MicrosoftProductionEvidence: Low40/100

Arvato streamlines supply chain logistics for efficiency and sustainability

Arvato Systems, a Germany-based logistics specialist, implemented AI-powered optimization for complex supply chain logistics using Microsoft Azure and integrated SAP solutions. The project focused on addressing challenges like order complexity, warehouse release processes, and transport optimization. Arvato blended its supply chain management expertise with Azure Cloud, SAP S/4HANA, SAP Extended Warehouse Management (EWM), and SAP Transportation Management (TM), along with its proprietary platbricks platform. Innovations included the use of machine learning, analytics, mobile logistics apps, and chatbots to modernize logistics operations for clients across various industries. Their services extended beyond IT implementation, covering strategic partnership, process redesign, education, and change management. The platform was designed to close digitization gaps and enable tailored supply chain optimization depending on individual client requirements. With this digital transformation, Arvato enabled logistics organizations to improve efficiency, customize processes, and ensure future-readiness. Key outcomes included enhanced operational efficiency and sustainability throughout the logistics chain. The integration also supported innovation continuity by exposing customers to emerging logistics trends. This solution positioned Arvato customers for continuous growth in dynamic global markets.

Organization
Arvato
Industry
Logistics
Location
Germany

Reported outcomes

Strategic outcomes

Speed & agilityImproved warehouse and transport efficiencyCustomer experience & trustEnabled tailored supply chain optimizationNew product / capabilityClosed digitization gaps in logisticsSustainability & ESGImproved operational sustainability
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
Customer
Arvato
Provider
Microsoft
Maturity
Production

Key outcomes included enhanced operational efficiency and sustainability throughout the logistics chain

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

  • 1AI-powered warehouse order optimization
  • 2Digitalized transport preparation and management
  • 3Supply chain process automation
  • Complex value chains required advanced logistics management.
  • Sub-optimal warehouse order releases affected service and efficiency.
  • Lack of digitized processes led to inefficiencies in logistics.
  • Need for real-time transport and warehouse utilization optimization.
  • Customers needed bespoke digital solutions for their unique supply chains.
  • Leveraged Microsoft Azure Cloud and SAP solutions integration for logistics optimization.
  • Applied machine learning and analytics to automate and enhance warehouse and transport management.
  • Implemented platbricks platform to fill digitization gaps or act as a standalone logistics solution.
  • Deployed mobile logistics apps, chatbots, and digital process reengineering for clients.
  • Enhanced efficiency of warehouse operations and transport planning.
  • Enabled individualized supply chain optimizations for diverse customer needs.
  • Closed digitization gaps in traditional logistics with custom cloud-based innovations.
  • Increased operational sustainability and agility for logistics partners.
Architecture

Order data is processed using Microsoft Azure Cloud and SAP S/4HANA. SAP Extended Warehouse Management (EWM) optimizes internal logistics and warehouse flows. SAP Transportation Management (TM) handles transport planning and execution. These interact with Arvato's platbricks platform, which digitizes additional logistics steps using analytics and automation. Mobile apps and chatbots interface with end users and logistics teams, enabling real-time insights and optimization.

Implementation partners1
Sources & evidence1
Evidence: Low40/100Evidence strength
  • Customer explicitly identified
  • Deployment status explicitly supported
  • Technical implementation details available
Publisher: us.arvato-systems.com

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