{"id":44469,"date":"2026-06-08T17:42:00","date_gmt":"2026-06-08T12:12:00","guid":{"rendered":"https:\/\/www.verdantis.com\/?p=44469"},"modified":"2026-06-12T14:53:09","modified_gmt":"2026-06-12T09:23:09","slug":"prevision-de-la-demanda-de-mantenimiento","status":"publish","type":"post","link":"https:\/\/www.verdantis.com\/es\/mro360\/prevision-de-la-demanda-de-mantenimiento\/","title":{"rendered":"Software de previsi\u00f3n de la demanda de mantenimiento"},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"44469\" class=\"elementor elementor-44469\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7a38daa e-flex e-con-boxed e-con e-parent\" data-id=\"7a38daa\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1cbd999 elementor-widget elementor-widget-html\" data-id=\"1cbd999\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<!-- ============================================================\r\n     MRO360 | Maintenance Demand Forecasting \u2014 SECTION 01: HERO\r\n     Elementor: paste into a single Custom HTML widget.\r\n     All CSS scoped under .vd-mdf-hero (no :root).\r\n     Target keyword: maintenance demand forecasting (H1)\r\n============================================================ -->\r\n<div class=\"vd-mdf-hero\">\r\n  <style>\r\n    @import url('https:\/\/fonts.googleapis.com\/css2?family=Open+Sans:wght@400;500;600;700;800&family=Roboto+Slab:wght@500;700&display=swap');\r\n\r\n    .vd-mdf-hero{\r\n      margin-left:calc(50% - 50vw);\r\n      margin-right:calc(50% - 50vw);\r\n      width:100vw;\r\n      box-sizing:border-box;\r\n      font-family:'Open Sans',Arial,sans-serif;\r\n      background:\r\n        radial-gradient(1100px 520px at 78% -10%, rgba(250,132,26,.18), 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div{font-size:11.5px;color:#cdd9f2;font-weight:600;}\r\n\r\n    @media(max-width:900px){\r\n      .vd-mdf-hero .wrap{grid-template-columns:1fr;gap:40px;padding:64px 22px 70px;}\r\n      .vd-mdf-hero .panel{order:2;}\r\n    }\r\n  <\/style>\r\n\r\n  <div class=\"wrap\">\r\n    <div class=\"copy\">\r\n      <p class=\"eyebrow\">MRO360 \u00b7 Inteligencia MRO basada en IA<\/p>\r\n      <h1 id=\"maintenance-demand-forecasting-that-reads-the-full-failure-signal-not-just-history\">Previsi\u00f3n de la demanda de mantenimiento que analiza el <span>se\u00f1al de fallo total<\/span>, no solo historia<\/h1>\r\n      <p class=\"lead\">MRO360 prev\u00e9 la demanda de piezas de repuesto a partir del historial de mantenimiento de los equipos, las notas sobre las causas fundamentales, las horas de funcionamiento y las \u00f3rdenes de trabajo en curso, y luego calcula las necesidades tanto preventivas como correctivas por referencia y por planta.<\/p>\r\n      <div class=\"cta-row\">\r\n        <a class=\"btn btn-primary\" href=\"#vd-mdf-demo\">Ver la previsi\u00f3n para los pr\u00f3ximos 6 meses \u2192<\/a>\r\n        <a class=\"btn btn-ghost\" href=\"#vd-mdf-engine\">C\u00f3mo funciona el motor<\/a>\r\n      <\/div>\r\n      <div class=\"chips\">\r\n        <span class=\"chip\">Multivariante, no solo hist\u00f3rico<\/span>\r\n        <span class=\"chip\">Demanda preventiva y correctiva<\/span>\r\n        <span class=\"chip\">Modelo de IA seleccionado autom\u00e1ticamente<\/span>\r\n        <span class=\"chip\">Funciona con tu ERP \/ CMMS<\/span>\r\n      <\/div>\r\n    <\/div>\r\n\r\n    <div class=\"panel\" aria-hidden=\"true\">\r\n      <div class=\"panel-head\">\r\n        <div class=\"t\">Previsi\u00f3n de demanda \u00b7 SKU 10-BRG-4471<small>Pr\u00f3ximos 6 meses \u00b7 unidades<\/small><\/div>\r\n        <div class=\"toggle\"><span class=\"on\">Mensual<\/span><span>Semanal<\/span><\/div>\r\n      <\/div>\r\n      <div class=\"bars\">\r\n        <div class=\"b\" style=\"height:46%\"><\/div>\r\n        <div class=\"b high\" style=\"height:88%\"><span class=\"lab\">ALTO<\/span><\/div>\r\n        <div class=\"b\" style=\"height:52%\"><\/div>\r\n        <div class=\"b\" style=\"height:34%\"><\/div>\r\n        <div class=\"b high\" style=\"height:78%\"><span class=\"lab\">ALTO<\/span><\/div>\r\n        <div class=\"b\" style=\"height:40%\"><\/div>\r\n      <\/div>\r\n      <div class=\"months\"><span>julio<\/span><span>Agosto<\/span><span>Sep<\/span><span>Oct<\/span><span>Nov<\/span><span>Dic<\/span><\/div>\r\n      <div class=\"legend\">\r\n        <div><i style=\"background:#1f63bd\"><\/i>Demanda de referencia<\/div>\r\n        <div><i style=\"background:#FA841A\"><\/i>Pico previsto<\/div>\r\n      <\/div>\r\n    <\/div>\r\n  <\/div>\r\n<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b5a5772 elementor-widget elementor-widget-html\" data-id=\"b5a5772\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<!-- ============================================================\r\n     SECTION 02: WHAT IS MAINTENANCE DEMAND FORECASTING\r\n     Captures informational semantic relevance (\"what is demand\r\n     forecasting\") in a CONTAINED way, then internal-links to the\r\n     full pillar article. Scoped under .vd-mdf-what\r\n     INTERNAL LINK TODO: confirm slug for the demand-forecasting pillar article.\r\n============================================================ -->\r\n<div class=\"vd-mdf-what\">\r\n  <style>\r\n    @import url('https:\/\/fonts.googleapis.com\/css2?family=Open+Sans:wght@400;500;600;700;800&display=swap');\r\n    .vd-mdf-what{font-family:'Open Sans',Arial,sans-serif;background:#ffffff;padding:78px 0;}\r\n    .vd-mdf-what *{box-sizing:border-box;}\r\n    .vd-mdf-what .wrap{max-width:1060px;margin:0 auto;padding:0 24px;}\r\n    .vd-mdf-what .grid{display:grid;grid-template-columns:1fr 1fr;gap:48px;align-items:start;}\r\n    .vd-mdf-what .eyebrow{font-size:13px;font-weight:700;letter-spacing:.12em;text-transform:uppercase;color:#FA841A;margin:0 0 14px;}\r\n    .vd-mdf-what h2{font-size:clamp(26px,3vw,34px);line-height:1.16;font-weight:800;color:#1A2434;margin:0 0 18px;letter-spacing:-.01em;}\r\n    .vd-mdf-what p{font-size:16px;line-height:1.68;color:#384152;margin:0 0 16px;}\r\n    .vd-mdf-what p a{color:#004DA9;font-weight:700;text-decoration:none;border-bottom:2px solid rgba(0,77,169,.25);}\r\n    .vd-mdf-what p a:hover{border-bottom-color:#004DA9;}\r\n    .vd-mdf-what .def{\r\n      background:linear-gradient(180deg,#f4f8ff,#eef4fe);border:1px solid #d8e4f7;border-left:4px solid #FA841A;\r\n      border-radius:12px;padding:24px 26px;\r\n    }\r\n    .vd-mdf-what .def h3{font-size:15px;font-weight:800;color:#024089;margin:0 0 10px;text-transform:uppercase;letter-spacing:.04em;}\r\n    .vd-mdf-what .def p{font-size:15.5px;color:#2b3445;margin:0;}\r\n    .vd-mdf-what .vs{display:grid;grid-template-columns:1fr 1fr;gap:14px;margin-top:20px;}\r\n    .vd-mdf-what .vs .card{background:#fff;border:1px solid #e1e3e7;border-radius:10px;padding:16px 16px 14px;}\r\n    .vd-mdf-what .vs .card .tag{font-size:11px;font-weight:800;letter-spacing:.06em;text-transform:uppercase;margin:0 0 8px;}\r\n    .vd-mdf-what .vs .card.gen .tag{color:#8a93a3;}\r\n    .vd-mdf-what .vs .card.mro .tag{color:#FA841A;}\r\n    .vd-mdf-what .vs .card p{font-size:13.5px;line-height:1.55;color:#46505f;margin:0;}\r\n    @media(max-width:840px){.vd-mdf-what .grid{grid-template-columns:1fr;gap:32px;}.vd-mdf-what .vs{grid-template-columns:1fr;}}\r\n  <\/style>\r\n\r\n  <div class=\"wrap\">\r\n    <div class=\"grid\">\r\n      <div>\r\n        <p class=\"eyebrow\">Los fundamentos<\/p>\r\n        <h2 id=\"what-is-maintenance-demand-forecasting\">\u00bfQu\u00e9 es la previsi\u00f3n de la demanda de mantenimiento?