It delineates many important benefits of such a diagnosis techniques. It also identifies possible pitfalls and recommends guidelines for the successful conduction of process diagnoses initiatives.

A significant number of studies have been conducted to analyze and understand the relationship between gas emissions and global temperature using conventional statistical approaches. However, these techniques follow assumptions of probabilistic modeling, where results can be associated with large errors. Furthermore, such traditional techniques cannot be applied to imprecise data. The purpose of this paper is to avoid strict assumptions when studying the complex relationships between variables by using the three innovative, up-to-date, statistical modeling tools: adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANNs) and fuzzy time series models.

These three approaches enabled us to effectively represent the relationship between global carbon dioxide (CO₂) emissions from the energy sector (oil, gas and coal) and the average global temperature increase. Temperature was used in this study (1900-2012). Investigations were conducted into the predictive power and performance of different fuzzy techniques against conventional methods and among the fuzzy techniques themselves.

A performance comparison of the ANFIS model against conventional techniques showed that the root means square error (RMSE) of ANFIS and conventional techniques were found to be 0.1157 and 0.1915, respectively. On the other hand, the correlation coefficients of ANN and the conventional technique were computed to be 0.93 and 0.69, respectively. Furthermore, the fuzzy-based time series analysis of CO₂ emissions and average global temperature using three fuzzy time series modeling techniques (Singh, Abbasov–Mamedova and NFTS) showed that the RMSE of fuzzy and conventional time series models were 110.51 and 1237.10, respectively.

The paper provides more awareness about fuzzy techniques application in CO₂ emissions studies.

These techniques can be extended to other models to assess the impact of CO₂ emission from other sectors.

The purpose of this paper is to review recent studies published from 2007 to 2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging topics and methods studied and to pointing future research directions in the field.

Articles on tourism and hotel demand modeling and forecasting published mostly in both science citation index and social sciences citation index journals were identified and analyzed.

This review finds that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, whereas disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior and consumer confidence indicators, among others. More sophisticated techniques such as nonlinear smooth transition regression, mixed-frequency modeling technique and nonparametric singular spectrum analysis have also been introduced to this research area.

The main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting.

This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices.

The value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

This study aims to examine the theoretical foundations for multivariate portfolio optimization algorithms under illiquid market conditions. In this study, special emphasis is devoted to the application of a risk-engine, which is based on the contemporary concept of liquidity-adjusted value-at-risk (LVaR), to multivariate optimization of investment portfolios.

This paper examines the modeling parameters of LVaR technique under event market settings and discusses how to integrate asset liquidity risk into LVaR models. Finally, the authors discuss scenario optimization algorithms for the assessment of structured investment portfolios and present a detailed operational methodology for computer programming purposes and prospective research design with the backing of a graphical flowchart.

To that end, the portfolio/risk manager can specify different closeout horizons and dependence measures and calculate the necessary LVaR and resulting investable portfolios. In addition, portfolio managers can compare the return/risk ratio and asset allocation of obtained investable portfolios with different liquidation horizons in relation to the conventional Markowitz´s mean-variance approach.

The examined optimization algorithms and modeling techniques have important practical applications for portfolio management and risk assessment, and can have many uses within machine learning and artificial intelligence, expert systems and smart financial applications, financial technology (FinTech), and within big data environments. In addition, it provide key real-world implications for portfolio/risk managers, treasury directors, risk management executives, policymakers and financial regulators to comply with the requirements of Basel III best practices on liquidly risk.

The proposed optimization algorithms can aid in advancing portfolios selection and management in financial markets by assessing investable portfolios subject to meaningful operational and financial constraints. Furthermore, the robust risk-algorithms and portfolio optimization techniques can aid in solving some real-world dilemmas under stressed and adverse market conditions, such as the effect of liquidity when it dries up in financial and commodity markets, the impact of correlations factors when there is a switching in their signs and the integration of the influence of the nonlinear and non-normal distribution of assets’ returns in portfolio optimization and management.

Because of the competitive economy, organizations today seek to rationalize, innovate and adapt to changing environments and circumstances as part of business process reengineering (BPR) efforts. Irrespective of the process reengineering program selected and the technique used to model it, BPR brings with it the issues of organizational and process changes, which involves managing organizational changes (also called “change management”). Change management is non‐trivial, as organizational changes are difficult to accomplish. Though some attempt has been made to model change management in enterprise information systems using conventional conceptual modeling techniques, they have just addressed “what” a change process is like, and they do not address “why” the process is the way it is.

The approach presents an actor‐dependency‐based technique for analyzing and modeling early‐phase requirements of organizational change management that provides the motivations, intents, and rationales behind the entities and activities.

A case study illustrates this approach.

This approach is novel in the sense that there is no similar intentional modeling approach for change management to the best of our knowledge. The approach is expected to be valuable because using this approach one can reason about the opportunities and changes that are associated with BPR and can incorporate prominently the issues related to change in the process of system analysis and design.

This paper seeks to present a conceptual modeling approach, which is new in the domain of information systems security risk assessment.

The approach is helpful for performing means‐end analysis, thereby uncovering the structural origin of security risks in information systems, and how the root‐causes of such risks can be controlled from the early stages of the projects.

