PurposeDifferent real cases indicate that the quality of a process is better monitored by a functional relationship rather than the traditional statistical process control (SPC) methods. This approach is referred to as profile monitoring. A serious objective in profile monitoring is the sensitivity of a model to very small changes of the process. The rapid progress of the precision manufacturing also indicates the importance of identifying very small shift types of a process/product profile curve. This sensitivity allows one to identify the fault of a process sooner compared to the case of lack of the capability.Design/methodology/approachThis paper proposed a new method to monitor very small shift types of a polynomial profile for phase II of the SPC. The proposed method was named as MGWMA-PF. The performance capability of the proposed approach was evaluated through several numerical examples. A real case study was also used to investigate the capability of the proposed model.FindingsThe results addressed that the proposed method was capable of detecting very small shift types effectively. The numerical report based on the average run length (ARL) term revealed the more sensitivity of the proposed model compared to other existing methods of the literature.Originality/valueThis paper proposes a new method to monitor very small shift types of a polynomial profile for phase II of the SPC. The proposed method provides detecting a very small change manifested itself to the process.
Semiparametric MEWMA for Phase II profile monitoring
