Dead band adjustment charts with asymmetric off-target costs, deterministic process drift and delayed dynamics
Feed-back control systems are a type of process control method in which a quantitative quality characteristic is measured at regular intervals of time, successive measures are used to estimate the current mean deviation of the quality characteristic from the target and, when necessary, adjustments o...
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Veröffentlicht in: | Journal of the Royal Statistical Society. Series D (The Statistician) 2003-01, Vol.52 (4), p.501-514 |
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description | Feed-back control systems are a type of process control method in which a quantitative quality characteristic is measured at regular intervals of time, successive measures are used to estimate the current mean deviation of the quality characteristic from the target and, when necessary, adjustments of an input compensatory variable are made to bring the estimated process mean back to the target. Important problems to be solved are how often to sample the quality characteristic and when and by how much to apply an adjustment, i.e. to determine the optimal sampling interval, action limits and amount of adjustment. This determination may be done to minimize the long run cost resulting from the combined effects of the costs of taking each observation, of making each adjustment and of being off target. When the adjustment cost is zero, adjustments are made at each sampling point (this is a repeated adjustment scheme). However, when the cost of making an adjustment is important, two different action limits must be chosen and an adjustment must be made only when the current estimate of the mean deviation of the quality characteristic from target jumps outside the action limits (this is a dead band scheme). We consider three situations requiring asymmetric action limits; these are deterministic process drift, asymmetric off-target costs and asymmetric underlying distributions. Deterministic process drift is often caused by tool wear. Asymmetric costs may be due to asymmetric tolerances and/or to different costs of exceeding positive and negative tolerances. Asymmetric distributions sometimes appear in essentially positive quality characteristics. In addition, we consider the possibility that there is a delay in taking appropriate actions; this delay is often caused by the tests and procedures that are required to sample the process. |
doi_str_mv | 10.1046/j.1467-9884.2003.00375.x |
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Important problems to be solved are how often to sample the quality characteristic and when and by how much to apply an adjustment, i.e. to determine the optimal sampling interval, action limits and amount of adjustment. This determination may be done to minimize the long run cost resulting from the combined effects of the costs of taking each observation, of making each adjustment and of being off target. When the adjustment cost is zero, adjustments are made at each sampling point (this is a repeated adjustment scheme). However, when the cost of making an adjustment is important, two different action limits must be chosen and an adjustment must be made only when the current estimate of the mean deviation of the quality characteristic from target jumps outside the action limits (this is a dead band scheme). We consider three situations requiring asymmetric action limits; these are deterministic process drift, asymmetric off-target costs and asymmetric underlying distributions. Deterministic process drift is often caused by tool wear. Asymmetric costs may be due to asymmetric tolerances and/or to different costs of exceeding positive and negative tolerances. Asymmetric distributions sometimes appear in essentially positive quality characteristics. 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Series D (The Statistician)</title><description>Feed-back control systems are a type of process control method in which a quantitative quality characteristic is measured at regular intervals of time, successive measures are used to estimate the current mean deviation of the quality characteristic from the target and, when necessary, adjustments of an input compensatory variable are made to bring the estimated process mean back to the target. Important problems to be solved are how often to sample the quality characteristic and when and by how much to apply an adjustment, i.e. to determine the optimal sampling interval, action limits and amount of adjustment. This determination may be done to minimize the long run cost resulting from the combined effects of the costs of taking each observation, of making each adjustment and of being off target. When the adjustment cost is zero, adjustments are made at each sampling point (this is a repeated adjustment scheme). However, when the cost of making an adjustment is important, two different action limits must be chosen and an adjustment must be made only when the current estimate of the mean deviation of the quality characteristic from target jumps outside the action limits (this is a dead band scheme). We consider three situations requiring asymmetric action limits; these are deterministic process drift, asymmetric off-target costs and asymmetric underlying distributions. Deterministic process drift is often caused by tool wear. Asymmetric costs may be due to asymmetric tolerances and/or to different costs of exceeding positive and negative tolerances. Asymmetric distributions sometimes appear in essentially positive quality characteristics. In addition, we consider the possibility that there is a delay in taking appropriate actions; this delay is often caused by the tests and procedures that are required to sample the process.