As a discipline, predictive analytics has been around for many decades and has been a hot topic in academia for many years. Its application in the field of demand planning, though, is still relatively untapped. Its effective uses in business are more an exception than a rule. Despite the mass of information available to us, and machine learning algorithms that can model the supply chain for insights, companies have barely scratched the surface with data analytics.
Part of the reason for this may be confusion about traditional demand planning and predictive analytics. Demand is for “something” and can be for a product or service. It manifests itself as a sale to an end-user, an order, a shipment, inter-plant transfer, distribution requirement, etc. Demand planning is a process and techniques used to create a demand plan.
Broadly speaking, there are two approaches to demand forecasting– one is to obtain information or make assumptions about patterns of past purchases, the other is to obtain information or make assumptions about external factors or the likely purchase behavior of the buyer.
Predictive Analytics Tells Us Not Only What Will Happen, But Why
While predictive analytics can be utilized to develop a demand plan, more often than not most demand planners still use only demand to forecast demand. Predictive analytics does not only forecast the demand itself but uses a systematic computational analysis of data or statistics (analytics) to try to determine why. Demand planning only creates an estimate of demand – predictive analytics creates an evaluation of what the future may be “if”.
Predictive Analytics Interprets A Wide Range Of Factors Affecting Demand
Predictive analytics is the philosophy of extracting information from data sets and using advanced statistical algorithms or even machine learning techniques to identify the likelihood of an unknown future outcome. The goal is to go beyond knowing what has happened to providing a best assessment of why or what drivers will impact something occurring in the future. Predictive analytics takes a more humanistic and sometimes intuitive logical approach. Instead of relying on past historical activities, predictive analytics analyzes the influencers, interactions, and activities of the actors (consumers) in demand.
Traditional Demand Planning asks – what did the item do last year?
The new era of Predictive Analytics asks – what does the consumer do when this happens?
Because of this, it is more than just a forecast of how much we will sell of an item next month but opening up a door into many more insights. This door is not limited to just supply chain either but brings the predictive analytics professional into every function and can add value to every business decision.
It Allows For Micro Targeting Campaigns
Applied to business, predictive models are used to analyze current data and historical facts in order to better understand customers, products and partners and to identify potential risks and opportunities for a company. It can be used for micro targeting campaigns to gain strategic advantages or used to determine the color and font on a website that drives the most traffic. As an online retailer, with predictive analytics you can understand how your page ranking, number of comments, ratings, and “winning the buy box” on Amazon impact your sales on any given day.
It translates to consumer loyalty and less churn of customers, and customizing experiences to make a higher probability of sales.
Predictive analytics has grown in prominence alongside the emergence of big data systems. As enterprises have amassed larger pools of data, they have created increased data mining opportunities to gain predictive insights. Heightened development and commercialization of machine learning tools have also helped expand predictive analytics capabilities. Because of this and the changing business environment, professionals in our field will continue to migrate to new ways of modeling and planning and start to see cross-over of these predictive models into traditional forecasting and planning as well.
Revealing the future by getting into the head of the consumers, rather than by analyzing the history of the item can pay enormous dividends. And, with the abundance of real-time consumer data available today, future demand for your organization’s products and services may be more precisely determined using predictive analytics rather than relying solely on traditional demand planning processes.
I’ll be speaking at the Predictive Business Analytics, Forecasting & Planning Conference in New Orleans from May 6-18, 2019. It’s a 2-day conference with an optional data science workshop, designed to get you up to speed with basics of predictive analytics, or get you to the cutting edge of the field with the the latest methodologies, tools and best practices.