Machine learning in logistics

Machine learning in logistics


Machine learning in logistics AVADA-MEDIA

Logistics is not just traffic data; it is planning the most efficient routes for movement and storage schemes for goods and services from the place of their production to the place of consumption.

Logistics is an area, with a competent organization of which, a tangible reduction in overhead costs is possible – and, on the contrary, if the logistics are organized incorrectly, the business suffers significant losses and loses customers. In today’s world, logistics processes have become so complicated that, along with conventional software, neural networks and artificial intelligence, in particular machine learning algorithms, come to the aid of specialists. These algorithms make it possible to analyze and interpret huge amounts of information that no human can handle.

The purpose of machine learning in logistics is to predict based on certain trends, but this, of course, is not limited to the use of artificial intelligence in logistics.

The algorithm will allow you to calculate the best (and not necessarily the shortest) route, give a forecast for the occupancy of warehouse premises for six months in advance, will serve for pattern recognition when analyzing labels on packaging or containers, and calculate the cost of transportation with such an amount of data that any accountant will save you. New information technologies make it possible to effectively monitor the customer base and predict the order even before the thought of it arises in the customer’s mind.

Machine learning in logistics

In which processes is the use of machine learning justified?


In which processes is the use of machine learning justified? AVADA-MEDIA

Machine learning algorithms allow you to establish a company’s business processes and influence processes such as procurement, delivery, inventory management, maintenance, interaction with suppliers and consumers, security, handling, etc.


For example, in purchasing, algorithms provide simplified ordering and batching for shipment. The automatic system monitors the data on the balances and finds a product or its analog from the supplier, and also generates an order. The person only has to confirm the order. This is very convenient in cases where you need to keep track of stock for thousands of items – for example, in huge supermarkets, including construction ones, machine learning algorithms constantly analyze purchase data and make additional orders in a timely manner.


Inventory management can be carried out using machine learning technologies and in another way. Namely, by analyzing photos of warehouse or store racks, the algorithm determines the availability and level of stocks and automatically generates an order for the purchase or delivery of the missing goods from the warehouse. The same algorithm can be used in marketing – namely, merchandising, the sphere of which is the physical placement of goods at points of sale in maximum accordance with consumer preferences. This is where machine learning in logistics is closely intertwined with marketing.

In the field of delivery, with the presence of special equipment, algorithms can track the movement of goods in real time, react to deviations from the schedule, and compare the planned and actual delivery times. The main goal is to minimize the delivery time and predict force majeure situations. Such forecasts may, in particular, be included in the calculation of the cost of transport services or the cost of insurance.


Maintenance in logistics can also be an area of ​​application for machine learning technologies. Any breakdowns in transport or storage equipment can result in significant losses – for example, when it comes to perishable goods that are transported in the freezer. At the same time, regular maintenance is also costly as the equipment must be removed from the production chain at this time. Machine learning algorithms resolve the contradiction – they analyze the information coming from a variety of sensors and predict the operation of the equipment. For example, for a freezer, this is a temperature graph, engine performance, freon pressure in the system, compressor operating mode, etc. The maintenance staff receives a signal about a possible malfunction and eliminates the problem in time, stopping only the problem freezer pointwise.


Machine learning can also be used to analyze consumer sentiment and trends. Thus, the business receives invaluable feedback on which product in which stores the chains consume more, and in which they do not take it at all. And accordingly, plan logistics and delivery to specific points of goods with the right articles and in the right volumes.

Where else is machine learning used in logistics?


Where else is machine learning used in logistics? AVADA-MEDIA

  • analysis of a large number of orders and their optimal distribution to transport units – in demand for delivery companies;
  • forecasting the profitability of transportation on certain routes, taking into account thousands of factors, including duties and weather conditions;
  • optimization of equipment operation in order to reload idle capacities;
  • accident rate forecast;
  • ensuring security and access control based on recognition of images and license plates;
  • creation of voice chat bots to work with a large number of clients.

AVADA MEDIA offers the development and implementation of logistics solutions related to machine learning algorithms. We have sufficient experience for this and professional senior and middle developers in the programming languages Python, Java, C ++, which are most often used to create machine learning algorithms.

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