Combining probabilistic forecasts of intermittent demand
Gespeichert in:
| Autor*in: |
Wang, Shengjie [verfasserIn] Kang, Yanfei [verfasserIn] Petropoulos, Fotios [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: European journal of operational research - Amsterdam [u.a.] : Elsevier, 1977, 315(2024), 3 vom: 16. Juni, Seite 1038-1048 |
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| Übergeordnetes Werk: |
volume:315 ; year:2024 ; number:3 ; day:16 ; month:06 ; pages:1038-1048 |
| Links: |
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| DOI / URN: |
10.1016/j.ejor.2024.01.032 |
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| Katalog-ID: |
1893907872 |
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| 982 | |2 26 |1 00 |x DE-206 |b In recent decades, new methods and approaches have been developed for forecasting intermittent demand series. However, the majority of research has focused on point forecasting, with little exploration into probabilistic intermittent demand forecasting. This is despite the fact that probabilistic forecasting is crucial for effective decision-making under uncertainty and inventory management. Additionally, most literature on this topic has focused solely on forecasting performance and has overlooked the inventory implications, which are directly relevant to intermittent demand. To address these gaps, this study aims to construct probabilistic forecasting combinations for intermittent demand while considering both forecasting accuracy and inventory control utility in obtaining combinations and evaluating forecasts. Our empirical findings demonstrate that combinations perform better than individual approaches for forecasting intermittent demand, but there is a trade-off between forecasting and inventory performance. | ||
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10.1016/j.ejor.2024.01.032 doi (DE-627)1893907872 (DE-599)KXP1893907872 DE-627 ger DE-627 rda eng Wang, Shengjie verfasserin (DE-588)1307090303 (DE-627)1867456265 aut Combining probabilistic forecasts of intermittent demand Shengjie Wang, Yanfei Kang, Fotios Petropoulos 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Forecasting (dpeaa)DE-206 Forecasting combination (dpeaa)DE-206 Intermittent demand (dpeaa)DE-206 Inventory management (dpeaa)DE-206 Probabilistic forecasting (dpeaa)DE-206 Kang, Yanfei verfasserin (DE-588)1192221540 (DE-627)1670543560 aut Petropoulos, Fotios verfasserin (DE-588)1050689852 (DE-627)784639051 (DE-576)404940706 aut Enthalten in European journal of operational research Amsterdam [u.a.] : Elsevier, 1977 315(2024), 3 vom: 16. Juni, Seite 1038-1048 Online-Ressource (DE-627)306713470 (DE-600)1501061-2 (DE-576)094058377 0377-2217 nnns volume:315 year:2024 number:3 day:16 month:06 pages:1038-1048 https://www.sciencedirect.com/science/article/pii/S0377221724000511/pdfft?md5=1d155fc6fd8a5357375fe2cc455b62b4&pid=1-s2.0-S0377221724000511-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.ejor.2024.01.032 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 315 2024 3 16 6 1038-1048 26 01 0206 4546668384 x1z 04-07-24 26 00 DE-206 In recent decades, new methods and approaches have been developed for forecasting intermittent demand series. However, the majority of research has focused on point forecasting, with little exploration into probabilistic intermittent demand forecasting. This is despite the fact that probabilistic forecasting is crucial for effective decision-making under uncertainty and inventory management. Additionally, most literature on this topic has focused solely on forecasting performance and has overlooked the inventory implications, which are directly relevant to intermittent demand. To address these gaps, this study aims to construct probabilistic forecasting combinations for intermittent demand while considering both forecasting accuracy and inventory control utility in obtaining combinations and evaluating forecasts. Our empirical findings demonstrate that combinations perform better than individual approaches for forecasting intermittent demand, but there is a trade-off between forecasting and inventory performance. |
| spelling |
10.1016/j.ejor.2024.01.032 doi (DE-627)1893907872 (DE-599)KXP1893907872 DE-627 ger DE-627 rda eng Wang, Shengjie verfasserin (DE-588)1307090303 (DE-627)1867456265 aut Combining probabilistic forecasts of intermittent demand Shengjie Wang, Yanfei Kang, Fotios Petropoulos 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Forecasting (dpeaa)DE-206 Forecasting combination (dpeaa)DE-206 Intermittent demand (dpeaa)DE-206 Inventory management (dpeaa)DE-206 Probabilistic forecasting (dpeaa)DE-206 Kang, Yanfei verfasserin (DE-588)1192221540 (DE-627)1670543560 aut Petropoulos, Fotios verfasserin (DE-588)1050689852 (DE-627)784639051 (DE-576)404940706 aut Enthalten in European journal of operational research Amsterdam [u.a.] : Elsevier, 1977 315(2024), 3 vom: 16. Juni, Seite 1038-1048 Online-Ressource (DE-627)306713470 (DE-600)1501061-2 (DE-576)094058377 0377-2217 nnns volume:315 year:2024 number:3 day:16 month:06 pages:1038-1048 https://www.sciencedirect.com/science/article/pii/S0377221724000511/pdfft?md5=1d155fc6fd8a5357375fe2cc455b62b4&pid=1-s2.0-S0377221724000511-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.ejor.2024.01.032 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 315 2024 3 16 6 1038-1048 26 01 0206 4546668384 x1z 04-07-24 26 00 DE-206 In recent decades, new methods and approaches have been