{"id":6861,"date":"2018-05-11T09:00:09","date_gmt":"2018-05-11T13:00:09","guid":{"rendered":"https:\/\/demand-planning.com\/?p=6861"},"modified":"2018-05-11T09:06:25","modified_gmt":"2018-05-11T13:06:25","slug":"weather-forecasting-and-business-forecasting","status":"publish","type":"post","link":"https:\/\/demand-planning.com\/2018\/05\/11\/weather-forecasting-and-business-forecasting\/","title":{"rendered":"Weather Forecasters, We Feel Your Pain"},"content":{"rendered":"

Demand Planners readily recognize the challenge of creating business forecasts, and perhaps have an appreciation for the thankless job weather forecasters have. Several times each day they appear on our TV screens, with audiences of millions eagerly anticipating their pearls of wisdom. And when they’re wrong and our vacation plans are ruined by rain we think “heck, with a job title like meteorologist they should get it right”. Perhaps they do, if we apply fair analysis and measurement.<\/strong><\/p>\n

It’s easy to simply state that business and weather forecasts are similar because they\u2019re both always wrong, but let\u2019s be careful in how we define what is wrong. If the weather forecast states tomorrow\u2019s high temperature will be 78 degrees, and it ends up at 79 degrees,\u00a0\u00a0is this really\u00a0 \u201cwrong\u201d? Would anyone have changed their daily plans, or travel or outdoor activities based on a single degree difference? Let’s relate that to a business forecast that predicts we’ll sell 10,000 units this month. If we sell 10,075 units, does this constitute being wrong?<\/p>\n

Forecasts should measure their accuracy in terms of relevance as opposed to absolute value<\/p><\/blockquote>\n

Our forecasts, whether for business or weather, should measure their accuracy in terms of relevance as opposed to absolute value<\/em>. Hence the use of forecast error measures such as MAPE<\/a>, WAPE and WMAPE<\/a>. We also appreciate forecasting a range versus a point estimate, thus in the above example we might predict a high of 76 \u2013 80 degrees, rather than 78.<\/p>\n

A major principle of business forecasting is that forecasts will incur more error as we project farther into the future. This is certainly true of weather forecasting as well. Many meteorologists will project a 5-day forecast, every morning during the week. Which day\u2019s forecast, from Monday through Friday, will do the best job of predicting Friday\u2019s actual temperature? It should be obvious that Friday\u2019s is best, although some cynics will prefer Saturday (after the fact, telling us what the temperature was the day before). It becomes a fool\u2019s errand to attempt to predict the temperature weeks, months or years into the future.<\/p>\n

\"business<\/p>\n

This may be countered by forecasting in more aggregate terms, as detailed forecasts have more error. For example, a projection of how many dollars of sales Ford Motor Company will have in North America for 2018 is more accurate than predicting how many F150 trucks, with crew cab, V8 and automatic transmission will sell between 3pm \u2013 5pm on July 10th<\/sup> in Binghamton, NY. Similarly, a forecast for the average temperature in the U.S.A. for 2018 is more accurate than predicting the temperature in Binghamton for July 10th<\/sup>.<\/p>\n

It is easy, and lazy, to avoid work and just assign all change to randomness in the market<\/p><\/blockquote>\n

Business forecasters recognize the only constant is change, which may result from random variability, trends and\/or seasonality. Weather forecasts share all of these demand patterns as well. It is easy, and lazy, to avoid work and just assign all change to randomness in the market\u2026 those damn customers are schizophrenic! A preferred approach is to recognize there is a reason demand exists, just as there is a scientific reason why the temperature today is warmer than yesterday. The best forecasters understand their products and markets and know scientifically and<\/em> intuitively what\u2019s happening, how it\u2019s happening and why it\u2019s happening.<\/p>\n

That said, there are still the one-off events, the outliers, what we may refer to as \u201cextreme weather events\u201d that are hard to predict and disruptive to our environments. Risk management will provide us with tools to predict, mitigate and recover from such events. Businesses would be well advised to have their own version of FEMA in place in this regard.<\/p>\n

[Ed: For further discussion on forecast relevance vs. absolute\u00a0absolute accuracy, check out Eric Wilson’s article titled <\/b>Stop Saying Forecasting Are Always Wrong<\/a>.<\/b><\/em><\/p>\n<\/span>","protected":false},"excerpt":{"rendered":"

Demand Planners readily recognize the challenge of creating business forecasts, and perhaps have an appreciation for the thankless job weather forecasters have. Several times each day they appear on our TV screens, with audiences of millions eagerly anticipating their pearls of wisdom. And when they’re wrong and our vacation plans are ruined by rain we […]<\/p>\n","protected":false},"author":5480,"featured_media":6866,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[387],"tags":[420],"class_list":{"0":"post-6861","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-models-and-methods","8":"tag-weather-forecast"},"_links":{"self":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts\/6861"}],"collection":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/users\/5480"}],"replies":[{"embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/comments?post=6861"}],"version-history":[{"count":0,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts\/6861\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/media\/6866"}],"wp:attachment":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/media?parent=6861"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/categories?post=6861"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/tags?post=6861"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}