Search results

Since 2012, with the Growth Decree 2.0, the Italian Government has been engaged in promoting the establishment and the growth of new innovative startups with a high technological value. The general objective of the policy is to promote sustainable growth, technological advancement and innovation. In the Decree framework, startups have to own an innovative character that is identified by at least one of the following three criteria: investments in research and development, qualified workforce, holding patents or registered software. Such three characteristics can be considered as innovation inputs able to create the conditions for a competitive advantage and greater economic performance. The research objective is to analyze how innovation inputs in innovative startups affect their economic performance. Each input factor can have a different impact on the company profitability. To this end, we apply the methodology SMAA for strategic management analysis and assessment (or SMAA-S) to detect the relation between innovation and profitability in Italian startups.

The financial crisis of 2007–2008 has brought critical attention to those financial institutions whose high destabilizing effects can have an impact on the financial system. Basel Committee (BIS) has introduced a set of regulations to identify such banks, defined global systemically important banks (G-SIBs). The purpose of this paper is to investigate the robustness of the BIS's methodology.

In this study, a multicriteria decision aid (MCDA) approach is considered, specifically HSMAA (hierarchy stochastic multiobjective acceptability analysis), which can take into account uncertainty on BIS's criteria weights as standard SMAA, but also uncertainty with respect to the subcriteria of the considered criteria. The results are analyzed to provide insights into the robustness of the BIS's methodology.

The obtained results indicate that overall the BIS's methodology does not appear robust. Within the two uncertainty scenarios considered, the results show a great variability in identifying the G-SIBs' systemic risk.

The paper proposes an analytical way to assess the robustness of the BIS's methodology. Moreover, the authors’ analysis reveals a great variability on the results, which highlight different issues for banks, regulators and enterprises.

The purpose of this paper is to review published approaches to supply chain risk management, to include identification and classification of types of risks, cases, and models. Specific aspects of risk in supply chains involving China are also addressed.

Literature review provides sources which are synthesized.

A generic framework is identified, then categorizations of supply chain risks are compared. Cases and models applied to the study of supply chain risk are reviewed briefly. A review of Chinese risk in the supply chain context is provided.

This review includes many current studies, and is a source of useful references for those examining supply chain risk.

Lean manufacturing (LM) is prevalent in the manufacturing industry; thus, focusing on fast and accurate lean tool implementation is the new paradigm in manufacturing. Value stream mapping (VSM) is one of the many LM tools. It is understood that combining LM implementation with VSM tools can generate better outcomes. This paper aims to develop an expert system for optimal sequencing of VSM tools for lean implementation.

A proposed artificial neural network (ANN) model is based on the analytic network process (ANP) devised for this study. It will facilitate the selection of VSM tools in an optimal sequence.

Considering different types of wastes and their level of occurrence, organizations need a set of specific tools that will be effective in the elimination of these wastes. The developed ANP model computes a level of interrelation between wastes and VSM tools. The ANN is designed and trained by data obtained from numerous case studies, so it can predict the accurate sequence of VSM tools for any new case data set.

The design and use of the ANN model provide an integrated result of both empirical and practical cases, which is more accurate because all viable aspects are then considered. The proposed modeling approach is validated through implementation in an automobile manufacturing company. It has resulted in benefits, namely, reduction in bias, time required, effort required and complexity of the decision process. More importantly, according to all performance criteria and subcriteria, the main goal of this research was satisfied by increasing the accuracy of selecting the appropriate VSM tools and their optimal sequence for lean implementation.

This paper investigates the financial strength of banks in Bangladesh and factors affecting the financial strength over the years 2010–2015 on 35 banks.

Additive value function with CAMEL rating (capital stength, asset quality, managerial efficiency, earning ability, liquidity) has been employed to calculate banks’ financial strength index (FSI). In the second stage, panel regression has been exercised to find out the determinants of banks’ financial strength.

Empirical finding exhibits that the Islamic banks of Bangladesh are financially stronger and outperform conventional and Islamic window banks with higher liquidity. In the ownership category, private banks have more financial strength with higher capital strength, asset quality, managerial efficiency and earning ability than public banks. Bank size, loan recovery, salary and banking sector development positively affect whereas the loan-asset negatively affect the bank’s financial strength in Bangladesh.

