←←All cases

Cloud-based BI solution for a food supply chain company

Client

A manufacturer of smart tray systems for transporting fresh produce

Industry

Services

BI platform development, data architecture design, cloud migration, data pipeline automation, data governance

Tech stack

Microsoft Azure, Azure Blob Storage , Azure Synapse Analytics, Azure Data Factory, Microsoft SSIS, Power BI, Microsoft Master Data Services (MDS), Snowflake

Challenge

A manufacturer of reusable smart trays for fresh food logistics—covering meat, seafood, fruits, and vegetables—was losing money because its analytics couldn't keep up with its operations. Without real-time access to business-critical data, logistics and warehouse teams had no reliable way to catch delivery delays or flag perishable inventory at risk before the damage was done. Spoilage was the most visible symptom. Outdated inventory data meant expiration thresholds were missed routinely. But the problem ran deeper: BI reports were slow to generate, incomplete when they arrived, and too stale to drive decisions in finance, logistics, or production. The organization needed a centralized platform that could serve as a single source of truth—and actually keep pace with a supply chain where hours matter.

Solution

To overhaul the customer’s logistics and BI workflows, ITRex designed and engineered a cloud-based Operational DataHub using Microsoft Azure services. The BI solution streamlined data ingestion, processing, and reporting and established a foundation for future AI integration. We took on the following challenges:

●

Centralizing data ingestion. The ITRex team integrated data from logistics, production, financial, and master data systems into Azure Blob Storage, creating unified, scalable access across all source systems.

●

Automating ETL pipelines. To automate and orchestrate data extraction, transformation, and loading (ETL) processes, we implemented Azure Data Factory and Microsoft SSIS services, achieving near real-time delivery of cleansed, accurate data to downstream systems.

●

Creating a single source of truth. Our data engineers organized and consolidated the ingested information in Azure Synapse Analytics, allowing the client's analytics and reporting teams to work with consistent, reliable data.

●

Migrating from Microsoft SSAS models to Microsoft Power BI datasets. We transitioned the existing analytical models from on-premises SSAS (Tabular) to Power BI datasets. This enabled a unified, scalable, and self-service analytics experience directly within the Power BI platform.

●

Implementing self-service business intelligence. ITRex built intuitive Power BI dashboards that let employees across logistics, warehousing, and finance access and create real-time reports without routing requests through IT or waiting on centralized report generation.

●

Establishing data governance. We implemented a governance framework with role-based access controls, reducing security exposure and giving the organization a foundation for scaling data use responsibly.

Architecture overview

●

Cloud platform: Microsoft Azure

●

Core data engine: Snowflake

●

Data processing & transformation: Azure Synapse Analytics

●

Data storage: Azure Blob Storage

●

Orchestration & automation: Azure Data Factory, Microsoft SSIS

●

Data visualization & reporting: Microsoft Power BI

●

MDM & data quality: Microsoft Master Data Services (MDS)

Business Intelligence Solution for a Food Supply Chain Company

Business Intelligence Solution for a Food Supply Chain

Impact

In fresh produce logistics, the margin for error is narrow—a missed expiration threshold or a delayed delivery decision can wipe out an entire shipment's profitability. The platform ITRex built closed that gap.

●

A 20% reduction in perishable goods spoilage, driven by near-real-time inventory visibility and optimized expiration tracking. Logistics teams could act on accurate stock data before goods crossed the threshold.

●

A ~10% uplift in revenue from higher sell-through rates and improved customer satisfaction. Fewer stockouts, better delivery performance, and sharper supply chain responsiveness translated directly into commercial outcomes.

●

A 20% improvement in operational efficiency, largely from eliminating roughly 65% of manual data preparation time. Automated ETL pipelines and self-service Power BI reporting freed logistics and finance teams from data handling mechanics, allowing them to focus on decision-making.

●

A 75% improvement in time-to-insight—the reporting cycle that previously took days or weeks now runs in minutes. When a supply chain disruption hits, the team sees it in the data fast enough to respond.

●

A platform built for what comes next. The cloud-native architecture and governance framework established readiness for machine learning and advanced forecasting—the logical next step for a business where better demand prediction has a direct line to margin improvement.