A method for analysing operational complexity in supply chains

This paper proposes a method for analysing the operational complexity in supply chains by using an entropic measure based on information theory. The proposed approach estimates the operational complexity at each stage of the supply chain and analyses the changes between stages. In this paper a stage is identified by the exchange of data and/or material. Through analysis the method identifies the stages where the operational complexity is both generated and propagated (exported, imported, generated or absorbed). Central to the method is the identification of a reference point within the supply chain. This is where the operational complexity is at a local minimum along the data transfer stages. Such a point can be thought of as a 'sink' for turbulence generated in the supply chain. Where it exists, it has the merit of stabilising the supply chain by attenuating uncertainty. However, the location of the reference point is also a matter of choice. If the preferred location is other than the current one, this is a trigger for management action. The analysis can help decide appropriate remedial action. More generally, the approach can assist logistics management by highlighting problem areas. An industrial application is presented to demonstrate the applicability of the method. © 2013 Operational Research Society Ltd. All rights reserved.

Shock transmission in a coupled beam system

This paper investigates the circumstances under which high peak acceleration can occur in the internal parts of a system when subjected to impulsive driving on the outside. Motivating examples include the design of packaging for transportation of fragile items. The system is modelled in an idealised form using two beams coupled with point connections. A Rayleigh-Ritz model of such coupled beams was validated against measurements on a particular beam system, then the model was used to explore the acceleration response to impulsive driving in the time, frequency and spatial domains. This study is restricted to linear vibration response and additional mechanisms for high internal acceleration due to nonlinear effects such as internal impacts are not considered. Using Monte Carlo simulation in which the indirectly driven beam was perturbed by randomly placed point masses a wide range of system behaviour was explored. This facilitates identification of vulnerable configurations that can lead to high internal acceleration. The results from the study indicate the possibility of curve veering influencing the peak acceleration amplification. The possibility of veering within an ensemble was found to be dependent on the relative coupling strength of the modes. Understanding of the mechanism may help to avoid vulnerable cases, either by design or by preparatory vibration testing. © 2013 Elsevier Ltd.

Bayesian Structured Prediction Using Gaussian Processes

We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing approaches, among others, conditional random fields (CRFs), maximum margin Markov networks (M3N), and structured support vector machines (SVMstruct), which embody only a subset of its properties. We present an inference procedure based on Markov Chain Monte Carlo. The framework can be instantiated for a wide range of structured objects such as linear chains, trees, grids, and other general graphs. As a proof of concept, the model is benchmarked on several natural language processing tasks and a video gesture segmentation task involving a linear chain structure. We show prediction accuracies for GPstruct which are comparable to or exceeding those of CRFs and SVMstruct.

Improving turbomachinery design process management

It is widely acknowledged that a company's ability to aquire market share, and hence its profitability, is very closely linked to the speed with which it can produce a new design. Indeed, a study by the U.K. Department of Trade and Industry has shown that the critical factor which determines profitability is the timely delivery of the new product. Late entry to market or high production costs dramatically reduce profits whilst an overrun on development cost has little significant effect. This paper describes a method which aims to assist the designer in producing higher performance turbomachinery designs more quickly by accelerating the process by which they are produced. The adopted approach combines an enhanced version of the 'Signposting' design process management methodology with industry-standard analysis codes and Computational Fluid Dynamics (CFD). It has been specifically configured to enable process-wide iteration, near instantaneous generation of guidance data for the designer and fully automatic data handling. A successful laboratory experiment based on the design of a large High Pressure Steam Turbine is described and this leads on to current work which incorporates the extension of the proven concept to a full industrial application for the design of Aeroengine Compressors with Rolls-Royce plc.

A systematic approach to materials information management for aerospace design

The materials information requirements of the aerospace sector are considered, specifically 'consolidation' (management of raw test data), 'analysis' (investigation of material trade-offs) and 'dissemination (secure distribution of data throughout an organization). An information architecture that satisfies the complex requirements of the aerospace materials industry is discussed and a case-study is presented. © 2003 by Granta Design Limited. Published by the American Institute of Aeronautics and Astronautics, Inc.