{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T17:04:26Z","timestamp":1780506266552,"version":"3.54.1"},"reference-count":47,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T00:00:00Z","timestamp":1697068800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["K"],"published-print":{"date-parts":[[2025,1,2]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>In this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The\u00a0use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>Numerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>Compared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.<\/jats:p><\/jats:sec>","DOI":"10.1108\/k-04-2023-0690","type":"journal-article","created":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T21:42:08Z","timestamp":1697060528000},"page":"110-133","source":"Crossref","is-referenced-by-count":4,"title":["Data-driven optimization for production planning with multiple demand features"],"prefix":"10.1108","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5351-4329","authenticated-orcid":false,"given":"Xiaoli","family":"Su","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijun","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3360-8886","authenticated-orcid":false,"given":"Bo","family":"Shao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Binlong","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2023,10,12]]},"reference":[{"issue":"2","key":"key2024121801340553900_ref001","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.jeconom.2020.04.006","article-title":"Predicting the VIX and the volatility risk premium: the role of short-run funding spreads volatility factors","volume":"220","year":"2021","journal-title":"Journal of Econometrics"},{"issue":"2","key":"key2024121801340553900_ref002","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.jeconom.2010.01.004","article-title":"Regression models with mixed sampling frequencies","volume":"158","year":"2010","journal-title":"Journal of Econometrics"},{"issue":"ahead-of-print","key":"key2024121801340553900_ref003","doi-asserted-by":"publisher","DOI":"10.1108\/K-09-2021-0853","article-title":"Acquiring supply chain agility through information technology capability: the role of demand forecasting in retail industry","volume":"ahead-of-print","year":"2022","journal-title":"Kybernetes"},{"issue":"2","key":"key2024121801340553900_ref004","first-page":"90","article-title":"The big data newsvendor: practical insights from machine learning","volume":"67","year":"2019","journal-title":"Operations Research"},{"issue":"2","key":"key2024121801340553900_ref005","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.ijforecast.2014.06.005","article-title":"Do high-frequency financial data help forecast oil prices? 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