Artificial intelligence(AI) is more than just another advance in technology. AI is revolutionizing daily life and it’s accelerating evolution across all industries, and logistics is no exception. The use of AI is opening a wide range of possibilities that represent drastic changes in the operation of logistics systems at all stages and phases of the supply chain.
Starting from the planning phase, through the use of AI in hand with big data, companies are becoming more dynamic in deducing the intent to consume certain products. Companies can now anticipate the demand of their markets. Additionally, they’re able to adapt early and operate logistics, accordingly, avoiding both inventory understocks and overstocks, and in turn saving resources.
As a result, operations are becoming more affordable for supply chain managers and logistics specialists, which can be directly translated into growth and competitive advantage.
There are concrete AI applications that are making a difference in logistics.
Automated Warehouses
The warehouse is at the heart of logistics, and the processes within warehousing represent both great challenges and opportunities. By using AI and empowering a management software with applied robotics, it’s possible to automate operations such as product transport and placement, allocating only the necessary resources for each task.
There are already “smart warehouses” where artificial intelligence allows visualizing the object data. It’s possible to practically automate all day-to-day operations of a warehouse. A good example of these are warehouses with online platforms that use artificial intelligence to automate processes such as goods receipt and wrapping. With barcode-free object recognition, products are sorted immediately after being unloaded from the containers. Simultaneously, while 3D wrapping, products are measured by AI and computer vision, there’s no need for barcode scanning.
Supply Chain Automation & Visibility
Many supply chain processes are susceptible to AI automation. From real-time purchase order monitoring and inventory updates to issuing supply orders; while improving goods traceability. It should be mentioned that, generally, investing in technology optimizes the supply chain.
Transport Coordination
AI will be able to define optimal routes for deliveries, as well as recalculate and/or modify routes in real time if necessary.
This allows visualizing and managing the ongoing transfers of merchandise, thus achieving superior warehouse management.
Increase Productivity
The use of algorithms and automated calculations result in better solutions that considerably increase warehouse productivity, which is especially true for online retailers. Additionally, there are now AI-controlled robots that ensure simultaneous and almost error-free communications.
Accounting Processing
AI is capable of handling essential business roles at high volumes. For example, processing millions of invoices from thousands of vendors, partners, and suppliers, lifting a huge burden off logistics accounting teams. Furthermore, AI technologies such as spoken language processing can extract essential information, such as billing amounts, account information, dates, addresses, and stakeholders among a great number of invoices. After classifying the data, an RPA (robotic process automation) bot can extract and enter the information into the software to generate a purchase order, make payments, and send the customer an email confirmation; all without human intervention. Some systems can even be used to detect invoice fraud.
Benefits of Integrating AI Into the Supply Chain
Task Automation
There are artificial vision systems capable of identifying errors or predicting needs, as well as alerting human resources those need to be solved. Thus, streamlining the process of merchandise management in the warehouse. Artificial intelligence can also be used to schedule or execute maintenance tasks at the warehouse and transport activities.
Demand Organization
Knowing the needs of the customer in advance is a crucial aspect when it comes to optimizing the supply chain. This is achieved by data collection, but also insights on macroeconomic, statistical, and consumer trends that address a better management of the supply chain. AI models include predictive components that allow tailored planning through considering different scenarios, both in the supply chain itself and in response to developments in world markets such as changes in demand, price fluctuations, supplier changes, etc. This allows planning ahead, because the AI system learns from changes and improves over time.
Solving Logistics Problems
AI creates models with more detailed descriptions to solve logistics problems.When testing and simulating steps are added, its validation becomes closer to real world reality. For example, in transport, combining artificial intelligence with analytics and big data helps to reduce costs. This also contributes to providing relevant information that will improve company sustainability indicators. AI also helps in warehouse and inventory management. If the SKUs are managed within the inventory, products can be located through AI in real time to monitor sales, show inventory status, to predict demand, and to detect errors or scams in the supply chain.
Supplier Tracking
Supplier delivery times directly affect supply chain effectiveness and productivity. By using artificial intelligence and analytics, it’s possible to quickly respond or even anticipate these contingencies. An example is building digital twins that can perform diagnostic and predictive tasks.
When used strategically, AI can reduce errors, minimize risks and repeated operations while offering many applications that can speed up the logistics process and ultimately increase competitiveness. The use of AI will certainly continue to impact on logistics processes in the future.