Optimizing Machine to Machine (M2M) Communication to Increase Throughput in Logistics
Our client – a large scale eCommerce shop – wanted to optimize the throughput of their logistics systems including autonomous, robotic subsystems. By analyzing network traffic and machine to machine communication within the warehouse niologic successfully identified potentials for optimization and set up a predictive system for order routing.
Challenge
The client had set up a warehouse system, in which robots automatically pick orders and box them for shipping. However, communication within the subsystems did not operate as fast as desired. Therefore, it was necessary to broadly analyze the system traffic and electronic communication. The aim was to identify steps, which can be optimized to increase processing speed and maximize throughput.
Procedure
Niologic performed a detailed analysis of network traffic within the warehouse system to fully understand the workflow. The network data between subsystems was automatically exported for analysis to Google Cloud Storage. The network data was then transformed using Python and Google Cloud Dataflow.
After processing, the transformed data was stored in Google BigQuery for further analysis and model training. When analyzing the data, it became evident, that a number of bugs occurred in the warehouse management system’s communication between robots and the control plane (warehouse management system). The throughput could be maximized by resolving multiple communication inefficiencies and better error handling.
Furthermore, some items in the warehouse are defined as not pickable by the robots (due to physical dimensions or image recognition) and thus require human interaction. Niologic successfully trained a model using Google AutoML and also BigQuery + external libraries to predict the probability of an item being successfully picked by learning from historic picking events.
Results and Customer Value
Through the analyses and optimizations carried out by niologic, the client gained valuable insights into their system. This allowed the client to perfectly understand the system’s bottleneck and potentials for optimization. Based on the newly gained knowledge, the company was now able to continuously improve their warehouse system and steadily increase throughput. Additionally, the deployment of the predictive system enabled the client to proactively plan order processing and required human interaction by load balancing between different pick&place technologies to maximize overall throughput.
