Date of publication:

24 Apr. 24

How To Use Artificial Intelligence For Optimization Of Logistic

Logistics plays a crucial role in ensuring the efficiency of economic processes in the modern world, supporting the necessary supply of services and goods in a timely and efficient manner. Increasing complexity of business processes, globalization of the economy, growth of production volumes, which require innovative approaches, create extraordinary conditions for logistics. These include artificial intelligence (AI), a technology that explores all sorts of ways to train a computer, robotics or analytical system to think like a human.

The authoritative company Gartner has received the results of research indicating a high interest of logistics companies in the introduction of artificial intelligence technologies in business. Thus, in 2024, almost half of such companies plan to actively invest in technologies that support artificial intelligence.

The Role Of Artificial Intelligence In The Logistics Industry And The Benefits Of Using Machine Learning

The high role of AI in the logistics industry is due to its networked nature, which is a natural foundation for the implementation of AI projects. If a company fails or deliberately refuses to implement AI, a real risk of loss of competitiveness is formed in the long term. This means that a business that does not recognize the timeliness and importance of using AI has every chance of being among the outsiders in the future, hopelessly lagging behind competitors.

AI has the potential to reduce costs, as well as reduce turnaround times, improve accuracy and productivity, which will inevitably impact the efficiency of business processes. By combining AI and robotic automation of technical processes, automated systems will be used to solve everyday tasks. The time freed up will allow employees to devote to the most important work functions that generate revenue.

Among the overall benefits of implementing AI and machine learning (ML) in logistics, researchers point to increased productivity, optimized processes, and satisfaction for both businesses and customers. These look as follows:

Cost reduction. Automation of many logistics processes ensures the savings of resources and labor forces. Without excluding demand, the lack of goods and excess stocks can be avoided, reducing the cost of loss and storage.

  • Reducing errors. In the process of making decisions, the Moscow Region allows you not to make human mistakes. Analysis of large data volumes systems allows us to draw only objective conclusions.
  • Increasing customer satisfaction. Effective routing and accurate forecasting of demand ensures the delivery of goods easily and on time.
  • Consumer satisfaction increases the possibility in real time to track the load with information about its condition and whereabouts.

The Use Of AI Algorithms To Analyze Market Demand And Forecasting

More and more logistics companies use artificial intelligence to optimize their forecasting processes. The use of predictive analytics, as well as machine learning algorithms, helps more accurately and quickly establish patterns in market trends and customer customer customer behavior. This enables companies more efficiently predict customer demand and simultaneously optimize the management of reserves, as well as supplies of supply chains.

Machine training can also contribute to:

  • better understanding of customer customer customer habits;
  • foresight of seasonal trends;
  • Determining the category of products that can be popular.

Having access to information in real time, retail sellers can make more reasonable decisions on the level of reserves, prices and advertising events.

The use of artificial intelligence in logistics can help in predicting the demand for products due to the features of a particular region and other parameters, among which, for example, weather conditions, various events. The forecasting also takes into account the state of affairs in warehouses using algorithms that allow optimizing the management of inventories on them.

Based on the analysis of past data, a “set of models” is formed, which itself determines the importance of each factor for a particular product at a particular point of sale. Such “models” with maximum detail are used for forecasting future periods. Such models are flexible to adjust and take into account new factors, self-learning, constantly adapting and developing, taking into account various demand trends.

Aiding AI In Inventory Management And Surplus Minimization

Inventory management is a very important task as it is money. Ineffective management leads to lower profitability and in the long run inevitably reduces the success of the company. AI can help improve the efficiency of inventory management, based on statistical information, current inventory, shipments and merchandising, predicting the needs of the company and making informed recommendations for purchases, moves and other operations. AI can determine which items are selling faster, which are selling slower, and which items have little or no demand.

This makes it possible to adjust inventories to avoid shortages as well as limit oversupply. Accordingly, it is possible to create optimal inventory. In addition, AI can make predictions of the right stock at the right time, which reduces the time it takes to deliver goods to the customer.

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Automization Of Route Planning

Due to the many unexpected events that can occur en route, the “last mile” (final delivery to the customer) is a complex and difficult process to manage. Among the constraints and negative factors are traffic jams, accidents, and temporary road closures. Drivers can provide a high quality of service to the end consumer by using route optimization solutions based on artificial intelligence. They allow creating efficient delivery routes in real time, taking into account the current traffic situation and events that occur along the route.

Using machine learning methods, AI collects data and uses it to make predictions, create an optimal route, and determine delivery time with high accuracy. Such data for analysis may include delivery area, customer type, weight and size of the parcel, among others. Road and weather conditions and data on traffic congestion during peak hours can also be used.

Reducing Travel Time And Optimizing Costs

Artificial Intelligence technologies can optimize the distance and route, the way goods are delivered. In addition, this helps to better coordinate the delivery time, to help reduce it, as well as a decrease in fuel costs.

Among the advantages of using AI:

  • Reducing emissions CO2;
  • Reducing the number of kilometers passed;
  • Reducing costs and reduction of delivery time;
  • Improving the quality of service and customer satisfaction.

An example is the American logistics company Coyote Logistics, using artificial intelligence, predicative analytics and machine learning, to compare information about the process of delivery with external data – weather and traffic in a real time. As a result, the company has the possibility of predicting problems that can affect transport chains, while developing an alternative supply plan.

Automation Of Warehouses And Their Robotization

Automation of warehouses and processing centers using AI helps to increase efficiency and reduce processing time.

The implementation of these and other processes involves the use of various technologies and sources of data, including:

  • The use of RFID margins and other technologies that allow you to track the location of goods and control their movement;
  • data collection from sensors installed on vehicles, warehouses and other logistics infrastructure facilities;
  • The use of software and programs for managing logistics processes that integrate all the collected data and automate a number of processes;
  • The use of data obtained from communication networks, such as the Internet of things (IOT), to monitor equipment and processes at a distance;
  • Analysis of external information and factors that affect the time of delivery.

