English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The AIB-SSEWMA chart is an auxiliary information-based (AIB) sum of squares exponentially weighted moving average (SSEWMA) chart using correlated auxiliary information to effectively monitor the shifts in the process mean and/or variability of a normally distributed process. The main focus of this study is proposed to combine an AIB sum of squares generally weighted moving average (AIB-SSGWMA) chart with the properties of the GWMA chart into an AIB-SSEWMA chart. Numerical simulations indicate that the AIB-SSGWMA chart based on average run lengths (ARLs) performs uniformly better than its counterparts, such as the AIB-SSEWMA, SSGWMA, and SSEWMA charts in detecting small process mean and/or variability shifts. In addition, the proposed chart is sensitive to small upward shifts in the mean as well as variability, as compared to the existing AIB-MaxGWMA chart. An example is used to illustrate the efficiency of the proposed AIB-SSGWMA chart in detecting small process shifts.

An enhanced sum of squares generally weighted moving average chart based on auxiliary information for process monitoring