IBM Logistics Sweden

SiB Solutions

Supply Chain Optimization

SiB Solutions needed a scalable, open and low-error way to process warehouse video and logistics events across customer sites without getting trapped in rigid monolithic architectures or vendor lock-in.

in 2020
over 30 sites across Europe and North America

Reported impact

Business outcome

The platform supports more than 30 sites across Europe and North America while helping SiB Solutions reduce infrastructure and operational expenses and deliver logistics services designed to eliminate costly warehouse errors.

  • Cost Reduction
  • Productivity Improvement
  • Scalability

Solution

SiB Solutions built its intelligent video and AI services on IBM Cloud beginning in 2020, using IBM Code Engine and IBM Event Streams, the managed Apache Kafka service, as the backbone for warehouse telemetry and video events. In customer warehouses, video analytics run onsite for data residency, and edge computers apply deep-learning models to the camera feeds so errors can be identified close to the process being observed. Those outputs are published by Kafka producers to Kafka topics and sent to cloud servers, where application logic executes and microservices coordinate logistics workflows. Event Streams gives the company the high-volume streaming fabric it needed to avoid brittle monoliths and connect onsite industrial environments to cloud services, while Code Engine supports the cloud-side runtime used to develop and operate the broader platform.

Data flow

Video is analyzed onsite in customer warehouses, edge computers process the footage with deep-learning models, and Kafka producers send the resulting events through IBM Event Streams to cloud servers on IBM Cloud where application logic and microservices coordinate logistics actions.

Solution architecture

3 components · 2 layers
  1. Ingest
    • IBM Event Streams Carries high-volume Kafka event streams between onsite edge processing and cloud microservices.
  2. Compute
    • IBM Cloud Hosts the cloud servers and broader platform that receive warehouse events and run application logic.
    • IBM Code Engine Supports the cloud-side runtime used to develop and operate the logistics application services.

Architecture clues

  • Forklift drivers can receive audible alerts and augmented reality overlays when loads are misplaced.
  • The architecture uses microservices rather than a large monolithic application.
  • Video analytics are run onsite to comply with data residency regulations.

Evidence from the source

"Event Streams on IBM Cloud is core in everything we develop"
"SiB Solutions has developed and improved its solutions since starting on IBM Cloud in 2020, using IBM Code Engine and IBM Event Streams"
"The output of this process is sent from Kafka producers over Kafka topics to cloud servers, where application logic takes place."
"the company now services over 30 sites across Europe and North America"
"video analytics are run onsite to comply with data residency regulations"
Transparency Evidence & methodology 75/100 reference value · 90% confidence · 2 sources

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 confidence90%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency9/10
    2 source references 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. IBM

    SiB Solutions customer story

    First-party
    https://www.ibm.com/case-studies/sib-solutions
    Source type
    Vendor-published customer story
    Published
    Apr 17, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date
  2. LinkedIn

    SiB Solutions | LinkedIn

    Independent secondary
    https://www.linkedin.com/company/sib-solutions-ab/

    LinkedIn's Organization JSON-LD lists addressLocality "Lund", addressCountry "SE", and numberOfEmployees value 30.

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.