Purpose
This paper presents economic and economic–statistical designs of the adaptive exponentially weighted moving average (AEWMA) control chart for monitoring the process mean. It also aims to compare the effect of estimated process parameters on the economic performance of three charts, which are Shewhart, exponentially weighted moving average and AEWMA control charts with economic–statistical design.
Design/methodology/approach
The optimal parameters of the control charts are obtained by applying the Lorenzen and Vance’s (1986) cost function. Comparisons between the economic–statistical and economic designs of the AEWMA control chart in terms of expected cost and statistical measures are performed. Also, comparisons are made between the economic performance of the three competing charts in terms of the average expected cost and standard deviation of expected cost.
Findings
This paper concludes that taking into account the economic factors and statistical properties in designing the AEWMA control chart leads to a slight increase in cost but in return the improvement in the statistical performance is substantial. In addition, under the estimated parameters case, the comparisons reveal that from the economic point of view the AEWMA chart is the most efficient chart when detecting shifts of different sizes.
Originality/value
The importance of the study stems from designing the AEWMA chart from both economic and statistical points of view because it has not been tackled before. In addition, this paper contributes to the literature by studying the effect of the estimated parameters on the performance of control charts with economic–statistical design.
Composite Indicator is considered the mathematical aggregation which has wide usage for monitoring performances, conducting benchmarks, analyzing policies, and communicating publicly. Human Development Index (HDI) is the most popular index which measures human development through average achievement in its main dimensions: Health status, education status, and living standard but it is faced with several critiques, positive and negative. Moreover, HDI was tested to have a positive and significant correlation with natural resource abundance. Therefore, based on Mathematical Programming approaches, previously tested for Composite Indicators' development, this research proposes a new calculated HDI using a Data Envelopment Analysis approach based on the Goal Programming model; including missing values' estimation. This new proposed HDI was validated through Sensitivity Analysis of Normalization and Weighting methods; in addition to Wilcoxon Signed Rank Test. The first test shows a positive high correlation between the proposed HDIs and the United Nations HDI. Those tests ensure that HDI rankings are highly correlated and that they are unchanged given the different normalization and weighting techniques. Moreover, they reflect that the paired sample mean is not the same. This highlights the advantageous property of the proposed HDI; preserving both the advantages of Goal Programming and Data Envelopment Analysis approaches, in addition to others.
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