Driving logistics efficiency with industrial Machine Learning

Executive summary

Client background

Our client is a Global Fortune 100 multinational engineering and technology company, based in Germany. Through a complex network of over 440 subsidiaries and regional entities, the company operates in over 60 countries worldwide. Its core operations are divided into four business sectors: Mobility, Industrial Technology, Consumer Goods, and Energy and Building Technology.

Business challenge

The client needed to streamline inventory management for more warehouses, but the existing logistics solution was difficult to scale. So it was essential to modernise the platform to improve the efficiency of internal logistics.

Sparknet approach
Sparknet experts have helped the client transform their logistics platform by introducing microservices architecture, DevOps best practices and implementing a number of advanced technologies including ML, AI, NLP, computer vision, etc.
Value delivered

The improved logistics platform will allow the client to scale fast to more warehouses, ensuring better performance and responsiveness.

Success story in detail

Business challenge: meeting customer needs

Being a leading global supplier of technology and services, our client has many factories, warehouses, and suppliers, as well as a lot of raw materials and finished goods, which circulate among them. To improve the logistics between its 400+ warehouses in over 60 countries, the client introduced an internal logistics platform. The platform is used by warehouse staff to efficiently allocate and manage goods and materials. However, after being in use in a few warehouses for several months, the platform turned out to have a lot of flaws and was unsuitable for further scaling.

Although our client had a vision that they needed to refactor the legacy platform, they did not have the comprehensive in-house expertise to address multiple technical issues and make the platform more efficient and scalable.

Sparknet approach: comprehensive transformation

Migrating to microservices The core reason why the platform was not scalable and inefficient was its monolithic architecture. Therefore, our Solution Architect designed and presented a new cloud-native infrastructure of the platform based on Azure Kubernetes, along with the suggested tech stack and the most efficient roadmap. Migrating to microservices allows smooth adding of new SaaS services: anomaly detection, delivery prediction, route recommendations, object detection in logistics, OCR (optical character recognition) of labels on boxes, Natural Language Processing for document verification, data mining, and sensor data processing. DevOps best practices The need for DevOps expertise was identified as another customer pain. Therefore, we are building the DevOps pipeline from scratch, setting up the environment for development and QA in Azure, and introducing CI/CD processes that allow us to easily assemble and deploy microservices to the environment. Computer Vision solution The core component of this project is the Computer Vision (CV) solution for docks that allows contactless tracking of goods with industrial optic sensors and Nvidia Jetson devices. Our client had CV algorithms written by another vendor, which were inefficient and unsuitable for production. Therefore, we found a top-notch CV expert with a Ph.D. degree to run the CV workstream. After careful examination of the existing algorithms, we decided to redevelop them completely. We changed the architecture of the solution and introduced Continuous Delivery for Machine Learning, which allows implementing continuously repeatable cycles of training, testing, deploying, monitoring, and operating the ML models. That is especially important given the global scale at which our client is operating. Multiplatform CV mobile app Also, our team designed the architecture of the multiplatform Computer Vision mobile app and is responsible for its end-to-end development. The app covers object detection, package damage detection, OCR, and NLP for document processing.
What we achieved together: driving automation and global scaling
The solution is in the development phase. The planned embedded computer vision solution for cameras installed in warehouses will allow our client to automatically detect arriving packages, scan barcodes, and change the delivery statuses of the boxes. And we are currently developing the computer vision pipeline for it. Thanks to the advanced CV algorithms and automated ML system, it is planned that cameras will read the barcodes and textual information under various conditions (i.e. different sizes of the boxes, camera angles, wet or stained labels). At the same time, the multiplatform mobile application will allow warehouse staff to scan barcodes and allocate the boxes efficiently in a warehouse.

Cloud-native microservices architecture enables the solution to scale fast to more than 400 warehouses in over 60 countries, ensuring better performance and responsiveness. Also, the redesigned architecture allows data streaming without delays and provides high load-carrying capacity. What’s more, the solution can be easily deployed on any cloud provider such as AWS, GCP, Azure, or even on-premise, providing our client with the flexibility to control expenses selecting the cloud provider with the best offering.


Benefits: improving the efficiency of internal logistics

The modernized and scalable logistics platform will significantly improve the efficiency of internal logistics in a number of ways:

  • Automating manual work and reducing paperwork for warehouse staff.
  • Streamlining inventory management for 400+ warehouses around the globe.
  • Tracking packages almost real-time, effectively managing the delivery statuses of boxes, and predicting warehouse load.
  • Package damage detection, thus eliminating defective packages.
  • More effective planning, reducing operational overhead and warehouse downtime.