{"id":8075,"date":"2019-11-18T09:38:05","date_gmt":"2019-11-18T14:38:05","guid":{"rendered":"https:\/\/demand-planning.com\/?p=8075"},"modified":"2019-11-18T09:38:05","modified_gmt":"2019-11-18T14:38:05","slug":"predictive-analytics-and-probabilistic-planning","status":"publish","type":"post","link":"https:\/\/demand-planning.com\/2019\/11\/18\/predictive-analytics-and-probabilistic-planning\/","title":{"rendered":"Predictive Analytics & Probabilistic Planning"},"content":{"rendered":"

What if we not only knew what the forecast will be for an item for the next period, but also understood a range of potential outcomes for that item?<\/p>\n

What if we didn\u2019t just have a number, but we also had a list of drivers that contributed to many possible numbers?<\/p>\n

What if we not only had an outcome, but could use probabilities to discover unseen outcomes?<\/p>\n

What if, instead of a single plan, we gave the business alternative scenarios that could play out?<\/p>\n

What if we didn\u2019t just forecast numbers but used predictive analytics to understand drivers, paint scenarios, and drive the demand you want?<\/p>\n

\u201cWhat if\u201d Is The Question That Underpins Predictive Analytics<\/h2>\n

\u201cWhat if\u201d is perhaps one of the more critical and important aspects of predictive analytics<\/a>. Predictive analytics and scenario planning<\/a> allow a business to respond to alternative situations more quickly and effectively. Predictive analytics<\/a>, using simulation techniques, can increase our knowledge and confidence in making informed decisions. Predictive analytics<\/a> focused on forecast drivers is information that helps us shape our future by telling us what actions should be taken that will lead to desired business conditions.<\/p>\n

The most basic part of forecasting is the assumption. As demand planners, assumptions are more important than numbers. Much of our job is managing them, interpreting them, and turning them into insights. Assumptions are numerous and help us break down complexity and uncertainty. Every business forecast contains assumptions.<\/p>\n

Another term for assumption may be “scenario”. A scenario, in this context, is a potential circumstance or combination of assumptions that could have a significant impact (whether good or ill) on an organization. In the messy world of people and behavior, there can be no forecast without a scenario. The only question is whether to make your assumptions explicit (known) or implicit (unknown). You have a choice: pick a single assumption (usually a single number) or use\u00a0predictive analytics<\/a>\u00a0to understand more variables and therefore more assumptions. The latter choice makes the variables known, and allows us to forecast more accurately.<\/p>\n

One Choice, Multiple Outcomes<\/strong><\/h2>\n

Scenario planning<\/a> and predictive analytics<\/a> are based on the premise that for every choice taken, there are several possible outcomes. By accurately identifying multiple variables that contribute to the forecast and preparing for each of these alternative scenarios, it is possible to be reasonably sure that the initial action was the correct one. This level of strategic foresight also allows for the creation of contingency plans that can be activated immediately, if the situation calls for action of that type.<\/p>\n

By using predictive analytics<\/a> and making the assumption known, it is possible to prepare in advance for the several potential outcomes<\/p><\/blockquote>\n

By using predictive analytics<\/a> and making the assumption known, it is possible to prepare in advance for the several potential outcomes rather than simply meeting them as they come along. The advance preparation can often save a great deal of time and money, as well as provide the company with intelligence that helps to defuse negative situations while maximizing the benefit from positive ones.<\/p>\n

At the core, this is what demand planning and predictive analytics<\/a> does. Their job is to take the questions that seem almost unanswerable (due to their complexity and the many unknowns) and try to manage the assumptions and develop answers. Each of the questions involves dozens of factors that can change the ultimate outcome. To help, there may be some good analytical approaches to addressing the unknowns and breaking down the complexity posed by such tough forecasting questions.<\/p>\n

More than numbers, demand planners manage assumptions and need to understand their individual contribution<\/p><\/blockquote>\n

We as demand planners live in the world of ambiguity and uncertainty and transform it into insights the business can use. More than managing numbers, we manage assumptions and need to understand their individual contribution. We use weighting and ratios and work towards the best fit of our data sets to the right model to minimize uncertainty and provide answers.<\/p>\n

Our world is changing as well, and we need to adapt. Predictive analytics<\/a> and probabilities just may be the train that is taking us into the future. We have already seen a shift from traditional time series modeling to predictive analytics due to omnichannel and e-planning, much of which is driven by regression models or even more sophisticated machine learning and probabilistic forecasting.<\/p>\n

Predictive Analytics Is About Probability<\/strong><\/h2>\n

One of the primary goals of predictive analytics<\/a> is to assign a probability to forecast drivers. With these probabilities you can understand (as unlikely as it may be) the likelihood of the black swan event occurring, or indeed a variety of other more day-to-day outcomes. Predictive analytics<\/a> can be used to create a number of different what-if scenarios, especially in the areas of risk assessment, customer buying trends, and business.\u00a0For example, it can be used with a business\u2019s sales history to determine when customers are most likely to make large purchases or which products will perform best.\u00a0It can also be used in a market as a whole to get an idea of when a business could safely try to expand without taking unnecessary risks.<\/p>\n

 <\/p>\n<\/span>","protected":false},"excerpt":{"rendered":"

What if we not only knew what the forecast will be for an item for the next period, but also understood a range of potential outcomes for that item? What if we didn\u2019t just have a number, but we also had a list of drivers that contributed to many possible numbers? What if we not […]<\/p>\n","protected":false},"author":3470,"featured_media":8079,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[390,386],"tags":[284,490,482],"class_list":{"0":"post-8075","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-advanced-analytics","8":"category-analytics","9":"tag-predictive-analytics","10":"tag-probabilistic-forecast","11":"tag-scenario-planning"},"_links":{"self":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts\/8075"}],"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\/3470"}],"replies":[{"embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/comments?post=8075"}],"version-history":[{"count":0,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/posts\/8075\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/media\/8079"}],"wp:attachment":[{"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/media?parent=8075"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/categories?post=8075"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/demand-planning.com\/wp-json\/wp\/v2\/tags?post=8075"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}