How Softweb maximized manufacturing efficiency or a leading baby products manufacturer
Our client, the baby products manufacturer, was a fast-growing company looking to optimize business operations and increase production efficiency. We built a data engineering and analytics platform to centralize the visibility of all operational aspects, reduce costs associated with running the operation, and allow users to track KPIs in real-time.
Industry: Manufacturing 
Business type: Enterprise - Services: Data Engineering, Cloud Migration, Data Visualization, Analytics Implementation
- Technology: Snowflake, Microsoft Power BI, Azure Data Factory, Azure Cloud, SQL
Client profile
With multiple production plants across North America, our client is a rapidly growing manufacturer of premium baby and toddler products. They design, manufacture, and distribute a wide range of infant care essentials.
Over the years, they expanded their product portfolio with eco-friendly, hypoallergenic, and plant-based baby products designed for sensitive skin and maximum absorbance. Their operations include product design, large-scale manufacturing, quality assurance, and coordinated supply chain management.
Technical challenges
Operational data was stored across legacy systems, spreadsheets, and reporting tools which were not connected. They needed the data at one place and in real-time for precision in high-speed production, adhesive application and bonding, material handling, consistent feeding of absorbent, and more. The major technical challenges were:
Manual processing of data
Production, financial, and quality data were stored on separate on-premises systems. Teams had to process data manually, which slowed operations and increased the risk of errors.
Data modeling using VLOOKUP
Data modeling relied on VLOOKUP formulas. They break when data structures change. Hence, it was challenging to compare data across departments and time periods.
Higher expenses
Without granular visibility into production waste, material costs, and process inefficiencies, our client’s manufacturing facilities were spending more.
Lack of data visualization capabilities
Without proper data visualization tools, interpreting data and generating accurate, timely reports remained slow and inefficient.
Our solution
Solutions delivered through a unified data engineering platform
We built, created, and deployed a cloud-based data engineering and analytics platform. The solution centralized the manufacturer’s operational data in a governed and scalable environment. Snowflake powered the core data warehouse, and Microsoft Power BI delivered reporting and visualization capabilities.
We eliminated legacy reporting methods and implemented a modern data pipeline. The new system included structured data, automated transformation processes, and dashboards designed for specific users. With this system in place, plant managers, finance teams, and operations leaders have access to real-time visibility of their KPIs.
Legacy system migration and cloud data warehouse setup on Snowflake for scalable, unified storage
We led the end-to-end migration of the client’s production and financial data from on-premises systems into Snowflake’s cloud data warehouse. Our data engineers reviewed source system schemas and mapped data relationships. The insights guided the design of a target warehouse architecture to support the client’s reporting and analytics needs.
With this migration, Snowflake became the only source of manufacturing data across all plants. The data models were set up to allow for operational and analytical workloads. The cloud infrastructure removed the bottlenecks to data availability that had previously limited the reporting team’s ability to generate reports.
Automated data ingestion and transformation pipelines for consistent, accurate data delivery
Using Azure Data Factory, we built fully automated ETL workflows to extract raw data from the client’s source systems. The pipelines transformed the data and loaded structured datasets into Snowflake. Our engineering team developed the architecture of these pipelines to accommodate the volume and type of manufacturing data (For example, production logs, costing data, quality standards, inventory feeds) to make them available for use.
Scheduled pipelines keep Power BI dashboards updated with the latest data. The architecture eliminates manual data handling, which reduces errors and maintains consistent formatting across all sources before loading into the warehouse.
Role-specific interactive dashboards deliver real-time KPI visibility across departments
We developed a set of Power BI dashboards aligned with the data needs of each functional team. Plant managers accessed dashboards that showed production output by line, shift, and facility. Finance teams accessed cost performance views with drill-downs into material spend, waste, and variance from plan. Operations leadership received an executive summary layer aggregating KPIs across all sites.
The design of each dashboard maximized the ease of interpreting information quickly. Users were able to apply filters to isolate time periods, product lines, and production facilities without any technical assistance. We designed visualizations, so anomalies and underperformance appear immediately. Early detection helped teams identify issues early and take corrective action across the manufacturing organization.
Cost variance and waste analytics to identify and remove sources of financial loss
We developed a targeted analytics module within the Power BI environment focused on financial performance. The module ingested cost data alongside production records and applied variance logic to surface deviations from expected spend. It gave finance and operations teams a shared view of where costs were running above plan and why.
Analysts were given access to granular data views that helped them to connect high-level cost variances to specific production runs, shifts, or input materials. The module enabled regular financial reviews with structured and data supported information instead of manual reports. Teams used these insights to prioritize process improvements, renegotiate supplier contracts, and strengthen cost controls in areas where waste was highest.
Centralized KPI performance benchmarking to enable consistent measurement
Our team collaborated with the client’s operational and analytic departments to define a set of standard KPIs. These KPIs measured production efficiency, quality of the product, cost of manufacturing, and throughput from the manufacturing process. All Power BI reports and dashboards used the same KPIs. The framework ensured every team calculated metrics in the same way instead of using different methods across departments.
Benchmarking logic was built into the data models so that each facility’s performance could be compared against internal targets and peer facilities. Leadership gained a structured view to identify which sites were excelling and which required support. It enabled data-driven improvements across the entire manufacturing network.
Business Goals and measurable outcomes
| Business objective | Business benefit delivered |
|---|---|
| Real-time KPI tracking | Achieved a 20% increase in data-driven decision-making efficiency through live KPI dashboards across all facilities. |
| Financial loss reduction | Delivered a 10% reduction in operational costs by surfacing hidden financial losses, using Power BI dashboards, across production lines. |
| Cost savings performance | Realized 25% enhanced operational efficiency by eliminating waste across material usage, scheduling, and production processes. |
| Unified data systems | Consolidated legacy systems, spreadsheets, and tools which were used separately into a single governed Snowflake data source. |
| Manufacturing intelligence | Aggregated facility-level KPIs into one executive view, enabling strategic resource allocation and performance benchmarking. |
Tech stack
- Cloud data warehouse
- Snowflake (centralized storage, unified data modeling, scalable query performance)
- ETL and data pipelines
- Azure Data Factory (automated extraction, transformation, and loading from legacy source systems)
- Business intelligence
- Microsoft Power BI (interactive dashboards, KPI reporting, cost analytics, drill-through)
- Cloud platform
- Microsoft Azure (pipeline orchestration, cloud infrastructure, secure data movement)
- Data modeling
- SQL (structured data models for production, cost, and quality datasets)
- KPI framework
- Standardized manufacturing KPI library across efficiency, quality, cost, and throughput
- Financial analytics
- Cost variance and loss identification module within Power BI
- Performance benchmarking
- Facility-level benchmarking and comparative performance tracking
- Data governance
- Governed Snowflake data models with role-based access and consistent metric definitions
- Source systems
- Legacy on-premise databases, operational spreadsheets, ERP data feeds
Similar Case Studies
How Softweb built a fleet management and vehicle auction platform for Emkay
Connect Now
Our experts would be eager to hear you.