developed for forecasting intermittent demand series. However, the majority of research has focused on point forecasting, with little exploration into probabilistic intermittent demand forecasting. This is despite the fact that probabilistic forecasting is crucial for effective decision-making under uncertainty and inventory management. Additionally, most literature on this topic has focused solely on forecasting performance and has overlooked the inventory implications, which are directly relevant to intermittent demand. To address these gaps, this study aims to construct probabilistic forecasting combinations for intermittent demand while considering both forecasting accuracy and inventory control utility in obtaining combinations and evaluating forecasts. Our empirical findings demonstrate that combinations perform better than individual approaches for forecasting intermittent demand, but there is a trade-off between forecasting and inventory performance. |
| allfields_unstemmed |
10.1016/j.ejor.2024.01.032 doi (DE-627)1893907872 (DE-599)KXP1893907872 DE-627 ger DE-627 rda eng Wang, Shengjie verfasserin (DE-588)1307090303 (DE-627)1867456265 aut Combining probabilistic forecasts of intermittent demand Shengjie Wang, Yanfei Kang, Fotios Petropoulos 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Forecasting (dpeaa)DE-206 Forecasting combination (dpeaa)DE-206 Intermittent demand (dpeaa)DE-206 Inventory management (dpeaa)DE-206 Probabilistic forecasting (dpeaa)DE-206 Kang, Yanfei verfasserin (DE-588)1192221540 (DE-627)1670543560 aut Petropoulos, Fotios verfasserin (DE-588)1050689852 (DE-627)784639051 (DE-576)404940706 aut Enthalten in European journal of operational research Amsterdam [u.a.] : Elsevier, 1977 315(2024), 3 vom: 16. Juni, Seite 1038-1048 Online-Ressource (DE-627)306713470 (DE-600)1501061-2 (DE-576)094058377 0377-2217 nnns volume:315 year:2024 number:3 day:16 month:06 pages:1038-1048 https://www.sciencedirect.com/science/article/pii/S0377221724000511/pdfft?md5=1d155fc6fd8a5357375fe2cc455b62b4&pid=1-s2.0-S0377221724000511-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.ejor.2024.01.032 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 315 2024 3 16 6 1038-1048 26 01 0206 4546668384 x1z 04-07-24 26 00 DE-206 In recent decades, new methods and approaches have been developed for forecasting intermittent demand series. However, the majority of research has focused on point forecasting, with little exploration into probabilistic intermittent demand forecasting. This is despite the fact that probabilistic forecasting is crucial for effective decision-making under uncertainty and inventory management. Additionally, most literature on this topic has focused solely on forecasting performance and has overlooked the inventory implications, which are directly relevant to intermittent demand. To address these gaps, this study aims to construct probabilistic forecasting combinations for intermittent demand while considering both forecasting accuracy and inventory control utility in obtaining combinations and evaluating forecasts. Our empirical findings demonstrate that combinations perform better than individual approaches for forecasting intermittent demand, but there is a trade-off between forecasting and inventory performance. |
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10.1016/j.ejor.2024.01.032 doi (DE-627)1893907872 (DE-599)KXP1893907872 DE-627 ger DE-627 rda eng Wang, Shengjie verfasserin (DE-588)1307090303 (DE-627)1867456265 aut Combining probabilistic forecasts of intermittent demand Shengjie Wang, Yanfei Kang, Fotios Petropoulos 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Forecasting (dpeaa)DE-206 Forecasting combination (dpeaa)DE-206 Intermittent demand (dpeaa)DE-206 Inventory management (dpeaa)DE-206 Probabilistic forecasting (dpeaa)DE-206 Kang, Yanfei verfasserin (DE-588)1192221540 (DE-627)1670543560 aut Petropoulos, Fotios verfasserin (DE-588)1050689852 (DE-627)784639051 (DE-576)404940706 aut Enthalten in European journal of operational research Amsterdam [u.a.] : Elsevier, 1977 315(2024), 3 vom: 16. Juni, Seite 1038-1048 Online-Ressource (DE-627)306713470 (DE-600)1501061-2 (DE-576)094058377 0377-2217 nnns volume:315 year:2024 number:3 day:16 month:06 pages:1038-1048 https://www.sciencedirect.com/science/article/pii/S0377221724000511/pdfft?md5=1d155fc6fd8a5357375fe2cc455b62b4&pid=1-s2.0-S0377221724000511-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.ejor.2024.01.032 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 315 2024 3 16 6 1038-1048 26 01 0206 4546668384 x1z 04-07-24 26 00 DE-206 In recent decades, new methods and approaches have been developed for forecasting intermittent demand series. However, the majority of research has focused on point forecasting, with little exploration into probabilistic intermittent demand forecasting. This is despite the fact that probabilistic forecasting is crucial for effective decision-making under uncertainty and inventory management. Additionally, most literature on this topic has focused solely on forecasting performance and has overlooked the inventory implications, which are directly relevant to intermittent demand. To address these gaps, this study aims to construct probabilistic forecasting combinations for intermittent demand while considering both forecasting accuracy and inventory control utility in obtaining combinations and evaluating forecasts. Our empirical findings demonstrate that combinations perform better than individual approaches for forecasting intermittent demand, but there is a trade-off between forecasting and inventory performance. |
