Abstract
Assessing eco-efficiency of companies is important to ensure the creation of wealth without compromising the needs of future generations. This work aims to extend the eco-efficiency concept by including in the assessment new features related to environmental benefits and environmental burdens. This concept is implemented using an innovative Directional Distance Function model, which searches for improvements in the magnitude of the indicators and in the composition of the resources consumed. This framework can help firms to become more sustainable by replacing non-renewable inputs with "greener" alternatives. We present an empirical application to large mining companies. Different scenarios regarding managerial priorities for adjustments to firms' economic and environmental indicators are explored. The results obtained and their managerial implications are discussed in the context of mining firms activity.

Abstract
130In the majority of European countries, the evaluation of schools is at the heart of the educational system as a means to guarantee the quality of education. Every year, in most countries around the world, students perform national exams. Their results are analyzed by several stakeholders, including governmental agencies, the media, and researchers on educational issues. At present, advances in information and communication technology (ICT) and data analysis techniques allow schools to make use of massive amounts of data in their daily management. This chapter focuses in particular on the use of students’? data to benchmark schools. It illustrates the potential contribution of the information gathered and analyzed through data analytics to promote the continuous improvement of schools’? educational processes. © 2018 by Taylor & Francis Group, LLC.

Abstract
In continental Portugal, the publicly financed supply of gastroenterology exams was limited since the end of the last century, restricted to a fixed set of private providers that was hired by the Portuguese state. This way of contracting created market entry barriers and is inefficient, since prices are administratively set. Besides, it produced access inequalities, because of the way that the supply was geographically distributed. This paper applies the Extended Kernel Density 4-Step Floating Catchment Area (EKD4SFCA) method to identify priority districts for the promotion of new supply by the state, in order to choose the appropriate way of contracting new private supply, as determined by current law, and to reduce access inequalities. The applied method enables the identification of the Portuguese regions with strong competition between health care providers and where patients’ access to publicly financed gastroenterology exams is relatively low. In these regions, the state should promote public bids to stimulate new supply, exploring thereby the potential for setting lower prices and reducing access inequalities.

Abstract
This paper presents a General Morphological Analysis (GMA) meta-model aiming to help decision-makers wishing to integrate sustainability concerns into the company strategy. This is made by joining Operational Research (OR) analysts, decision-makers and stakeholders as participants in the problem structuring and formulation process. This is particularly relevant in societal issues, where public transport companies are particularly important. Indeed, public transport companies play a quite visible role in the dimensions of corporate social responsibility, namely because of four reasons: (i) they provide daily services crucial to mass customers' mobility; (ii) their investments are usually of high value and rather sensitive to technological development; (iii) they play a crucial role in the energy sector and (iv) are strongly dependent upon macro-policies. © 2017 The Authors. Published by Elsevier B.V.

Abstract
A cancer registry is a standardized tool to produce population-based data on cancer incidence and survival. Cancer registries can retrieve and store information on all cancer cases occurring in a defined population. The main sources of data on cancer cases usually include: treatment and diagnostic facilities (oncology centres or hospital departments, pathology laboratories, or imaging facilities etc.) and the official territorial death registry. The aim of this paper is to evaluate the north regional cancer registry (RORENO) of Portugal using a qualitative research. We want to characterize: the main functionalities and core processes, team involved, different healthcare institutions in the regional network and an identification of issues and potential improvements. RORENO links data of thirteen-two healthcare institutions and is responsible for the production of cancer incidence and survival report for this region. In our semi-structure interviews and observation of RORENO we identified a serious problem due to a lack of an automatic integration of data from the different sources. Most of the data are inserted manually in the system and this implies an extra effort from the RORENO team. At this moment RORENO team are still collecting data from 2011. In a near future it is crucial to automatize the integration of data linking the different healthcare institutions in the region. However, it is important to think which functionalities this system should give to the institutions in the network to maximize the engagement with the project. More than a database this should be a source of knowledge available to all the collaborative oncologic network.

Abstract
Credit-card fraud leads to billions of dollars in losses for online merchants. With the development of machine learning algorithms, researchers have been finding increasingly sophisticated ways to detect fraud, but practical implementations are rarely reported. We describe the development and deployment of a fraud detection system in a large e-tail merchant. The paper explores the combination of manual and automatic classification, gives insights into the complete development process and compares different machine learning methods. The paper can thus help researchers and practitioners to design and implement data mining based systems for fraud detection or similar problems. This project has contributed not only with an automatic system, but also with insights to the fraud analysts for improving their manual revision process, which resulted in an overall superior performance.