</description><subject>Capital costs</subject><subject>Cost control</subject><subject>Cost functions</subject><subject>Determinism</subject><subject>Error rates</subject><subject>Exact sciences and technology</subject><subject>Inference from stochastic processes; time series analysis</subject><subject>Integrated moving average model</subject><subject>Linear inference, regression</subject><subject>Long run cost</subject><subject>Long run costs</subject><subject>Mathematics</subject><subject>Minimization of cost</subject><subject>Modeling</subject><subject>Probability and statistics</subject><subject>Process drift</subject><subject>Samples</subject><subject>Sampling interval</subject><subject>Sciences and techniques of general use</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Statistical process control</subject><subject>Statistics</subject><subject>Time series</subject><subject>Time series models</subject><subject>Unit costs</subject><issn>0039-0526</issn><issn>1467-9884</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><recordid>eNqNUUtv1DAQjhBILIV_wMEXOJHUzsNODhxQCwtSBYIWkLhYs_aEOuTRerzq5t_jNNVyRbI1I38Pez4nCRM8E7yUp10mSqnSpq7LLOe8yOJWVXZ4lGyOwONkE0-blFe5fJo8I-o4Fyqvqk0yniNYtoPRMrDdnsKAY2DmGnwgdufCNQOahwGDd4ZNbZsG8L8xMiYK9IZZDOgHNzoKEb_xk0EiZr1rA1s8LfYwY6zzCIMz9Dx50kJP-OKhniTfP7y_OvuYXnzZfjp7d5GashBVKlqzs1I2VjXCNEoBNAYlQBtHktYKVRpeKrMzCppIKUWLSsa5Gw64MwKKk-T16hufdLtHCnpwZLDvYcRpT7pQdc5FWUVivRKNn4g8tvrGuwH8rAXXS8C600uOeslRLwHr-4D1IUpfPdwBZKBvPYzG0T99VeSyyXnkvV15d67H-b_99bfLy_PYRf3LVd9RmPxRX4q8jivC6QrHP8DDEQb_R0u1OP38vNW_iu3VV1n80HnxF3WJqXc</recordid><startdate>20030101</startdate><enddate>20030101</enddate><creator>LUCENO, Alberto</creator><general>Blackwell Publishing</general><general>Blackwell Publishers</general><general>Blackwell</general><scope>BSCLL</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope></search><sort><creationdate>20030101</creationdate><title>Dead band adjustment charts with asymmetric off-target costs, deterministic process drift and delayed dynamics</title><author>LUCENO, Alberto</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4315-1fcbd669d791c977aa9ce6aaf1466dd174c047cbc7a979141fe7603790aebc1a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Capital costs</topic><topic>Cost control</topic><topic>Cost functions</topic><topic>Determinism</topic><topic>Error rates</topic><topic>Exact sciences and technology</topic><topic>Inference from stochastic processes; time series analysis</topic><topic>Integrated moving average model</topic><topic>Linear inference, regression</topic><topic>Long run cost</topic><topic>Long run costs</topic><topic>Mathematics</topic><topic>Minimization of cost</topic><topic>Modeling</topic><topic>Probability and statistics</topic><topic>Process drift</topic><topic>Samples</topic><topic>Sampling interval</topic><topic>Sciences and techniques of general use</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Statistical process control</topic><topic>Statistics</topic><topic>Time series</topic><topic>Time series models</topic><topic>Unit costs</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>LUCENO, Alberto</creatorcontrib><collection>Istex</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><jtitle>Journal of the Royal Statistical Society. 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Series D (The Statistician)</jtitle><date>2003-01-01</date><risdate>2003</risdate><volume>52</volume><issue>4</issue><spage>501</spage><epage>514</epage><pages>501-514</pages><issn>0039-0526</issn><eissn>1467-9884</eissn><coden>STTNAP</coden><abstract>Feed-back control systems are a type of process control method in which a quantitative quality characteristic is measured at regular intervals of time, successive measures are used to estimate the current mean deviation of the quality characteristic from the target and, when necessary, adjustments of an input compensatory variable are made to bring the estimated process mean back to the target. Important problems to be solved are how often to sample the quality characteristic and when and by how much to apply an adjustment, i.e. to determine the optimal sampling interval, action limits and amount of adjustment. This determination may be done to minimize the long run cost resulting from the combined effects of the costs of taking each observation, of making each adjustment and of being off target. When the adjustment cost is zero, adjustments are made at each sampling point (this is a repeated adjustment scheme). However, when the cost of making an adjustment is important, two different action limits must be chosen and an adjustment must be made only when the current estimate of the mean deviation of the quality characteristic from target jumps outside the action limits (this is a dead band scheme). We consider three situations requiring asymmetric action limits; these are deterministic process drift, asymmetric off-target costs and asymmetric underlying distributions. Deterministic process drift is often caused by tool wear. Asymmetric costs may be due to asymmetric tolerances and/or to different costs of exceeding positive and negative tolerances. Asymmetric distributions sometimes appear in essentially positive quality characteristics. In addition, we consider the possibility that there is a delay in taking appropriate actions; this delay is often caused by the tests and procedures that are required to sample the process.</abstract><cop>Oxford, UK</cop><pub>Blackwell Publishing</pub><doi>10.1046/j.1467-9884.2003.00375.x</doi><tpages>14</tpages></addata></record> |
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source | JSTOR Mathematics and Statistics; EBSCOhost Business Source Complete; Alma/SFX Local Collection; JSTOR |
subjects | Capital costs Cost control Cost functions Determinism Error rates Exact sciences and technology Inference from stochastic processes time series analysis Integrated moving average model Linear inference, regression Long run cost Long run costs Mathematics Minimization of cost Modeling Probability and statistics Process drift Samples Sampling interval Sciences and techniques of general use Statistical analysis Statistical methods Statistical process control Statistics Time series Time series models Unit costs |
title | Dead band adjustment charts with asymmetric off-target costs, deterministic process drift and delayed dynamics |
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