Search results

This study seeks to build a financial uncertainty index for Saudi Arabia. This index serves as a leading indicator of Saudi economic activity and helps to describe economic fluctuations and forecast economic trends.

This study adopts an extension of the Jurado et al. (2015) procedure by combining financial uncertainty factors with their net spillover effects on GDP and inflation to construct an aggregate financial uncertainty index. The authors consider 13 monthly financial variables for Saudi Arabia from January 2010 to June 2021.

The empirical results show that the constructed financial uncertainty estimates are good leading indicators of economic activity. The robustness analysis suggests that the authors’ proposed financial uncertainty estimators outperform the alternative estimates used by other existing approaches to estimate the financial conditions index.

To the best of the authors’ knowledge, this is the first attempt at constructing a financial uncertainty index for Saudi Arabia. This study extends the empirical literature, from which the authors propose a novel conceptual framework for building a financial uncertainty index by combining the approach of Jurado et al. (2015) and the time-varying connectedness network approach proposed by Antonakakis et al. (2020)

The concept of sustainable product design (SPD) is gaining significant attention in recent research. However, due to inherent uncertainties associated with new product development and incorporation of multiple qualitative and quantitative criteria; SPD is a complex and challenging task. The purpose of this paper is to introduce a novel approach by integrating quality function deployment (QFD), multi-criteria decision making (MCDM) technique and Six Sigma evaluation for facilitating SPD in the context of Industry 4.0.

The customer requirements are evaluated through the neutrosophic-decision-making trial and evaluation laboratory-analytic network process (DEMATEL-ANP)-based approach followed by utilizing QFD matrix to estimate the weights of the engineering characteristics (EC). The Six Sigma method is then employed to evaluate the alternatives’ design based on the ECs’ values.

The effectiveness of the suggested approach is illustrated through an example. The result indicates that utilization of the neutrosophic MCDM technique with integration of Six Sigma methodology provides a simple, effective and computationally inexpensive method for SPD.

The proposed approach is helpful in upstream evaluation of the product design with limited experimental/numerical data, maintaining a strong competitive position in the market and enhancing customer satisfaction.

This work provides a novel approach to objectively quantify performance of SPD under the paradigm of Industry 4.0 using the integration of QFD-based hybrid MCDM with Six Sigma method.

Industry 4.0 is recognised as a competitive strategy that helps implementers optimise their value chain. However, its adoption poses several challenges. This study investigates and ranks the drivers and barriers to implementing Industry 4.0 in the Thai sugar industry, the world's second-largest sugar exporter. It also evaluates the industry's readiness for Industry 4.0.

The drivers and impediments were identified based on a systematic literature review (SLR) and further investigated using a questionnaire, expert interviews, Pearson's correlation and nonparametric statistical analyses. The IMPULS model was used to assess the industry's readiness.

Most companies expect to minimise costs, develop employees and improve various elements of operational performance and data tracking capability. Thai sugar producers are still at a low readiness level to deploy Industry 4.0. High investment is the major challenge. Small businesses struggle to hire competent employees, collaborate with a highly credible technology provider and adapt to new solutions.

The findings can serve as a benchmark or guide for sugar manufacturers and companies in other sectors, where Industry 4.0 technologies are not yet widely utilised, to overcome existing roadblocks and make strategic decisions. They can also assist governments in developing policies that foster digital transformation and increase national competitiveness.

There is a scarcity of research on Industry 4.0 execution in the sugar industry. This study addresses this gap by investigating the reasons for the hesitancy of sugar producers to pursue Industry 4.0 and proposing solutions.

Assessing inputs and outputs is a significant aspect of taking decisions while there are complex and multistage processes in many examinations. Due to the presence of interval performance measures in various real-world studies, the purpose of this study is to address the changes of interval inputs of two-stage processes for the perturbations of interval outputs of two-stage systems, given that the overall efficiency scores are maintained.

