Developing talent from a supply-demand perspective : an optimization model for managers

While executives emphasize that human resources (HR) are a firm’s biggest asset, the level of research attention devoted to planning talent pipelines for complex global organizational environments does not reflect this emphasis. Numerous challenges exist in establishing human resource management str...
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Autor*in:	Moheb-Alizadeh, Hadi [verfasserIn] Handfield, Robert B. - 1964- [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Übergeordnetes Werk:	Enthalten in: Logistics - Basel : MDPI AG, 2017, 1(2017), 1 vom: Sept., Seite 1-29
Übergeordnetes Werk:	volume:1 ; year:2017 ; number:1 ; month:09 ; pages:1-29

Links:	Link aufrufen Link aufrufen Terms of use

DOI / URN:	10.3390/logistics1010005

Katalog-ID:	168249778X

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allfields	10.3390/logistics1010005 doi (DE-627)168249778X (DE-599)KXP168249778X DE-627 ger DE-627 rda eng Moheb-Alizadeh, Hadi verfasserin aut Developing talent from a supply-demand perspective an optimization model for managers Hadi Moheb-Alizadeh and Robert B. Handfield 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier While executives emphasize that human resources (HR) are a firm’s biggest asset, the level of research attention devoted to planning talent pipelines for complex global organizational environments does not reflect this emphasis. Numerous challenges exist in establishing human resource management strategies aligned with strategic operations planning and growth strategies. We generalize the problem of managing talent from a supply-demand standpoint through a resource acquisition lens, to an industrial business case where an organization recruits for multiple roles given a limited pool of potential candidates acquired through a limited number of recruiting channels. In this context, we develop an innovative analytical model in a stochastic environment to assist managers with talent planning in their organizations. We apply supply chain concepts to the problem, whereby individuals with specific competencies are treated as unique products. We first develop a multi-period mixed integer nonlinear programming model and then exploit chance-constrained programming to a linearized instance of the model to handle stochastic parameters, which follow any arbitrary distribution functions. Next, we use an empirical study to validate the model with a large global manufacturing company, and demonstrate how the proposed model can effectively manage talents in a practical context. A stochastic analysis on the implemented case study reveals that a reasonable improvement is derived from incorporating randomness into the problem. Handfield, Robert B. 1964- verfasserin (DE-588)1071826794 (DE-627)826360181 (DE-576)433330090 aut Enthalten in Logistics Basel : MDPI AG, 2017 1(2017), 1 vom: Sept., Seite 1-29 Online-Ressource (DE-627)1000929906 (DE-600)2908937-2 (DE-576)494570555 2305-6290 nnns volume:1 year:2017 number:1 month:09 pages:1-29 https://doi.org/10.3390/logistics1010005 Resolving-System kostenfrei https://www.mdpi.com/2305-6290/1/1/5/pdf Verlag kostenfrei https://creativecommons.org/licenses/by/4.0/ Verlag Terms of use GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 1 2017 1 9 1-29 26 01 0206 3546371445 x1z 22-11-19 2403 01 DE-LFER 3596670861 00 --%%-- --%%-- n --%%-- l01 18-02-20 2403 01 DE-LFER https://doi.org/10.3390/logistics1010005 2403 01 DE-LFER https://www.mdpi.com/2305-6290/1/1/5/pdf 26 00 DE-206 56 chance-constrained programming 26 00 DE-206 56 nonlinear programming 26 00 DE-206 56 stochastic programming 26 00 DE-206 56 talent management
spelling	10.3390/logistics1010005 doi (DE-627)168249778X (DE-599)KXP168249778X DE-627 ger DE-627 rda eng Moheb-Alizadeh, Hadi verfasserin aut Developing talent from a supply-demand perspective an optimization model for managers Hadi Moheb-Alizadeh and Robert B. Handfield 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier While executives emphasize that human resources (HR) are a firm’s biggest asset, the level of research attention devoted to planning talent pipelines for complex global organizational environments does not reflect this emphasis. Numerous challenges exist in establishing human resource management strategies aligned with strategic operations planning and growth strategies. We generalize the problem of managing talent from a supply-demand standpoint through a resource acquisition lens, to an industrial business case where an organization recruits for multiple roles given a limited pool of potential candidates acquired through a limited number of recruiting channels. In this context, we develop an innovative analytical model in a stochastic environment to assist managers with talent planning in their organizations. We apply supply chain concepts to the problem, whereby individuals with specific competencies are treated as unique products. We first develop a multi-period mixed integer nonlinear programming model and then exploit chance-constrained programming to a linearized instance of the model to handle stochastic parameters, which follow any arbitrary distribution functions. Next, we use an empirical study to validate the model with a large global manufacturing company, and demonstrate how the proposed model can effectively manage talents in a practical context. A stochastic analysis on the implemented case study reveals that a reasonable improvement is derived from incorporating randomness into