Abstract
This paper proposes an innovative composite indicator to evaluate Corporate Social Responsibility (CSR). The methodology proposed involves two stages. The first stage specifies an optimization model, based on Directional Distance Functions, to obtain a relative measure of CSR at the firm level that can guide performance improvements. This model allows distinguishing the firms with best practices from those with potential for improvement and can suggest targets for future achievements. In the second stage, a goal programming model is proposed to identify a common set of weights for the key performance indicators, enabling the evaluation of all firms on common grounds. These weights are used to construct an industry ranking, which is based on the distance of firms to a common frontier of technology that respects the trade-offs determined for the industry. An illustrative application of the method proposed is presented at the end of the paper. The indicators considered in the evaluation were selected according to international standards and guidelines applicable to mining firms. All dimensions of the Triple Bottom Line (economic, environmental and social) were taken into account. The results and their managerial implications are discussed with the objective of promoting the awareness of CSR levels in the mining activity, supporting the sustainable development of industrial activities.

Abstract
In recent years, cognitive robots have started to find their way into manufacturing halls. However, the full potential of these robots can only be exploited through (a) an integration of the robots with the Manufacturing Execution System (MES), (b) a new and simpler way of programming based on robot skills, automated task planning, and knowledge modeling, and (c) enabling the robots to function in a shared human/robot workspace with the ability to handle unexpected situations. The STAMINA project has built a robotic system that meets these objectives for an automotive kitting application, which has also been tested, validated, and demonstrated in a relevant environment (TRL6). This paper describes the STAMINA robot system and the evaluation of this system on a series of realistic kitting tasks. The structure of the system, evaluation methodology, and experimental results, are presented along with the insights and experiences gained from this work.

Abstract
Promotional tools such as cross-market discounts have been increasingly used as a means to increase customer satisfaction and sales. This paper aims to assess whether the implementation of a cross-market discount campaign by a retailing company encouraged customers to increase their purchases level. It contributes to the literature by using neural networks to detect novelties in a real context involving cross-market discounts. Besides the computation of point predictions, the methodology proposed involves the estimation of neural networks prediction intervals. Sales predictions are compared with the observed values in order to detect significant changes in customers' spending. The use of neural networks is validated through the comparison with the forecasting estimates of support vector regression, regression trees, and linear regression. The results reveal that the promotional campaign under analysis did not significantly impact the sales of the rewarded customers.

Abstract
Disruptive requirements that currently drive the so-called Industry 4.0 (I4.0) are increasingly present in today's industries, where factories are forced to innovate in search of improvement in the quality of manufacturing of products aligned with the reductions of: manufacturing time, environmental and cost impacts with the manufacturing process. For this, an Information Systems (IS) architecture is proposed to reduce the negative impacts on an industrial operation caused by manual configuration failures in manufacturing systems, machines that are worn out in the production process and unstable integrations between industrial subsystems. The suggested SI model uses the Viable Systems Model adapted to Maintenance 4.0 technologies (Cyber-physical Systems (CPS), Manufacturing Execution Systems (MES), Data Mining and Digital Manufacturing concepts/technologies) with the goal to create an automatic purchase flow to replace parts by mitigating impending failures in industrial equipment through data mining and predictive analysis.

Abstract
Cutting phases occur in many production processes when a larger object must be cut into multiple smaller pieces. Some examples of relevant industries being clothing, footwear, metalware and furniture. The cutting phase is composed of two stages. The first stage consists of finding a good layout for the set of small pieces that must be cut from the larger object and minimizing some objective such as raw-material waste (The Cutting and Packing Problem). Once this good layout has been established, it is provided as input for the second stage which consists of determining the path to cut the pieces which minimizes another objective, such as the total cutting time or distance (The Cutting Path Determination Problem). This second stage is crucial for efficient production planning. Only one linear mathematical model has previously been proposed for the Cutting Path Determination Problem. In this paper, this problem is addressed using two exact approaches based on the Rural Postman Problem (RPP) and the Traveling Salesman Problem (TSP). The RPP approach, in particular, is able to produce optimal solutions for instances containing more than 2000 edges in under 1 h.

Abstract
Across the world, several developing countries with recent wind energy (WE) diffusion have been demonstrating an interest in expanding WE generation quickly and expressively. In Brazil, this fast and remarkable WE diffusion process started in the past decade, facing a variety of barriers. This research proposes to reliably identify and analyse current barriers to the implementation of onshore wind farms in the country, aiming at informing other developing countries with the intention to expand WE utilization about potential barriers, as well as assisting the Brazilian WE sector in its efforts to keep growing sustainably. To achieve this research goal, 41 key stakeholders from the Brazilian WE sector were interviewed. Based on a content analysis of the interviews, twenty-four barriers where identified, three of which with particular relevance: poor transmission infrastructure, unattractive financial loans and unstable macroeconomic environment. The data collected suggests a serious risk that Brazil's WE sector stops growing if the recovery of its national economy, the reestablishment of regular energy auctions, the access to competitive credit, and improvements to the transmission system infrastructure keep being held back. In the face of this scenario, this study concludes by providing policy recommendations concerning the sustainability of the Brazilian WE sector.