Design for Six Sigma Digital Model for Manufacturing Process Design

The transition to digital manufacturing has become more important as the quantity and quality of the use of computer systems in manufacturing companies has increased. It has become necessary to model, simulate and analyse all machines, tools, and raw materials to optimise the manufacturing process. It is even better to determine the best possible solution at the stage of defining the manufacturing process by using technologies that analyse data from simulations to calculate an optimal design before it is even built. In this paper, Design for Six Sigma (DFSS) principles are applied to analyse different scenarios using digital twin models for simulation to determine the best configuration for the manufacturing system. The simulation results were combined with multi-criteria decision-making (MCDM) methods to define a model with the best possible overall equipment effectiveness (OEE). The OEE parameter reliability was identified as the most influential factor in the final determination of the most effective and economical manufacturing process configuration

Multi-Criteria Model for the Selection of New Process Equipment in Casting Manufacturing: A Case Study

The decision to introduce new process equipment is always a complex and risky process. An important competitive advantage in the automotive industry is the integration of new process equipment into the production system using different decision-making models composed of measurable key indicators, scientific methods and empirical values, study defines a multi-criteria model based on various scientific sources combined with extensive manufacturing experience, which is an important tool for making quality decisions when introducing new equipment or technology. The study considers five main areas of indicators and characteristics: internal, external, financial, knowledge level, and other important indicators. By applying the Delphi method, the importance of the indicators is defined by multidisciplinary experts and the order of each characteristic is determined, which is then prioritized using MCDM methods to select the final solution. The paper presents the model definition and a practical application of the model to the castings manufacturing in the automotive industry