Documents disponibles écrits par cet auteur

Content analysis of OR job advertisements to infer required skills / Sodhi, M. S. in Journal of the operational research society (JORS), Vol. 61 N° 9 (Septembre 2010) 

Public ISBD [article]  in Journal of the operational research society (JORS) > Vol. 61 N° 9 (Septembre 2010) . - pp. 1315–1327 Titre : Content analysis of OR job advertisements to infer required skills Type de document : texte imprimé Auteurs : Sodhi, M. S., Auteur ; B-G. Son, Auteur Année de publication : 2011 Article en page(s) : pp. 1315–1327 Note générale : Recherche opérationnelle Langues : Anglais (eng) Mots-clés : Content  analysis  Manpower  planning  Statistics  Education Index. décimale : 001.424 Résumé : The paper presents an empirical method to infer employers' requirements of operational research (OR) skills by analysing the text of online job ads using content analysis. The method entailed collecting more than a thousand job ads from online sources, creating a hierarchy of sets of OR-related words and phrases, and then analysing the job ads using content analysis software to count the numbers of ads using the hierarchy of words and phrases. The method is particularly well suited for the periodic analysis of job ads to understand changes and trends, and is replicable in that, when carried out using our proposed keywords, it would yield the same results for any set of OR job ads. DEWEY : 001.424 ISSN : 0361-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v61/n9/abs/jors200980a.html [article] Content analysis of OR job advertisements to infer required skills [texte imprimé] / Sodhi, M. S., Auteur ; B-G. Son, Auteur . - 2011 . - pp. 1315–1327. Recherche opérationnelle Langues : Anglais (eng) in Journal of the operational research society (JORS) > Vol. 61 N° 9 (Septembre 2010) . - pp. 1315–1327 Mots-clés : Content  analysis  Manpower  planning  Statistics  Education Index. décimale : 001.424 Résumé : The paper presents an empirical method to infer employers' requirements of operational research (OR) skills by analysing the text of online job ads using content analysis. The method entailed collecting more than a thousand job ads from online sources, creating a hierarchy of sets of OR-related words and phrases, and then analysing the job ads using content analysis software to count the numbers of ads using the hierarchy of words and phrases. The method is particularly well suited for the periodic analysis of job ads to understand changes and trends, and is replicable in that, when carried out using our proposed keywords, it would yield the same results for any set of OR job ads. DEWEY : 001.424 ISSN : 0361-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v61/n9/abs/jors200980a.html

Determining supply requirement in the sales-and-operations-planning (S&OP) process under demand uncertainty: a stochastic programming formulation and a spreadsheet implementation / Sodhi, M. S. in Journal of the operational research society (JORS), Vol. 62 N° 3 Special issue (Mars 2011) 

Public ISBD [article]  in Journal of the operational research society (JORS) > Vol. 62 N° 3 Special issue (Mars 2011) . - pp. 526–536 Titre : Determining supply requirement in the sales-and-operations-planning (S&OP) process under demand uncertainty: a stochastic programming formulation and a spreadsheet implementation Type de document : texte imprimé Auteurs : Sodhi, M. S., Auteur ; Tang, C. S., Auteur Année de publication : 2011 Article en page(s) : pp. 526–536 Note générale : Recherche opérationnelle Langues : Anglais (eng) Mots-clés : Stochastic  programming  Supply-chain  planning  Demand  uncertainty  Sales-and-operations  planning  (S&OP)  Conditional  value-at-risk  (cVaR)  Spreadsheet Index. décimale : 001.424 Résumé : We show how to extend the demand-planning stage of the sales-and-operations-planning (S&OP) process with a spreadsheet implementation of a stochastic programming model that determines the supply requirement while optimally trading off risks of unmet demand, excess inventory, and inadequate liquidity in the presence of demand uncertainty. We first present the model that minimizes the weighted sum of respective conditional value-at-risk (cVaR) metrics over demand scenarios in the form of a binomial tree. The output of this model is the supply requirement to be used in the supply-planning stage of the S&OP process. Next we show how row-and-column aggregation of the model reduces its size from exponential (2T) in the number of time periods T in the planning horizon to merely square (T2). Finally, we demonstrate the tractability of this aggregated model in an Excel spreadsheet implementation with a numerical example with 26 time periods. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v62/n3/abs/jors201093a.html [article] Determining supply requirement in the sales-and-operations-planning (S&OP) process under demand uncertainty: a stochastic programming formulation and a spreadsheet implementation [texte imprimé] / Sodhi, M. S., Auteur ; Tang, C. S., Auteur . - 2011 . - pp. 526–536. Recherche opérationnelle Langues : Anglais (eng) in Journal of the operational research society (JORS) > Vol. 62 N° 3 Special issue (Mars 2011) . - pp. 526–536 Mots-clés : Stochastic  programming  Supply-chain  planning  Demand  uncertainty  Sales-and-operations  planning  (S&OP)  Conditional  value-at-risk  (cVaR)  Spreadsheet Index. décimale : 001.424 Résumé : We show how to extend the demand-planning stage of the sales-and-operations-planning (S&OP) process with a spreadsheet implementation of a stochastic programming model that determines the supply requirement while optimally trading off risks of unmet demand, excess inventory, and inadequate liquidity in the presence of demand uncertainty. We first present the model that minimizes the weighted sum of respective conditional value-at-risk (cVaR) metrics over demand scenarios in the form of a binomial tree. The output of this model is the supply requirement to be used in the supply-planning stage of the S&OP process. Next we show how row-and-column aggregation of the model reduces its size from exponential (2T) in the number of time periods T in the planning horizon to merely square (T2). Finally, we demonstrate the tractability of this aggregated model in an Excel spreadsheet implementation with a numerical example with 26 time periods. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v62/n3/abs/jors201093a.html