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Gross before fulfillment and claims
⌘FILTERSApplied across every view
THE OPERATIONS PULSE / INTERACTIVE REPORT
See the system.
Not just the numbers.
Explore the connections between delivery reliability, network efficiency, customer experience, and profitability.
48,000 source records21 months modeled12 destination regions
01 — COMMAND CENTER
48,000 shipments in viewPerformance snapshot
All metrics recalculate with your selections. Values are from modeled sample records.
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Among completed deliveries
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All statuses in selected period
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Revenue − fulfillment − claims
TREND ANALYSIS
MONTHLYShipment volume & SLA performance
DELIVERY QUALITY
MIXShipment outcomes
DESTINATION MARKETS
Highest-volume regions
LEAD TIME DISTRIBUTION
Delivery duration profile
02 / OPERATIONS INTELLIGENCE
Every route tells a story.
Analyze the transportation network across hubs, lanes, distance, and service-level commitments.
Active distribution hubs—
Average completed transit—
Repeated delivery attempts—
GEOGRAPHIC FLOW
LANE HEATMAPOrigin → destination volume matrix
SITE COMPARISON
On-time performance by hub
CAPACITY BREAKDOWN
Exceptions by region
SERVICE DELIVERY
SLA vs actual transit hours
OPERATING CORRELATION
Route distance × transit time
03 / UNIT ECONOMICSRevenue is a signal.
Revenue is a signal.
Margin is the truth.
See which services, channels and distribution hubs make the network sustainable.
Gross revenue—
Fulfillment + claims—
Net contribution—
FINANCIAL TREND
Monthly revenue vs operating cost
REVENUE SOURCES
Acquisition channels
PRODUCT ECONOMICS
Contribution margin by service
COST STRUCTURE
Cost & claims by service
GROWTH CONCENTRATION
Revenue distribution across the hub network
04 / EXCEPTION INTELLIGENCE
Make hidden risks visible.
Locate delay drivers, non-delivery patterns and customer satisfaction signals before they compound.
Late deliveries—
Returns + lost / damaged—
Mean customer satisfaction—
QUALITY TREND
Weekly late-delivery incidence
EXECUTION COST
Delivery attempt distribution
ROOT-CAUSE REVIEW
Exception reasons — Pareto analysis
CUSTOMER EXPERIENCE
CSAT by shipment outcome
VARIABILITY
Transit time distribution by service
CARRIER BENCHMARK
On-time rate by delivery partner
05 / DECISION SIMULATION
What happens if?
Test growth and cost trade-offs using historical averages as a baseline. Illustrative scenarios, not predictions.
Set your assumptions
Controls update the contribution forecast instantly.
Shipment volume change+12%
-30%+60%
Revenue per order change+4%
-20%+30%
Operating cost per order-5%
-25%+35%
MODELED MONTHLY CONTRIBUTION
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—
Expected revenue—
Expected cost—
Modeled margin—
Simple deterministic sensitivity model, not a validated demand forecast. A 6-month historical average is used as the baseline for the filtered dataset.
SCENARIO BRIDGE
How assumptions change contribution
SENSITIVITY
Price vs cost improvement
06 / RAW DATA EXPLORER
Go from chart to record.
Search, sort and export individual records. All data is fictitious and generated for demonstration.
Shipment records
—| ID ↕ | Date ↕ | Origin ↕ | Destination ↕ | Service ↕ | Status ↕ | SLA / actual ↕ | Revenue ↕ | Contribution ↕ |
|---|
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1
IDs, route distances, economic values and customer satisfaction scores are synthetic. The generated dataset does not contain customer PII.
07 / BEHIND THE WORK
Turning complexity into clarity.
This application is a working, self-initiated demonstration of data analysis, dashboard engineering and operational storytelling.
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PORTFOLIO OWNER & CREATORRay Gerraldo
Ray Gerraldo
S Tambunan.
Data Analytics · Operations Intelligence · GIS
Operations-focused analyst with experience in logistics control and performance monitoring. Interested in making complicated datasets useful through reproducible analysis, clear visualizations and decision-oriented tools.
View verified LinkedIn profile ↗ENGINEERING APPROACH
What's under the hood?
48k generated shipments, 19 fields per record, cross-dimensional filtering, 20+ data visualizations, KPI recalculations, Pareto root-cause analysis, and scenario modeling. A Python generator, example SQL queries and data dictionary accompany the downloadable source.