Actually, an interval inverse two-stage data envelopment analysis (DEA) model is proposed to plan resources. To illustrate, an interval two-stage network DEA model with external interval inputs and outputs and also its inverse problem are suggested to estimate the upper and lower bounds of the entire efficiency and the stages efficiency along with the variations of interval inputs.

An example from the literature and a real case study of the banking industry are applied to demonstrate the introduced approach. The results show the proposed approach is suitable to estimate the resources of two-stage systems when interval measures are presented.

To the best of the authors’ knowledge, there is no study to estimate the fluctuation of imprecise inputs related to network structures for the changes of imprecise outputs while the interval efficiency of network processes is maintained. Accordingly, this paper considers the resource planning problem when there are imprecise and interval measures in two-stage networks.

The idea of this study is to provide a solid Financial Condition Index (FCI) that allows the monetary transmission policy to be monitored in a country which in recent decades has suffered from major financial and monetary crises.

The authors construct three FCIs for Mexico to analyse the role of financial asset prices in formulating monetary policy under an inflation-targeting regime. Using monthly data from 1995 to 2017, the authors estimate FCIs with two different methodologies and build the index by taking into account the mechanism of transmission of monetary policy and incorporating the most relevant financial variables.

This study’s results show that, likewise for developing countries as Mexico, an FCI could be a useful tool for managing monetary policy in reducing macroeconomic fluctuations.

Apart from building a predictor of possible financial stress, the authors construct an FCI for a central bank that pursues inflation targeting and to analyse the role of financial asset prices in formulating monetary policy.

1. We construct three FCIs for Mexico to analyse the role of financial asset prices in formulating monetary policy under an inflation-targeting regime.

2. The FCIs are based on (1) a vector autoregression model (VAR); (2) an autoregressive distributed lag model (ARDL) and (3) a factor-augmented vector autoregression model (FAVAR).

3. FCI could become a new target for monetary policy within a hybrid inflation-targeting framework.

4. FCI could be a good tool for managing monetary policy in developing countries with a low-inflation environment.

We construct three FCIs for Mexico to analyse the role of financial asset prices in formulating monetary policy under an inflation-targeting regime.

The FCIs are based on (1) a vector autoregression model (VAR); (2) an autoregressive distributed lag model (ARDL) and (3) a factor-augmented vector autoregression model (FAVAR).

FCI could become a new target for monetary policy within a hybrid inflation-targeting framework.

FCI could be a good tool for managing monetary policy in developing countries with a low-inflation environment.

The purpose of this study is to estimate inputs (outputs) and flexible measures when outputs (inputs) are changed provided that the relative efficiency values remain without change.

A novel inverse data envelopment analysis (DEA) approach with flexible measures is proposed in this research to assess inputs (outputs) and flexible measures when outputs (inputs) are perturbed on condition that the relative efficiency scores remain unchanged. Furthermore, flexible inverse DEA approaches proposed in this study are used for a numerical example from the literature and an application of Iranian banking industry to clarify and validate them.

The findings show that including flexible measures into the investigation effects on the changes of performance measures estimated and leads to more reasonable achievements.

The traditional inverse DEA models usually investigate the changes of some determinate input-output factors for the changes of other given input-output indicators assuming that the efficiency values are preserved. However, there are situations that the changes of performance measures should be tackled while some measures, called flexible measures, can play either input or output roles. Accordingly, inverse DEA optimization models with flexible measures are rendered in this paper to address these issues.

This study aims to minimize the transportation-related cost in distribution while utilizing a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach. An increased cost in distribution is a major problem for many companies due to the absence of efficient planning methods to overcome operational challenges in distinct distribution networks. The problem addressed in this study is to minimize the transportation-related cost in distribution while using a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach which has not gained the adequate attention in the literature.

This study formulated the transportation problem as a vehicle routing problem with a heterogeneous fixed fleet and workload balancing, which is a combinatorial optimization problem of the NP-hard category. The model was solved using both the simulated annealing and a genetic algorithm (GA) adopting distinct local search operators. A greedy approach has been used in generating an initial solution for both algorithms. The paired t-test has been used in selecting the best algorithm. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet compositions of the heterogeneous fleet. Results were analyzed using analysis of variance (ANOVA) and Hsu’s MCB methods to identify the best scenario.

