Artificial intelligence (AI) and logistics and distribution processes
The possibility of imitating human intelligence, in order to perform activities through systems that are capable of processing information, executing activities related to decision making and reducing the margins of error that are usually committed by the simple condition of fallibility of human beings, is, nowadays, a reality.
We are referring to artificial intelligence (AI), a tool that must be taken into consideration when streamlining logistics and distribution processes in a global economy that demands greater speed and efficiency in all its operational processes. Through algorithms and computations, A.I. is becoming a fundamental trend for the growth of the entire digital infrastructure at the level of business activities, companies, corporations, institutions and public organizations. It is about materializing the deployment of programs that are capable of creating personalized recommendations for consumers, performing online searches and purchases, inventory planning, increasing the efficiency of logistic processes and making, in general terms, all commercial activity more efficient.
Artificial intelligence (AI), conceived as a type of technology that is capable of accompanying the decision-making process by virtue of solving concrete problems, operating through a wide range of possibilities, through expert systems that are capable of making inferences to provide solutions based on prior knowledge of the context in which they emerge, is also designed for the development of other systems of greater complexity that have properties for autonomy, self-regulation and self-control.
Let's take a look at some systems and models that are currently being used in production and marketing activities. In general terms, we can mention the following: systems that make possible the recognition of high-power generic patterns (support vector machines), without resulting in a performance overhead; systems based on learning processes based on probabilistic events (hidden Markov systems), use of techniques for reasoning in situations of uncertainty (fuzzy systems), use of evolutionary computation, which is based on the application of concepts that are often used in the biological sciences and that allow the codification of aspects related to population, mutation and survival of the fittest. Regarding the latter, it is worth mentioning that the sequence of finite steps for problem solving (algorithms) is based on evolutionary, genetic and collective intelligence elements.
As can be seen, A.I., having diverse applications related to diagnosis, prediction, sequence of operations, design and interpretation of data, has in turn diverse fields of application. We could mention: medicine, engineering, administration, manufacturing systems, management systems, computer systems, distribution, among others.
Today, business activities make use of: voice assistants, which recognize human language, making it possible to execute all the functions that are commanded; smartphones, to expand connectivity levels; predictive modes, which make it possible to generate words by simply pressing a button for each letter; map and directions systems, capable of analyzing geographic and spatial information; and many other specialized programs and applications, which contribute to a greater streamlining of production processes.
Today, it is possible that intelligent systems are able to perform functions for predictive analysis, achieving the maximization of the life of business assets and the improvement of information, which is linked to the status and behavior of demand to answer the big question: what should be sold in order to maintain full customer satisfaction? It is a matter of thoroughly understanding that the process of automation at the enterprise level and the use of intelligent machines is causing a shift in the procedures and tasks that were previously performed manually and routinely. Nowadays, the programming levels based on new technologies (AI) lead to be performed in less time and with fewer resources.
At the level of logistics activity and goods distribution processes, we can mention those technologies based on A.I., which specialize, on the one hand, in production and warehouse logistics, detecting anomalies and processing information to improve the quality of goods and services required by customers.
On the other hand, there are those related to route optimization and that offer alternatives when initiating certain journeys. These are intelligent systems based on the comparison of geographic data and the analysis of data related to the current status of time-space conditions (weather conditions or information related to traffic flow). Applications such as PlannerPro by Beetrack, offer great help in the design and planning of delivery routes, optimizing available resources, allowing efficient delivery through the allocation of schedules and responsibilities and offering improved quality of services required by customers.
As can be seen, artificial intelligence (AI) has been contributing to the reduction of time in the logistics and distribution chain, optimizing inventory operations and increasing productivity levels in a highly competitive global reality.