The realization of agile enterprises requires substantial development of the underpinning modelling, information management and knowledge representation technologies. This paper introduces a resource model that has been developed to support the dynamic, aggregate planning of manufacturing operations within large, complex production networks during the formative stages of design. The main goal of aggregate planning is the measurement of product manufacturability and the evaluation of alternative design configurations and manufacturing scenarios, through the allocation of multiple parts to remote facilities within the supply network. The term ‘resource aware’ planning is used to indicate the creation of a dynamic interrelationship between the planning entities (products and processes) and the enterprise resources, including humans and machines. The technologies employed for implementing the pilot methods include the aggregate resource modelling methodology, a web-centric co-development environment, unique methods for enriching planning entities with knowledge and evolutionary computing methods for the rapid generation and optimization of production routes. A pilot resource aware planning system has been implemented that supports many innovative modes of operation, the initial testing of which was very encouraging.
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.
Designers have no way of establishing, and hence controlling, the likely production consequences of design decisions during the early stages of new product introduction. Aggregate process modelling is a newly developed methodology for the identification and manufacturability assessment of production routeings for partially specified product configurations. The novelty of the proposed approach lies in the close integration of process models with existing product and resource information and the provision of feedback regarding manufacturing issues to control downstream design processes. A description of the methods for early estimation of product manufacturability and their potential application in a design support system, able to prevent the progression of design ideas that would be costly, difficult or even impossible to manufacture, is presented in this paper.
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.
This paper describes a novel methodology for optimizing the wide range of early planning choices available for the manufacture of complex products, across a distributed and dynamically changing enterprise. A hybrid simulated annealing and greedy algorithm has been developed to optimize such planning decisions. The hybrid algorithm and associated methods detailed within the paper allow the utilization of key attributes from the core product, process and resource digital models to create an initial valid plan and a dynamic ‘manufacturing phase space’. Within this phase space, the detailed methods, heuristics and algorithms are deployed in order to optimize the initial plan by evaluating alternative processes and resources. The evaluation is based on an alternative's impact on the overall solution obtained by applying user-defined cardinal weightings on quality, cost, delivery and knowledge (QCD + K) metrics. To enable the application of business objectives on QCD + K, methods were developed for the conversion of quality, delivery and knowledge into a cost equivalent. The paper is concluded with an example based upon the collaborating company's aerospace products.
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)
