Remanufacturing processes have been widely identified as being important elements of a sustainable economy. However, the commercial viability of this product recovery route is significantly influenced by several operational challenges that are different from those that occur in conventional manufacturing. One of the most widely used systematic process improvement methods in conventional manufacturing is lean production, which seeks to minimize all forms of waste throughout an operation. As the academic study of lean best practices in remanufacturing processes is relatively new, there is a lack of knowledge regarding implementation and impact. As such, this paper presents multiple case studies investigating the application of lean methods in industrial remanufacturing operations. These studies focus on the automotive industry, where both conventional and remanufacturing operations have been the leading adopters of lean thinking and practice. The results of the investigation identify specific lean methods that help manage the inherent complexity of the remanufacturing processes, and consequently improve the overall productivity of the process. Similarly, factors that limit the application of lean practices within remanufacturing are also identified and discussed. Matrices for opportunities and threats are developed to enable the simple adoption of these findings. Overall, the research provides a more holistic understanding of the application of lean within a remanufacturing environment, with benefits for both academia and industry.

Feature recognition from a solid model can be treated as a pattern-directed search for specific shape properties. However, to extend the range of features and of interacting situations in which it can be of use, there is a requirement to characterize a richer set of shape properties on which search will be based. Sequences of edges that are within the convex hull of a face (‘remnant’ loops’ and edge loops that are shared between several features add to the discriminating power of feature hunting techniques. These shape properties have been exploited in the implementation of a feature analysis system that operates on 2-5D object representations in a solid model and which derives a regional feature structure suited to three-axis machining. The motivation for its development was to provide a bridging link between a solid model representation and a knowledge-based, computer-aided, process planning system for prismatic parts.

This paper provides a novel methodology for human-driven decision support for capacity allocation in labour-intensive manufacturing systems. In such systems (where output depends solely on human labour) it is essential that any changes aimed at improving productivity are informed by the workers’ actual working practices, rather than attempting to implement strategies based on an idealised representation of a theoretical production process. This paper reports how worker position data (obtained by localisation sensors) can be used as input to process mining algorithms to generate a data-driven process model to understand how manufacturing tasks are actually performed and how this model can then be used to build a discrete event simulation to investigate the performance of capacity allocation adjustments made to the original working practice observed in the data. The proposed methodology is demonstrated using a real-world dataset generated by a manual assembly line involving six workers performing six manufacturing tasks. It is found that, with small capacity adjustments, one can reduce the completion time by 7% (i.e., without requiring any additional workers), and with an additional worker a 16% reduction in completion time can be achieved by increasing the capacity of the bottleneck tasks which take relatively longer time than others.

This paper focuses on data-driven prediction of lead times for product orders based on the real-time production state captured at the arrival instants of orders in make-to-order production environments. In particular, we consider a sophisticated manufacturing system where a large number of measurements about the production state are available (e.g. sensor data). In response to this complex prediction challenge, we present a novel ensemble hierarchical deep learning algorithm comprised of three deep neural networks. One of these networks acts as a generalist, while the other two function as specialists for different products. Hierarchical ensemble methods have previously been successfully utilised in addressing various multi-class classification problems. In this paper, we extend this approach to encompass the regression task of lead time prediction. We demonstrate the suitability of our algorithm in two separate case studies. The first case study uses one of the largest manufacturing datasets available, the Bosch production line dataset. The second case study uses synthetic datasets generated from a reliability-based model of a multi-product, make-to-order production system, inspired by the Bosch production line. In both case studies, we demonstrate that our algorithm provides high-accuracy predictions and significantly outperforms selected benchmarks including the single deep neural network. Moreover, we find that prediction accuracy is significantly higher in the synthetic dataset, which suggests that there is complexity (i.e. subtle interactions) in industrial manufacturing processes that are not easily reproduced in artificial models

Collaborative re-use of design and manufacturing data is one way that global e-commerce could significantly reduce development costs and lead times of new products. However the proliferation of web based catalogues for standard components (e.g. brochure-ware for nuts and washers etc.) only hints at the possible productivity gains if a 3D search engine could be created. Consequently the research reported here is motivated by the belief that shape matching technology is key to enabling a much deeper form of Internet-based collaborative commerce. This paper describes the coarse “shape” filters that support a 3D, Internet-based search engine, known as ShapeSifter, whose ultimate aim is to enable the location of parts already in production that have a shape similar to a desired “newly designed” part. The challenge of the research is to identify the key metrics that produce meaningful characterizations of 3D models for similarity comparison purposes. In this context the work reported focuses on the use of novel indices based on an object’s convex-hull and surface area distribution to carry out a coarse filtering of candidates prior to the application of more detailed analysis (e.g. the construction of multi-dimensional feature vectors). In doing this, the paper describes the crucial role played by a benchmark database of geometrical similar objects.