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The role of clusters in the development and growth of local and national economies has been extensively studied and discussed in global literature. Different methodologies are used for analysing the impact these have in national and regional economies, such as the input–output (IO) and gravity models. This paper aims to detail the methodologies present in the literature and propose a new robust theoretical framework, which facilitates the evaluation and comparison among maritime clusters in terms of attractiveness assisting stakeholders to devise strategies, which will attract companies.

An index is created composed of five key categories, namely, infrastructure, financing, governance, manpower and institution/legislation. For the analysis of the index, multi-attribute utility theory (MAUT) is used as a tool to evaluate the importance and performance of the different attributes using both quantitative and qualitative criteria. The methodology has been tested via the use the Piraeus maritime cluster.

The framework has been tested on its robustness and friendliness to the user providing useful insights to the stakeholders. Among the results has been the importance of the finance, manpower and infrastructure attributes, which appear to promote the cluster’s attractiveness. In addition, legislation and institutional partnerships, along with Government support, need to take place improve the performance of the cluster.

A key limitation is the fact that the methodology has been tested in a single case. Applying the methodological framework in a wider sample of clusters will significantly improve the present work.

The proposed model takes further existing research in the field via adopting the philosophy of the World Bank’s Logistics Performance Index. Among the benefits of the proposed index is that it offers the flexibility and robustness to compare among different maritime clusters globally and can be readily used as a benchmarking policy tool at national, regional and global levels at any given point in time and attribute dimension.

This study aims to identify to what extent the economic factor effect is more salient in shaping inward foreign direct investment (IFDI) than are institutional factors in G-20 inflow patterns.

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was applied using the World Bank Governance and Development Indicators, followed by a panel data technique over the period 2005-2015 to estimate the connections between the different dimensions of economics, institutions and IFDI in the G-20.

Results showed that countries with better economic performance contrasting with the governance indicators are more effective at attracting IFDI. However, the correlation between FDI intensity and governance indicators has been found relatively weak, which may suggest a more controversial role of institutions as determinants of IFDI.

This quantitative approach uses a country-level set of variables; therefore, the authors suggest the development of more firm-level analysis of the impact of institutions. Also, the limitation of the TOPSIS method itself is based on heuristic assumptions.

The main findings point to a relatively low impact of institutions on IFDI. The authors suggest that the global financial crisis has changed the rationale of decision-making by multinational companies.

The originality of the present study was to apply a multi criteria decision-making technique on FDI’s analysis combined with institutional data.

Limited facilities operating and modernization budgets require organizations to carefully identify, prioritize and authorize projects to ensure allocated resources align with strategic objectives. Traditional facility prioritization methods using risk matrices can be improved to increase granularity in categorization and avoid mathematical error or human cognitive biases. These limitations restrict the utility of prioritizations and if erroneously used to select projects for funding, they can lead to wasted resources. This paper aims to propose a novel facility prioritization methodology that corrects these assessment design and implementation issues.

A Mamdani fuzzy logic inference system is coupled with a traditional, categorical risk assessment framework to understand a facilities’ consequence of failure and its effect on an organization’s strategic objectives. Model performance is evaluated using the US Air Force’s facility portfolio, which has been previously assessed, treating facility replicability and interruptability as minimization objectives. The fuzzy logic inference system is built to account for these objectives, but as proof of ease-of-adaptation, facility dependency is added as an additional risk assessment criterion.

Results of the fuzzy logic-based approach show a high degree of consistency with the traditional approach, though the value of the information provided by the framework developed here is considerably higher, as it creates a continuous set of facility prioritizations that are unbiased. The fuzzy logic framework is likely suitable for implementation by diverse, spatially distributed organizations in which decision-makers seek to balance risk assessment complexity with an output value.

This paper fills the identified need for portfolio management strategies that focus on prioritizing projects by risk to organizational operations or objectives.

This study presents the development of a supply chain (SC) observatory, which is a benchmarking solution to support companies within the same industry in understanding their positioning in terms of SC performance.

A case study is used to demonstrate the set-up of the observatory. Twelve experts on automatic equipment for the wrapping and packaging industry were asked to select a set of performance criteria taken from the literature and evaluate their importance for the chosen industry using multi-criteria decision-making (MCDM) techniques. To handle the high number of criteria without requiring a high amount of time-consuming effort from decision-makers (DMs), five subjective, parsimonious methods for criteria weighting are applied and compared.

A benchmarking methodology is presented and discussed, aimed at DMs in the considered industry. Ten companies were ranked with regard to SC performance. The ranking solution of the companies was on average robust since the general structure of the ranking was very similar for all five weighting methodologies, though simplified-analytic hierarchy process (AHP) was the method with the greatest ability to discriminate between the criteria of importance and was considered faster to carry out and more quickly understood by the decision-makers.

Developing an SC observatory usually requires managing a large number of alternatives and criteria. The developed methodology uses parsimonious weighting methods, providing DMs with an easy-to-use and time-saving tool. A future research step will be to complete the methodology by defining the minimum variation required for one or more criteria to reach a specific position in the ranking through the implementation of a post-fact analysis.

Governments around the world instituted guidelines for calculating energy efficiency of vehicles not only by models, but by the whole universe of new vehicles registered. This paper compiles Multi-criteria decision-making (MCDM) studies related to automotive industry. We applied a Systematic Literature Review on MCDM studies published until 2015 to identify patterns on MCDM applications to design vehicles more fuel efficient in order to achieve full compliance with energy efficiency guidelines (e.g., Inovar-Auto). From 339 papers, 45 papers have been identified as describing some MCDM technique and correlation to automotive industry. We classified the most common MCDM technique and application in the automotive industry. Integrated approaches were more usual than individual ones. Application of fuzzy methods to tackle uncertainties in the data was also observed. Despite the maturity in the use of MCDM in several areas of knowledge, and intensive use in the automotive industry, none of them are directly linked to car design for energy efficiency. Analytic Hierarchy Process was identified as the common technique applied in the automotive industry.