{"id":1541,"date":"2024-09-04T21:38:08","date_gmt":"2024-09-04T21:38:08","guid":{"rendered":"https:\/\/tellusant.com\/?p=1541"},"modified":"2025-05-21T12:50:22","modified_gmt":"2025-05-21T12:50:22","slug":"normas-de-prevision-estrategica","status":"publish","type":"post","link":"https:\/\/tellusant.com\/es\/strategic-forecasting-rules\/","title":{"rendered":"Reglas de previsi\u00f3n estrat\u00e9gica"},"content":{"rendered":"
\n

Cada paso puede parecer f\u00e1cil, pero en combinaci\u00f3n somos \u00fanicos en el mundo para hacer modelos a largo plazo como estos. C\u00f3mo lo hacemos en la pr\u00e1ctica es algo que nos pertenece. Lo que ves aqu\u00ed son algunos de los componentes b\u00e1sicos, a alto nivel.<\/p>\n

No creamos modelos para los clientes en s\u00ed. Los utilizamos para apoyar nuestros productos de planificaci\u00f3n estrat\u00e9gica, en los que integramos modelos predictivos. Nos centramos en ayudar a mejorar la toma de decisiones, no en ofrecer modelos.<\/p>\n

Dicho esto, nuestros modelos predictivos son tan buenos o mejores que cualquier otro del mundo. El \u00faltimo ejemplo es PACE (Precios adaptados a la Econom\u00eda de Consumo)<\/a>que se basa en lo que describimos aqu\u00ed, y mucho m\u00e1s.<\/p>\n

>Reglas de previsi\u00f3n estrat\u00e9gica<\/h1>\n<\/div>\n
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Anclaje ONE-Model<\/h4>\n

Las predicciones a largo plazo (de 3 a 10 a\u00f1os) deben basarse en una visi\u00f3n de futuro. S\u00f3lo se dispone de unas pocas series de datos de este tipo.<\/p>\n

\"Tellusant:regla<\/p>\n

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Predecir s\u00f3lo con datos hist\u00f3ricos es una tonter\u00eda. Sabemos m\u00e1s sobre el futuro de lo que sugiere este planteamiento.<\/p>\n

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DOS-Herramientas a largo plazo frente a herramientas a corto plazo<\/h4>\n

Las herramientas para las predicciones a largo plazo son diferentes de las que se utilizan para las predicciones a corto plazo. La elasticidad de los ingresos tiene una importancia fundamental. La elasticidad de los precios, menos.<\/p>\n

\"Tellusant:<\/p>\n

\n

A menudo, los analistas ampl\u00edan los modelos existentes a corto plazo cuando la alta direcci\u00f3n solicita una perspectiva a largo plazo. Esto suele ser una mala idea.<\/p>\n

\n<\/div>\n

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TRES-Distribuci\u00f3n de ingresos<\/h4>\n

La distribuci\u00f3n de la renta dice mucho m\u00e1s sobre el futuro que el uso de medias. Es lo que se denomina econom\u00eda distributiva y mejora notablemente la precisi\u00f3n.<\/p>\n<\/div>\n

\"Tellusant:<\/p>\n

\n

Con demasiada frecuencia, los analistas utilizan en sus modelos conceptos anticuados como las medias, lo que reduce la credibilidad y la precisi\u00f3n.<\/p>\n

\n<\/div>\n

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CUATRO-Esfuerzo laboral<\/h4>\n

El esfuerzo de trabajo necesario para comprar un producto tiene una importancia fundamental en los modelos a largo plazo. Este esfuerzo suele medirse en minutos de trabajo.<\/p>\n

\"Tellusant:<\/p>\n

\n

La mayor\u00eda de los modelos de demanda no tienen en cuenta el esfuerzo laboral, ni est\u00e1tica ni din\u00e1micamente. Sin embargo, ayuda a explicar por qu\u00e9 algunos mercados despegan de repente.<\/p>\n

\n<\/div>\n

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FIVE-S-Curves<\/h4>\n

Los modelos no lineales son m\u00e1s potentes que las regresiones lineales est\u00e1ndar. Las curvas en S suelen representar bien la demanda y est\u00e1n relacionadas matem\u00e1ticamente con las elasticidades.<\/p>\n

\"Tellusant:<\/p>\n

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La elasticidad de los ingresos suele disminuir a lo largo del horizonte previsto. Por tanto, el uso de modelos lineales conduce a visiones exageradas de la demanda futura.<\/p>\n

\n<\/div>\n

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SEIS-Paridad de poder adquisitivo (PPA)<\/h4>\n

Los precios ajustados a la paridad del poder adquisitivo son un mejor indicador que los precios est\u00e1ndar basados en el tipo de cambio. Esto es especialmente cierto en los pa\u00edses menos ricos.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

\"Tellusant:<\/p>\n

\n

Muchas empresas subestiman las oportunidades de mercado en los pa\u00edses emergentes bas\u00e1ndose en una hip\u00f3tesis err\u00f3nea sobre el tipo de cambio en los modelos de demanda.<\/p>\n

\n<\/div>\n

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SIETE: Agrupaci\u00f3n de datos<\/h4>\n

La puesta en com\u00fan de datos entre pa\u00edses es una t\u00e9cnica poderosa. Tambi\u00e9n se aplica al pensamiento l\u00f3gico. Utilice el mayor n\u00famero posible de pa\u00edses en los an\u00e1lisis.<\/p>\n

\"Tellusant:<\/p>\n

A menudo, los analistas en su pa\u00eds de origen modelan s\u00f3lo su pa\u00eds de origen. Esto no es bueno. Gran parte de los conocimientos aplicables al pa\u00eds de origen proceden de los pa\u00edses de comparaci\u00f3n.<\/p>\n

\n

Estas son las siete reglas de previsi\u00f3n estrat\u00e9gica que Tellusant utiliza, por ejemplo, TelluBase<\/a> y PoluSim<\/a>.<\/p>\n

\n

Encontrar\u00e1 m\u00e1s informaci\u00f3n sobre los m\u00e9todos de construcci\u00f3n de modelos en el art\u00edculo acad\u00e9mico \u00bfD\u00f3nde est\u00e1 el mercado?<\/a> por Canback y D'Agnese.<\/p>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"

Este breve documento describe lo sofisticados y cuidadosos que son nuestros modelos de predicci\u00f3n a largo plazo y permite a los lectores espiar bajo el cap\u00f3.<\/p>","protected":false},"author":1,"featured_media":3389,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"cybocfi_hide_featured_image":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[24,19,58],"tags":[45,46,47,48],"class_list":["post-1541","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ideas-thoughts-category","category-products-solutions-category","category-strategic-forecasting-category","tag-affordability","tag-forecasting","tag-predictive-models","tag-strategic-planning"],"acf":[],"yoast_head":"\nStrategic Forecasting Rules | Tellusant<\/title>\n<meta name=\"description\" content=\"This post describes our strategic forecasting rules and dicsusses some of our methods. 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