N-iX Case Studies Driving Efficiency in Logistics with Industrial Machine Learning

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	Driving Efficiency in Logistics with Industrial Machine Learning N-iX

Driving Efficiency in Logistics with Industrial Machine Learning

N-iX

Technology Category	Analytics & Modeling - Data Mining Analytics & Modeling - Machine Learning
Applicable Industries	Consumer Goods Transportation
Applicable Functions	Logistics & Transportation Warehouse & Inventory Management
Use Cases	Computer Vision Inventory Management
Services	Data Science Services Training
Challenge	The client, a Global Fortune 100 multinational engineering and technology company, was facing challenges with its existing logistics platform. The platform was used to manage goods and materials across its 400+ warehouses in over 60 countries. However, the platform had several flaws and was unsuitable for further scaling. The monolithic architecture of the platform made it inefficient and difficult to scale. The client had a vision to refactor the legacy platform but lacked the comprehensive in-house expertise to address the technical issues and make the platform more efficient and scalable. The client needed a solution that would streamline inventory management for more warehouses and improve the efficiency of internal logistics. Read More
About Customer	The client is a Global Fortune 100 multinational engineering and technology company based in Germany. The company operates in over 60 countries worldwide through a complex network of over 440 subsidiaries and regional entities. Its core operations are divided into four business sectors: Mobility, Industrial Technology, Consumer Goods, and Energy and Building Technology. Being a leading global supplier of technology and services, the client has many factories, warehouses, and suppliers, as well as a lot of raw materials and finished goods, which circulate among them. Read More
Solution	N-iX experts helped the client transform their logistics platform by introducing a microservices architecture, DevOps best practices, and implementing advanced technologies including Machine Learning (ML), Artificial Intelligence (AI), Natural Language Processing (NLP), and computer vision. The new cloud-native infrastructure of the platform was based on Azure Kubernetes. The migration to microservices allowed for the smooth addition of new SaaS services like anomaly detection, delivery prediction, route recommendations, object detection in logistics, OCR of labels on boxes, NLP for document verification, data mining, and sensor data processing. A DevOps pipeline was built from scratch, setting up the environment for development and QA in Azure, and introducing CI/CD processes. A Computer Vision (CV) solution for docks was developed that allows contactless tracking of goods with industrial optic sensors and Nvidia Jetson devices. A multiplatform CV mobile app was also developed that covers object detection, package damage detection, OCR, and NLP for document processing. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Data Mining

Analytics & Modeling - Machine Learning

Applicable Industries

Consumer Goods

Transportation

Applicable Functions

Logistics & Transportation

Warehouse & Inventory Management

Use Cases

Computer Vision

Inventory Management

Services

Data Science Services

Training

Challenge

The client, a Global Fortune 100 multinational engineering and technology company, was facing challenges with its existing logistics platform. The platform was used to manage goods and materials across its 400+ warehouses in over 60 countries. However, the platform had several flaws and was unsuitable for further scaling. The monolithic architecture of the platform made it inefficient and difficult to scale. The client had a vision to refactor the legacy platform but lacked the comprehensive in-house expertise to address the technical issues and make the platform more efficient and scalable. The client needed a solution that would streamline inventory management for more warehouses and improve the efficiency of internal logistics.

About Customer

The client is a Global Fortune 100 multinational engineering and technology company based in Germany. The company operates in over 60 countries worldwide through a complex network of over 440 subsidiaries and regional entities. Its core operations are divided into four business sectors: Mobility, Industrial Technology, Consumer Goods, and Energy and Building Technology. Being a leading global supplier of technology and services, the client has many factories, warehouses, and suppliers, as well as a lot of raw materials and finished goods, which circulate among them.

Solution

N-iX experts helped the client transform their logistics platform by introducing a microservices architecture, DevOps best practices, and implementing advanced technologies including Machine Learning (ML), Artificial Intelligence (AI), Natural Language Processing (NLP), and computer vision. The new cloud-native infrastructure of the platform was based on Azure Kubernetes. The migration to microservices allowed for the smooth addition of new SaaS services like anomaly detection, delivery prediction, route recommendations, object detection in logistics, OCR of labels on boxes, NLP for document verification, data mining, and sensor data processing. A DevOps pipeline was built from scratch, setting up the environment for development and QA in Azure, and introducing CI/CD processes. A Computer Vision (CV) solution for docks was developed that allows contactless tracking of goods with industrial optic sensors and Nvidia Jetson devices. A multiplatform CV mobile app was also developed that covers object detection, package damage detection, OCR, and NLP for document processing.

Impact #1	The modernized and scalable logistics platform significantly improved the efficiency of internal logistics. The cloud-native microservices architecture enabled the solution to scale fast to more than 400 warehouses in over 60 countries, ensuring better performance and responsiveness. The redesigned architecture allowed data streaming without delays and provided high load-carrying capacity. The solution could be easily deployed on any cloud provider such as AWS, GCP, Azure, or even on-premise, providing the client with the flexibility to control expenses by selecting the cloud provider with the best offering. The embedded computer vision solution for cameras installed in warehouses allowed the client to automatically detect arriving packages, scan barcodes, and change the delivery statuses of the boxes. The multiplatform mobile application allowed warehouse staff to scan barcodes and allocate the boxes efficiently in a warehouse.

Impact #1

The modernized and scalable logistics platform significantly improved the efficiency of internal logistics. The cloud-native microservices architecture enabled the solution to scale fast to more than 400 warehouses in over 60 countries, ensuring better performance and responsiveness. The redesigned architecture allowed data streaming without delays and provided high load-carrying capacity. The solution could be easily deployed on any cloud provider such as AWS, GCP, Azure, or even on-premise, providing the client with the flexibility to control expenses by selecting the cloud provider with the best offering. The embedded computer vision solution for cameras installed in warehouses allowed the client to automatically detect arriving packages, scan barcodes, and change the delivery statuses of the boxes. The multiplatform mobile application allowed warehouse staff to scan barcodes and allocate the boxes efficiently in a warehouse.

Benefit #1	Streamlined inventory management for 400+ warehouses around the globe.
Benefit #2	Real-time tracking of packages, effectively managing the delivery statuses of boxes, and predicting warehouse load.
Benefit #3	Automated manual work and reduced paperwork for warehouse staff.

Benefit #1

Streamlined inventory management for 400+ warehouses around the globe.

Benefit #2

Real-time tracking of packages, effectively managing the delivery statuses of boxes, and predicting warehouse load.

Benefit #3

Automated manual work and reduced paperwork for warehouse staff.

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Overview

Driving Efficiency in Logistics with Industrial Machine Learning

Operational Impact

Quantitative Benefit