An Example Of Effective Robotization Of Warehouses

The most obvious example of successful implementation of artificial intelligence technologies and robotization of warehouses can be the Chinese company Alibaba. The corporation has the world’s largest automated warehouse. In it, robotic devices are engaged in picking and packing goods for delivery to customers, which currently perform 70% of the warehouse’s work.

Their advantages are numerous and obvious. An important among others – when moving in the process of completing tasks, robots can hold up to 500 kg. Each device is equipped with special sensors that prevent collisions, Wi-Fi for calling by employees.

In Automation Of The Inventory Process

Inventory in the warehouse is a time -consuming and complex process. It requires the attention and perseverance of warehouse personnel in addition to attracting additional resources. In addition, work is often carried out at a height, associated with a risk to humans, it is necessary to use specialized equipment. AI is able to simplify and facilitate this task. An example is the introduction by L’Oréal, an unmanned system of inventory in order to solve problems and minimize risks.

It looks like this: the drone, which is equipped with an on -board camera, flies past the racks for each position and tier to conduct an inventory. Video processing with artificial intelligence allows the drone to read barcodes, to recognize empty places. In addition, he is able to take into account the height of the layers and recognize where the end of one cell and the beginning of another.

Monitoring And Accounting Of Equipment Using AI

Artificial intelligence can be effective for monitoring and accounting of equipment, provided that predicative maintenance is used. This strategy involves continuous monitoring of the state of equipment under the usual conditions of use and forecasting the remaining life. Predicative maintenance uses models to predict failures of components of a particular unit, in contrast to preventive and reactive maintenance, helping to reduce the number of failures or simply prevent them.

This helps to plan maintenance in advance by minimizing downtime. Diagnostic tools based on AI allow manufacturers to identify conditions that can cause a breakdown, and intervene until its onset. Machine training models provide opportunities for predicting the remaining life of equipment and preparing for repair.

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Ensuring The Structural Transparency Of The Supply Chain With AI

The possibilities of ensuring the transparency of the supply chain can be divided into two vast groups – structural and dynamic. Artificial intelligence is able to effectively improve each of them depending on their features.

Structural transparency based on AI includes:

  • Classical risk management is a cyclic approach to identifying and solving potential risks for the supply chain.
  • network display – documenting the assets of the company and key partners, with an illustration of their location and relationships.
  • Evaluation of the network – determining the risks inherent in the design of the network.
  • Computer modeling – modeling based on the actual performance of the network.

Dynamic Transparency (In Real Time) Of The Supply Chain With AI

Dynamic transparency based on AI is less developed and is in the stage of improvement. Types of such transparency include:

    1. Monitoring – collection and observation of signals indicating the performance and state of functions of the supply chain is desirable – in real time.
    2. forecasting – to assess the future state of the supply chain, signals are used in real time.
    3. proposals – to make decisions and recommendations of actions using the possibilities and minimizing the influence of failures, the algorithmic capabilities of the dispatch tower and the signals of the supply chain in real time are used.
    4. Autonomous execution – Automation of the processes allows the dispatching point to independently respond in real time to the signals of the supply chain to extract benefits from the possibilities and minimize the influence of the failures.

Trends And Directions Of Development Of AI In Logistics

Studies conducted in the United States regarding the influence of AI on economics and logistics in particular showed that in the near future, in 20% of all professions, 50% of operations will be performed using AI.

Specialists tracked and distinguished various directions of its development, among which:

      • Development of demand forecasting systems. The use of AI to analyze consumer behavior and purchase data improves demand forecasting and reduces stock costs.
      • The use of AI and machine learning to manage warehouse and inventory. Optimizes the costs of storage and management of stocks, and also reduces the likelihood of errors.

Among the trends that are considered relevant for the development of AI in logistics, experts also call:

      • development of robotization. Robots of routine tasks in orders processing and in warehouses allows you to reduce personnel costs and increase performance.
      • Optimization of delivery routes. Artificial intelligence algorithms optimize the delivery trajectory and improve the effectiveness of this process.
      • Development of logistics management software. Artificial intelligence allows you to create software systems for logistics management that allow you to effectively coordinate the processes of logistics and delivery.

The Risks In The Introduction Of AI In Logistics And Development Prospects In The Future

Experts express fears that systems under the control of AI may not cope with complex and unexpected scenarios. This is due to the fact that when failing of the AI systems, individual processes can be paralyzed. Based on this, for such situations, logistics companies should have a backup plan. And also – if necessary, be able to quickly adjust the AI algorithms.

Experts also believe that the global of the implementation of AI will depend on reliable cybersecurity. If there is no reliability of collecting, preserving and transmitting data to partners, this from the advantage turns into a problem. Increasing the value of data for launching systems for business makes them more valuable for attackers. Also, before resolving the issue of the legal status of AI or at least responsibility for the decisions and actions taken, artificial intelligence will only be an auxiliary tool for humans.

According to experts, prospects in the development of logistics in AI are wide and diverse. They will touch, firstly, his abilities:

      1. automate manual and laborious processes.
      2. Analyze large volumes of data for making balanced decisions.
      3. Predict the delivery time of departures.
      4. automatically process customer requests and complaints.
      5. Teach and support employees.
      6. Provide the security of info -flowers.

The main advantage can be considered that AI will change the operating model of logistics from reactive to predictable. It will work ahead, providing higher results with optimal costs within the company, operating interactions and outside the company. AI will complement human capabilities, allow us to eliminate routine work by shifting the focus of employees who are engaged in logistics to more productive and important tasks.

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