<\/h2>\r\n        <p>La previsi\u00f3n de la demanda de mantenimiento es el proceso de calcular cu\u00e1ntas piezas de repuesto y consumibles necesitar\u00e1 una instalaci\u00f3n en un periodo futuro, de modo que el stock est\u00e9 disponible en el momento en que una orden de trabajo lo requiera, sin inmovilizar capital circulante en piezas que permanecen sin utilizar.<\/p>\r\n        <p>En el \u00e1mbito del mantenimiento, reparaci\u00f3n y revisi\u00f3n (MRO), la previsi\u00f3n es m\u00e1s compleja que en el comercio minorista o la producci\u00f3n, ya que la demanda depende de las aver\u00edas de los equipos y de las intervenciones de mantenimiento, y no de un consumo constante. Dos piezas id\u00e9nticas pueden tener una demanda muy diferente en funci\u00f3n del activo al que dan servicio, su antig\u00fcedad y su historial de aver\u00edas.<\/p>\r\n      <\/div>\r\n\r\n      <div>\r\n        <div class=\"def\">\r\n          <h3 id=\"in-one-line\">En una sola l\u00ednea<\/h3>\r\n          <p>La previsi\u00f3n de la demanda permite predecir <strong>consumo futuro de piezas<\/strong>; en materia de mantenimiento, ese consumo depende de <strong>estado de los activos y actividades de mantenimiento<\/strong>, y no solo el uso anterior.<\/p>\r\n        <\/div>\r\n        <div class=\"vs\">\r\n          <div class=\"card gen\">\r\n            <p class=\"tag\">Previsiones generales<\/p>\r\n            <p>Prev\u00e9 la demanda futura bas\u00e1ndose en las tendencias hist\u00f3ricas de ventas o consumo y en la estacionalidad.<\/p>\r\n          <\/div>\r\n          <div class=\"card mro\">\r\n            <p class=\"tag\">Previsi\u00f3n de MRO<\/p>\r\n            <p>Incluye modos de fallo, \u00f3rdenes de trabajo, horas de funcionamiento y nivel de criticidad para predecir cu\u00e1ndo se necesitar\u00e1 realmente una pieza.<\/p>\r\n          <\/div>\r\n        <\/div>\r\n      <\/div>\r\n    <\/div>\r\n  <\/div>\r\n<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a403a1f elementor-widget elementor-widget-html\" data-id=\"a403a1f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<!-- ============================================================\r\n     SECTION 03: WHY HISTORICAL-ONLY FORECASTING FAILS IN MRO\r\n     Problem framing \/ differentiation. 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Esto funciona cuando el uso es constante y predecible. La demanda de mantenimiento no es constante: depende de las aver\u00edas, las paradas programadas y el estado de los activos, por lo que el historial por s\u00ed solo no suele detectar los picos que realmente importan.<\/p>\r\n    <\/div>\r\n\r\n    <div class=\"cards\">\r\n      <div class=\"card\">\r\n        <p class=\"n\">01<\/p>\r\n        <h3 id=\"spikes-hide-in-the-average\">Los picos se ocultan en la media<\/h3>\r\n        <p>Los modelos hist\u00f3ricos suavizados aten\u00faan los picos de mantenimiento correctivo que, en realidad, provocan la falta de existencias, por lo que el stock de seguridad se establece en funci\u00f3n de una curva de demanda que el equipo nunca sigue.<\/p>\r\n      <\/div>\r\n      <div class=\"card\">\r\n        <p class=\"n\">02<\/p>\r\n        <h3 id=\"identical-parts-different-demand\">Piezas id\u00e9nticas, demanda diferente<\/h3>\r\n        <p>El mismo SKU se comporta de forma diferente en una instalaci\u00f3n antigua de un solo tren que en una redundante. El historial de uso no tiene en cuenta ese contexto; los datos de fallos y la criticidad s\u00ed lo hacen.<\/p>\r\n      <\/div>\r\n      <div class=\"card\">\r\n        <p class=\"n\">03<\/p>\r\n        <h3 id=\"planned-and-unplanned-are-blended\">Lo planificado y lo imprevisto se entremezclan<\/h3>\r\n        <p>Agrupar el consumo preventivo y el correctivo en una sola cifra hace que ambos queden ocultos. La l\u00f3gica de reposici\u00f3n acaba siendo err\u00f3nea para ambos, ya que no era la adecuada para ninguno de ellos.<\/p>\r\n      <\/div>\r\n    <\/div>\r\n\r\n    <div class=\"pull\">La mayor\u00eda de los casos de falta de existencias no se deben a fallos en la gesti\u00f3n del inventario. Se deben a <strong>fallos en la informaci\u00f3n<\/strong>: los datos necesarios para pronosticar la demanda ya se encuentran en su ERP, su sistema de gesti\u00f3n de mantenimiento (CMMS) y los registros de mantenimiento.<\/div>\r\n  <\/div>\r\n<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4214a99 elementor-widget elementor-widget-html\" data-id=\"4214a99\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<!-- ============================================================\r\n     SECTION 04: THE MULTIVARIATE SIGNAL ENGINE\r\n     The heart of the page. Pulls directly from the demo script:\r\n     maintenance history, long\/short tag, maintenance notes, RCA,\r\n     install date, operating hours + ERP consumption signals.\r\n     Scoped under .vd-mdf-engine  (anchor id=\"vd-mdf-engine\")\r\n============================================================ -->\r\n<div class=\"vd-mdf-engine\" id=\"vd-mdf-engine\">\r\n  <style>\r\n    @import url('https:\/\/fonts.googleapis.com\/css2?family=Open+Sans:wght@400;500;600;700;800&display=swap');\r\n    .vd-mdf-engine{font-family:'Open Sans',Arial,sans-serif;background:#f6f8fb;padding:84px 0;}\r\n    .vd-mdf-engine *{box-sizing:border-box;}\r\n    .vd-mdf-engine .wrap{max-width:1140px;margin:0 auto;padding:0 24px;}\r\n    .vd-mdf-engine .head{text-align:center;max-width:760px;margin:0 auto 50px;}\r\n    .vd-mdf-engine .eyebrow{font-size:13px;font-weight:700;letter-spacing:.12em;text-transform:uppercase;color:#FA841A;margin:0 0 14px;}\r\n    .vd-mdf-engine h2{font-size:clamp(26px,3.2vw,36px);line-height:1.14;font-weight:800;color:#1A2434;margin:0 0 16px;letter-spacing:-.01em;}\r\n    .vd-mdf-engine .head p{font-size:16.5px;line-height:1.64;color:#46505f;margin:0;}\r\n\r\n    .vd-mdf-engine .flow{display:grid;grid-template-columns:1fr auto 1fr;gap:30px;align-items:center;}\r\n    .vd-mdf-engine .signals{display:grid;grid-template-columns:1fr 1fr;gap:12px;}\r\n    .vd-mdf-engine .sig{background:#fff;border:1px solid #e1e3e7;border-radius:11px;padding:15px 15px 13px;box-shadow:0 2px 10px rgba(26,36,52,.04);}\r\n    .vd-mdf-engine .sig .ico{width:34px;height:34px;border-radius:8px;background:#eef4fe;display:flex;align-items:center;justify-content:center;margin-bottom:10px;}\r\n    .vd-mdf-engine .sig .ico svg{width:18px;height:18px;stroke:#004DA9;fill:none;stroke-width:2;}\r\n    .vd-mdf-engine .sig h4{font-size:14px;font-weight:700;color:#1A2434;margin:0 0 4px;}\r\n    .vd-mdf-engine .sig p{font-size:12.5px;line-height:1.5;color:#5a6472;margin:0;}\r\n\r\n    .vd-mdf-engine .arrow{display:flex;flex-direction:column;align-items:center;gap:10px;}\r\n    .vd-mdf-engine .arrow .core{\r\n      width:120px;height:120px;border-radius:22px;\r\n      background:linear-gradient(140deg,#024089,#004DA9);color:#fff;\r\n      display:flex;flex-direction:column;align-items:center;justify-content:center;text-align:center;\r\n      box-shadow:0 18px 40px rgba(0,77,169,.34);padding:12px;\r\n    }\r\n    .vd-mdf-engine .arrow .core b{font-size:13px;font-weight:800;line-height:1.2;}\r\n    .vd-mdf-engine .arrow .core span{font-size:10.5px;color:#cfe0fb;margin-top:5px;}\r\n    .vd-mdf-engine .arrow .dash{font-size:22px;color:#FA841A;font-weight:800;}\r\n\r\n    .vd-mdf-engine .out{background:#fff;border:1px solid #d8e4f7;border-top:4px solid #FA841A;border-radius:14px;padding:24px;}\r\n    .vd-mdf-engine .out h3{font-size:16px;font-weight:800;color:#024089;margin:0 0 14px;}\r\n    .vd-mdf-engine .out .line{display:flex;align-items:center;gap:10px;padding:10px 0;border-bottom:1px dashed #e6ebf3;}\r\n    .vd-mdf-engine .out .line:last-child{border-bottom:0;}\r\n    .vd-mdf-engine .out .line b{font-size:14px;color:#1A2434;font-weight:700;}\r\n    .vd-mdf-engine .out .line span{font-size:12.5px;color:#6a7585;margin-left:auto;font-weight:600;}\r\n    .vd-mdf-engine .out .dot{width:9px;height:9px;border-radius:50%;background:#FA841A;flex:none;}\r\n\r\n    @media(max-width:920px){\r\n      .vd-mdf-engine .flow{grid-template-columns:1fr;gap:26px;}\r\n      .vd-mdf-engine .arrow{flex-direction:row;}\r\n      .vd-mdf-engine .arrow .dash{transform:rotate(90deg);}\r\n    }\r\n    @media(max-width:520px){.vd-mdf-engine .signals{grid-template-columns:1fr;}}\r\n  <\/style>\r\n\r\n  <div class=\"wrap\">\r\n    <div class=\"head\">\r\n      <p class=\"eyebrow\">C\u00f3mo realiza sus previsiones MRO360<\/p>\r\n      <h2 id=\"a-multivariate-engine-not-a-usage-chart\">Un motor multivariante, no un gr\u00e1fico de uso<\/h2>\r\n      <p>MRO360 va mucho m\u00e1s all\u00e1 del consumo hist\u00f3rico. Analiza el historial de mantenimiento del equipo, las notas del propio t\u00e9cnico, los an\u00e1lisis de las causas fundamentales, la antig\u00fcedad y el tiempo de funcionamiento, y luego combina todos estos datos para obtener una cifra de demanda por SKU.<\/p>\r\n    <\/div>\r\n\r\n    <div class=\"flow\">\r\n      <!-- inputs -->\r\n      <div class=\"signals\">\r\n        <div class=\"sig\">\r\n          <div class=\"ico\"><svg viewbox=\"0 0 24 24\"><path d=\"M3 3v18h18\"\/><path d=\"M7 14l4-4 3 3 5-6\"\/><\/svg><\/div>\r\n          <h4>Consumo hist\u00f3rico<\/h4>\r\n          <p>Movimientos y uso de mercanc\u00edas desde los m\u00f3dulos de materiales e inventario.<\/p>\r\n        <\/div>\r\n        <div class=\"sig\">\r\n          <div class=\"ico\"><svg viewbox=\"0 0 24 24\"><path d=\"M4 4h16v4H4z\"\/><path d=\"M4 12h10M4 16h7\"\/><\/svg><\/div>\r\n          <h4>Historial de mantenimiento de los equipos<\/h4>\r\n          <p>Fallos, reparaciones y sustituciones anteriores de cada activo.<\/p>\r\n        <\/div>\r\n        <div class=\"sig\">\r\n          <div class=\"ico\"><svg viewbox=\"0 0 24 24\"><path d=\"M14 3v5h5\"\/><path d=\"M5 3h9l5 5v13H5z\"\/><path d=\"M8 13h7M8 17h5\"\/><\/svg><\/div>\r\n          <h4>Etiquetas largas y cortas \u00b7 notas<\/h4>\r\n          <p>Notas de mantenimiento en formato de texto libre que describen las causas de la aver\u00eda del equipo.<\/p>\r\n        <\/div>\r\n        <div class=\"sig\">\r\n          <div class=\"ico\"><svg viewbox=\"0 0 24 24\"><circle cx=\"11\" cy=\"11\" r=\"7\"\/><path d=\"M21 21l-4-4\"\/><\/svg><\/div>\r\n          <h4>An\u00e1lisis de las causas fundamentales<\/h4>\r\n          <p>Causas de fallo documentadas que indican un riesgo de recurrencia.<\/p>\r\n        <\/div>\r\n        <div class=\"sig\">\r\n          <div class=\"ico\"><svg viewbox=\"0 0 24 24\"><rect x=\"3\" y=\"4\" width=\"18\" height=\"17\" rx=\"2\"\/><path d=\"M3 9h18M8 2v4M16 2v4\"\/><\/svg><\/div>\r\n          <h4>Fecha de instalaci\u00f3n<\/h4>\r\n          <p>Antig\u00fcedad del activo y fase del ciclo de vida en relaci\u00f3n con el fallo previsto.<\/p>\r\n        <\/div>\r\n        <div class=\"sig\">\r\n          <div class=\"ico\"><svg viewbox=\"0 0 24 24\"><circle cx=\"12\" cy=\"12\" r=\"9\"\/><path d=\"M12 7v5l3 3\"\/><\/svg><\/div>\r\n          <h4>Horario de atenci\u00f3n<\/h4>\r\n          <p>Tiempo de funcionamiento real que sirve de base para las estimaciones del desgaste y del tiempo hasta la aver\u00eda.<\/p>\r\n        <\/div>\r\n      <\/div>\r\n\r\n      <!-- core -->\r\n      <div class=\"arrow\">\r\n        <div class=\"dash\">\u2192<\/div>\r\n        <div class=\"core\"><b>MRO360<br>Motor de previsiones<\/b><span>IA agencial frente a un modelo de referencia estad\u00edstico<\/span><\/div>\r\n        <div class=\"dash\">\u2192<\/div>\r\n      <\/div>\r\n\r\n      <!-- output -->\r\n      <div class=\"out\">\r\n        <h3 id=\"demand-forecast-per-sku\">Previsi\u00f3n de la demanda por SKU<\/h3>\r\n        <div class=\"line\"><span class=\"dot\"><\/span><b>Demanda preventiva<\/b><span>programado<\/span><\/div>\r\n        <div class=\"line\"><span class=\"dot\"><\/span><b>Demanda correctiva<\/b><span>basado en los errores<\/span><\/div>\r\n        <div class=\"line\"><span class=\"dot\"><\/span><b>Previsi\u00f3n a seis meses<\/b><span>mensual + semanal<\/span><\/div>\r\n        <div class=\"line\"><span class=\"dot\"><\/span><b>Se han se\u00f1alado los m\u00e1ximos y m\u00ednimos<\/b><span>por per\u00edodo<\/span><\/div>\r\n        <div class=\"line\"><span class=\"dot\"><\/span><b>A nivel de planta y a escala mundial<\/b><span>banner enrollable<\/span><\/div>\r\n      <\/div>\r\n    <\/div>\r\n  <\/div>\r\n<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e26f8f0 elementor-widget elementor-widget-html\" data-id=\"e26f8f0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<!-- ============================================================\r\n     SECTION 05: DUAL-ENGINE + MULTIPLE AI MODELS, AUTO-SELECTED\r\n     From script: \"multiple AI models available... system auto\r\n     recommends the best available AI model for your domain.