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The article aims to identify the scientific challenges and point out future research directions on Cost Engineering. The research areas covered in this article include Design Cost; Manufacturing Cost; Operating Cost; Life Cycle Cost; Risk and Uncertainty management and Affordability Engineering. Collected information at the Academic Forum on Cost Engineering held at Cranfield University in 2008 and further literature review findings are presented. The forum set the scope of the Cost Engineering research, a brainstorming was held on the forum and literatures were further reviewed to understand the current and future practices in cost engineering. The main benefits of the article include coverage of the current research on cost engineering from different perspectives and the future research areas on Cost Engineering.

Static system modelling for a manufacturing organisation does not closely reflect the manufacturing process. A dynamic model more closely simulates the processes involved, and can provide a richer model. This also applies to cost modelling, where some operational details are assumed or obtained from other systems. In order to provide a holistic modelling system, an experimental system has been built closely to couple a generative cost modelling system and a discrete event simulation system. This paper describes the system, its advantages and shortcomings, and the results obtained for the planning of an assembly process.

This paper discusses the theoretical aspects and applications of a novel methodology for exploiting a knowledge management editor tool to structure organizational knowledge and integrate it with product development activities. An organizational knowledge framework for capturing and representing manufacturing know-how has been developed using an ontological approach. The captured knowledge is converted into the industry-standard eXtensible mark-up language (XML) and then shared within a web-centric product data management (PDM) system to support a collaborative and distributed product development environment. The key business benefit of adopting such an approach arises from the closer integration between the key technical and business activities taking place during early design. In particular the effectiveness of decision making is increased.

Product design is not a linear process, with all stages of a product advancing at the same rate. Re-use of past design elements in conjunction with introducing new, loosely specified parts within a modern distributed and collaborative design environment presents a problem in accurately estimating likely quality, cost and delivery metrics. This paper presents a new methodology termed 'Feature Elasticity', which determines the impact of change on a product's design in terms of its effect upon the relevant process plan as part of an internet-enabled and knowledge-enriched aggregate process-planning system. The methodology is demonstrated via an example based upon an industrial case study.

The calculation and optimization of product manufacturability during the preliminary stages of design are critical to achieving reduced time to market, high quality and low cost. An aggregate planning method, which translates early product characteristics into manufacturing requirements, forms the basis of a new intelligent support system for which the manufacturing evaluation, optimization and reporting functions are described in this paper. The system 'intelligently explores' the many alternative processing technologies and equipment choices available, seeking solutions that best satisfy a multi-criteria objective function encapsulating quality, cost, delivery and knowledge criteria. The addition of knowledge factors means that both quantitative and qualitative factors can be used to rank alternatives. Importantly, the system prioritizes each element according to its potential for improvement. The designer is thus presented with the opportunity to redefine the design elements or process specifications, which would yield the greatest improvements in production.