How SYNA.OS LOGISTICS reduces the number of vehicles by 30 percent

Processing the entire order pool on time, even with fewer transport vehicles? That’s possible with SYNA.OS LOGISTICS. Thanks to AI-based automated decision making and process optimization, orders are distributed more intelligently. This saves on expensive hardware and reduces delays.


A car consists of several thousand individual parts. If the exterior mirror fails to reach the production line on time, there is a risk of interrupted production: the conveyor belts come to a standstill, resulting in expensive downtimes. This makes it all the more important for the required parts to arrive at the right place reliably, efficiently and on time. Increasingly, mobile robots are responsible for transport within factories, also known as AGVs (Automated Guided Vehicles) or AMRs (Autonomous Mobile Robots). If these vehicles are controlled using the software solution SYNA.OS LOGISTICS, the number of vehicles can be reduced by up to 30 percent and delays can be decreased significantly. How is that possible?


Holistic approach: Always keeping everything in view


One customer example can demonstrate how these savings are achieved. For this customer, SYNAOS simulated multiple scenarios: In the first scenario, SYNA.OS LOGISTICS optimized the distribution of tasks among a total of 59 driverless vehicles. The adaptive algorithm does not proceed according to a rigid plan: instead, it considers complex dependencies, travel times and the entire order pool. As a result, the first order is not necessarily assigned to the fastest vehicle – what matters is the best choice for overall optimization. Its travel time might be longer, but less time will still be required overall: The AI-based algorithm proceeds using a holistic approach and anticipates the interplay of global factors in its analysis. Orders and vehicles are combined optimally as far as possible.

With holistic optimization using SYNA.OS LOGISTICS, only 59 vehicles are required to process the order pool. The AI algorithms keep delays within the acceptable range (less than 5 minutes) even during peak loads. Delays only occur in isolated cases – surges in delays are not discernible.

Reacting flexibly to disruptions


The result: Delays are reduced and there are fewer problems and late orders. The software checks continuously and in real time whether acute changes are required. For instance, if a vehicle suddenly breaks down, the adaptive algorithm immediately calculates alternative solutions and prevents a production shutdown in the worst case. Extensive monitoring also makes it possible to flexibly push orders forward in case of downtimes. Vehicles are always used to their ideal capacity.


Fewer vehicles, fewer delays


In the second scenario of the simulation, the orders were distributed step-by-step – this is still the common standard in many logistics centers and factories. These strict rules stipulate that an order is always assigned to the best available vehicle in each case. Without the smart software from SYNAOS, delays occur over the course of a simulated eight-hour shift and accumulate by the end of the shift:

With step-by-step planning, the fastest available vehicle executes the order in each case. This is a classic planning approach. But this results in enormous delays and a “surge” in late orders. The simulation demonstrates: 59 vehicles are not enough in this case to efficiently process the orders. Customers have two options: Planning for an additional 17 vehicles, or using SYNA.OS LOGISTICS with its AI-based approach.

In order to bring delays down to the same level as Scenario 1 without abandoning step-by-step scheduling, 76 vehicles would be needed instead of 59. Inversely, this means: By using SYNA.OS LOGISTICS and its sophisticated algorithm, 17 vehicles can be eliminated – that’s nearly 30 percent!


In summary: Clever software saves resources


The powerful software ecosystem from SYNAOS distributes orders based on smart and flexible rules. All relevant information is channeled into usage planning for machines in real time. As a result, SYNA.OS LOGISTICS detects delays immediately and compensates for them with rapid rescheduling. The algorithm calculated 250,000 combinations per second, so it always finds the best solution to manage vehicles as efficiently as possible.

Our extensive simulations have demonstrated that AI-based holistic fleet optimization requires fewer vehicles than a classical planning approach. The number of vehicles can be reduced by roughly 30 percent thanks to intelligent management. (These practice-oriented simulations reflect production in the automotive industry. The energy management of the vehicles was not considered in the process.) Another advantage: If fewer vehicles are moving around, the traffic network is significantly relieved and obstructions occur less frequently. The exterior mirror reaches the production line on time and can be installed without any delay.