Logistics Data Analytics & Digital Transformation

Industry: 3PL Transportation & Warehouse Fulfillment (United States)

Challenge: A mature U.S. 3PL operator was losing money across transportation and warehouse operations.

Driver paperwork and Excel-based invoicing failed to capture all billable activities. Operational data was isolated across WMS, telematics, and CRM systems. There was no real-time visibility into margins by customer, route, or service.

In a thin-margin industry, fragmented systems were turning operational complexity into financial loss.

Operational complexity increased dramatically:

Transportation revenue depended on driver time, wait time, kilometers, fuel adjustments, storage, and ad-hoc services.
Warehouse profitability depended on SKU movement, storage duration, and handling costs.
Finance had no real-time margin visibility.
Sales pipeline data was disconnected from operations.
Historical WMS data was locked in a legacy system.
Invoice generation relied heavily on manual Excel processes.

The consequences were severe:

Revenue leakage due to unbilled driver wait times and ancillary services
Inconsistent invoice logic across customers
No clear understanding of margin per client, route, or service type
Inability to connect pipeline growth with operational capacity
Growing dependency on third-party data connectors (Fivetran) with limited flexibility

In logistics, margins are thin. Small inaccuracies compound into significant financial impact.

Solution

We designed and implemented a logistics data analytics architecture that connected operations, finance, sales, and warehouse intelligence into a unified ecosystem.

Here is how we did it.

Margin Visibility in Workday Adaptive Planning

From WMS Transactions to Financial Cube Modeling

Finance teams were unable to properly analyze margin by:

Customer
Route
Service type
Warehouse operation

We built an automated pipeline that:

Extracts operational data from the Synapse Warehouse Management System
Standardizes cost and revenue mappings
Transforms operational metrics into financial-ready models
Pushes structured data into the Workday Adaptive Planning cube

This enabled:

Margin analysis by customer and service line
Scenario planning and forecasting
Alignment between operational performance and financial reporting

For the first time, finance could see operational reality reflected in financial planning without having to manually export Excel files and upload them to Workday.

Automated Transportation Invoicing from Samsara Telemetry

Eliminating Revenue Leakage

Manual invoicing in logistics is dangerous. Driver wait times, detention charges, load handling, kilometers driven, and storage often get partially billed or not billed at all. The client previously relied on Excel-based processes.

We developed a custom invoicing engine that:

Pulls truck and driver telemetry data from Samsara
Calculates billable hours, wait times, distance, and load activity
Applies customer-specific pricing logic
Generates structured invoice-ready datasets

This ensured:

All billable activities were captured
Invoice consistency across customers
Reduced manual processing time
Increased billing accuracy

In industries where margins are often 5–10%, invoice precision is critical.

Historical WMS Migration & Future-Proof Reporting

Synapse → Deposco Transition Without Losing Visibility

The company transitioned warehouse management systems from Synapse to Deposco. Historical data risked being siloed permanently.

We:

Extracted and standardized legacy Synapse historical data
Modeled it into a unified schema
Designed reporting logic to seamlessly join legacy and future Deposco data
Ensured continuity in Power BI reporting

The result:

Leadership could analyze multi-year warehouse performance despite platform migration. No data loss. No reporting discontinuity.

ABC Velocity Analysis & SKU Segmentation

Inventory Intelligence Beyond Storage

Warehouse profitability depends on understanding:

Fast movers
Slow movers
Dead stock
Storage cost vs turnover

We implemented ABC velocity segmentation:

Classified SKUs based on movement and revenue contribution
Identified storage inefficiencies
Enabled optimized space allocation
Provided data-driven insights into working capital usage

This moved warehouse reporting from reactive to strategic.

Replacing Fivetran with Python HubSpot → Snowflake Sync

Reducing Dependency & Increasing Control

Sales pipeline visibility was disconnected from operations. The company relied on Fivetran to sync HubSpot data, which:

Limited transformation control
Increased recurring costs
Restricted schema optimization

We designed a custom data pipeline that:

Extracts HubSpot data directly
Loads it into Snowflake
Applies transformation logic tailored to reporting needs
Integrates seamlessly with Power BI

This enabled:

Full pipeline-to-revenue visibility
Lead-to-customer lifecycle analysis
Operational capacity forecasting

And reduced reliance on generic connectors.

Result

Operational Intelligence Instead of Spreadsheet Guesswork

After implementation:

Finance gained real-time margin visibility
Invoice accuracy improved and revenue leakage was reduced
Warehouse performance became measurable across system migrations
SKU segmentation improved inventory planning
Sales and operations data became connected
Third-party data dependency was reduced

The company moved from fragmented reporting to structured logistics data analytics infrastructure. Instead of reacting to financial surprises at month-end, leadership now monitors operational performance continuously.

Technologies and Tools

Snowflake, Python, Power BI, Workday Adaptive Planning, HubSpot API, Samsara API, Custom ETL Pipelines

Witanalytica