Downstream performance prediction for a manufacturing system using neural networks and six-sigma improvement techniques

Intelligent techniques have been applied in a range of industrial environments [Meziane F, Vadera S, Kobbacy K, Proudlove N. Intelligent systems in manufacturing: current developments and future prospects. Integrated Manuf Syst 2000;11(4):218–38; Stephanopoulos G, Han C. Intelligent systems in process engineering: a review. Comput Chem Eng, 1996;20 (6–7):743–91; Johnston AB, Maguire LP, McGinnity TM. Using business improvement techniques to inform the optimisation of production cycle time: an industrial case study. Proceedings of the IEEE SMC UK-RI Chapter conference 2004 on intelligent cybernetic systems. September 7–8, 2004 ISSN:1744–9189; Proudlove NC, Vadera S, Kobbacy KAH. Intelligent management systems in operations: A review. J Oper Res Soc, 1998;49(7):682–99] although their implementation is not the first choice of many process engineers. In contrast process engineers in a diverse range of manufacturing environments regularly deploy business improvement techniques, such as the six-sigma methodology. Such techniques aim to control and subsequently identify the relationship between the process inputs and outputs so that a process engineer can more accurately predict how the process output shall perform based on the system inputs. Factors such as cost reduction, automatic process control or simply process prediction may be the defining factors in establishing prediction models.