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10.1016/j.ejor.2024.01.032 doi (DE-627)1893907872 (DE-599)KXP1893907872 DE-627 ger DE-627 rda eng Wang, Shengjie verfasserin (DE-588)1307090303 (DE-627)1867456265 aut Combining probabilistic forecasts of intermittent demand Shengjie Wang, Yanfei Kang, Fotios Petropoulos 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Forecasting (dpeaa)DE-206 Forecasting combination (dpeaa)DE-206 Intermittent demand (dpeaa)DE-206 Inventory management (dpeaa)DE-206 Probabilistic forecasting (dpeaa)DE-206 Kang, Yanfei verfasserin (DE-588)1192221540 (DE-627)1670543560 aut Petropoulos, Fotios verfasserin (DE-588)1050689852 (DE-627)784639051 (DE-576)404940706 aut Enthalten in European journal of operational research Amsterdam [u.a.] : Elsevier, 1977 315(2024), 3 vom: 16. Juni, Seite 1038-1048 Online-Ressource (DE-627)306713470 (DE-600)1501061-2 (DE-576)094058377 0377-2217 nnns volume:315 year:2024 number:3 day:16 month:06 pages:1038-1048 https://www.sciencedirect.com/science/article/pii/S0377221724000511/pdfft?md5=1d155fc6fd8a5357375fe2cc455b62b4&pid=1-s2.0-S0377221724000511-main.pdf Verlag lizenzpflichtig https://doi.org/10.1016/j.ejor.2024.01.032 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 315 2024 3 16 6 1038-1048 26 01 0206 4546668384 x1z 04-07-24 26 00 DE-206 In recent decades, new methods and approaches have been developed for forecasting intermittent demand series. However, the majority of research has focused on point forecasting, with little exploration into probabilistic intermittent demand forecasting. This is despite the fact that probabilistic forecasting is crucial for effective decision-making under uncertainty and inventory management. Additionally, most literature on this topic has focused solely on forecasting performance and has overlooked the inventory implications, which are directly relevant to intermittent demand. To address these gaps, this study aims to construct probabilistic forecasting combinations for intermittent demand while considering both forecasting accuracy and inventory control utility in obtaining combinations and evaluating forecasts. Our empirical findings demonstrate that combinations perform better than individual approaches for forecasting intermittent demand, but there is a trade-off between forecasting and inventory performance. |
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26 00 DE-206 In recent decades, new methods and approaches have been developed for forecasting intermittent demand series. However, the majority of research has focused on point forecasting, with little exploration into probabilistic intermittent demand forecasting. This is despite the fact that probabilistic forecasting is crucial for effective decision-making under uncertainty and inventory management. Additionally, most literature on this topic has focused solely on forecasting performance and has overlooked the inventory implications, which are directly relevant to intermittent demand. To address these gaps, this study aims to construct probabilistic forecasting combinations for intermittent demand while considering both forecasting accuracy and inventory control utility in obtaining combinations and evaluating forecasts. Our empirical findings demonstrate that combinations perform better than individual approaches for forecasting intermittent demand, but there is a trade-off between forecasting and inventory performance Combining probabilistic forecasts of intermittent demand Shengjie Wang, Yanfei Kang, Fotios Petropoulos Forecasting (dpeaa)DE-206 Forecasting combination (dpeaa)DE-206 Intermittent demand (dpeaa)DE-206 Inventory management (dpeaa)DE-206 Probabilistic forecasting (dpeaa)DE-206 |
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ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">315</subfield><subfield code="j">2024</subfield><subfield code="e">3</subfield><subfield code="b">16</subfield><subfield code="c">6</subfield><subfield code="h">1038-1048</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4546668384</subfield><subfield code="y">x1z</subfield><subfield code="z">04-07-24</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">In recent decades, new methods and approaches have been developed for forecasting intermittent demand series. However, the majority of research has focused on point forecasting, with little exploration into probabilistic intermittent demand forecasting. This is despite the fact that probabilistic forecasting is crucial for effective decision-making under uncertainty and inventory management. Additionally, most literature on this topic has focused solely on forecasting performance and has overlooked the inventory implications, which are directly relevant to intermittent demand. To address these gaps, this study aims to construct probabilistic forecasting combinations for intermittent demand while considering both forecasting accuracy and inventory control utility in obtaining combinations and evaluating forecasts. Our empirical findings demonstrate that combinations perform better than individual approaches for forecasting intermittent demand, but there is a trade-off between forecasting and inventory performance.</subfield></datafield></record></collection>
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| score |
7.401434 |