\"\r\n     Plus PDF Module 06 dual-engine framing. Scoped .vd-mdf-models\r\n============================================================ -->\r\n<div class=\"vd-mdf-models\">\r\n  <style>\r\n    @import url('https:\/\/fonts.googleapis.com\/css2?family=Open+Sans:wght@400;500;600;700;800&display=swap');\r\n    .vd-mdf-models{\r\n      margin-left:calc(50% - 50vw);margin-right:calc(50% - 50vw);width:100vw;\r\n      font-family:'Open Sans',Arial,sans-serif;\r\n      background:linear-gradient(135deg,#024089,#004DA9);color:#fff;padding:84px 0;\r\n    }\r\n    .vd-mdf-models *{box-sizing:border-box;}\r\n    .vd-mdf-models .wrap{max-width:1140px;margin:0 auto;padding:0 24px;}\r\n    .vd-mdf-models .head{max-width:740px;margin:0 0 46px;}\r\n    .vd-mdf-models .eyebrow{font-size:13px;font-weight:700;letter-spacing:.12em;text-transform:uppercase;color:#FFC619;margin:0 0 14px;}\r\n    .vd-mdf-models h2{font-size:clamp(26px,3.2vw,36px);line-height:1.14;font-weight:800;margin:0 0 16px;letter-spacing:-.01em;}\r\n    .vd-mdf-models .head p{font-size:16.5px;line-height:1.64;color:#d6e2f7;margin:0;}\r\n\r\n    .vd-mdf-models .duo{display:grid;grid-template-columns:1fr 1fr;gap:20px;margin-bottom:34px;}\r\n    .vd-mdf-models .eng{background:rgba(255,255,255,.06);border:1px solid rgba(255,255,255,.16);border-radius:14px;padding:26px;}\r\n    .vd-mdf-models .eng .badge{display:inline-block;font-size:11px;font-weight:800;letter-spacing:.06em;text-transform:uppercase;padding:5px 11px;border-radius:20px;margin-bottom:14px;}\r\n    .vd-mdf-models .eng.stat .badge{background:rgba(255,255,255,.16);color:#fff;}\r\n    .vd-mdf-models .eng.ai .badge{background:#FA841A;color:#1A2434;}\r\n    .vd-mdf-models .eng h3{font-size:19px;font-weight:700;margin:0 0 10px;}\r\n    .vd-mdf-models .eng p{font-size:14.5px;line-height:1.6;color:#cdd9ef;margin:0;}\r\n\r\n    .vd-mdf-models .auto{\r\n      background:rgba(0,0,0,.18);border:1px solid rgba(255,255,255,.14);border-radius:14px;\r\n      padding:28px;display:grid;grid-template-columns:1.1fr 1fr;gap:30px;align-items:center;\r\n    }\r\n    .vd-mdf-models .auto h3{font-size:21px;font-weight:800;margin:0 0 12px;}\r\n    .vd-mdf-models .auto h3 span{color:#FFC619;}\r\n    .vd-mdf-models .auto p{font-size:15px;line-height:1.6;color:#d6e2f7;margin:0;}\r\n    .vd-mdf-models .picker{background:#fff;border-radius:11px;padding:16px;color:#1A2434;box-shadow:0 16px 40px rgba(0,0,0,.3);}\r\n    .vd-mdf-models .picker .pk{display:flex;align-items:center;gap:10px;padding:11px 12px;border-radius:8px;margin-bottom:8px;border:1px solid #e1e3e7;font-size:13px;font-weight:600;}\r\n    .vd-mdf-models .picker .pk:last-child{margin-bottom:0;}\r\n    .vd-mdf-models .picker .pk.best{border-color:#FA841A;background:#fff7ee;}\r\n    .vd-mdf-models .picker .pk .mname{flex:1;}\r\n    .vd-mdf-models .picker .pk .acc{font-size:12px;font-weight:800;color:#024089;}\r\n    .vd-mdf-models .picker .pk.best .acc{color:#FA841A;}\r\n    .vd-mdf-models .picker .pk .star{color:#FA841A;font-weight:800;}\r\n    .vd-mdf-models .picker .pk .mut{color:#9aa3b1;}\r\n    .vd-mdf-models .picker .cap{font-size:11px;color:#8a93a3;margin:10px 2px 0;font-weight:600;}\r\n    @media(max-width:860px){.vd-mdf-models .duo{grid-template-columns:1fr;}.vd-mdf-models .auto{grid-template-columns:1fr;gap:22px;}}\r\n  <\/style>\r\n\r\n  <div class=\"wrap\">\r\n    <div class=\"head\">\r\n      <p class=\"eyebrow\">Los modelos de predicci\u00f3n<\/p>\r\n      <h2 id=\"two-engines-working-together-with-the-right-ai-model-chosen-for-you\">Dos motores que funcionan en conjunto, con el modelo de IA m\u00e1s adecuado para ti<\/h2>\r\n      <p>Un motor estad\u00edstico establece la base estructural. Un motor de inteligencia artificial aporta contexto y reconocimiento de patrones: picos de recuperaci\u00f3n, fallos correlacionados y se\u00f1ales estacionales que el an\u00e1lisis matem\u00e1tico por s\u00ed solo no detectar\u00eda.<\/p>\r\n    <\/div>\r\n\r\n    <div class=\"duo\">\r\n      <div class=\"eng stat\">\r\n        <span class=\"badge\">Motor 1<\/span>\r\n        <h3 id=\"statistical-baseline\">Referencia estad\u00edstica<\/h3>\r\n        <p>Los modelos probados basados en series temporales y en el consumo determinan el patr\u00f3n estructural de la demanda a partir de los datos hist\u00f3ricos de consumo, lo que constituye la base s\u00f3lida sobre la que se asienta toda previsi\u00f3n.<\/p>\r\n      <\/div>\r\n      <div class=\"eng ai\">\r\n        <span class=\"badge\">Motor 2<\/span>\r\n        <h3 id=\"agentic-ai-context-layer\">Capa de contexto de IA agentiva<\/h3>\r\n        <p>Los modelos entrenados espec\u00edficamente para este fin integran el historial de aver\u00edas, las notas de mantenimiento y las se\u00f1ales de las \u00f3rdenes de trabajo, perfeccionando as\u00ed la l\u00ednea de base con el contexto operativo espec\u00edfico de sus plantas.<\/p>\r\n      <\/div>\r\n    <\/div>\r\n\r\n    <div class=\"auto\">\r\n      <div>\r\n        <h3 id=\"multiple-models-one-auto-recommended-fit\">Varios modelos. <span>Una combinaci\u00f3n recomendada autom\u00e1ticamente.<\/span><\/h3>\r\n        <p>MRO360 cuenta con m\u00faltiples modelos de IA y eval\u00faa cu\u00e1l ofrece el mejor rendimiento para tu \u00e1mbito y tus datos, para luego recomendarlo autom\u00e1ticamente. No es necesario ajustar los modelos manualmente, y la elecci\u00f3n es siempre transparente.<\/p>\r\n      <\/div>\r\n      <div class=\"picker\" aria-hidden=\"true\">\r\n        <div class=\"pk best\"><span class=\"star\">\u2605<\/span><span class=\"mname\">Recomendaciones autom\u00e1ticas para tu dominio<\/span><span class=\"acc\">La mejor opci\u00f3n<\/span><\/div>\r\n        <div class=\"pk\"><span class=\"mut\">\u25cb<\/span><span class=\"mname\">Modelo con refuerzo de gradiente<\/span><span class=\"acc\">Alt<\/span><\/div>\r\n        <div class=\"pk\"><span class=\"mut\">\u25cb<\/span><span class=\"mname\">Series temporales + demanda intermitente<\/span><span class=\"acc\">Alt<\/span><\/div>\r\n        <p class=\"cap\">El sistema selecciona autom\u00e1ticamente; los planificadores pueden anular esta selecci\u00f3n<\/p>\r\n      <\/div>\r\n    <\/div>\r\n  <\/div>\r\n<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2149e53 elementor-widget elementor-widget-html\" data-id=\"2149e53\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<!