IBM Software / SaaS global

IBM Sterling Order Management team

IT Operations Automation · Real-time Analytics

The IBM Sterling OMS SaaS team had to maintain near-perfect uptime and performance for retailers during events like Black Friday while operating a highly distributed microservices estate with hundreds of integrations, tens of thousands of containers, and massive order and API volumes.

1M spans per second
20+ IBM Cloud Kubernetes Service clusters
20B API calls in one week
25 million order lines optimized by the Sterling OMS AI engine

Reported impact

Business outcome

Instana helped the OMS team get through the 2024 holiday peak without major incidents, sustain huge order and API loads, and support better client retention and support metrics while Sterling’s AI engine optimized millions of order decisions for retailers.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth
  • Scalability

Solution

IBM migrated Sterling OMS from a competing monitoring tool to IBM Instana so the service could observe and optimize its multitenant retail workload end to end. The SaaS platform itself runs real-time order and inventory management across more than 400 customizable environments on IBM Cloud Kubernetes Service, backed by IBM Db2, NoSQL stores, Cassandra, MongoDB, Elastic Search, IBM MQ, Kafka, Istio service mesh, and an Apache Spark/Iceberg lakehouse used for AI workloads. Instana collects telemetry across those components so the team can see every touchpoint and integration in real time, identify issues proactively, and protect the 99.9999% uptime commitment. During holiday peaks, Sterling’s AI engine uses live demand and inventory signals to optimize order sourcing while Instana absorbs nearly a million spans per second, giving the team the operational visibility needed to keep fulfillment systems stable at retail scale.

Data flow

Retail orders, inventory events, and API traffic enter Sterling OMS through its Kubernetes-hosted microservices and messaging layers, persistent data lands in Db2 and related stores, Spark and the lakehouse support optimization workloads, and Instana continuously ingests telemetry so the team can see performance and resolve issues across the whole fulfillment path.

Solution architecture

9 components · 6 layers
  1. Ingest
    • IBM MQ Handles messaging within the OMS transaction environment.
    • Kafka Streams event data across the distributed OMS platform.
  2. Compute
    • Apache Spark Supports AI workloads tied to order and inventory optimization.
  3. Storage
    • IBM Db2 Stores part of the order-management data powering the SaaS application.
    • Iceberg Data Lakehouse Provides the lakehouse foundation used by AI workloads in the OMS stack.
  4. Serving
    • IBM Sterling Order Management Runs the multi-tenant SaaS order and inventory workflows used by retail customers during peak commerce periods.
  5. Orchestration
    • IBM Cloud Kubernetes Service Hosts the large Kubernetes estate that scales OMS microservices horizontally and vertically.
    • Istio Service Mesh Manages service networking across the large microservices environment.
  6. Governance
    • IBM Instana Observability Provides real-time observability across integrations, microservices, and infrastructure so the team can detect and resolve issues quickly.

Architecture clues

  • Instana migration was completed before the 2024 holiday season to prepare for retail peak.
  • The platform supports over 400 customizable environments with multi-tenant microservices.
  • The stack combines Db2, NoSQL, Cassandra, MongoDB, Elastic Search, IBM MQ, Kafka, Istio, Spark, and an Iceberg lakehouse.

Evidence from the source

“20+ IBM Cloud Kubernetes Service clusters, 900+ worker nodes, 60,000 active containers”
“At its busiest, the platform processed more than 62,000 transactions per second, resulting in close to 1 million spans per second ingested by Instana.”
“During the 2024 holiday surge, 25 million order lines were optimized by the Sterling OMS AI engine.”
“Since the start of the migration in summer 2023, Instana has played a key role in our success by enabling rapid issue resolution and reducing Mean Time to Resolution (MTTR)”
“The OMS SaaS product suite supports over 400 customizable environments”
Transparency Evidence & methodology 100/100 reference value · 89% 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 value100/100
  • 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 impact25/25
    5 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 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 transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    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

    IBM Sterling Order Management team customer story

    First-party
    https://www.ibm.com/case-studies/sterling-oms-instana
    Source type
    Vendor-published customer story
    Published
    May 21, 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

Date evidence

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