Capgemini Automotive Global

Iveco Group

Real-time Analytics · Supply Chain Optimization

Iveco Group needed global visibility and tighter control over its complex, heterogeneous supply-chain processes so it could make faster decisions and improve efficiency and sustainability.

20 industrial sites
31 research and development centers
6-8 months to acquire the right awareness
8 major global brands

Reported impact

Business outcome

Iveco gained a scalable digital platform for shipment traceability, planning, supplier coordination, and sustainability analysis, improving responsiveness and creating a reusable foundation for future logistics use cases.

  • Cost Reduction
  • Productivity Improvement
  • Scalability

Solution

Iveco Group worked with Capgemini and AWS to build a Digital Control Tower that turns fragmented supply-chain data into a single operational planning and monitoring environment. The program began with an agile design phase that mapped heterogeneous systems, defined use cases, and created a data catalog so logistics, manufacturing, and partner information could be normalized and shared. Built on AWS cloud technology, the Control Tower centralizes data collection, source management, and core workflows, then layers AI and machine-learning use cases across traceability, supplier arbitration, dock scheduling, yard management, ETA analysis, and CO2 tracking. Serverless cloud services and collaborative tools let suppliers and OEMs participate in the same planning loop, while end-to-end traceability and sustainability reporting give Iveco a reusable digital platform it can extend incrementally as new supply-chain use cases are prioritized.

Data flow

Supply-chain and shipment data from heterogeneous internal systems are cataloged and centralized in the Control Tower on AWS, where traceability, ETA monitoring, supplier arbitration, and CO2 calculations can be applied. Suppliers and OEMs interact through collaborative tools, and the resulting operational signals feed planning, exception management, and sustainability reporting across the network.

Solution architecture

4 components · 2 layers
  1. Compute
    • Amazon Web Services (AWS) Provides the cloud foundation for centralized data collection, serverless processing, and AI-enabled supply-chain services.
  2. Orchestration
    • Control Tower Serves as the central environment for monitoring shipments, data flows, KPIs, and supply-chain processes globally.
    • Dock Scheduling Automates the planning and tracking of arrivals and supplier deliveries to reduce queues and improve coordination.
    • Yard Management Helps manage arriving shipments as part of the broader inbound-planning workflow.

Architecture clues

  • Serverless cloud storage was used to optimize resources on demand
  • The platform includes E2E Inbound Traceability and Supplier Arbitration use cases
  • The solution automates global CO2 calculations for Scope 3 transport emissions
  • The team used a Scrum-based design methodology

Evidence from the source

"Based on AWS cloud technology, the solution allowed Iveco Group to create a data platform and a strategic plan that covered data collection, source management, the centralization of heterogenous processes, and the use of AI and machine learning solutions"
"E2E Inbound Traceability, provides Iveco to track and monitor all shipments through the Control Tower."
"capable of optimizing data collection from many different sources, automating the calculation of CO2 emitted globally"
"the project team identified that Iveco Group would benefit from the dock scheduling, which allows a company to plan, organize, and track shipments accurately thanks to an algorithm"
Transparency Evidence & methodology 75/100 reference value · 93% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value75/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence93%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability25/25
    12 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency10/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Capgemini

    Iveco Group’s intelligent supply chain ensures efficiency and sustainability - Capgemini India

    First-party
    https://www.capgemini.com/in-en/news/client-stories/iveco-groups-intelligent-supply-chain-ensures-efficiency-and-sustainability/
    Source type
    Vendor-published customer story
    Published
    May 27, 2024
    Supports
    • Customer
    • Region
    • Company size
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • Business outcome
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page May 27, 2024 · Capgemini · First-party
    Open date source
Not separately sourced Industry. Treat these normalized attributes as lower-confidence until a dedicated source is added.