Beyond theory: An examination of lean new product introduction practices in the UK

Interest is growing around the application of lean techniques to new product introduction (NPI). Although a relatively emergent topic compared with the application of 'lean' within the factory, since 2000 there has been an exponential rise in the literature on this subject. However, much of this work focuses on describing and extolling the virtues of the 'Toyota approach' to design. Therefore, by way of a stock take for the UK, the present authors' research has set out to understand how well lean product design practices have been adopted by leading manufacturers. This has been achieved by carrying out in-depth case studies with three carefully selected manufacturers of complex engineered products. This paper describes these studies, the detailed results and subsequent findings, and concludes that both the awareness and adoption of practices is generally embryonic and far removed from the theory advocated in the literature. © IMechE 2007.

Testing of Depth-Encoded Hough Voting for Infrastructure Object Detection

The lack of viable methods to map and label existing infrastructure is one of the engineering grand challenges for the 21st century. For instance, over two thirds of the effort needed to geometrically model even simple infrastructure is spent on manually converting a cloud of points to a 3D model. The result is that few facilities today have a complete record of as-built information and that as-built models are not produced for the vast majority of new construction and retrofit projects. This leads to rework and design changes that can cost up to 10% of the installed costs. Automatically detecting building components could address this challenge. However, existing methods for detecting building components are not view and scale-invariant, or have only been validated in restricted scenarios that require a priori knowledge without considering occlusions. This leads to their constrained applicability in complex civil infrastructure scenes. In this paper, we test a pose-invariant method of labeling existing infrastructure. This method simultaneously detects objects and estimates their poses. It takes advantage of a recent novel formulation for object detection and customizes it to generic civil infrastructure scenes. Our preliminary experiments demonstrate that this method achieves convincing recognition results.