Statistical Control by Monitoring and Adjustment

Box, George E. P.; Luceño, Alberto; Paniagua‐Quiñones, María Del Carmen

doi:10.1002/9781118164532

Cited by 137 publications

(158 citation statements)

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“…The phase planes each have associated efficient frontiers, which arise from the constrained optimisation problem. This new conceptual framework enables us to extend the previous work of, for example, Box and Lucerno ( [14]) by applying methods of statistical process control such as feedback mechanisms to any forecasting method rather than just simple exponential smoothing.…”

Section: Introductionmentioning

confidence: 93%

Process control in an agile supply chain network

Pearson

Masson

Swain

2010

International Journal of Production Economics

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The work comprises a new theoretical development applied to aid decision making in an increasingly important commercial sector. Agile supply, where small volumes of high margin, short life cycle innovative products are offered, is increasingly carried out through a complex global supply chain network. We outline an equilibrium solution in such a supply chain network, which works through limited cooperation and coordination along edges (links) in the network.The links constitute the stochastic modelling entities rather than the nodes of the network. We utilise newly developed phase plane analysis to identify, model and predict characteristic behaviour in supply chain networks. The phase plane charts profile the flow of inventory and identify out of control conditions. They maintain quality within the network, as well as intelligently track the way the network evolves in conditions of changing variability. The methodology is essentially distribution free, relying as it does on the study of forecasting errors, and can be used to examine contractual details as well as strategic and game theoretical concepts between decision-making components (agents) of a network. We illustrate with typical data drawn from supply chain agile fashion products.

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Section: Introductionmentioning

confidence: 93%

Process control in an agile supply chain network

Pearson

Masson

Swain

2010

International Journal of Production Economics

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“…However, a critical change-point is the point where a major shift in the trend is recognised. There are several approaches to performing a trend analyses and 'changepoint analyses' (Page, 1955;Chow, 1960;Page, 1957;Hinkley, 1971;Hinkley and Schechtman, 1987;Pettitt, 1980;Box and Luceño, 1997;Sibanda and Sibanda, 2007;Fujisaki et al 2008;Taylor, 2000a;Taylor, 2011 Here, the CUSUMs are not the CUSUMs of the values instead they are the CUSUMs of differences between the values and the average. "These differences sum up to zero, so the CUSUM always ends at zero (S43=0)" (Taylor, 2000a).…”

Section: Stages Of Socioeconomic Transitionmentioning

confidence: 99%

“…According to the results of this particular technique in our study, we have not found absolute convergence yet, in any of the indicators selected in this study. Reply: There are several approaches to perform a trend analyses and 'changepoint analyses' (Page, 1955;Chow, 1960;Page, 1957;Hinkley, 1971;Hinkley and Schechtman, 1987;Pettitt, 1980;Box and Luceño, 1997;Sibanda and Sibanda, 2007;Fujisaki et al 2008;Taylor, 2000a;Taylor, 2011). The Chow test is a statistical and econometric test of whether the coefficients in two linear regressions on different data sets are equal or The Chow test, commonly used in econometrics to test for structural breaks.…”

Section: Replymentioning

confidence: 99%

Demographic Convergence and its Linkage with Health Inequalities in India

Goli

2014

SSRN Journal

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“…Lu and Reynolds (1999) investigate the steady state ARL of cumulative sum (CUSUM), EWMA, and Shewhart control charts for auto correlated data modeled as a first order autoregressive process plus an additional random error term. Last, Box and Luceno (1997) considering quality monitoring by feedback adjustment from additional laboratory test and hospital information.…”

Section: Univariate (Shewhart) Control Chartsmentioning

confidence: 99%

The quality movement in hospital care

Jarrett

2013

Qual Quant

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The purpose is to introduce the demand for the quality movement practice in hospital care. . We show both the need and application of quality monitoring, especially the need monitoring activities having auto correlated data flows of which there are many in the hospital environment. The goal is to control the flow of quality care data in the dynamic behavior of these systems of acre in hospitals. These monitoring systems are designed to control and improve changes in the hospital care environment. AbstractThe purpose is to introduce the demand for the quality movement practice in hospital care. . We show both the need and application of quality monitoring, especially the need monitoring activities having auto correlated data flows of which there are many in the hospital environment. The goal is to control the flow of quality care data in the dynamic behavior of these systems of acre in hospitals. These monitoring systems are designed to control and improve changes in the hospital care environment.

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Statistical Control by Monitoring and Adjustment

Cited by 137 publications

References 0 publications

Process control in an agile supply chain network

Process control in an agile supply chain network

Demographic Convergence and its Linkage with Health Inequalities in India

The quality movement in hospital care

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