Suppliers Selection In Manufacturing Industries And Associated Multi-Objective Desicion Making Methods: Past, Present And The Future

Nowadays, many manufacturing companies have decided to use other companies’ competencies and outsource part of their manufacturing processes and business to suppliers globally in order to reduce costs, improve quality of products, explore or expand new markets, and offer better services to customers, etc. The decisions have rendered manufacturing organizations with new challenges. Organizations need to evaluate their suppliers' performance, and take account of their weakness and strength in order to win and survive in highly competitive global marketplaces. Hence, suppliers evaluation and selection are taken as an important strategy for manufactring enterprises. This paper aims to provide a comprehensive and critical review on suppliers selection and the formulation of different criteria for suppliers selection, the associated multi-objctive decision makings, selecion algorithms, and their implementation and application perspectives. Furthermore, individual and integrated suppliers selection approaches are presented, including Analytic hierarchy process (AHP), Analytic network process (ANP), and Mathematical programming (MP). Linear programming (LP), Integer programming (IP), Data envelopment analysis (DEA) and Goal programming (GP) are discussed with in-depth. The paper concludes with further discussion on the potential and application of suppliers selection approach for the broad manufacturing industry

Manufacturing approach to production changeover complexity reduction and its implementation through smart surfaces and process optimization

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonManufacturing approach to complexity reduction and its implementation through smart s and process optimizationIn the last two decades or so, we have seen rapid development in sustainable manufacturing and technologies, which have come to play an increasingly vital role in the manufacturing process improvement and agility enhancement at a manufacturing company. Manufacturers have to responsively compete in the market with a sustainable manner. The main aims of most production facilities is to minimize manufacturing costs while addressing the variety and quality needs of the products. This necessity endorses the importance of flexibility, reconfiguration and responsiveness. To be responsive to the customers’ dynamic needs and reduce production costs, manufacturers often have to produce a variety of products on a single production system but further supported with technical means on agility and sustainability. It often takes time and resources to switch from one product to another on the same production system. Producing a variety of products on a single production system also increases the manufacturing complexity associated with the system and processes. Modern complex products or equipment may have thousands of parts and take a tedious number of manufacturing/assembly steps to make these products. The setup time and resources used in the changeover process are a completely loss while there is no production taking place. For sustainable manufacturing there is an immediate need to eliminate or minimize these loses due to cleaning, changeover and setup while the manufacturing system and production line being shut down. This can be overcome by scientific analysis and understanding of each of the steps of production setup, and some sustainable techniques can be applied to reduce setup time/changeover and improve sustainability of the manufacturing system/process. It is essentially important to investigate the design of a sustainable manufacturing system and the underlying complexity in a scientific manner, so to minimize the production changeover and greatly enhance the productivity and efficiency from the view point of sustainability while supported by multiscale modelling and analysis. This doctoral research aims to investigate the key bottleneck issues in a food packaging manufacturing system through the multiscale sustainable manufacturing approach and the associated implementation perspectives. The approach is described in details in chapter 3 and chapter 4. The research is focused on design of smart surfaces applicable to the packaging equipment and its impact on reducing production changeover and complexity towards a sustainable manufacturing system, which is thoroughly undertaken in light of multiscale modelling and analysis and system engineering simulations. A food manufacturing case study is conducted and actual data is used in liaison with an industrial partner company. In the study, three aspects are considered in production changeover both qualitatively and quantitatively, including reduction of complexity, cleaning of the equipment/machines, and sustainability. Different aspects of the complexity are discussed and explored, and corresponding experiments carried out but focused on using different micro surface structures. Most of the time consumed during changeover is on the cleaning of metal conveyors or machines. Therefore, metal surface structures in micro scale are studied in-depth. A self-cleaning property of ultra-hydrophobic surfaces is investigated and applied to reduce the frequency of the cleaning on the conveyor panel surfaces and thus to reduce the time consumed on their cleaning. Process mapping and facility layout are also studied and discussed during this doctoral study to improve the production changeover process at the macro scale. Additionally, recommendations for automation are made and explored to improve the manufacturing facility performance. A new simulation model is developed for the dedicated food packaging manufacturing system, which can be used as a ‘virtual factory’ and to help model the existing production setup and the process optimization while with the underlying thinking on the scales of both macro and micro combined in a sustainable manufacturing manner