Trends that will shape yard management in 2025

Own web apps thanks to no-code and low-code technologies

Michael Rölli, Vice President Customer Management at myleo / dsc. © myleo / dsc

Web apps help to digitize paper-based routine processes, such as ticking off checklists, and make them evaluable. In professional yard management systems, companies can "program" web apps themselves. This is made possible by integrated no-code or low-code app editors. No-code or low-code refers to a technology that allows you to create your own software applications with virtually no programming knowledge. While low-code applications still require a certain minimum level of basic programming knowledge, no-code applications can be used completely without this. This means that companies are able to map any requirements themselves within a very short time and integrate them into the yard management solutions.

The fact that more and more yard processes are being digitized and automated naturally also benefits the control station. Smart yard management also offers employees decisive advantages in their day-to-day work thanks to targeted tracking. The control center can use ETA forecasts to detect deviations even from a great distance and intervene to correct ongoing processes. Based on information from the process and IoT sensors, it is possible to draw conclusions about the quality of planning. Information from truck telematics systems, but also from integrated systems, is the basis for efficient loading point control and throughput and capacity optimization on the factory premises.

Slot and yard management: clearly defining KPIs and using data

In the area of slot and yard management in particular, there are a number of different KPIs to measure strategic goals in terms of growth and also in relation to reducing process costs:

- Number of booked slots per loading or unloading resource per material group

- Punctuality of truck drivers per forwarder / supplier and logistics discipline

- Time per activity (e.g. check-in, waiting time until call-off, loading and unloading time, weighing, etc.)

- Lead time from arrival to departure

- Statistics per driver and logistical discipline

By specifically measuring and analyzing those KPIs, companies can:

- Optimize personnel and resource planning in loading and unloading

- Measure the performance of suppliers and forwarders for quality discussions and negotiations

- Evaluate and validate possible demurrage charges

- Optimize and distribute workload peaks depending on the door

- Measure and reduce throughput times

AI and machine learning make mountains of data analyzable

The information generated in the course of a digital yard process offers considerable potential for data analysis. Artificial intelligence (AI) and machine learning (ML) can be used to evaluate this data based on facts. For planning reliability, this would mean concrete predictions, a calculation of loading and unloading times for the future instead of guesses or estimates. The use of AI systems promises new potential to increase productivity and efficiency and reduce costs. It should also be noted that AI systems do not threaten the professional existence of employees. Rather, they help them to solve the complex challenges of yard logistics. AI algorithms are particularly helpful where several hundred or thousand transport transactions take place every day. This is because these requirements go far beyond human planning capabilities. Networking yard management and AI enables users to run simulations that allow them to plan several days into the future.