STORY INLINE POST
In the fast-paced world of logistics, every second counts and every decision has a significant impact on efficiency and profitability. The logistics industry, which drives the flow of goods and services around the world, is constantly evolving. In this context, predictive modeling has become an essential tool that is revolutionizing the way logistics operations are managed.
When we talk about predictive models we refer to algorithms or artificial intelligence (AI) systems that use historical data and, in real time, create forecasts or predictions about future events. These models are used in various fields, such as economics, science, healthcare and currently in logistics operations, to make informed decisions and anticipate outcomes based on patterns and trends identified in the data.
The process of creating a model or predictive analytics begins with the collection of internal and external data; a statistical model is then formulated that looks for patterns and relationships that can indicate behaviors and trends into the future. For example, a predictive model can forecast the increase in demand for a product based on data patterns.
As more information is gathered, the model can be adjusted to improve its accuracy, and here AI plays a key role, turning a predictive model into a "treasure map" that tells us where gold is most likely to be found based on conditions and locations where it has been found before.
In the logistics industry, this "treasure map" will help us predict situations ranging from the need to replace a truck part before it breaks to how many packages will be delivered on a given day or which product will be in high demand in order to fill shelves on time and avoid shortages. In this way, any operation can be accurately planned.
Obtaining real-time information is one of the main benefits of predictive models in logistics, allowing us to prevent problems before they occur and achieve cost savings; for example, optimizing routes with these tools can reduce fuel costs by 10% to 15%, in addition to improving delivery times by 20% to 25%.
Based on historical data and in real time, the best possible route is determined, the number of daily deliveries or the frequency of deliveries is increased and risk areas are avoided, taking into account factors such as traffic, weather conditions, reports of truck theft and cargo restrictions, among other variables.
In terms of inventory management, predictive models can accurately predict future demand, which will help us to maintain optimal stock levels and avoid shortages or excess inventory, reducing storage costs by 10% to 20%.
Automating processes through predictive modeling also helps minimize the possibility of human error. By leveraging real-time data analytics, faster and more accurate decisions can be made, improving operational efficiency. According to a McKinsey & Company report, automating logistics processes with predictive modeling reduces human error by 30% and increases efficiency by 25%.
Predictive models are not infallible and working with low volumes of data could lead to statistically weak, unstable and unreliable model results. Using incorrect metrics to evaluate the performance of a model also generates errors and mainly, ignoring experts in the field when building a model is a misconception, a predictive model is not a magic act where data is inserted into a computer and out of nowhere it throws predictions. The knowledge of data mining experts is a fundamental piece to achieve accurate results from what has been learned in the past. Therefore, collaboration between experts and those who know the business is essential for better results. Everything built is based on that information.
The logistics industry of the future is predictive, with the implementation of flexible processes capable of adapting to all kinds of situations, as we experienced during the COVID-19 pandemic, maintaining quality, reducing time and achieving greater profitability. All this would not be possible without the support of technology to detect inefficiencies and bottlenecks as quickly as possible and thus maintain traceability and control.
Onest Logistics, a leading Mexican company in the industry, remains at the forefront and in constant evolution through the adoption of predictive models that allow us to improve services for our customers and be a strategic part of their business growth. Undoubtedly, the advance of technology will allow us to strengthen predictive logistics, adapting to changes in real time, in addition to optimizing resources and improving efficiency throughout the supply chain.
Big data, artificial intelligence and machine-learning have burst onto the logistics scene and their presence will increase in order to achieve a clear goal, which is prediction with the aim of better planning, more time and lower costs.
These tools are essential to stay competitive in a world where speed and accuracy are key to decision-making. In this way, predictive models are a catalyst for a more efficient future.
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