@article{
 title = {Inventory management with dynamic Bayesian network software systems},
 type = {article},
 year = {2011},
 pages = {290-300},
 volume = {87 LNBIP},
 websites = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960252796&partnerID=40&md5=eb146c853a01e790065fb5811e2e0a32},
 id = {d7d8ebc6-d8f6-3556-91db-d14de95f4b38},
 created = {2015-04-22T21:36:21.000Z},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {b1fc9654-2f96-39de-a2ca-bc6069a08a77},
 last_modified = {2017-03-14T13:43:36.285Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Inventory management at a single or multiple levels of a supply chain is usually performed with computations such as Economic Order Quantity or Markov Decision Processes. The former makes many unrealistic assumptions and the later requires specialist Operations Research knowledge to implement. Dynamic Bayesian networks provide an alternative framework which is accessible to non-specialist managers through off-the-shelf graphical software systems. We show how such systems may be deployed to model a simple inventory problem, and learn an improved solution over EOQ. We discuss how these systems can allow managers to model additional risk factors throughout a supply chain through intuitive, incremental extensions to the Bayesian networks. © 2011 Springer-Verlag.},
 bibtype = {article},
 author = {Taylor, M.a and Fox, C.b},
 doi = {10.1007/978-3-642-21863-7_25},
 journal = {Lecture Notes in Business Information Processing}
}

{"_id":"QowLKiSntFMjdRRmL","authorIDs":[],"author_short":["Taylor, M.","Fox, C."],"bibbaseid":"taylor-fox-inventorymanagementwithdynamicbayesiannetworksoftwaresystems-2011","bibdata":{"title":"Inventory management with dynamic Bayesian network software systems","type":"article","year":"2011","pages":"290-300","volume":"87 LNBIP","websites":"http://www.scopus.com/inward/record.url?eid=2-s2.0-79960252796&partnerID=40&md5=eb146c853a01e790065fb5811e2e0a32","id":"d7d8ebc6-d8f6-3556-91db-d14de95f4b38","created":"2015-04-22T21:36:21.000Z","file_attached":false,"profile_id":"95e10851-cdf3-31de-9f82-1ab629e601b0","group_id":"b1fc9654-2f96-39de-a2ca-bc6069a08a77","last_modified":"2017-03-14T13:43:36.285Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"private_publication":false,"abstract":"Inventory management at a single or multiple levels of a supply chain is usually performed with computations such as Economic Order Quantity or Markov Decision Processes. The former makes many unrealistic assumptions and the later requires specialist Operations Research knowledge to implement. Dynamic Bayesian networks provide an alternative framework which is accessible to non-specialist managers through off-the-shelf graphical software systems. We show how such systems may be deployed to model a simple inventory problem, and learn an improved solution over EOQ. We discuss how these systems can allow managers to model additional risk factors throughout a supply chain through intuitive, incremental extensions to the Bayesian networks. © 2011 Springer-Verlag.","bibtype":"article","author":"Taylor, M.a and Fox, C.b","doi":"10.1007/978-3-642-21863-7_25","journal":"Lecture Notes in Business Information Processing","bibtex":"@article{\n title = {Inventory management with dynamic Bayesian network software systems},\n type = {article},\n year = {2011},\n pages = {290-300},\n volume = {87 LNBIP},\n websites = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960252796&partnerID=40&md5=eb146c853a01e790065fb5811e2e0a32},\n id = {d7d8ebc6-d8f6-3556-91db-d14de95f4b38},\n created = {2015-04-22T21:36:21.000Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {b1fc9654-2f96-39de-a2ca-bc6069a08a77},\n last_modified = {2017-03-14T13:43:36.285Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Inventory management at a single or multiple levels of a supply chain is usually performed with computations such as Economic Order Quantity or Markov Decision Processes. The former makes many unrealistic assumptions and the later requires specialist Operations Research knowledge to implement. Dynamic Bayesian networks provide an alternative framework which is accessible to non-specialist managers through off-the-shelf graphical software systems. We show how such systems may be deployed to model a simple inventory problem, and learn an improved solution over EOQ. We discuss how these systems can allow managers to model additional risk factors throughout a supply chain through intuitive, incremental extensions to the Bayesian networks. © 2011 Springer-Verlag.},\n bibtype = {article},\n author = {Taylor, M.a and Fox, C.b},\n doi = {10.1007/978-3-642-21863-7_25},\n journal = {Lecture Notes in Business Information Processing}\n}","author_short":["Taylor, M.","Fox, C."],"urls":{"Website":"http://www.scopus.com/inward/record.url?eid=2-s2.0-79960252796&partnerID=40&md5=eb146c853a01e790065fb5811e2e0a32"},"biburl":"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0","bibbaseid":"taylor-fox-inventorymanagementwithdynamicbayesiannetworksoftwaresystems-2011","role":"author","metadata":{"authorlinks":{}},"downloads":5},"bibtype":"article","biburl":"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0","creationDate":"2015-04-23T01:31:49.490Z","downloads":5,"keywords":[],"search_terms":["inventory","management","dynamic","bayesian","network","software","systems","taylor","fox"],"title":"Inventory management with dynamic Bayesian network software systems","year":2011,"dataSources":["8hwkQbNZz66Dc7LzM","ya2CyA73rpZseyrZ8","2252seNhipfTmjEBQ"]}