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Logistics · Supply Chain · US
📊 Business Intelligence & Data

BI Infrastructure Rebuilt for US Supply Chain Leader —
Same-Day Close, 65% Time Saved

A fragmented, multi-system reporting environment consolidated into a single source of truth. Monthly close moved from 3 days to same day.

65%
Reduction in time spent on reporting and data assembly
3→0d
Month-end close time reduced — from 3 days to same day
1
Single source of truth replacing 6 disconnected systems
100%
Real-time data — no stale weekly exports
— The Situation

The challenge and the solution

// The Challenge

Six systems, six different numbers, and a finance team spending 3 days closing the books.

A major US supply chain company operated with six separate data systems — SAP for finance, a custom WMS for warehouse operations, a TMS for transport, Salesforce for customer data, spreadsheets for KPI tracking, and an outdated BI tool for reporting. Each system had its own data model. Numbers conflicted. Finance teams spent three days every month-end manually reconciling everything before they could close the books.

  • Six disconnected data systems with no single source of truth
  • Monthly close taking 3 days — reconciliation done manually across all systems
  • Weekly operations report took 8 hours to assemble from multiple exports
  • Different departments using different numbers — causing executive-level confusion
  • No real-time visibility — decisions made on last week's data
// The Solution

SAP ETL pipeline, standardised data warehouse, and role-based Power BI dashboards.

We rebuilt the BI infrastructure from the ground up — starting with a clean data architecture. SAP financial data, WMS operational data, and TMS transport data were pulled into a centralised Azure Synapse data warehouse via automated ETL pipelines. One canonical set of numbers. Role-based Power BI dashboards for finance, operations, and executive leadership. Live data — no weekly exports.

  • Azure Synapse data warehouse — single source of truth for all business data
  • Automated ETL pipelines from SAP, WMS, TMS, and Salesforce — hourly refresh
  • Data quality rules enforced at ingestion — conflicting records flagged before they reach reports
  • Role-based Power BI dashboards — finance, operations, executive, and driver-level views
  • Month-end close automated — finance team reviews AI-assembled report on day 1

— What We Built

Six components of the solution

Every piece designed to solve a specific part of the problem — integrated into one system that works end-to-end.

🏗️

Azure Synapse Data Warehouse

Centralised data warehouse replacing six disconnected sources. Single canonical data model. All business data flowing in — finance, operations, transport, customer.
🔄

Automated ETL Pipelines

Pipelines from SAP (finance), WMS (warehouse), TMS (transport), and Salesforce (customer). Hourly refresh. Data quality checks at ingestion — bad records quarantined and flagged.
📊
Role-Based Power BI Dashboards
Executive dashboard (P&L, margins, KPIs), Finance dashboard (actuals vs budget, variance), Operations dashboard (throughput, SLA, capacity), and Driver-level operational views.
🤖

AI-Powered Month-End Close

Month-end report assembled automatically from the data warehouse. Finance team receives a draft close pack on day 1 — reviews and signs off rather than building from scratch.

Real-Time Anomaly Alerts

Continuous monitoring of key metrics — throughput, on-time delivery, margin. Deviations from baseline trigger Slack alerts to the relevant team within hours of occurrence.
💬

Natural Language Data Queries

Executives query the data in plain English via a connected AI assistant. "What was our on-time delivery rate by carrier last month?" — answered in seconds without an analyst.

— Results

What this delivered for the client

The numbers — measured outcomes

65%
Reduction in time spent on report assembly — weekly operations report from 8 hours to 45 minutes
Day 1
Month-end close achieved — down from day 3, releasing finance team to value-added analysis
1
Single source of truth — all departments using the same numbers from one canonical system
Real-time
Data freshness — vs weekly exports that were outdated before they were distributed
Finance team freed from reconciliation

With automated ETL handling data quality and reconciliation, the finance team stopped spending three days on month-end data assembly and started spending that time on analysis and strategic work.

Executive decisions on current data

Previously, executive decisions were made from last week's exports. With live dashboards, the leadership team made decisions on data that was at most one hour old — fundamentally changing how quickly the business could respond to issues.

Cross-department data disputes eliminated

"Which number is right?" was a weekly occurrence before the BI rebuild. After: one source of truth, one number, no disputes. Departmental alignment improved measurably.

"Very good service — friendly and helpful with a high level of technical understanding and competence. Listens, makes suggestions, and delivers very quickly. The BI rebuild has transformed how we run the business."
G
Gerhard
US Supply Chain Leader · Finance Director
— Delivery Timeline

How we delivered it

From the initial audit to live deployment — every stage designed to minimise risk and maximise speed to value.

Week 1–3
🔍
Data Architecture & Discovery
Mapped all six data sources, their schemas, and conflict patterns. Designed the target data model. Identified the top 20 data quality issues causing reconciliation failures.
Week 4–7
🏗️
Data Warehouse & ETL Build
Azure Synapse warehouse provisioned. ETL pipelines built from SAP, WMS, TMS, and Salesforce. Data quality rules implemented. First clean consolidated dataset produced at week 7.
Week 8–10
📊
Dashboard Development
Power BI dashboards built for all four audience levels. Tested with end users from each department. Iteration cycle. Live data connected and validated against legacy reports.
Week 11–12
🚀
Go-Live & First Month-End
Full deployment. First automated month-end close at week 12 — finance team received draft close pack on day 1. Time saved vs previous process: 3 days.
— Technology stack
Azure Synapse AnalyticsSAP BI / ETLPower BI PremiumPython PipelinesAzure Data FactorySalesforce API

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