Purpose:　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.　Design/methodology/approach:　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 use　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.　Findings:　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.　Originality/value:　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.　©　2023,　Emerald　Publishing　Limited.