the problem. Handfield, Robert B. 1964- verfasserin (DE-588)1071826794 (DE-627)826360181 (DE-576)433330090 aut Enthalten in Logistics Basel : MDPI AG, 2017 1(2017), 1 vom: Sept., Seite 1-29 Online-Ressource (DE-627)1000929906 (DE-600)2908937-2 (DE-576)494570555 2305-6290 nnns volume:1 year:2017 number:1 month:09 pages:1-29 https://doi.org/10.3390/logistics1010005 Resolving-System kostenfrei https://www.mdpi.com/2305-6290/1/1/5/pdf Verlag kostenfrei https://creativecommons.org/licenses/by/4.0/ Verlag Terms of use GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 1 2017 1 9 1-29 26 01 0206 3546371445 x1z 22-11-19 2403 01 DE-LFER 3596670861 00 --%%-- --%%-- n --%%-- l01 18-02-20 2403 01 DE-LFER https://doi.org/10.3390/logistics1010005 2403 01 DE-LFER https://www.mdpi.com/2305-6290/1/1/5/pdf 26 00 DE-206 56 chance-constrained programming 26 00 DE-206 56 nonlinear programming 26 00 DE-206 56 stochastic programming 26 00 DE-206 56 talent management
allfields_unstemmed	10.3390/logistics1010005 doi (DE-627)168249778X (DE-599)KXP168249778X DE-627 ger DE-627 rda eng Moheb-Alizadeh, Hadi verfasserin aut Developing talent from a supply-demand perspective an optimization model for managers Hadi Moheb-Alizadeh and Robert B. Handfield 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier While executives emphasize that human resources (HR) are a firm’s biggest asset, the level of research attention devoted to planning talent pipelines for complex global organizational environments does not reflect this emphasis. Numerous challenges exist in establishing human resource management strategies aligned with strategic operations planning and growth strategies. We generalize the problem of managing talent from a supply-demand standpoint through a resource acquisition lens, to an industrial business case where an organization recruits for multiple roles given a limited pool of potential candidates acquired through a limited number of recruiting channels. In this context, we develop an innovative analytical model in a stochastic environment to assist managers with talent planning in their organizations. We apply supply chain concepts to the problem, whereby individuals with specific competencies are treated as unique products. We first develop a multi-period mixed integer nonlinear programming model and then exploit chance-constrained programming to a linearized instance of the model to handle stochastic parameters, which follow any arbitrary distribution functions. Next, we use an empirical study to validate the model with a large global manufacturing company, and demonstrate how the proposed model can effectively manage talents in a practical context. A stochastic analysis on the implemented case study reveals that a reasonable improvement is derived from incorporating randomness into the problem. Handfield, Robert B. 1964- verfasserin (DE-588)1071826794 (DE-627)826360181 (DE-576)433330090 aut Enthalten in Logistics Basel : MDPI AG, 2017 1(2017), 1 vom: Sept., Seite 1-29 Online-Ressource (DE-627)1000929906 (DE-600)2908937-2 (DE-576)494570555 2305-6290 nnns volume:1 year:2017 number:1 month:09 pages:1-29 https://doi.org/10.3390/logistics1010005 Resolving-System kostenfrei https://www.mdpi.com/2305-6290/1/1/5/pdf Verlag kostenfrei https://creativecommons.org/licenses/by/4.0/ Verlag Terms of use GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 1 2017 1 9 1-29 26 01 0206 3546371445 x1z 22-11-19 2403 01 DE-LFER 3596670861 00 --%%-- --%%-- n --%%-- l01 18-02-20 2403 01 DE-LFER https://doi.org/10.3390/logistics1010005 2403 01 DE-LFER https://www.mdpi.com/2305-6290/1/1/5/pdf 26 00 DE-206 56 chance-constrained programming 26 00 DE-206 56 nonlinear programming 26 00 DE-206 56 stochastic programming 26 00 DE-206 56 talent management
allfieldsGer	10.3390/logistics1010005 doi (DE-627)168249778X (DE-599)KXP168249778X DE-627 ger DE-627 rda eng Moheb-Alizadeh, Hadi verfasserin aut Developing talent from a supply-demand perspective an optimization model for managers Hadi Moheb-Alizadeh and Robert B. Handfield 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier While executives emphasize that human resources (HR) are a firm’s biggest asset, the level of research attention devoted to planning talent pipelines for complex global organizational environments does not reflect this emphasis. Numerous challenges exist in establishing human resource management strategies aligned with strategic operations planning and growth strategies. We generalize the problem of managing talent from a supply-demand standpoint through a resource acquisition lens, to an industrial business case where an organization recruits for multiple roles given a limited pool of potential candidates acquired through a limited number of recruiting channels. In this context, we develop an innovative analytical model in a stochastic environment to assist managers with talent planning in their organizations. We apply supply chain concepts to the problem, whereby individuals with specific competencies are treated as unique products. We first develop a multi-period mixed integer nonlinear programming model and then exploit chance-constrained programming to a linearized instance of the model to handle stochastic parameters, which follow any arbitrary distribution functions. Next, we use an empirical study to validate the model with a large global manufacturing company, and demonstrate how the proposed model can effectively manage talents in a practical context. A stochastic analysis on the implemented case study reveals that a reasonable