Inventory Policy with Parametric Demand: Operational Statistics, Linear Correction, and Regression
In this paper, we consider data‐driven approaches to the problem of inventory control. We first consider the approach of operational statistics and review related results which enable us to maximize a priori expected profit uniformly over all parameter values, when the demand distribution is known u...
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Veröffentlicht in: | Production and operations management 2012-03, Vol.21 (2), p.291-308 |
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description | In this paper, we consider data‐driven approaches to the problem of inventory control. We first consider the approach of operational statistics and review related results which enable us to maximize a priori expected profit uniformly over all parameter values, when the demand distribution is known up to the location and scale parameters. For the case of the unknown shape parameter, we first suggest a heuristic approach based on operational statistics to obtain improved ordering policies and illustrate the same for the case of a Pareto demand distribution. In more general cases where the heuristic is not applicable, we suggest linear correction and support vector regression approaches to better estimate ordering policies, and illustrate these using a Gamma demand distribution. In certain cases, our proposed approaches are found to yield significant improvements. |
doi_str_mv | 10.1111/j.1937-5956.2011.01261.x |
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Oper. Manag</addtitle><description>In this paper, we consider data‐driven approaches to the problem of inventory control. We first consider the approach of operational statistics and review related results which enable us to maximize a priori expected profit uniformly over all parameter values, when the demand distribution is known up to the location and scale parameters. For the case of the unknown shape parameter, we first suggest a heuristic approach based on operational statistics to obtain improved ordering policies and illustrate the same for the case of a Pareto demand distribution. In more general cases where the heuristic is not applicable, we suggest linear correction and support vector regression approaches to better estimate ordering policies, and illustrate these using a Gamma demand distribution. In certain cases, our proposed approaches are found to yield significant improvements.</description><subject>demand ambiguity</subject><subject>Estimates</subject><subject>Expected values</subject><subject>Inventory</subject><subject>Inventory control</subject><subject>Inventory management</subject><subject>Linear programming</subject><subject>model uncertainty</subject><subject>newsvendor model</subject><subject>operational statistics</subject><subject>Order quantity</subject><subject>Regression analysis</subject><subject>Standard deviation</subject><subject>Studies</subject><issn>1059-1478</issn><issn>1937-5956</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNkF9PgzAUxYnRxDn9Do3Pgi2lLfhgYqZT43SLf6JvTekuE2QwW3Tj21vE6Kt96W3uOefe_jwPERwQd46LgCRU-CxhPAgxIQEmISfBZssb_Da2XY1Z4pNIxLvenrUFxljQEA-89Lr6hKqpTYtmdZnrFq3z5hXNlFFLaEyu0TksVTU_QdMVGNXkdaVK9NC4yja5tkdoklegDBrVxoDu-kfI6dE9LAxY69773k6mSgsHP_fQexpfPI6u_Mn08np0NvF1xDjxCQZOdSZSpdWcZjGNQMQ4TDEPVRqLjAPjmqhUMzHXOFKKaWCaJyntPhMxOvQO-9yVqd8_wDayqD-MW9fKJKGhoJyEThT3Im1qaw1kcmXypTKtJFh2QGUhO26y4yY7oPIbqNw462lvXecltP_2ydn09qErXQDrA6xawN9y_xjs9z6HHDa_g5V5k1xQweTz3aUULwyPb8N7eUO_ADkznEE</recordid><startdate>201203</startdate><enddate>201203</enddate><creator>Ramamurthy, Vivek</creator><creator>George Shanthikumar, J.</creator><creator>Shen, Zuo-Jun Max</creator><general>Blackwell Publishing Ltd</general><general>SAGE Publications</general><general>Blackwell Publishers Inc</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>0U~</scope><scope>1-H</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8AO</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>K60</scope><scope>K6~</scope><scope>L.-</scope><scope>L.0</scope><scope>M0C</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>S0X</scope></search><sort><creationdate>201203</creationdate><title>Inventory Policy with Parametric Demand: Operational Statistics, Linear Correction, and Regression</title><author>Ramamurthy, Vivek ; George Shanthikumar, J. ; Shen, Zuo-Jun Max</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4561-10e63cf7bacad3f834e7802b062ab87f6e56c1abc57dc04aa5ce5c69b30007453</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>demand ambiguity</topic><topic>Estimates</topic><topic>Expected values</topic><topic>Inventory</topic><topic>Inventory control</topic><topic>Inventory management</topic><topic>Linear programming</topic><topic>model uncertainty</topic><topic>newsvendor model</topic><topic>operational statistics</topic><topic>Order quantity</topic><topic>Regression analysis</topic><topic>Standard deviation</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ramamurthy, Vivek</creatorcontrib><creatorcontrib>George Shanthikumar, J.</creatorcontrib><creatorcontrib>Shen, Zuo-Jun Max</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Global News & ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>ABI/INFORM Global</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><jtitle>Production and operations management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ramamurthy, Vivek</au><au>George Shanthikumar, J.</au><au>Shen, Zuo-Jun Max</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Inventory Policy with Parametric Demand: Operational Statistics, Linear Correction, and Regression</atitle><jtitle>Production and operations management</jtitle><addtitle>Prod. Oper. Manag</addtitle><date>2012-03</date><risdate>2012</risdate><volume>21</volume><issue>2</issue><spage>291</spage><epage>308</epage><pages>291-308</pages><issn>1059-1478</issn><eissn>1937-5956</eissn><coden>POMAEN</coden><abstract>In this paper, we consider data‐driven approaches to the problem of inventory control. We first consider the approach of operational statistics and review related results which enable us to maximize a priori expected profit uniformly over all parameter values, when the demand distribution is known up to the location and scale parameters. For the case of the unknown shape parameter, we first suggest a heuristic approach based on operational statistics to obtain improved ordering policies and illustrate the same for the case of a Pareto demand distribution. In more general cases where the heuristic is not applicable, we suggest linear correction and support vector regression approaches to better estimate ordering policies, and illustrate these using a Gamma demand distribution. In certain cases, our proposed approaches are found to yield significant improvements.</abstract><cop>Los Angeles, CA</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1111/j.1937-5956.2011.01261.x</doi><tpages>18</tpages></addata></record> |
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source | Wiley Journals; Business Source Complete; SAGE Complete A-Z List |
subjects | demand ambiguity Estimates Expected values Inventory Inventory control Inventory management Linear programming model uncertainty newsvendor model operational statistics Order quantity Regression analysis Standard deviation Studies |
title | Inventory Policy with Parametric Demand: Operational Statistics, Linear Correction, and Regression |
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