Abstract

Abstract
The adoption of mobile payment systems is known to face multiple concerns regarding security, usability and value proposition. In this work, we start from the assumption that initial acceptance will always be weak because of the lack of an established usage frame. Instead, we focus on understanding how we can leverage upon the real contact with the technology to create a solid path for gradual acceptance through the development of new practices and the increasing perception of value. In this study, we report on our findings with a real-world prototype of a NFC-based payment system. We identify a set of design lessons that may help to improve the initial phases of NFC-based payment deployments and provide a path for the adoption that focuses on positive initial user experiences and early adoption scenarios. © Springer International Publishing 2013.

Abstract
In this paper, a new efficient algorithm named combined local search heuristics which comprise two local search heuristics, Variable Neighborhood Descent (VND) and Random Neighbor Selection (RNS), is designed and proposed to solve two-dimensional guillotine bin packing problems. The objective of these problems is to pack smaller pieces of rectangular items into large rectangular bins without overlapping such that the total number of used bins is minimized. A constructive heuristic (CH) is conceived to construct a solution by packing items into bins with the use of a defined item packing sequence. VND and RNS, which consist of three deterministic neighborhood structures and three random neighbor selection operators, respectively, are used for improving a solution given by the CH. Benchmark instances were adopted to verify the effectiveness of the designed algorithm via computational experiments. Computational results show that, in terms of the quality of solutions, the proposed approach is better than other heuristics and metaheuristics. In terms of computational times, the proposed algorithm cannot be compared to other algorithms and the computational experiments cannot offer enough evidence of showing any good running-time behavior of the proposed algorithm because of different models of computers used. However, from a practical point of view, easy implementation and reasonable and affordable computational times confirm the usefulness of the proposed algorithm.

Abstract
Since the automotive industry is one of the most competitive markets, key players should be capable of evolving their management strategy from a reactive to proactive approach. This way, it is critical for this type of companies to explore a new performance management approach where an effective interaction between the strategic and operational layers should be achieved. In line with this vision, a framework is presented that helps stakeholders make decisions based on the ability to anticipate future performance behaviours. Using leading indicators as reference, the key idea is to structure and model the existing knowledge within a mathematical tool, in order to project the manufacturing system's behaviour into the future. In order to show the reliability and importance of this framework, this paper presents a research performed at an automotive plant. The aim is to model the factors affecting energy consumption and thus estimate the future behaviour in terms of sustainability issues.

Abstract
Due to the increasing globalization process and the current economic situation, the power has shifted from the producer to the costumer, forcing companies to become more aware of the market needs. In order to become more customer-oriented, companies have been enhancing their management capabilities by shifting from a functional oriented approach to a process centered strategy, where core inter-firms processes can be seamlessly monitored and controlled. Since it is not possible to manage a system if its performance cannot be measured continuously during its entire life cycle, it is necessary to explore flexible and agile performance measurement and management systems as they are important tools capable of supporting the achievement of the strategic goals on the operational side. In the recent years several research projects have developed techniques and tools that support the collaboration. However they are restricted to the business level. In order to achieve the goals with the best performance, innovative and appropriate process monitoring and control mechanisms are needed. Consequently, this research provides an innovative solution that facilitates not only the gathering of operational and strategic information but also the assessment of collaborative manufacturing processes behavior following a fuzzy approach. © Springer International Publishing Switzerland 2013.

Abstract
Currently, performance analysis on complex manufacturing systems is performed in an ad hoc way since the main objective is to verify if the strategy designed has been helping companies achieve their targets following a reactive approach. However, more and more companies are performing in competitive markets, forcing them to become more proactive than reactive. This way, a simple approach is no longer suitable for this type of companies, and a stronger and effective interaction between the strategic and operational layers is key. Therefore, this research proposes a framework composed of both qualitative and quantitative methods that allow decision-makers to better understand their production system. Moreover, using key leading indicators as reference, the idea is to provide companies with the ability to anticipate future performance behaviors based not only on the knowledge acquired, but also on a mathematical tool that will synthesize this knowledge and infer future performance behaviors. This paper explores a critical issue for contemporary industrial organizations and sustainability issues concerning energy consumption. In this scope, an important research was performed aiming at modeling and understanding the normal behavior of electricity consumption, as well as the factors affecting energy consumption in the painting line of an automotive plant. © Springer International Publishing Switzerland 2013.