AI in logistics. 5 best tools

In today’s world, where every second matters and every resource is valuable, logistics faces new challenges. Introducing AI in logistics opens up new horizons for the industry, allowing for time and cost savings, as well as increased operational efficiency and transparency. Let’s take a closer look at how artificial intelligence can revolutionize logistics and which AI tools in logistics are currently setting the standards and how they can help businesses gain a competitive advantage. Read on.

AI in logistics - table of contents

AI in logistics. How to improve the delivery system using artificial intelligence?

The logistics sector is undergoing transformation. According to forecasts by Allied Market Research, the value of this industry will reach 13 trillion dollars by 2027. It is AI that provides real-time alerts about operational issues, allowing companies to react promptly and ensure timely deliveries.

Thanks to AI algorithms, it is possible to ensure data accuracy for decision-making and predict inventory needs to avoid shortages of popular products. AI also identifies the cheapest and most efficient delivery routes, resulting in cost savings. Here are a few key ways in which AI impacts the logistics sector:

Resource management – AI enhances operational efficiency. For example, AI systems analyze fuel consumption and driver working hours to optimize schedules and delivery routes.
Evolution and learning from trends – thanks to AI, companies automate processes and stay competitive. Algorithms learn from seasonal sales patterns to better forecast future demand.
Parcel tracking – AI helps in monitoring deliveries to ensure they are on time. AI-based tracking systems notify the company about potential delays in transportation.
Supply chain transparency – AI enables quick issue resolution. AI dashboards allow real-time identification and resolution of bottlenecks in the supply chain.
Data management – AI ensures accuracy and consistency of data. AI systems monitor and update product data in real time, ensuring its accuracy throughout the supply chain.

AI in logistics. Best tools

The development of AI technology in logistics has opened up new possibilities for companies to improve supply chain management. Let’s take a look at the most advanced tools that assist in achieving these goals.

IBM Watson Supply Chain

IBM Watson Supply Chain is a tool that, through AI, provides organizations with real-time insights, predictive tips, and recommendations for action. This enables inventory management optimization, demand forecasting, and supplier relationships through the analysis of data from various sources. IBM Watson Supply Chain Insights is an AI-based solution that:

increases supply chain visibility,
provides insights, allowing better data management and practical guidance.

This allows for more effective mitigation of disruptions and risks, as well as improvement in decision-making and performance across the entire supply chain.

Source: IBM (https://www.ibm.com/products/planning-analytics/supply-chain-planning)

SAP Ariba

SAP Ariba is a cloud-based platform for procurement and supply chain management that leverages AI to streamline the processes of sourcing goods, managing suppliers, and negotiating contracts. Its AI-supported analytical engine helps companies identify potential risks and opportunities to enhance efficiency and safety in their supply chain.

AI in logistics applied by SAP Ariba is a procurement and expense management service that allows suppliers and buyers to connect and conduct business on one platform. It provides a comprehensive set of solutions for managing the entire procurement process and building ethical and ecological supply chains.

The biggest advantage of SAP Ariba is the ability to seamlessly integrate with other SAP tools to ensure comprehensive business support in terms of digital services and expertise. This results in reducing financial and operational disruptions and lowering the risk associated with suppliers. The Ariba Network is a key component of SAP Ariba, powered by SAP HANA, providing a platform for managing catalogs, offers, purchases, and invoices.

Source: SAP (https://www.sap.com/poland/products/spend-management/ariba-login.html)

o9 Solutions

o9 Solutions offers an Integrated Business Planning (IBP) platform supported by AI that helps organizations align processes from three main areas of the company:

supply chain,
sales department, and
finance area.

Advanced demand forecasting capabilities enable companies to optimize inventory levels, reduce order fulfillment time, and enhance customer satisfaction. o9 Solutions is an AI-supported planning and decision-making platform that enables true Integrated Business Planning (IBP) for global companies. It offers a suite of solutions for supply chain planning and analysis, retail planning, and production scheduling.

The o9 Control Tower dashboard enables quick and informed decision-making based on data. The o9 Solutions platform, offering AI solutions in logistics, helps companies manage complex processes by integrating best practices and enabling data-driven strategic business planning.

