Abstract
In this paper, we address the irregular strip packing problem (or nesting problem) where irregular shapes have to be placed on strips representing a piece of material whose width is constant and length is virtually unlimited. We explore a constructive heuristic that relies on the use of graphical processing units to accelerate the computation of different geometrical operations. The heuristic relies on static selection processes, which assume that a sequence of pieces to be placed is defined a priori. Here, the emphasis is put on the analysis of the impact of these sequences on the global performance of the solution algorithm. Computational results on benchmark datasets are provided to support this analysis, and guide the selection of the most promising methods to generate these sequences.

Abstract
Due to the current demands from the market, technological improvements and action of competitors, manufacturing companies are pushed to compete in shorter product development cycles. This poses a great challenge, as the conventional product development cycle is shortened. Hence, companies are forced to introduce some improvement action plans and adopt certain manufacturing operational strategies to remain competitive and achieve a good market share. Due to the importance of this topic, in this paper we revise the current manufacturing improvement plans in the literature, to establish the basis of our future research work. The findings of this work point towards the need to develop a system that integrates the evaluation of the manufacturing improvement plans in the future overall performance of the manufacturing plant and aid in the process of decision-making.

Abstract
Quality control, failure analysis and improvement are central elements in manufacturing. Total Quality Management (TQM) provides several quality oriented tools and techniques which, in the event of things, are not always applicable. The increased use of Information Technology (IT) in manufacturing means increased data availability and improved potential for knowledge extraction. Exploiting this knowledge requires data storage and processing facilities with demanding, time consuming sessions for interpretation. Without suitable tools and techniques, knowledge remains hidden in databases. This paper presents a method to help identify root causes of nonconformities (NCs) using a pattern identification approach. Hereby, a general framework, Knowledge Discovery in Databases (KDD), is adapted. This adaptation involves incorporating an economic concentration measure, the Herfindahl-Hirschman Index (HHI), as the data mining algorithm. After presenting the theoretical background, a new methodology is proposed. The suggested approach can be regarded as a quality tool to help make root cause identification of failures simpler and more agile. A case study from the automotive industry is examined using this tool. Results are obtained and presented in the form of matrix based patterns. They suggest that concentration indices help indicate possible root causes of NCs, warranting further investigation in this area.

Abstract
In today's business, consumers are challenging manufacturers by increasingly demanding low volumes of innovative, fashionable and inexpensive products; adopting high quality standards; responding in smaller intervals of time, and with high configurability and parameterization. This omnipresent scenario poses challenging opportunities for collaborative networked organizations. The present paper addresses the main collaborative business processes tailored for responsiveness and efficient use of knowledge on customized manufacturing environments through a lean-based framework proposal for collaborative networks inspired in the Set-Based Concurrent Engineering (SBCE) methodology. In particular this work describes the ongoing implementation of the proposed concepts through a web-based collaborative portal.

Abstract
A virtual enterprise management platform is proposed with the objective to simplify the formation, management, adaptation and monitoring of the dynamic manufacturing process in VE. Within this platform an integration black box is highlighted to monitor the process data. This black box is used as a smart object to collect updated data or information from the business process or equipment within the VE. The raw data for predictive maintenance is collected by the black box from programmable logic controllers via gateway interface. A dashboard user interface is also designed and developed within the scope of this research that acts as a visualization tool to display the process monitored data. The theme of this research is designed and developed within the scope of the European Commission NMP priority of the Seventh RTD Framework Programme for the ADVENTURE (ADaptive Virtual ENterprise ManufacTURing Environment) project. © Springer International Publishing Switzerland 2015.

Abstract
Selecting quality improvement projects among a variety of mutually competing ones is characterised by prioritisation. In practice, this is often done by the rule of thumb, following a single attribute criterion or by subjective preferences of individual stakeholders of a project team. An approach to track systematically nonconformities and to identify those critical ones that should be prioritised based on multi-attribute criteria is presented. The approach is introduced and its purpose is explained. After the background of relevant topics is given, the method's importance in research and for practitioners is derived. Steps of the approach are portrayed and an application case of a mature industrial company, integrated in the supply-chain of the automotive industry, is presented. Elements of quality tools and techniques are used as attributes, namely failure mode and effect analysis and Pareto diagram. Results based on industrial data indicate that the use of this approach contributes to support informed and structured prioritisation decisions, which foster a more efficient improvement of future quality-based projects.