The solutions generated by both algorithms were subjected to the t-test, and the results revealed that the GA outperformed in solution quality in planning a heterogeneous fleet for distribution with load balancing. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet utilization with different compositions of the heterogeneous fleet. Results were analyzed using ANOVA and Hsu’s MCB method and found that removing the lowest capacities trucks enhances the average vehicle utilization with reduced travel distance.

The developed model has considered both planning of heterogeneous fleet and the requirement of work load balancing which are very common industry needs, however, have not been addressed adequately either individually or collectively in the literature. The adopted solution methodologies to solve the NP-hard distribution problem consist of metaheuristics, statistical analysis and scenario analysis are another significant contribution. The planning of distribution operations not only addresses operational-level decision, through a scenario analysis, but also strategic-level decision has also been considered.

The planning of distribution operations not only addresses operational-level decisions, but also strategic-level decisions conducting a scenario analysis.

Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

This paper extends the authors’ previous research work investigating resilience for municipal infrastructure from an asset management perspective. Therefore, this paper aims to formulate a pavement resilience index while incorporating asset management and the associated resilience indicators from the authors’ previous research work.

This paper introduces a set of holistic-based key indicators that reflect municipal infrastructure resiliency. Thenceforth, the indicators were integrated using the weighted sum mean method to form the proposed resilience index. Resilience indicators weights were determined using principal components analysis (PCA) via IBM SPSS®. The developed framework for the PCA was built based on an optimization model output to generate the required weights for the desired resilience index. The output optimization data were adjusted using the standardization method before performing PCA.

This paper offers a mathematical approach to generating a resilience index for municipal infrastructure. The statistical tests conducted throughout the study showed a high significance level. Therefore, using PCA was proper for the resilience indicators data. The proposed framework is beneficial for asset management experts, where introducing the proposed index will provide ease of use to decision-makers regarding pavement network maintenance planning.

The resilience indicators used need to be updated beyond what is mentioned in this paper to include asset redundancy and structural asset capacity. Using clustering as a validation tool is an excellent opportunity for other researchers to examine the resilience index for each pavement corridor individually pertaining to the resulting clusters.

This paper provides a unique example of integrating resilience and asset management concepts and serves as a vital step toward a comprehensive integration approach between the two concepts. The used PCA framework offers dynamic resilience indicators weights and, therefore, a dynamic resilience index. Resiliency is a dynamic feature for infrastructure systems. It differs during their life cycle with the change in maintenance and rehabilitation plans, systems retrofit and the occurring disruptive events throughout their life cycle. Therefore, the PCA technique was the preferred method used where it is data-based oriented and eliminates the subjectivity while driving indicators weights.

Although real estate investment decisions are among the most important managerial decisions, such decisions are usually made in an ad hoc fashion in Southeast Asia. The purpose of this study is to present a two-tier multi-criteria decision-making model for real estate investment decisions across three rapidly growing but significantly understudied Southeast Asian countries: Cambodia, Myanmar and Vietnam.

Using three data sources (secondary data, two surveys and nearly 100 experts and senior executives), the authors applied a combination of the Analytic Hierarchy Process and the Simple Additive Weighting (or weighted sum) methods as two special cases of multi-criteria decision-making to assess nine real estate investment projects across Cambodia, Myanmar and Vietnam.

The results of this study indicated that Vietnam, Cambodia and Myanmar were the first, second and third most preferred countries for real estate investments, respectively. Moreover, the results clearly show a trade-off between perceived country risk and financial returns, indicating that a higher perceived country risk can be compensated for with higher financial returns.

Real estate investment decisions are usually made in an ad hoc manner in Southeast Asia. This study helps investors make more informed decisions when investing in real estate projects across three rapidly growing but significantly understudied Southeast Asian countries: Cambodia, Myanmar and Vietnam.