During engineering processes, there is a high error rate during the production of individual parts caused by the high number of used parts, their possible combinations as well as design changes and adaptions. Flawed parts cannot be used in the production process and must be discarded. Although a professional team supervises quality assurance in design change processes, this process results in enormous follow-up costs covering the disposal of flawed parts, moulds and tools. Thus, the process is slowed down considerably while the costs for staff and material rise. This situation is caused by the lack of planning security for manual and partially compatible processes and the consequent need for an efficient automatic supervisory body.
The core to KENDAXA’s solution is a learning mechanism based on the existing production data. KENDAXA prepares the available data extracted from the production silos and creates a standardised escalation and control structure covering all departments. This mechanism will identify and eliminate flawed plans relying on data aggregation as well as historical data and data profiling. In examining these processes’ functionality based on existing historical production data, the learning mechanism achieves the optimisation of the production-pricing-ratio. Consequently, this optimisation, combined with intelligent controlling, causes the reduction of staff costs for quality assurance of design change processes and a reduced error rate in the list of components
Based on the optimised dataset and its control, errors can be eradicated early, and flawed production is drastically reduced. Additionally, staff costs are cut, mobilising and optimising the use of the available capacities and resources.
Error-related Costs p.a.