Source: o9 Solutions (https://o9solutions.com/solutions/supply-chain/)

FourKites

FourKites is a real-time supply chain monitoring platform that utilizes AI and machine learning to predict the arrival times of shipments and optimize transportation routes. As a result, companies can reduce transportation costs, increase customer satisfaction, and minimize the environmental impact of logistics operations.

One of FourKites’ clients, Henkel, benefits from using AI in logistics by having access to real-time data on the location and estimated time of arrival (ETA) of shipments. This allows them to better plan their tasks and respond to potential delays. FourKites has also brought other advantages to Henkel, such as time and cost savings, improved quality, fair dispute resolution, and avoiding penalties for delays. In 2023, Henkel planned to track nearly a million shipments using FourKites.

Oracle SCM

Oracle SCM is one of the most sophisticated AI tools in logistics. It provides a set of AI-supported supply chain management tools that enhance decision-making, optimize processes, and drive operational performance throughout the supply chain (Oracle Supply Chain Management (SCM)). Some of these tools include:

Oracle Intelligent Track and Trace – meaning a tool for tracking the carrier’s route and the shipment’s path,
Oracle Demand Management – a demand management tool that allows controlling inventory levels even in large enterprises,
Oracle Supply Chain Planning – a module used for planning supply chains in the company,
Oracle Transportation Management – a transport management platform,
Oracle Warehouse Management – a tool for controlling warehouses and deliveries.

Oracle SCM (Supply Chain Management) is a comprehensive set of applications designed to manage the supply chain with increased efficiency and visibility. It includes a range of features, such as product lifecycle management, supply chain planning, procurement, logistics, and order management. An AI-powered logistics tool can also integrate with Internet of Things (IoT) devices and blockchain to meet modern supply chain challenges.

Oracle uses not only AI and ML (Machine Learning) in logistics, which accelerate data analysis, revealing issues related to employees and supply chain inefficiencies. Modern solutions collaborating with AI in logistics also include voice interfaces and Natural Language Processing (NLP), improving not only accessibility and speed but also data analysis and decision-making skills.

The most significant innovation, however, is predictive analytics. It allows comparing future sales orders with staffing levels to uncover skill gaps and identify needs related to warehouse volume or vehicle availability. All this is aimed at reducing disruptions in the supply chain.

Source: Oracle (https://www.oracle.com/scm/product-tours/#supply-chain-planning)

What problems can arise when using AI in logistics?

Introducing AI into logistics comes with challenges. The initial transformation requires significant investments and digitalization of the enterprise. AI algorithms in logistics can be complex, which may initially make it difficult to understand the decisions proposed by newly implemented management systems.

Ensuring data security is also essential to protect operational integrity and customer trust. Additionally, AI systems trained on insufficient quality data can lead to erroneous decisions and algorithmic biases. Therefore, it is important to prioritize employee training and thorough data collection for optimizing transportation right from the start of implementing AI in logistics within a company.

The future of AI in logistics

AI transforms logistics, streamlining operations, reducing delivery costs, and providing companies with a strategic advantage. The capabilities of AI enable companies to increasingly:

optimize supply chains – AI in logistics allows for more precise planning and resource management,
plan routes – thanks to artificial intelligence, it’s possible to find the most efficient routes for transporting goods,
gain a strategic advantage – companies using AI in logistics gain an advantage over the competition by continuously refining both delivery systems and management methods over time.

The outline of the future scenario with AI in logistics may look as follows: companies will increasingly rely on AI for demand forecasting, automation of warehouse processes, and optimization of delivery routes. The utilization of artificial intelligence in management, planning, and the creation of future strategies will also grow.

Summary

AI in logistics brings significant benefits but also poses challenges. Companies considering AI should approach implementations thoughtfully, seeking guidance from logistics AI experts to ensure both the advantages and effectiveness of the technology are maximized in a secure and controlled manner.

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Author: Robert Whitney
JavaScript expert and instructor who coaches IT departments. His main goal is to up-level team productivity by teaching others how to effectively cooperate while coding.
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