Use Case Intelligence RSS Search use cases Search
Snowflake Logistics Reading, PA

Penske Logistics

Real-time Analytics

On-premises reporting across SQL Server and mainframe systems made performance data hard to use and left Penske without a secure, practical way to turn many operational indicators into actionable manager guidance.

Read the original story Explore all use cases
<15 days to build a new AI summarization model
business analysts create BI reports with companywide data spanning five years in just 15 minutes
nearly 30 data columns in a dashboard

Reported impact

Business outcome

Penske built a new AI summarization model in under 15 days, delivers five years of companywide reporting in minutes, and gives managers clearer guidance that improves associate safety, performance, and retention.

  • Productivity Improvement
  • Risk Reduction

Solution

Penske Logistics moved reporting and generative AI workloads onto Snowflake on Azure so operational, safety, and workforce data could be analyzed together and summarized at scale. The company centralizes indicators from logistics systems, telematics, and other business sources in Snowflake, replacing limited SQL Server and mainframe reporting with a scalable cloud platform. Business analysts and leaders now use self-service reporting over companywide data spanning five years, while data scientists build repeatable analysis processes on the same governed foundation. Penske then uses Cortex AI to run LLMs directly inside Snowflake, turning nearly 30 dashboard columns into concise talking points and recommendations for each associate without bringing in an external AI vendor. Because the models stay in Snowflake’s governed perimeter, the team launched its first summarization model in less than 15 days and gave managers faster insight to improve safety, performance, and retention.

Data flow

Operational performance data from logistics systems, telematics devices, and other business sources flows into Snowflake on Azure. Tableau exposes historical reporting for analysts and leaders, while Cortex AI converts multi-column performance indicators into summaries and recommendations for managers.

Solution architecture

3 components · 3 layers
  1. Compute
    • Cortex AI Runs LLM-based summarization inside Snowflake to turn many performance indicators into actionable manager guidance.
  2. Storage
    • Snowflake Centralizes Penske performance data and provides the governed cloud platform for reporting and AI workloads.
  3. Serving
    • Tableau Provides certified self-service reporting so analysts and managers can explore multi-year data directly.

Architecture clues

  • Certified self-service reporting reduces dependence on central reporting teams.
  • Cortex AI keeps LLM inference inside Snowflake's governed perimeter.
  • Snowflake runs on Azure for Penske's centralized platform.

Evidence from the source

Cortex AI made it easy and cost-effective for the team to securely bring LLMs to their data
With Snowflake on Azure, Penske Logistics has centralized data in a secure environment
analysis was a manual process to parse through nearly 30 data columns in a dashboard
less than 15 days to build a new AI summarization model
produce relative talking points and recommendations for each associate
Transparency Evidence & methodology 100/100 reference value · 79% 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
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • 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 provenance0/10
    The publication date has no documented provenance.
  • 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. Snowflake

    Penske Logistics customer story

    First-party
    https://www.snowflake.com/en/customers/all-customers/case-study/penske/
    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 Mar 10, 2025 · 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 Apr 17, 2026 · Snowflake · 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.

Related

Similar use cases

See all like this
Snowflake Archived · Feb 11, 2026 GLS Logistics Germany GLS needed to replace a high-maintenance on-prem analytics platform that could explain past delays but could not predict future delivery problems, and whose slow queries, outages, and operational overhead limited the… 21 months Real-time AnalyticsSupply Chain Optimization Amazon Web Services (AWS) · Snowflake AI Data Cloud Snowflake Modified · Oct 9, 2026 DataHub Software / SaaS Palo Alto, California A fragmented, self-managed observability stack forced engineers to jump between tools, left monitoring on the same infrastructure it monitored, and drove high AWS cost and incident blind spots. $100K+ annual AWS savings IT Operations AutomationReal-time Analytics OPAL · Observe agent · Observe by Snowflake Snowflake Archived · Oct 8, 2026 Mercury Financial Services San Francisco, California replicate the company’s critical operational database into Snowflake fast enough to support trusted analytics, product decisions, and risk use cases without overloading production systems. 0 Replication incidents since the migration Real-time Analytics Snowflake · Snowflake Openflow