improvement is derived from incorporating randomness into the problem. Handfield, Robert B. 1964- verfasserin (DE-588)1071826794 (DE-627)826360181 (DE-576)433330090 aut Enthalten in Logistics Basel : MDPI AG, 2017 1(2017), 1 vom: Sept., Seite 1-29 Online-Ressource (DE-627)1000929906 (DE-600)2908937-2 (DE-576)494570555 2305-6290 nnns volume:1 year:2017 number:1 month:09 pages:1-29 https://doi.org/10.3390/logistics1010005 Resolving-System kostenfrei https://www.mdpi.com/2305-6290/1/1/5/pdf Verlag kostenfrei https://creativecommons.org/licenses/by/4.0/ Verlag Terms of use GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 1 2017 1 9 1-29 26 01 0206 3546371445 x1z 22-11-19 2403 01 DE-LFER 3596670861 00 --%%-- --%%-- n --%%-- l01 18-02-20 2403 01 DE-LFER https://doi.org/10.3390/logistics1010005 2403 01 DE-LFER https://www.mdpi.com/2305-6290/1/1/5/pdf 26 00 DE-206 56 chance-constrained programming 26 00 DE-206 56 nonlinear programming 26 00 DE-206 56 stochastic programming 26 00 DE-206 56 talent management
allfieldsSound	10.3390/logistics1010005 doi (DE-627)168249778X (DE-599)KXP168249778X DE-627 ger DE-627 rda eng Moheb-Alizadeh, Hadi verfasserin aut Developing talent from a supply-demand perspective an optimization model for managers Hadi Moheb-Alizadeh and Robert B. Handfield 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier While executives emphasize that human resources (HR) are a firm’s biggest asset, the level of research attention devoted to planning talent pipelines for complex global organizational environments does not reflect this emphasis. Numerous challenges exist in establishing human resource management strategies aligned with strategic operations planning and growth strategies. We generalize the problem of managing talent from a supply-demand standpoint through a resource acquisition lens, to an industrial business case where an organization recruits for multiple roles given a limited pool of potential candidates acquired through a limited number of recruiting channels. In this context, we develop an innovative analytical model in a stochastic environment to assist managers with talent planning in their organizations. We apply supply chain concepts to the problem, whereby individuals with specific competencies are treated as unique products. We first develop a multi-period mixed integer nonlinear programming model and then exploit chance-constrained programming to a linearized instance of the model to handle stochastic parameters, which follow any arbitrary distribution functions. Next, we use an empirical study to validate the model with a large global manufacturing company, and demonstrate how the proposed model can effectively manage talents in a practical context. A stochastic analysis on the implemented case study reveals that a reasonable improvement is derived from incorporating randomness into the problem. Handfield, Robert B. 1964- verfasserin (DE-588)1071826794 (DE-627)826360181 (DE-576)433330090 aut Enthalten in Logistics Basel : MDPI AG, 2017 1(2017), 1 vom: Sept., Seite 1-29 Online-Ressource (DE-627)1000929906 (DE-600)2908937-2 (DE-576)494570555 2305-6290 nnns volume:1 year:2017 number:1 month:09 pages:1-29 https://doi.org/10.3390/logistics1010005 Resolving-System kostenfrei https://www.mdpi.com/2305-6290/1/1/5/pdf Verlag kostenfrei https://creativecommons.org/licenses/by/4.0/ Verlag Terms of use GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2050 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 1 2017 1 9 1-29 26 01 0206 3546371445 x1z 22-11-19 2403 01 DE-LFER 3596670861 00 --%%-- --%%-- n --%%-- l01 18-02-20 2403 01 DE-LFER https://doi.org/10.3390/logistics1010005 2403 01 DE-LFER https://www.mdpi.com/2305-6290/1/1/5/pdf 26 00 DE-206 56 chance-constrained programming 26 00 DE-206 56 nonlinear programming 26 00 DE-206 56 stochastic programming 26 00 DE-206 56 talent management
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spellingShingle	Moheb-Alizadeh, Hadi 26 chance-constrained programming 26 nonlinear programming 26 stochastic programming 26 talent management Developing talent from a supply-demand perspective an optimization model for managers
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topic_title	26 00 DE-206 56 chance-constrained programming 26 00 DE-206 56 nonlinear programming 26 00 DE-206 56 stochastic programming 26 00 DE-206 56 talent management Developing talent from a supply-demand perspective an optimization model for managers Hadi Moheb-Alizadeh and Robert B. Handfield
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title_full	Developing talent from a supply-demand perspective an optimization model for managers Hadi Moheb-Alizadeh and Robert B. Handfield
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title_auth	Developing talent from a supply-demand perspective an optimization model for managers