Though some attempts have previously been made to model security risk assessment in information systems using conventional modeling techniques such as data flow diagrams and UML, the previous works have analyzed and modeled the same just by addressing “what” a process is like. However, they do not address “why” the process is the way it is.

The approach addresses the limitation of the existing security risk assessment models by exploring the strategic dependencies between the actors of a system and analyzing the motivations, intents and rationales behind the different entities and activities constituting the system.

The purpose of this paper is to review the available reduced order modeling approaches in the literature for predicting the flow and specially temperature fields inside data centers in terms of the involved design parameters.

This paper begins with a motivation for flow/thermal modeling needs for designing an energy‐efficient thermal management system in data centers. Recent studies on air velocity and temperature field simulations in data centers through computational fluid dynamics/heat transfer (CFD/HT) are reviewed. Meta‐modeling and reduced order modeling are tools to generate accurate and rapid surrogate models for a complex system. These tools, with a focus on low‐dimensional models of turbulent flows are reviewed. Reduced order modeling techniques based on turbulent coherent structures identification, in particular the proper orthogonal decomposition (POD) are explained and reviewed in more details. Then, the available approaches for rapid thermal modeling of data centers are reviewed. Finally, recent studies on generating POD‐based reduced order thermal models of data centers are reviewed and representative results are presented and compared for a case study.

It is concluded that low‐dimensional models are needed in order to predict the multi‐parameter dependent thermal behavior of data centers accurately and rapidly for design and control purposes. POD‐based techniques have shown great approximation for multi‐parameter thermal modeling of data centers. It is believed that wavelet‐based techniques due to the their ability to separate between coherent and incoherent structures – something that POD cannot do – can be considered as new promising tools for reduced order thermal modeling of complex electronic systems such as data centers

The paper reviews different numerical methods and provides the reader with some insight for reduced order thermal modeling of complex convective systems such as data centers.

Process models are a valuable tool in the design and configuration of enterprises. However, current modeling techniques have shortcomings that prevent them from fully supporting the analysis required to design an enterprise. This is especially true when considering the needs of modeling highly distributed and temporary multi‐company enterprises such as fractal or virtual enterprises. This paper presents a modeling scheme that supports a process‐centered approach to the analysis and design of both conventional and extended enterprises. Using a holon‐based approach to model the components of an enterprise, it allows for the development of integrated business rule, activity, resource, business process, and organizational views of the enterprise using the IDEF suite of modeling methods. The scheme is built around a central IDEF5 model of the enterprise from which the other views are extracted. The paper also describes the technique for developing an enterprise model using the scheme.

The quality of a Business Process (BP) model is vital for the successful accomplishment of all its lifecycle phases. Indeed, a high-quality BP model makes its implementation, execution and evaluation easier. In the literature, the improvement of BP model quality has been dealt with using several techniques. For instance, modeling guidelines, refactoring techniques, and transformation rules are the most used ones. The purpose of this paper is to exploit existing initiatives in this field to help designers improve their BP models.

This paper draws up a systematic inventory of the existing approaches to improve the quality of BP models. Moreover, it provides a comparative evaluation with the aim of identifying the particularities of each approach as well as the common gaps in the state of the art. Finally, it proposes a guiding framework, called BP-Quality, that supports designers in improving the quality of their BP models.

The usability of BP-Quality is illustrated through a case study and a set of experiments. The preliminary experimental evaluation of this guiding framework shows encouraging results.

The proposed guiding framework has the merit of exploiting existing initiatives in the field of BP quality improvement. In addition, it customizes and optimizes the quality improvement process according to the particularities of each BP model.

The purpose of this paper is to describe a technique for modeling transformer internal faults using transmission line modeling (TLM) method. In this technique, a model for simulating a two winding single phase transformer is modified to be suitable for simulating an internal fault in both windings.

TLM technique is mainly used for modeling transformer internal faults. This was first developed in early 1970s for modeling two‐dimensional field problems. Since, then, it has been extended to cover three dimensional problems and circuit simulations. This technique helps to solve integro‐differential equations of the analyzed circuit. TLM simulations of a single phase transformer are compared to a custom built transformer in laboratory environment.

It has been concluded from the real time studies that if an internal fault occurs on the primary or secondary winding, the primary current will increase a bit and secondary current does not change much. However, a very big circulating current flows in the shorted turns. This phenomenon requires a detailed modeling aspect in TLM simulations. Therefore, a detailed inductance calculation including leakages is included in the simulations. This is a very important point in testing and evaluating protective relays. Since, the remnant flux in the transformer core is unknown at the beginning of the TLM simulation, all TLM initial conditions are accepted as zero.

The modeling technique presented in this paper is based on a low frequency (up to a few kHz) model of the custom‐built transformer. A detailed capacitance model must be added to obtain a high‐frequency model of the transformer. A detailed arc model, aging problem of the windings will be applied to model with TLM + finite element method.

Using TLM technique for dynamical modeling of transformer internal faults is the main contribution. This is an extended version of an earlier referenced paper of the authors and includes inductance calculation, leakages calculation, and BH curve simulation while the referenced paper only includes piecewise linear inductance values. This modeling approach may help power engineers and power system experts understand the behavior of the transformer under internal faults.