CONTAINERMANAGEMENT
CONTAINERMANAGEMENT
Load carriers such as crates, pallets, frames, containers, and ULDs sometimes disappear, are located where they are not needed, or are missing elsewhere. Getting an overview of where containers are often requires a lot of effort, as their data is often collected in multiple systems. However, container management is most efficient when its four core aspects – needs assessment, tracking, distribution, and transportation organization – are integrated into a single process. Intelligent and AI based container management software effectively links these four areas and directs the use of loading aids for greater efficiency.
Humans and AI Need to Work Together
The more containers and the larger the network, the greater the complexity and the more decisions that need to be made. To enable intelligent software to help users manage the container cycle, complex processes and rules are mapped into algorithms. Artificial intelligence and machine learning are used to analyze large amounts of data about container movements, manual interventions in the process, etc., to identify patterns for which no rule has yet been created in the optimization software. These new rules can be automatically applied by the system. Humans remain in control and can reject or adjust the decisions made by the system. Through this real-time interaction between man and machine, container management makes a lasting contribution to the company’s success.
The Rule of Three in Container Management
Warehouse A has nine totes. Warehouse B is missing four totes. How many totes will warehouse C need next week?
At first glance, subtracting and adding load carriers and their accounts seems like a simple arithmetic task, but in day-to-day pooling, it turns out to be a complex function with many unknowns. Many software solutions are easy to manage and track, but when it comes to distribution and scheduling, the wheat is separated from the chaff. And when it comes to reliable forecasting of the required load carriers, the number of top candidates becomes very small.
Fachartikel
Algorithmen versenden Ladungsträger
Schnell ist die Frage beantwortet, wo Machine Learning und Künstliche Intelligenz uns im privaten Alltag begegnen: Heizung regulieren, Musik hören, Navigieren, et cetera. Diese Wünsche erfüllt uns künstliche Intelligenz automatisch oder nach einem Sprachbefehl. Und Vorschläge macht sie dank Machine Learning auch: Wer sich dieses Lied angehört hat, dem könnten auch die folgenden Lieder gefallen. Und in der Logistik? Wo sind die Einsatzgebiete dieser Technologie im Behältermanagement?
Optimized Container Management
Knowing where and how many containers are located makes it possible to control and use them efficiently. This is possible through the use of tracking and tracing methods and AI based container management software. This reduces the need for expensive new purchases, error-prone manual entries, and time-consuming coordination and significantly increases the profitability of containers.
Track Containers
Knowing the status and location of containers in the network is fundamental to efficient container tracking. Container-related data is generated at various points in the network. It is done directly through auto-ID and telematics solutions and indirectly through ERP, warehouse management, or other systems. This information is processed in real-time by specialized software. In this way, companies always have an overview of the current distribution of containers.
Inventory Management
The software processes the data from the container movements in real-time to always display the current container inventory. This not only assists the users of the system with inventories, but also enables them to respond immediately to any requirements, such as orders or call-offs.
Common Database
Specialized container management software is linked to existing IT systems that are relevant to container management (e.g., ERP system, production planning system). Data from these systems and tracking and tracing are continuously processed by the container management system and made available to users.
An AI based container management system provides:
- An overview of carrier locations;
- Reduced control costs;
- Reduced logistics costs;
- Increased security of supply;
- Low operating costs, and;
- Faster decision-making.
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Circular Economy
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The goal of container management is to achieve a cost-effective, demand-driven distribution of load carriers.
When this balancing act is mastered, container management makes a lasting contribution to the company's success.
INFORM optimizes daily
> 5.2 million containers and returnable transport packaging.
Focus: Sustainable Container Cycle with AI
An economically and environmentally sustainable container loop is the goal of many companies. AI can contribute to this by supporting the four key cornerstones:
1. Determine demand
How many containers are needed, where, and when? Algorithms use historical data, ad hoc bookings, warehouse space analysis, etc. to create accurate demand forecasts.
2. tracking
What containers are in transit? How many are loaded, how many empty? AI processes current status messages (RFID, barcode, ERP, etc.) in real time.
3. distribution
How many containers from warehouse A to warehouse B? Or would it be better to go to C? Based on a precise demand forecast, the accounts of the parties involved are optimized.
4. transportation
Whether internally in the AI or via an interface to the Transportation Management System (TMS), containers arrive at their destination on time without a lot of manual effort.
Intelligent Software for Container Management combines the four cornerstones and optimizes the use of load carriers to achieve greater cost efficiency.
