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MongoDB Manufacturing Global

Zebra Technologies

Predictive Maintenance

“Our previous approach wasn’t the best — it risked data getting out of sync.

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Reported impact

Business outcome

Historical data helps to spot trends and identify issues that might occur in other devices.

  • Cost Reduction
  • Customer Experience
  • Scalability

Solution

“Our previous approach wasn’t the best — it risked data getting out of sync. Historical data helps to spot trends and identify issues that might occur in other devices. Zebra improves big data analytics using MongoDB Atlas and Google Cloud Industry Software & Technology Product MongoDB Atlas MongoDB Atlas Search Use Case Edge Computing Customer since 2014 Smart devices are everywhere, from our pockets to our factories to our cars. “Savanna detects existing issues and uses predictive intelligence to prevent others from happening.” The platform is built on highly-scalable, highly-available architecture using Google Kubernetes Engine and other Google Cloud Platform (GC) technologies.

Data flow

Operational events and application data flow into MongoDB Atlas, where the platform stores current state and supports real-time queries for downstream applications and integrations such as Google Cloud, BigQuery, Google Kubernetes Engine.

Solution architecture

2 components · 2 layers
  1. Storage
    • MongoDB Atlas Stores the operational data and provides the managed document database foundation for Zebra Technologies's workload.
  2. Serving
    • MongoDB Atlas Search Provides integrated full-text and faceted search across Zebra Technologies's application data.

Architecture clues

  • Autoscaling is used to absorb demand spikes and control costs.
  • Full-text or faceted search runs inside the MongoDB platform.
  • Kubernetes is part of the deployment architecture.
  • The deployment integrates with Google Cloud.

Evidence from the source

Historical data helps to spot trends and identify issues that might occur in other devices.
“Our previous approach wasn’t the best — it risked data getting out of sync.
Transparency Evidence & methodology 59/100 reference value · 67% 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 value59/100
  • Problem clarity12/20
    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 specificity12/20
    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 confidence67%
  • 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 evidence10/20
    2 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    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. MongoDB

    Zebra Technologies customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/zebra
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived May 25, 2024 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Jul 16, 2025 · MongoDB · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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