Developing a framework for make-or-buy decisions

The make-or-buy question represents a fundamental dilemma faced by many companies. Companies have finite resources and cannot always afford to have all manufacturing technologies in-house. This has resulted in an increasing awareness of the importance of make-or-buy decisions. This paper reports on the development of a make-or-buy framework to address the make-or-buy decision for either a specific individual part or family of parts. Firstly, a literature review of the principal make-or-buy approaches is discussed. Secondly, the development of a make-or-buy framework is described and the framework is explained and illustrated using case studies. Thirdly, the operationalisation of the framework is outlined. The paper concludes with a discussion of its contribution to both the academic understanding of the subject, and the improvement of industrial practice.

Developing a framework for mapping industrial emergence

The industrial landscape is becoming increasingly complex and dynamic, with innovative technologies stimulating the emergence of new industries and business models. This paper presents a preliminary framework for mapping industrial emergence, based on roadmapping principles, in order to understand the nature and characteristics of such phenomena. The focus at this stage is on historical examples of industrial emergence, with the preliminary framework based on observations from 20 'quick scan' maps, one of which is used to illustrate the framework. The learning from these historical cases, combined with further industrial consultation and literature review, will be used to develop practical methods for strategy and policy application. The paper concludes by summarising key learning points and further work needed to achieve these outcomes. © 2009 PICMET.

SBLOCK: A framework for efficient stencil-based PDE solvers on multi-core platforms

We present a new software framework for the implementation of applications that use stencil computations on block-structured grids to solve partial differential equations. A key feature of the framework is the extensive use of automatic source code generation which is used to achieve high performance on a range of leading multi-core processors. Results are presented for a simple model stencil running on Intel and AMD CPUs as well as the NVIDIA GT200 GPU. The generality of the framework is demonstrated through the implementation of a complete application consisting of many different stencil computations, taken from the field of computational fluid dynamics. © 2010 IEEE.

A FEniCS-based programming framework for modeling turbulent flow by the Reynolds-averaged Navier-Stokes equations

Finding an appropriate turbulence model for a given flow case usually calls for extensive experimentation with both models and numerical solution methods. This work presents the design and implementation of a flexible, programmable software framework for assisting with numerical experiments in computational turbulence. The framework targets Reynolds-averaged Navier-Stokes models, discretized by finite element methods. The novel implementation makes use of Python and the FEniCS package, the combination of which leads to compact and reusable code, where model- and solver-specific code resemble closely the mathematical formulation of equations and algorithms. The presented ideas and programming techniques are also applicable to other fields that involve systems of nonlinear partial differential equations. We demonstrate the framework in two applications and investigate the impact of various linearizations on the convergence properties of nonlinear solvers for a Reynolds-averaged Navier-Stokes model. © 2011 Elsevier Ltd.