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The purpose of this paper is to develop a new control chart using two EWMA statistics called the hybrid exponentially weighted moving average (HEWMA) chart to improve the sensitivity of EWMA chart proposed by Zhang et al. (2014). When mean and variance of process are not constants, the use of control chart using coefficient of variation (CV) is a successful approach.

The control chart using EWMA statistics has ability to detect moderate and small shifts in the process. The authors present the designing of the proposed HEWMA statistics control chart called the HEWMA_CV chart based on two hybrid EWMA (HEWMA) statistics. The proposed control chart utilizes the current information and previous information to make decision about the state of control chart.

In this paper, the authors will present the designing of HEWMA statistics control chart called the HEWMA_CV chart. The efficiency of the proposed control chart is shown using the simulated data and real data from the industry. The application of proposed chart on the real data shows that the proposed chart has ability to detect shift in the process and it is superior than existing chart in terms of average run length (ARL).

The design and implementation of the proposed control chart on a real data shows that it can be applied in several industries, such as chemical industry, biological assays, etc.

The practical application of HEWMA chart using coefficient variation is gaining extensive adequacy. The design and implementation of the HEWMA chart offers a new approach in the detection of small process mean shift.

In practice, when mean and variance of process are not constants, the use of control chart using CV is a successful approach. In this paper, the authors designed a new control chart using two EWMA statistics called the HEWMA chart to improve the sensitivity of EWMA chart. The comparison shows that the proposed chart is superior than the existing chart in terms of ARL.