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Case study

Shop Floor Intelligence in Manufacturing with Azure Automation and Odoo Integration5.0 rating5 Stars Rated On Clutch

Odoo

Industry

Manufacturing & Industrial Engineering

Services Used

Azure Data Engineering, Business Intelligence (BI), Data Governance, Data Warehousing

Region

Middle East

Engagement Model

End to End Project Lifecycle Management

Function/Department

IT and Technology Support

About Client

  • A $200M+ Oman-based manufacturing company with 8+ working plants across regional and global markets.
  • By creating a legacy for itself in the industry, the company is responsible for producing high-quality industrial machinery and components.

Problem STATEMENT

During the initial discussions, the client faced several challenges while scaling its ERP-driven data and analytics initiatives. These limitations impacted data accessibility, decision-making, and overall backend efficiency:

Fragmented Data Landscape:

Data across Odoo ERP and other business systems remained siloed, limiting cross-functional visibility and unified reporting.

Limited Reporting Capabilities:

Native ERP reporting lacked real-time insights and advanced BI capabilities, restricting production and finance teams from accessing actionable dashboards.

Manual Data Handling:

Heavy reliance on Excel-based reporting and manual data extraction led to delays, inconsistencies, and increased dependency on operational teams.

Lack of a Scalable Data Platform:

The absence of a centralized, cloud-based architecture limited seamless data integration and restricted historical and trend-based analysis.

Operational Visibility Gaps:

Plant and leadership teams lacked unified dashboards to monitor inventory, production, and supply chain performance in a cohesive manner.

Data Governance Challenges:

There was no standardized approach to ensure data quality, access control, and auditability across different business functions.

Data Governance & Security Gaps:

Sensitive operational data lacked standardized access controls, data lineage, and compliance frameworks, increasing risk and limiting trust in data systems.

Solution

After establishing the set of execution problems the company was facing, the data engineering team at DataToBiz delivered a data and analytics transformation setup on Azure, custom to their ERP-driven operations. 

Unified Data Platform on Azure:

Established a centralized data platform by integrating Odoo ERP data across finance, inventory, procurement, and manufacturing into a single, trusted source of truth.

Automated Data Pipelines & Standardization:

Built automated data pipelines and transformation layers to ensure clean, standardized, and near-live data availability for reporting and analytics.

Live Operational Visibility:

Enabled faster and more accurate tracking of production, inventory, and financial performance, improving responsiveness across business functions.

Self-Service BI:

Replaced manual reporting with interactive Power BI dashboards, empowering teams with drill-down insights and self-service analytics.

Advanced Analytics Enablement:

Enabled use cases such as demand forecasting, inventory optimization, and production performance tracking to drive better planning and efficiency.

Data Governance & Secure Access:

Established governance frameworks with role-based access controls to ensure data quality, compliance, and secure access across the organization.

Scalable & Future-Ready Architecture:

Designed a robust architecture to support high-volume data, ongoing digital initiatives, and enterprise-wide adoption of analytics and AI.

Technical Implementation

The solution was built on a cloud-native Azure data and analytics stack, designed for scalability, secure integration, and enterprise-grade reporting.

Data Ingestion:
Azure Data Factory and APIs integrated data from Odoo across finance, inventory, procurement, and manufacturing.

Data Platform:
ADLS Gen2 with a Bronze, Silver, and Gold architecture enabled centralized and scalable data storage.

Data Processing:
Azure Databricks with Spark handled data transformation, cleansing, and KPI standardization.

Data Modeling:
Azure Synapse and SQL layers provided optimized, analytics-ready data models.

Semantic Layer and BI:
Power BI datasets with row-level security powered interactive, role-based dashboards for business users.

Advanced Analytics:
Python-based models enabled forecasting and inventory optimization.

Security and Deployment:
Microsoft Entra ID, role-based access control, encryption, and CI/CD pipelines ensured secure and scalable operations.

No changes were made to the core ERP system. The implementation focused entirely on data, analytics, and AI enablement within the Azure ecosystem.

Technical Architecture

Odoo Integration framework

Business Impact

Reduced Manual Reporting Effort

Manual effort across ERP reporting decreased by 30%, with 15+ Odoo-based reports automated. Teams moved away from spreadsheet-heavy consolidation to more streamlined, insight-focused workflows.

Faster Decision-Making

Near-live BI dashboards replaced delayed reporting cycles, improving decision-making speed by 40% across finance, procurement, and operations.

Improved Inventory Visibility

Centralized tracking of inventory data improved stock visibility by 25%, reducing instances of overstocking and stockouts.

Increased Operational Efficiency

Unified insights across procurement, production, and finance led to a 20% improvement in overall operational efficiency.

Expanded Data Accessibility

Enabled 60+ business users with role-based Power BI dashboards, improving data adoption and cross-functional visibility.

Conclusion

All-in-all, the entire collaboration enabled stronger production visibility, tighter inventory control, and more responsive supply chain operations. With faster access to reliable data, the business is better equipped to scale across plants and demand fluctuations.

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Frequently Asked Questions(FAQs)

How do I know if a solution like this is right for my business?

A solution like this is a good fit if your teams spend more time collecting and reconciling data than acting on it, if reports from different departments do not match, or if decisions depend on delayed or manual reporting. It also fits when your data is growing in volume or sources, and your current setup struggles to keep up.

DataToBiz starts with a discovery call to review your goals, data sources, and reporting gaps. You get a clear view of the expected outcomes, effort, and timeline before any commitment.

Can DataToBiz customize the solution for my industry and current data setup?

Yes. Every solution DataToBiz delivers is configured around your industry, business processes, and existing data environment. The team assesses your current systems, data quality, and reporting needs first, then designs a solution that works with what you already have instead of replacing it. This applies across industries such as manufacturing, retail, healthcare, finance, and logistics, and across setups ranging from legacy databases to cloud data platforms.

Which tools, platforms, and data sources does DataToBiz work with?

DataToBiz works across the major analytics, cloud, and data engineering stacks. For BI and visualization, this includes Power BI, Tableau, and Looker. For cloud platforms, it includes Microsoft Azure, AWS, and Google Cloud. For data warehousing and processing, it includes Snowflake, Databricks, and SQL-based databases. Data sources include ERP and CRM systems, spreadsheets, APIs, IoT and sensor data, and third-party applications.

If your stack includes a tool not listed here, the team can assess compatibility during discovery.

How long does a project like this take with DataToBiz?

Most projects with DataToBiz take from a few weeks to a few months. The timeline depends on how ready your data infrastructure is for the initiative and how many systems need to connect. Smaller solutions go live quickly, while larger programs roll out in phases so you see value early. After an initial discovery call, you receive a timeline with clear milestones.

How does DataToBiz keep my data secure and compliant during the entire engagement?

Security and governance is part of the plan from the first conversation. DataToBiz works within your security policies and follows the regulations that apply to your industry. The project usually Access stays limited to the people who need it, and confidentiality terms cover the engagement from kickoff to handover.

What support and results can I expect after the solution goes live?

The work does not stop at go-live. Your team gets a proper handover and guidance on using the solution, and DataToBiz stays available for questions and improvements. The business impact of the project is measured against the goals set at the start, so you can see exactly what changed for your company.

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