Purpose
– The purpose of this paper is to provide a demonstration of the application of techniques for robust optimization for improvement of the injection moulding processes in an injection moulding small and medium sized enterprise (SME).
Design/methodology/approach
– A critical to quality characteristic (CtQ) which is connected to assembly problems is the subject of investigation. The CtQ is not directly measurable. The variation in a dimension of a product, which is correlated to the CtQ, is studied using design of experiments (DoE) and Taguchi methods. A two-cavity mould is used in the injection moulding process. To evaluate the robustness of the process using signal-to-noise analysis, the data were transformed to compensate for the systematic differences between the mould cavities.
Findings
– The initial results showed that finding optimal process parameter settings commonly valid for both cavities was impossible. After a modification of the mould, the experiments were rerun and optimal settings could be found.
Practical implications
– Applying DoE techniques in small and medium-sized injection moulding companies is far from common practice. This case study demonstrates a method to apply DoE with five process parameters which can serve as a standard method to prepare production when a new mould is used for the first time.
Originality/value
– The originality is connected to the combination of the applied methods and, in the context of the case study, carried out in an SME unfamiliar with the power of the applied methods. The value of the paper is to demonstrate the power of the most powerful technique in quality engineering to improve an injection moulding process within the context of SMEs. The authors would accentuate the point that the true power becomes visible when this powerful technique is introduced into an organization with very little understanding of the technique. In addition, the case study is valuable to practitioners because it proposes a new scientific and systematic approach to understand and optimize the start-up of the moulding process.