Adaptive CUSUM charts, referred to as ACUSUM charts, have attracted considerable attention from the research community. By adjusting the reference parameter k dynamically, an ACUSUM chart may achieve a better performance over a range of mean shifts than conventional CUSUM charts that are designed for maximal detection effectiveness at a particular level of process shift. This article studies a new feature of the ACUSUM chart related to an additional charting parameter w, i.e., the exponential of the sample mean shift in (x _t – μ ₀) ^w . The ACUSUM chart can be enhanced by adapting this parameter w according to the on-line estimated value of the mean shift, in conjunction with the reference parameter k. The testing cases reveal that this new adaptive CUSUM chart not only outperforms the earlier ACUSUM chart to a substantial degree, but also works as well as the most effective combined schemes consisting of a few CUSUM and/or charts. Furthermore, this enhanced ACUSUM chart is easier to design and implement in a computerized environment compared with those combined schemes. In addition, a general-purpose optimization algorithm is proposed to assist the designs of various CUSUM charts. This paper demonstrates that this algorithm can significantly improve the performance of many CUSUM charts over the entire process shift range. Moreover, a systematic performance comparison of eight CUSUM charts is presented. The findings from this comparison are useful aids for SPC practitioners to select an appropriate CUSUM chart for real applications. [Supplementary materials are available for this article. Go to the publisher's online edition of IIE Transactions for the following free supplementary resource: Appendix]

Big consumer data provide new opportunities for business administrators to explore the value to fulfil customer requirements (CRs). Generally, they are presented as purchase records, online behaviour, etc. However, distinctive characteristics of big data, Volume, Variety, Velocity and Value or ‘4Vs’, lead to many conventional methods for customer understanding potentially fail to handle such data. A visible research gap with practical significance is to develop a framework to deal with big consumer data for CRs understanding. Accordingly, a research study is conducted to exploit the value of these data in the perspective of product designers. It starts with the identification of product features and sentiment polarities from big consumer opinion data. A Kalman filter method is then employed to forecast the trends of CRs and a Bayesian method is proposed to compare products. The objective is to help designers to understand the changes of CRs and their competitive advantages. Finally, using opinion data in Amazon.com, a case study is presented to illustrate how the proposed techniques are applied. This research is argued to incorporate an interdisciplinary collaboration between computer science and engineering design. It aims to facilitate designers by exploiting valuable information from big consumer data for market-driven product design.