abstract	While executives emphasize that human resources (HR) are a firm’s biggest asset, the level of research attention devoted to planning talent pipelines for complex global organizational environments does not reflect this emphasis. Numerous challenges exist in establishing human resource management strategies aligned with strategic operations planning and growth strategies. We generalize the problem of managing talent from a supply-demand standpoint through a resource acquisition lens, to an industrial business case where an organization recruits for multiple roles given a limited pool of potential candidates acquired through a limited number of recruiting channels. In this context, we develop an innovative analytical model in a stochastic environment to assist managers with talent planning in their organizations. We apply supply chain concepts to the problem, whereby individuals with specific competencies are treated as unique products. We first develop a multi-period mixed integer nonlinear programming model and then exploit chance-constrained programming to a linearized instance of the model to handle stochastic parameters, which follow any arbitrary distribution functions. Next, we use an empirical study to validate the model with a large global manufacturing company, and demonstrate how the proposed model can effectively manage talents in a practical context. A stochastic analysis on the implemented case study reveals that a reasonable improvement is derived from incorporating randomness into the problem.
abstractGer	While executives emphasize that human resources (HR) are a firm’s biggest asset, the level of research attention devoted to planning talent pipelines for complex global organizational environments does not reflect this emphasis. Numerous challenges exist in establishing human resource management strategies aligned with strategic operations planning and growth strategies. We generalize the problem of managing talent from a supply-demand standpoint through a resource acquisition lens, to an industrial business case where an organization recruits for multiple roles given a limited pool of potential candidates acquired through a limited number of recruiting channels. In this context, we develop an innovative analytical model in a stochastic environment to assist managers with talent planning in their organizations. We apply supply chain concepts to the problem, whereby individuals with specific competencies are treated as unique products. We first develop a multi-period mixed integer nonlinear programming model and then exploit chance-constrained programming to a linearized instance of the model to handle stochastic parameters, which follow any arbitrary distribution functions. Next, we use an empirical study to validate the model with a large global manufacturing company, and demonstrate how the proposed model can effectively manage talents in a practical context. A stochastic analysis on the implemented case study reveals that a reasonable improvement is derived from incorporating randomness into the problem.
abstract_unstemmed	While executives emphasize that human resources (HR) are a firm’s biggest asset, the level of research attention devoted to planning talent pipelines for complex global organizational environments does not reflect this emphasis. Numerous challenges exist in establishing human resource management strategies aligned with strategic operations planning and growth strategies. We generalize the problem of managing talent from a supply-demand standpoint through a resource acquisition lens, to an industrial business case where an organization recruits for multiple roles given a limited pool of potential candidates acquired through a limited number of recruiting channels. In this context, we develop an innovative analytical model in a stochastic environment to assist managers with talent planning in their organizations. We apply supply chain concepts to the problem, whereby individuals with specific competencies are treated as unique products. We first develop a multi-period mixed integer nonlinear programming model and then exploit chance-constrained programming to a linearized instance of the model to handle stochastic parameters, which follow any arbitrary distribution functions. Next, we use an empirical study to validate the model with a large global manufacturing company, and demonstrate how the proposed model can effectively manage talents in a practical context. A stochastic analysis on the implemented case study reveals that a reasonable improvement is derived from incorporating randomness into the problem.
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title_short	Developing talent from a supply-demand perspective
url	https://doi.org/10.3390/logistics1010005 https://www.mdpi.com/2305-6290/1/1/5/pdf https://creativecommons.org/licenses/by/4.0/
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Numerous challenges exist in establishing human resource management strategies aligned with strategic operations planning and growth strategies. We generalize the problem of managing talent from a supply-demand standpoint through a resource acquisition lens, to an industrial business case where an organization recruits for multiple roles given a limited pool of potential candidates acquired through a limited number of recruiting channels. In this context, we develop an innovative analytical model in a stochastic environment to assist managers with talent planning in their organizations. We apply supply chain concepts to the problem, whereby individuals with specific competencies are treated as unique products. We first develop a multi-period mixed integer nonlinear programming model and then exploit chance-constrained programming to a linearized instance of the model to handle stochastic parameters, which follow any arbitrary distribution functions. Next, we use an empirical study to validate the model with a large global manufacturing company, and demonstrate how the proposed model can effectively manage talents in a practical context. 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Developing talent from a supply-demand perspective : an optimization model for managers

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