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An integer multiple objective non‐linear mathematical programming formulation is developed for simultaneously forming part/machine cells. In the proposed model, generic capability units which are termed as resource elements are used to define the processing requirements of parts and processing capabilities of machine tools. Machine capabilities are not generally taken into account in the previous cell formation procedures. Explicit consideration of unique and overlapping machine capabilities can result in better manufacturing cell designs with higher utilisation levels and less machine duplication. The proposed cell formation model has distinguishing features. Several important cell formation objectives, such as minimisation of part dissimilarity (based on production requirements and processing sequences of parts) in formed cells, minimisation of cell load imbalance, and minimisation of extra capacity requirements for cell formation, are considered. In order to solve the mathematical programming model, a simulated annealing algorithm is developed. Cooperative game theoretic approach is applied for evaluating multiple objectives.

The decomposition of production facilities into efficient cells is one of the areas attracting increasing research attention due to the performance benefits which cellular manufacturing offers. One of the problems associated with cell formation is the restrictive character of the existing task formalization models resulting in most cases from the use of single machine tool routeings in representing the component requirements. In contrast, the industrial reality provides a more complex picture with multiple choice of processing routeings in terms of available machine alternatives which need to be considered in order to achieve an “optimum” cellular decomposition of manufacturing facilities. Presents a facility decomposition approach based on multiple choice of processing alternatives for each component. The decision making is based on a generic description of the component processing routeings using unique machine capability patterns ‐ “resource elements”. Manufacturing cells are formed using concurrent fuzzy clustering methodology and a validation procedure for selection of the “optimum” facility partition.

The paper seeks to report on some of the preliminary results of an ongoing scoping study into the shape of the manufacturing enterprise of the future.

This paper evolved through a combination of literature review, focused group discussions, interviews and a questionnaire survey of six aerospace companies in the UK. It is primarily an attempt to provide a broad framework for synthesizing some of the information generally available as a contribution to the current debate regarding the future of manufacturing systems.

The results to date show that the product development process and supply network efficiency are the two most significant domains influencing manufacturing responsiveness. Within those domains, customer driven product development and supply chain design, intelligent and flexible technology, producibility analysis, integrated product and process development and the concurrency of the extended manufacturing enterprise are considered as the most significant elements towards achieving responsiveness. In addition a Responsive Manufacturing Model (RMM) is provided.

The RMM reported in the paper is at an early state of development and the work is ongoing to refine it further. The development of appropriate measures and methods of assessment for the various facets and attributes of manufacturing responsiveness is an important step towards full model development which is still to be addressed.

The process of structuring the various elements influencing manufacturing responsiveness into logical groups in a hierarchical model has proved very useful during model development. It proved a significant aid during the focused group discussions and interviews that preceded completion of the questionnaire. The results to date are very encouraging and provide several interesting insights into the domains and elements of manufacturing responsiveness and the relative importance attached to them in the UK aerospace sector.

The work was funded by EPSRC (IMI) research grant as it was the first attempt in this field over within the UK. The proposed model and the obtained results have led to another research project funded by EPSRC over three years to further investigate the proposed model and the implication of its implementation.

For high technology companies, the successful acquisition and management of technology to enable the development and manufacture of innovative products is a key factor in their competitiveness. Seeks to present an integrated technology road‐mapping methodology that enables management to define its technology requirements, taking account of financial and other issues, to assess proposed technology projects against these requirements and to create a balanced technology project portfolio.

The methodology consists of six steps or phases; the first three steps produce a set of technology requirements based on a company's business drivers, products and competitive position; the last three steps enable the creation and assessment of a portfolio of research and development projects.

Applications of the methodology in industry have demonstrated that the integrated nature of the process, from a derivation of technology requirements to investment decision making, improves the clarity and transparency of decision making. In particular, the linking of technology requirements assessment to portfolio generation makes it easier to justify the assignment of resources to technology assessment.

The methodology has been applied successfully in a high technology manufacturing environment. The formalized methodology ensures that assumptions and preferences have to be externalized and justified. In addition, the results of a road‐mapping or project assessment session can be re‐examined at a later date in order to ascertain the reasoning behind decisions taken.

An integrated road‐mapping methodology is presented which utilizes both financial and non‐financial (including intangible) factors to provide guidance and enable the objective selection and assessment of a portfolio of technology projects. This software‐supported methodology has been applied successfully in high technology manufacturing companies.

This paper discusses research on machining and repairing of turbomachinery components which are generally complex geometries and made up of difficult to machine materials (nickel super alloys or titanium alloys).

The approaches, methods and methodologies used for machining and repairing of blades are reviewed as well as the comparisons between them are made.

Particularly, the most recent blade machining and repair techniques using high flexible machine tools and industrial robots, are mentioned.

The limitation of the approaches, methods and methodologies are given and supported by real practical application examples.

This paper presents a state of the art review of research in machining and repairing of turbomachinery components, which have been mainly done in the last decade. The paper act as a reference, gathering the works about turbomachinery components from a manufacturing point of view.

Reports on ongoing research in developing generative process planning systems. Presents a feature‐based model for describing component geometry and its structural aspects (connectivity) and a hierarchical structure for form features definition and classification, as well as methods for representing the capabilities of machine tools. These models form the basis for decision making in the prototype process planning system GENPLAN. As an example, reports on how the models are being used for reasoning about component geometry and a linguistic approach that is adopted for machine tool selection in process planning.

The purpose of this paper is to provide a management tool to maximise the effective time-to-market of a portfolio given the competitive monitoring activities.

From the constant monitoring of competition and market needs, it is proposed to define a time-to-need, time when the market may consume the product under development and competitor will not provide a solution before. This time-to-need is proposed to be defined by an expert committee in a periodical meeting of the portfolio. Once it is identified the time-to-need and the time-to-market (project management), it is possible to manage resources in order to maximise the portfolio outputs.

The application of the mentioned approach in an automotive industry showed improvements on number of launched new products per year (double) and on number of patented product launched (four times more).

This approach applies on projects of medium to long term (more than two years) because the resource management can consume set up time. The presented results in this work were based in a single case, which can limit the expected results of the application of this methodology.

This approach enables a constant alignment among experts and a better deployment of resources.

This work provides a practical tool to promote better resource allocation in a portfolio. It can also be an enabler of innovation projects once it finds resources potential to fund the more front end work.

In this paper manufacturing responsiveness is related to the ability of manufacturing systems to utilise its existing resources to make a rapid and balanced response to the predictable and unpredictable changes. Better understanding of the inherent (hidden) flexibility that exists within a manufacturing system can therefore lead to significant improvement in system performance and responsiveness. In the reported research a conceptual framework for representing the capabilities of machine tools and machining facilities using generic capabilities units termed “resource elements” is presented as well as a mathematical basis of calculating the manufacturing system flexibility using the resource elements. Simulations are used to examine manufacturing system performance and compare resource element‐based scheduling with conventional machine‐based approaches. The results show that significant improvements in system performance and the system’s ability to cope with disturbances can be achieved if manufacturing facilities are represented and scheduled based on the resource elements concept.