Automated process modelling and continuous improvement.
This thesis discusses and demonstrates the benefits of simulating and optimising a manufacturing control system in order to improve flow of production material through a system with high variety low volume output requirements. The need for and factors affecting synchronous flow are also discussed along with the consequences of poor flow and various solutions for overcoming it. A study into and comparison of various planning and control methodologies designed to promote flow of material through a manufacturing system was carried out to identify a suitable system to model. The research objectives are; • Identify the best system to model that will promote flow, • Identify the potential failure mechanisms within that system that exist and have not been yet resolved, • Produce a model that can fully resolve or reduce the probability of the identified failure mechanisms having an effect. This research led to an investigation into the main elements of a Drum-Buffer-Rope (DBR) environment in order to generate a comprehensive description of the requirements for DBR implementation and operation and attempt to improve the limitations that have been identified via the research literature. These requirements have been grouped into three areas, i.e.: a. plant layout and kanban controls, b. planning and control, and c. DBR infrastructure. A DBR model was developed combined with Genetic Algorithms with the aim of maximising the throughput level for an individual product mix. The results of the experiments have identified new knowledge on how DBR processes facilitate and impede material flow synchronisation within high variety/low volume manufacturing environments. The research results were limited to the assumptions made and constraints of the model, this research has highlighted that as such a model becomes more complex it also becomes more volatile and more difficult to control, leading to the conclusions that more research is required by extending the complexity of the model by adding more product mix and system variability to compare results with the results of this research. After which it will be expected that the model will be useful to enable a quick system response to large variations in product demand within the mixed model manufacturing industry.
- PhD