-- ============================================================\r\n     SECTION 06: PREVENTIVE + CORRECTIVE DEMAND\r\n     From script: forecast \"consists not only for preventive as\r\n     well as corrective orders\". PDF: planned\/corrective\/emergency\r\n     forecasted independently. Scoped under .vd-mdf-types\r\n============================================================ -->\r\n<div class=\"vd-mdf-types\">\r\n  <style>\r\n    @import url('https:\/\/fonts.googleapis.com\/css2?family=Open+Sans:wght@400;500;600;700;800&display=swap');\r\n    .vd-mdf-types{font-family:'Open Sans',Arial,sans-serif;background:#ffffff;padding:84px 0;}\r\n    .vd-mdf-types *{box-sizing:border-box;}\r\n    .vd-mdf-types .wrap{max-width:1120px;margin:0 auto;padding:0 24px;}\r\n    .vd-mdf-types .head{max-width:740px;margin:0 0 44px;}\r\n    .vd-mdf-types .eyebrow{font-size:13px;font-weight:700;letter-spacing:.12em;text-transform:uppercase;color:#FA841A;margin:0 0 14px;}\r\n    .vd-mdf-types h2{font-size:clamp(26px,3.2vw,36px);line-height:1.14;font-weight:800;color:#1A2434;margin:0 0 16px;letter-spacing:-.01em;}\r\n    .vd-mdf-types .head p{font-size:16.5px;line-height:1.64;color:#46505f;margin:0;}\r\n    .vd-mdf-types .row{display:grid;grid-template-columns:repeat(3,1fr);gap:18px;}\r\n    .vd-mdf-types .t{border-radius:14px;padding:26px 24px;border:1px solid #e1e3e7;position:relative;overflow:hidden;}\r\n    .vd-mdf-types .t::before{content:\"\";position:absolute;top:0;left:0;right:0;height:4px;}\r\n    .vd-mdf-types .t.prev::before{background:#004DA9;}\r\n    .vd-mdf-types .t.corr::before{background:#FA841A;}\r\n    .vd-mdf-types .t.emer::before{background:#FFC619;}\r\n    .vd-mdf-types .t .tag{font-size:11px;font-weight:800;letter-spacing:.07em;text-transform:uppercase;margin:0 0 12px;}\r\n    .vd-mdf-types .t.prev .tag{color:#004DA9;}\r\n    .vd-mdf-types .t.corr .tag{color:#FA841A;}\r\n    .vd-mdf-types .t.emer .tag{color:#d99500;}\r\n    .vd-mdf-types .t h3{font-size:19px;font-weight:700;color:#1A2434;margin:0 0 10px;}\r\n    .vd-mdf-types .t p{font-size:14.5px;line-height:1.6;color:#4d5765;margin:0 0 14px;}\r\n    .vd-mdf-types .t .basis{font-size:12.5px;color:#6a7585;background:#f6f8fb;border-radius:8px;padding:10px 12px;font-weight:600;}\r\n    .vd-mdf-types .note{\r\n      margin-top:30px;text-align:center;font-size:15.5px;line-height:1.6;color:#384152;max-width:840px;margin-left:auto;margin-right:auto;\r\n    }\r\n    .vd-mdf-types .note strong{color:#024089;}\r\n    @media(max-width:820px){.vd-mdf-types .row{grid-template-columns:1fr;}}\r\n  <\/style>\r\n\r\n  <div class=\"wrap\">\r\n    <div class=\"head\">\r\n      <p class=\"eyebrow\">Previsi\u00f3n por tipo de mantenimiento<\/p>\r\n      <h2 id=\"preventive-and-corrective-demand-forecasted-independently\">Demanda preventiva y correctiva, pronosticada de forma independiente<\/h2>\r\n      <p>Una cifra global \u00fanica no es adecuada para ninguno de los dos casos. MRO360 distingue entre la demanda programada y la provocada por aver\u00edas, y luego las combina en un total preciso por SKU, de modo que la l\u00f3gica de reposici\u00f3n refleje c\u00f3mo se consume realmente cada pieza.<\/p>\r\n    <\/div>\r\n\r\n    <div class=\"row\">\r\n      <div class=\"t prev\">\r\n        <p class=\"tag\">Preventivo<\/p>\r\n        <h3 id=\"scheduled-demand\">Demanda programada<\/h3>\r\n        <p>Basado en el calendario de mantenimiento y los planes de mantenimiento preventivo, y no solo en medias hist\u00f3ricas. Un consumo predecible y basado en el tiempo que permite una planificaci\u00f3n rigurosa.<\/p>\r\n        <div class=\"basis\">Base: calendario de PM \u00b7 planes de trabajo \u00b7 calendario<\/div>\r\n      <\/div>\r\n      <div class=\"t corr\">\r\n        <p class=\"tag\">Correctivo<\/p>\r\n        <h3 id=\"failure-driven-demand\">Demanda impulsada por los fallos<\/h3>\r\n        <p>Estimados a partir del historial de aver\u00edas, los patrones de las causas fundamentales, la antig\u00fcedad de los activos y las horas de funcionamiento, estos picos son imposibles de prever con un modelo basado \u00fanicamente en el uso.<\/p>\r\n        <div class=\"basis\">Fundamentos: modos de fallo \u00b7 RCA \u00b7 tiempo de funcionamiento \u00b7 MTBF<\/div>\r\n      <\/div>\r\n      <div class=\"t emer\">\r\n        <p class=\"tag\">Emergencia<\/p>\r\n        <h3 id=\"unplanned-exposure\">Exposici\u00f3n involuntaria<\/h3>\r\n        <p>Se detecta cuando el estado o las se\u00f1ales predictivas indican un fallo inminente, de modo que se dispone de un margen de seguridad antes de que se produzca la aver\u00eda, y no despu\u00e9s.<\/p>\r\n        <div class=\"basis\">Fundamento: se\u00f1ales de estado \u00b7 criticidad<\/div>\r\n      <\/div>\r\n    <\/div>\r\n\r\n    <p class=\"note\">Dado que cada flujo se modela por separado, <strong>stock de seguridad y puntos de reabastecimiento<\/strong> se puede comparar con la curva de demanda real de cada pieza.<\/p>\r\n  <\/div>\r\n<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-23a3d42 elementor-widget elementor-widget-html\" data-id=\"23a3d42\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<!-- ============================================================\r\n     SECTION 07: THE FORECAST OUTPUT (product UI)\r\n     From script: 6-month expected demand, monthly + weekly view,\r\n     highs and lows highlighted per material. CSS-only toggle so it\r\n     works inside Elementor without relying on external JS.\r\n     Scoped under .vd-mdf-demo  (anchor id=\"vd-mdf-demo\")\r\n============================================================ -->\r\n<div class=\"vd-mdf-demo\" id=\"vd-mdf-demo\">\r\n  <style>\r\n    @import url('https:\/\/fonts.googleapis.com\/css2?family=Open+Sans:wght@400;500;600;700;800&display=swap');\r\n    .vd-mdf-demo{\r\n      margin-left:calc(50% - 50vw);margin-right:calc(50% - 50vw);width:100vw;\r\n      font-family:'Open Sans',Arial,sans-serif;background:#f6f8fb;padding:84px 0;\r\n    }\r\n    .vd-mdf-demo *{box-sizing:border-box;}\r\n    .vd-mdf-demo .wrap{max-width:1080px;margin:0 auto;padding:0 24px;}\r\n    .vd-mdf-demo .head{text-align:center;max-width:720px;margin:0 auto 40px;}\r\n    .vd-mdf-demo .eyebrow{font-size:13px;font-weight:700;letter-spacing:.12em;text-transform:uppercase;color:#FA841A;margin:0 0 14px;}\r\n    .vd-mdf-demo h2{font-size:clamp(26px,3.2vw,36px);line-height:1.14;font-weight:800;color:#1A2434;margin:0 0 16px;letter-spacing:-.01em;}\r\n    .vd-mdf-demo .head p{font-size:16.5px;line-height:1.64;color:#46505f;margin:0;}\r\n\r\n    \/* app frame *\/\r\n    .vd-mdf-demo .app{background:#fff;border:1px solid #dde3ec;border-radius:16px;box-shadow:0 26px 60px rgba(26,36,52,.12);overflow:hidden;}\r\n    .vd-mdf-demo .topbar{display:flex;align-items:center;gap:8px;padding:13px 18px;background:#1A2434;}\r\n    .vd-mdf-demo .topbar i{width:11px;height:11px;border-radius:50%;display:inline-block;}\r\n    .vd-mdf-demo .topbar .url{margin-left:14px;font-size:12.5px;color:#aeb8c7;font-weight:600;}\r\n    .vd-mdf-demo .topbar .url b{color:#fff;}\r\n    .vd-mdf-demo .body{padding:26px 28px 30px;}\r\n    .vd-mdf-demo .barhead{display:flex;flex-wrap:wrap;align-items:flex-end;justify-content:space-between;gap:14px;margin-bottom:22px;}\r\n    .vd-mdf-demo .barhead .meta .sku{font-size:18px;font-weight:800;color:#1A2434;margin:0;}\r\n    .vd-mdf-demo .barhead .meta .sub{font-size:13px;color:#6a7585;margin:4px 0 0;font-weight:600;}\r\n    .vd-mdf-demo .barhead .total{text-align:right;}\r\n    .vd-mdf-demo .barhead .total .v{font-size:26px;font-weight:800;color:#004DA9;line-height:1;}\r\n    .vd-mdf-demo .barhead .total .l{font-size:11.5px;color:#8a93a3;font-weight:700;text-transform:uppercase;letter-spacing:.05em;margin-top:5px;}\r\n\r\n    \/* CSS toggle *\/\r\n    .vd-mdf-demo .switch{display:inline-flex;background:#eef2f8;border-radius:9px;padding:4px;margin-bottom:24px;}\r\n    .vd-mdf-demo .switch label{font-size:13px;font-weight:700;color:#6a7585;padding:8px 18px;border-radius:6px;cursor:pointer;transition:.15s;}\r\n    .vd-mdf-demo input[name=\"vd-mdf-view\"]{position:absolute;opacity:0;pointer-events:none;}\r\n    .vd-mdf-demo #vd-mdf-m:checked ~ .body label[for=\"vd-mdf-m\"]{background:#FA841A;color:#1A2434;}\r\n    .vd-mdf-demo #vd-mdf-w:checked ~ .body label[for=\"vd-mdf-w\"]{background:#FA841A;color:#1A2434;}\r\n    .vd-mdf-demo .view{display:none;}\r\n    .vd-mdf-demo #vd-mdf-m:checked ~ .body .view.monthly{display:block;}\r\n    .vd-mdf-demo #vd-mdf-w:checked ~ .body .view.weekly{display:block;}\r\n\r\n    \/* chart *\/\r\n    .vd-mdf-demo .chart{position:relative;border-left:2px solid #e1e3e7;border-bottom:2px solid #e1e3e7;padding:34px 4px 0;}\r\n    .vd-mdf-demo .bars{display:flex;align-items:flex-end;gap:10px;height:200px;}\r\n    .vd-mdf-demo .weekly .bars{gap:5px;}\r\n    .vd-mdf-demo .col{flex:1;display:flex;flex-direction:column;align-items:center;justify-content:flex-end;height:100%;position:relative;}\r\n    .vd-mdf-demo .col .bar{width:78%;border-radius:6px 6px 0 0;background:linear-gradient(180deg,#4d8ce0,#1f63bd);transition:.2s;}\r\n    .vd-mdf-demo .col.high .bar{background:linear-gradient(180deg,#FFC619,#FA841A);}\r\n    .vd-mdf-demo .col.low .bar{background:linear-gradient(180deg,#cdd9ef,#9fb6df);}\r\n    .vd-mdf-demo .col .pill{position:absolute;top:-22px;font-size:9.5px;font-weight:800;letter-spacing:.04em;padding:2px 7px;border-radius:20px;white-space:nowrap;}\r\n    .vd-mdf-demo .col.high .pill{background:#fff2dc;color:#d97a00;}\r\n    .vd-mdf-demo .col.low .pill{background:#eef2f8;color:#5a6472;}\r\n    .vd-mdf-demo .col .xl{position:absolute;bottom:-26px;font-size:11px;font-weight:600;color:#6a7585;}\r\n    .vd-mdf-demo .chart{margin-bottom:34px;}\r\n    .vd-mdf-demo .legend{display:flex;flex-wrap:wrap;gap:18px;justify-content:center;padding-top:8px;}\r\n    .vd-mdf-demo .legend div{font-size:12.5px;color:#46505f;font-weight:600;}\r\n    .vd-mdf-demo .legend i{display:inline-block;width:12px;height:12px;border-radius:3px;margin-right:7px;vertical-align:-1px;}\r\n    .vd-mdf-demo .caption{text-align:center;font-size:13px;color:#8a93a3;margin-top:18px;font-weight:600;}\r\n    @media(max-width:560px){.vd-mdf-demo .weekly{display:none !important;}.vd-mdf-demo .switch label[for=\"vd-mdf-w\"]{opacity:.4;}}\r\n  <\/style>\r\n\r\n  <div class=\"wrap\">\r\n    <div class=\"head\">\r\n      <p class=\"eyebrow\">M\u00edralo en el producto<\/p>\r\n      <h2 id=\"six-months-of-demand-by-month-and-by-week\">La evoluci\u00f3n de la demanda durante seis meses, por meses y por semanas<\/h2>\r\n      <p>Elige cualquier material y MRO360 calcula la demanda prevista para los pr\u00f3ximos seis meses, se\u00f1alando los picos y los m\u00ednimos para que los planificadores sepan exactamente cu\u00e1ndo aumenta la presi\u00f3n.<\/p>\r\n    <\/div>\r\n\r\n    <div class=\"app\">\r\n      <input type=\"radio\" name=\"vd-mdf-view\" id=\"vd-mdf-m\" checked>\r\n      <input type=\"radio\" name=\"vd-mdf-view\" id=\"vd-mdf-w\">\r\n\r\n      <div class=\"topbar\">\r\n        <i style=\"background:#FA841A\"><\/i><i style=\"background:#FDA300\"><\/i><i style=\"background:#FFC619\"><\/i>\r\n        <span class=\"url\">MRO360 \u00b7 <b>Previsi\u00f3n de la demanda<\/b><\/span>\r\n      <\/div>\r\n\r\n      <div class=\"body\">\r\n        <div class=\"barhead\">\r\n          <div class=\"meta\">\r\n            <p class=\"sku\">Referencia 10-BRG-4471 \u00b7 Cojinete de eje de alta presi\u00f3n<\/p>\r\n            <p class=\"sub\">Planta: Refiner\u00eda A \u00b7 Se ha aplicado el modelo recomendado autom\u00e1ticamente<\/p>\r\n          <\/div>\r\n          <div class=\"total\"><div class=\"v\">142 unidades<\/div><div class=\"l\">Previsi\u00f3n para los pr\u00f3ximos seis meses<\/div><\/div>\r\n        <\/div>\r\n\r\n        <div class=\"switch\">\r\n          <label for=\"vd-mdf-m\">Vista mensual<\/label>\r\n          <label for=\"vd-mdf-w\">Vista semanal<\/label>\r\n        <\/div>\r\n\r\n        <!-- MONTHLY -->\r\n        <div class=\"view monthly\">\r\n          <div class=\"chart\">\r\n            <div class=\"bars\">\r\n              <div class=\"col\"><div class=\"bar\" style=\"height:44%\"><\/div><span class=\"xl\">julio<\/span><\/div>\r\n              <div class=\"col high\"><div class=\"pill\">PEAK<\/div><div class=\"bar\" style=\"height:90%\"><\/div><span class=\"xl\">Agosto<\/span><\/div>\r\n              <div class=\"col\"><div class=\"bar\" style=\"height:55%\"><\/div><span class=\"xl\">Sep<\/span><\/div>\r\n              <div class=\"col low\"><div class=\"pill\">BAJA<\/div><div class=\"bar\" style=\"height:28%\"><\/div><span class=\"xl\">Oct<\/span><\/div>\r\n              <div class=\"col high\"><div class=\"pill\">PEAK<\/div><div class=\"bar\" style=\"height:80%\"><\/div><span class=\"xl\">Nov<\/span><\/div>\r\n              <div class=\"col\"><div class=\"bar\" style=\"height:42%\"><\/div><span class=\"xl\">Dic<\/span><\/div>\r\n            <\/div>\r\n          <\/div>\r\n          <div class=\"legend\">\r\n            <div><i style=\"background:#1f63bd\"><\/i>Demanda de referencia<\/div>\r\n            <div><i style=\"background:#FA841A\"><\/i>M\u00e1xima prevista<\/div>\r\n            <div><i style=\"background:#9fb6df\"><\/i>M\u00ednima prevista<\/div>\r\n          <\/div>\r\n        <\/div>\r\n\r\n        <!