Predictive analysis – statistical techniques such as predictive modeling, machine learning, and data mining to analyze data and make predictions.
Has become a very fashionable concept, and we will probably use it more and more often. Its use can help with production activities and more. It is also important for the supply chain.
Let’s start with the definition – predictive analytics means using statistics and algorithms to determine the likelihood of future outcomes based on that historical information. In essence, it’s about transforming the industry from human decision-making to data-driven decision-making. And there are a lot of them.
In practice, predictive analytics is nothing more than the ability to determine what might happen in the future. Somebody will say – because it’s obvious, they’ve been trying to do it for years. Yes, but experts say it is no longer the proverbial “coffee grounds guessing. Only modern methods of collecting information, their scale, and analytical methods allow forecasting with satisfactory accuracy.
The increasing availability of computers and more user-friendly software also contributes to this. Thanks to them, predictive analytics is no longer the domain of mathematicians alone; it can also be used by business analysts.
Predictive Analytics helps predict accidents
Transportation executives and logistics operators use predictive analytics to solve problems as well as to explore new opportunities.
The list of typical applications is long.
– Fraud detection (e.g., on the Internet),
– better execution of individual processes,
– inventory forecasting and resource management,
– price optimization.
As a result, the goal is to increase efficiency on the one hand and minimize risk on the other.
All of these features also apply to the supply chain. This includes, for example, predicting equipment failures and reducing risks related to safety and reliability, which is particularly important in the case of automated warehouses and autonomous transport systems.
Predictive analytics will help optimize routes and resource utilization
According to Transmetrics experts, thanks to predictive analytics, logistics operators and carriers can plan actions based on customer needs and customer behavior (demand and supply forecasts) weeks or even months in advance. This optimizes the use of funds in logistics. Thanks to the analytics of transportation management support software (TMS), you can predict potential threats, adjusting your actions accordingly.
Predictive analytics are also useful for last-mile delivery planning, optimizing routes and schedules where possible, eliminating burdensome downtime in traffic jams, and helping to coordinate freight shipments on a shared basis.
Experts emphasize that investing in predictive analytics solutions is no longer an option; it has become a necessity.
Choosing the data critical to success
What can companies do to start implementing such solutions? According to Transmetrics, in some cases, the first step may be to hire or appoint a dedicated person to manage the company’s digital transformation and build an information-driven supply chain.
The second option is to partner with an outside technology vendor to provide predictive analytics products and services tailored to transportation and logistics needs.
The key to using predictive analytics is to understand the problem that needs to be solved. The next step is to select the data whose analysis will help solve that problem. In the case of the supply chain, this is usually data collected by various sensors and recorders. The resulting data should be prepared for analysis according to a previously defined “key.” When building a predictive model, the nature and amount of data collected are taken into account.
Predictive modeling requires a team approach. It takes people who understand the specifics of a particular business problem, who can prepare the data for analysis, and then provide the appropriate analytical infrastructure to build and implement the model.