This study has its limitations despite its importance. CAMELS is a more improved form than using CAMEL. But because of the data deficiency on “S” which represents sensitivity, it would not be possible to use CAMELS framework. Further researchers could incorporate this.

Government and banks should allow Islamic banks to enter the market on easy terms because of their outstanding performance in the existing market. In addition, banks should provide loans with consideration so that they cannot create credit risk. In addition, they should calculate composite financial strength annually to understand which components they need to work on.

This study extends the extant result on the composite FSI. It is hard to examine the financial strength of banks using only ratio value, which misleads most of the time. The study offers evidence on how the FSI provides more rigorous results and what are the factors contribute most to the financial strength of banks.

This study aims to develop a model for project portfolio selection considering organizational goals such as budgets, sustainability cash flow and reinvestment strategy under an uncertain environment.

A multi-objective mathematical programming model is proposed for project selection, which takes the social, environmental and financial aspects into account as the objectives of the project portfolio selection problem. The project evaluation and selection process in one of the large capitals in the Middle East with numerous urban construction projects was considered as a real case study, in which the subjects of environmental and social sustainability are of great importance. Then, the most significant criteria for project evaluation and selection based on sustainability were identified and ranked using the fuzzy best-worst method (BWM).

The criterion of “defining clear and real objectives” was ranked first, “project investment return period” was ranked second, “minimum changes in the predicted range” was ranked third, and the other ten sustainability indicators were ranked as well. Next, the presented mathematical programming model was solved using the augmented e-constraint method. The sensitivity analysis indicated that increasing the amount of investments in projects would increase their net present value. Also, increased investment had no effect on sustainability, while decreased investment caused sustainability to not being optimal.

This study focuses on the impact of the amount of investments on projects, and the associated costs of sustainable projects. Further to the authors' knowledge, there has been no relevant study taking uncertainty into account. Also, very few studies proposed a mathematical programming model for the project portfolio selection problem. Moreover, this research uses the brainstorming and Delphi method to identify the sustainability indicators influencing the organization and screens the evaluation indicators. Furthermore, the weights of the evaluation indicators are determined using the fuzzy BWM based on the consistency of opinions.

The purpose of this paper is to develop a method for solving the multiple attribute decision-making (MADM) problem in which the decision maker can provide the five types of attribute aspirations, namely: benefit type with requirements; cost type with requirements; interval type; benefit type; and cost type.

First, for each type of attribute aspiration, the calculation formula of utility values of alternative concerning attributes is given. Then, using the calculation formulae, the attribute values are transformed into the corresponding utility values. On the basis of this, the overall ranking value of each alternative is calculated. Further, a ranking order of alternatives can be determined according to the obtained overall ranking values.

Research shows that it is necessary to develop the method for MADM with attribute aspirations. The example shows that the proposed method is applicable.

The proposed method can be applied to the selection of wastewater treatment technologies or other areas.

This paper proposes a new MADM method with multiple types of attribute aspirations. It develops and enriches the existing MADM methods.

This study presents a reliability-based and sustainability-informed maintenance optimization model for telecommunications equipment. It considers several risk attributes associated with sustainability dimensions (i.e. social, economic and environmental aspects).

Many companies have developed long-term strategies to promote higher resource utilization, which has led to a paradigm shift in the role of maintenance. In parallel, reliability has been recognized as a fundamental factor in characterizing the optimal blend of maintenance strategies for a given system. It is essential for accurate failure prediction, which contributes toward more efficient use of all resources.

The corresponding subattributes are identified based on expert opinions and then incorporated into the proposed model. Subsequently, using the multiobjective particle swarm optimization (MOPSO), the proposed sustainability risks together with the maintenance costs are optimized, and the proper blend of maintenance strategies is identified.

Effective management of all human and natural resources, which are particularly emphasized by the concept of sustainability, has attracted much attention in recent years. However, contributions that effectively apply this concept in maintenance problems are very few and very few studies have attempted to quantify sustainability.