-- WEEKLY -->\r\n        <div class=\"view weekly\">\r\n          <div class=\"chart\">\r\n            <div class=\"bars\">\r\n              <div class=\"col\"><div class=\"bar\" style=\"height:30%\"><\/div><span class=\"xl\">W1<\/span><\/div>\r\n              <div class=\"col\"><div class=\"bar\" style=\"height:38%\"><\/div><span class=\"xl\">W2<\/span><\/div>\r\n              <div class=\"col\"><div class=\"bar\" style=\"height:52%\"><\/div><span class=\"xl\">W3<\/span><\/div>\r\n              <div class=\"col high\"><div class=\"pill\">PEAK<\/div><div class=\"bar\" style=\"height:92%\"><\/div><span class=\"xl\">W4<\/span><\/div>\r\n              <div class=\"col\"><div class=\"bar\" style=\"height:60%\"><\/div><span class=\"xl\">W5<\/span><\/div>\r\n              <div class=\"col\"><div class=\"bar\" style=\"height:48%\"><\/div><span class=\"xl\">Semana 6<\/span><\/div>\r\n              <div class=\"col low\"><div class=\"pill\">BAJA<\/div><div class=\"bar\" style=\"height:22%\"><\/div><span class=\"xl\">W7<\/span><\/div>\r\n              <div class=\"col\"><div class=\"bar\" style=\"height:36%\"><\/div><span class=\"xl\">W8<\/span><\/div>\r\n              <div class=\"col\"><div class=\"bar\" style=\"height:50%\"><\/div><span class=\"xl\">W9<\/span><\/div>\r\n              <div class=\"col high\"><div class=\"pill\">PEAK<\/div><div class=\"bar\" style=\"height:84%\"><\/div><span class=\"xl\">W10<\/span><\/div>\r\n              <div class=\"col\"><div class=\"bar\" style=\"height:44%\"><\/div><span class=\"xl\">W11<\/span><\/div>\r\n              <div class=\"col\"><div class=\"bar\" style=\"height:32%\"><\/div><span class=\"xl\">W12<\/span><\/div>\r\n            <\/div>\r\n          <\/div>\r\n          <div class=\"legend\">\r\n            <div><i style=\"background:#1f63bd\"><\/i>Demanda de referencia<\/div>\r\n            <div><i style=\"background:#FA841A\"><\/i>M\u00e1xima prevista<\/div>\r\n            <div><i style=\"background:#9fb6df\"><\/i>M\u00ednima prevista<\/div>\r\n          <\/div>\r\n        <\/div>\r\n\r\n        <p class=\"caption\">Imagen ilustrativa. Las previsiones se generan por SKU a partir de los datos de su propio ERP, CMMS y de mantenimiento.<\/p>\r\n      <\/div>\r\n    <\/div>\r\n  <\/div>\r\n<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-29986fd elementor-widget elementor-widget-html\" data-id=\"29986fd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<!-- ============================================================\r\n     SECTION 08: DEMAND FORECASTING METHODS\r\n     Targets informational keyword \"demand forecasting methods\"\r\n     within a commercial page, then routes depth to the pillar\r\n     article via internal link. Scoped under .vd-mdf-methods\r\n     INTERNAL LINK TODO: confirm pillar slug.\r\n============================================================ -->\r\n<div class=\"vd-mdf-methods\">\r\n  <style>\r\n    @import url('https:\/\/fonts.googleapis.com\/css2?family=Open+Sans:wght@400;500;600;700;800&display=swap');\r\n    .vd-mdf-methods{font-family:'Open Sans',Arial,sans-serif;background:#fff;padding:84px 0;}\r\n    .vd-mdf-methods *{box-sizing:border-box;}\r\n    .vd-mdf-methods .wrap{max-width:1100px;margin:0 auto;padding:0 24px;}\r\n    .vd-mdf-methods .head{max-width:740px;margin:0 0 42px;}\r\n    .vd-mdf-methods .eyebrow{font-size:13px;font-weight:700;letter-spacing:.12em;text-transform:uppercase;color:#FA841A;margin:0 0 14px;}\r\n    .vd-mdf-methods h2{font-size:clamp(26px,3.2vw,36px);line-height:1.14;font-weight:800;color:#1A2434;margin:0 0 16px;letter-spacing:-.01em;}\r\n    .vd-mdf-methods .head p{font-size:16.5px;line-height:1.64;color:#46505f;margin:0;}\r\n    .vd-mdf-methods .grid{display:grid;grid-template-columns:repeat(2,1fr);gap:18px;}\r\n    .vd-mdf-methods .m{border:1px solid #e1e3e7;border-radius:13px;padding:24px;background:#fbfcfe;}\r\n    .vd-mdf-methods .m h3{font-size:17px;font-weight:700;color:#024089;margin:0 0 8px;display:flex;align-items:center;gap:10px;}\r\n    .vd-mdf-methods .m h3 .num{width:28px;height:28px;border-radius:8px;background:#eef4fe;color:#004DA9;font-size:13px;font-weight:800;display:flex;align-items:center;justify-content:center;}\r\n    .vd-mdf-methods .m p{font-size:14px;line-height:1.6;color:#4d5765;margin:0 0 12px;}\r\n    .vd-mdf-methods .m .use{font-size:12.5px;font-weight:700;color:#6a7585;}\r\n    .vd-mdf-methods .m .use span{color:#FA841A;}\r\n    .vd-mdf-methods .m.flag{background:linear-gradient(150deg,#fff7ee,#fff);border-color:#fad9b3;border-left:4px solid #FA841A;}\r\n    .vd-mdf-methods .m.flag h3{color:#1A2434;}\r\n    .vd-mdf-methods .m.flag h3 .num{background:#FA841A;color:#1A2434;}\r\n    .vd-mdf-methods .closer{\r\n      margin-top:30px;background:#1A2434;border-radius:13px;padding:26px 28px;color:#fff;\r\n      display:flex;flex-wrap:wrap;align-items:center;justify-content:space-between;gap:18px;\r\n    }\r\n    .vd-mdf-methods .closer p{margin:0;font-size:15.5px;line-height:1.5;max-width:640px;color:#dbe3ef;}\r\n    .vd-mdf-methods .closer p b{color:#FFC619;}\r\n    .vd-mdf-methods .closer a{flex:none;text-decoration:none;background:#FA841A;color:#1A2434;font-weight:700;font-size:14px;border-radius:8px;padding:13px 22px;}\r\n    @media(max-width:780px){.vd-mdf-methods .grid{grid-template-columns:1fr;}}\r\n  <\/style>\r\n\r\n  <div class=\"wrap\">\r\n    <div class=\"head\">\r\n      <p class=\"eyebrow\">Los enfoques<\/p>\r\n      <h2 id=\"demand-forecasting-methods-and-which-one-mro360-uses\">M\u00e9todos de previsi\u00f3n de la demanda y cu\u00e1l utiliza MRO360<\/h2>\r\n      <p>La mayor\u00eda de los m\u00e9todos de previsi\u00f3n de la demanda se pueden clasificar en unas pocas categor\u00edas. Cada una tiene su lugar; la diferencia radica en la cantidad de contexto operativo que cada m\u00e9todo es capaz de asimilar. A continuaci\u00f3n, te ofrecemos un resumen.<\/p>\r\n    <\/div>\r\n\r\n    <div class=\"grid\">\r\n      <div class=\"m\">\r\n        <h3 id=\"1time-series-statistical\"><span class=\"num\">1<\/span>Series temporales y estad\u00edstica<\/h3>\r\n        <p>Las medias m\u00f3viles, el suavizado exponencial y los modelos de tipo ARIMA realizan proyecciones a partir de los datos hist\u00f3ricos de consumo y la estacionalidad.<\/p>\r\n        <p class=\"use\">Ideal para: <span>un uso estable y predecible<\/span><\/p>\r\n      <\/div>\r\n      <div class=\"m\">\r\n        <h3 id=\"2consumption-based-planning\"><span class=\"num\">2<\/span>Planificaci\u00f3n basada en el consumo<\/h3>\r\n        <p>La l\u00f3gica nativa del ERP (como MRP o MM-CBP) realiza los pedidos de reposici\u00f3n bas\u00e1ndose en los movimientos de mercanc\u00edas anteriores y en los plazos de entrega de toda la red de materiales.<\/p>\r\n        <p class=\"use\">Ideal para: <span>piezas de gran volumen y producci\u00f3n constante<\/span><\/p>\r\n      <\/div>\r\n      <div class=\"m\">\r\n        <h3 id=\"3movement-classification\"><span class=\"num\">3<\/span>Clasificaci\u00f3n de los movimientos<\/h3>\r\n        <p>ABC-XYZ y la segmentaci\u00f3n en r\u00e1pido, lento y sin movimiento ajustan la pol\u00edtica en funci\u00f3n de la velocidad y la variabilidad, sacando a la luz el stock muerto y el excedente.<\/p>\r\n        <p class=\"use\">Ideal para: <span>racionalizaci\u00f3n a nivel de cartera<\/span><\/p>\r\n      <\/div>\r\n      <div class=\"m flag\">\r\n        <h3 id=\"4ai-native-multivariate\"><span class=\"num\">4<\/span>Multivariante nativo de IA<\/h3>\r\n        <p>El enfoque de MRO360: una base de datos estad\u00edstica combinada con una IA aut\u00f3noma que analiza el historial de aver\u00edas, las notas de mantenimiento, la antig\u00fcedad de los activos y las \u00f3rdenes de trabajo, y distingue entre las necesidades de mantenimiento preventivo y las de mantenimiento correctivo.<\/p>\r\n        <p class=\"use\">Ideal para: <span>demanda de mantenimiento, reparaci\u00f3n y revisi\u00f3n (MRO) impulsada por las aver\u00edas<\/span><\/p>\r\n      <\/div>\r\n    <\/div>\r\n\r\n    <div class=\"closer\">\r\n      <p>El m\u00e9todo adecuado depende de la pieza. MRO360 aplica <b>m\u00e9todos estad\u00edsticos y de inteligencia artificial combinados<\/b> y selecciona autom\u00e1ticamente la opci\u00f3n m\u00e1s adecuada para cada dominio, en lugar de hacer pasar todas las referencias por un \u00fanico modelo.<\/p>\r\n      <a href=\"https:\/\/www.verdantis.com\/contact\/\">M\u00e1s informaci\u00f3n \u2192<\/a>\r\n    <\/div>\r\n  <\/div>\r\n<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cbb4f5e elementor-widget elementor-widget-html\" data-id=\"cbb4f5e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<!-- ============================================================\r\n     SECTION 09: VALUE + CTA\r\n     Outcomes are mechanism-based. The two percentage figures tie\r\n     directly to demand forecasting (emergency spend, stockouts)\r\n     and are taken from the MRO360 deck.\r\n     >>> VERIFY: confirm these ranges with product\/legal before\r\n         external publication (standing Verdantis caveat). <<<\r\n     Scoped under .vd-mdf-value\r\n============================================================ -->\r\n<div class=\"vd-mdf-value\">\r\n  <style>\r\n    @import url('https:\/\/fonts.googleapis.com\/css2?family=Open+Sans:wght@400;500;600;700;800&display=swap');\r\n    .vd-mdf-value{\r\n      margin-left:calc(50% - 50vw);margin-right:calc(50% - 50vw);width:100vw;\r\n      font-family:'Open Sans',Arial,sans-serif;\r\n      background:\r\n        radial-gradient(900px 480px at 18% 0%, rgba(250,132,26,.18), transparent 60%),\r\n        linear-gradient(135deg,#024089,#003a82);color:#fff;padding:84px 0;\r\n    }\r\n    .vd-mdf-value *{box-sizing:border-box;}\r\n    .vd-mdf-value .wrap{max-width:1100px;margin:0 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.disc{font-size:12px;color:#9fb2da;margin:20px 2px 0;}\r\n    @media(max-width:860px){.vd-mdf-value .outcomes{grid-template-columns:1fr 1fr;}}\r\n    @media(max-width:520px){.vd-mdf-value .outcomes{grid-template-columns:1fr;}.vd-mdf-value .cta{flex-direction:column;align-items:flex-start;}}\r\n  <\/style>\r\n\r\n  <div class=\"wrap\">\r\n    <div class=\"head\">\r\n      <p class=\"eyebrow\">Por qu\u00e9 merece la pena realizar previsiones precisas<\/p>\r\n      <h2 id=\"forecast-the-demand-and-the-downstream-problems-stop-compounding\">Si se prev\u00e9 la demanda, los problemas posteriores dejar\u00e1n de agravarse<\/h2>\r\n      <p>Cuando la previsi\u00f3n distingue entre la demanda preventiva y la correctiva e identifica la se\u00f1al de fallo total, los planificadores dejan de reaccionar ante los desabastecimientos y comienzan a gestionarlos como excepciones poco frecuentes y se\u00f1aladas.<\/p>\r\n    <\/div>\r\n\r\n    <div class=\"outcomes\">\r\n      <div class=\"o\">\r\n        <p class=\"v\">50-70%<\/p>\r\n        <p class=\"l\">Se han reducido los gastos de emergencia<\/p>\r\n        <p class=\"d\">Menos pedidos de \u00faltima hora a precios elevados en los pr\u00f3ximos 12 meses.<\/p>\r\n      <\/div>\r\n      <div class=\"o\">\r\n        <p class=\"v\">90%+<\/p>\r\n        <p class=\"l\">Se han eliminado los casos de falta de existencias<\/p>\r\n        <p class=\"d\">Las piezas cr\u00edticas estar\u00e1n disponibles cuando la orden de trabajo las requiera.<\/p>\r\n      <\/div>\r\n      <div class=\"o\">\r\n        <p class=\"v\">Por referencia<\/p>\r\n        <p class=\"l\">Nivel de detalle de la previsi\u00f3n<\/p>\r\n        <p class=\"d\">Demanda modelizada por pieza, planta y tipo de mantenimiento.<\/p>\r\n      <\/div>\r\n      <div class=\"o\">\r\n        <p class=\"v\">Autoaprendizaje<\/p>\r\n        <p class=\"l\">Compuestos de precisi\u00f3n<\/p>\r\n        <p class=\"d\">Cada modificaci\u00f3n del planificador se registra y mejora el modelo.<\/p>\r\n      <\/div>\r\n    <\/div>\r\n\r\n    <div class=\"cta\">\r\n      <div>\r\n        <h3 id=\"see-your-own-demand-forecast\">Consulta tu propia previsi\u00f3n de demanda<\/h3>\r\n        <p>Traiga una muestra de sus datos de mantenimiento e inventario. Le mostraremos la previsi\u00f3n semestral que genera MRO360 para sus referencias m\u00e1s problem\u00e1ticas.<\/p>\r\n      <\/div>\r\n      <div class=\"btns\">\r\n        <a class=\"p\" href=\"\/contact\/\">Solicitar una demostraci\u00f3n<\/a>\r\n        <a class=\"g\" href=\"\/mro360\/\">Descubre MRO360<\/a>\r\n      <\/div>\r\n    <\/div>\r\n\r\n    <p class=\"disc\">Los rangos de resultados reflejan las implementaciones de MRO360 en entornos con gran cantidad de activos y var\u00edan en funci\u00f3n de la calidad de los datos, el perfil de los activos y el grado de madurez actual.<\/p>\r\n  <\/div>\r\n<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>La previsi\u00f3n de la demanda de piezas de repuesto difiere de la previsi\u00f3n de ventas o de producci\u00f3n. Descubre qu\u00e9 m\u00e9todos funcionan, en qu\u00e9 casos falla la f\u00f3rmula est\u00e1ndar en el sector MRO y c\u00f3mo MRO360 prev\u00e9 el consumo a nivel de planta.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[308],"tags":[],"class_list":["post-44469","post","type-post","status-publish","format-standard","hentry","category-page"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.verdantis.com\/es\/wp-json\/wp\/v2\/posts\/44469","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.verdantis.com\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.verdantis.com\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.verdantis.com\/es\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.verdantis.com\/es\/wp-json\/wp\/v2\/comments?post=44469"}],"version-history":[{"count":22,"href":"https:\/\/www.verdantis.com\/es\/wp-json\/wp\/v2\/posts\/44469\/revisions"}],"predecessor-version":[{"id":44945,"href":"https:\/\/www.verdantis.com\/es\/wp-json\/wp\/v2\/posts\/44469\/revisions\/44945"}],"wp:attachment":[{"href":"https:\/\/www.verdantis.com\/es\/wp-json\/wp\/v2\/media?parent=44469"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.verdantis.com\/es\/wp-json\/wp\/v2\/categories?post=44469"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.verdantis.com\/es\/wp-json\/wp\/v2\/tags?post=44469"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}