5.0
"APPWRK IT Solutions Private Limited has demonstrated high confidence in their commitment..."
Highlights
- Unilever is one of the world’s largest FMCG enterprises with 280+ factories and a global network of warehouses, operating a geographically dispersed supply chain, manufacturing, and distribution ecosystems across multiple regions and markets.
- The most immediate business demand emerged for real-time operational visibility and standardized performance monitoring across inventory management, core operations, logistics, warehouse management, and yard logistics to support faster, KPI-driven decision-making across sites.
- In a strategic partnership with APPWRK, Unilever conceptualized and built ByDashboards, a unified Enterprise Analytics and Business Intelligence platform to eliminate fragmented reporting, inconsistent KPIs, heavy dependency on IT teams, and to enable centralized visualization of data across all factory and site locations on a single platform, establishing a single source of truth across business functions and geographies.
- The platform enabled self-service KPI creation, custom dashboard design, and reusable reporting templates, delivering real-time visibility, faster insights, and scalable analytics adoption across sites and regions.
Tech Stack
- Visualization & User Interface: ReactJS, ApexCharts
- Application Layer: .NET Core
- Data Sources & Integration: BlueYonder WMS Integration, REST APIs, Event-Driven APIs
- Hosting / Runtime: Azure App Service (Backend APIs), Azure Static Web Apps (Frontend)
- API Management & Security Gateway: Azure Front Door
- Identity & Access Management: Azure AD (Entra ID)
- Data Storage: Azure SQL Database, Azure Blob Storage
- Cloud Platform & Hosting: Microsoft Azure
- Notifications & Communication: Azure Communication Services (Email)
- Performance Strategy: Query optimization, indexing, and pre-aggregated reporting datasets.
Tools & Technologies
React JS
.NET Core
BlueYonder Integration
REST-API
ApexCharts
Microsoft Azure
Azure Services
Overview
Unilever, one of the world’s largest FMCG enterprises, operates a geographically dispersed network of factories, warehouses, and logistics operations that generate large volumes of operational data. As the organization scaled across regions and sites, achieving enterprise-wide visibility across core operations, like inventory management, logistics, warehouse, and yard management, became increasingly challenging due to decentralized reporting.
While operational systems produced rich data, inconsistent KPIs, static site-specific dashboards, and repeated IT involvement created friction in analysis and delayed decision-making. Limited cross-location comparability and fragmented reporting reduced confidence in enterprise-level performance insights.
In a strategic partnership with APPWRK, Unilever developed ByDashboards, a next-generation Enterprise Analytics and Business Intelligence Platform designed to centralize analytics and significantly reduce data analysis friction. The platform enables fully customizable, self-service dashboards where users can select sites, define KPIs, choose visualization formats, and instantly generate performance views without IT dependency.
Integrated directly with operational warehouse systems and built on a unified enterprise database, ByDashboards established a single source of truth for analytics and reporting. This shift enabled a move from rigid, tool-driven reporting to a flexible, business-led analytics model, improving data reliability, accelerating decision-making, and ensuring consistent KPI governance across factories, warehouses, and regions.
Challenges
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Fragmented Digital Analytics Backbone
The absence of a unified analytics backbone led to fragmented reporting across factories, warehouses, and logistics operations, creating system dependencies, limiting enterprise-wide visibility, and preventing connected factory insights and cross-site benchmarking.
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Manufacturing and Operations Data Inconsistency
Inconsistent KPI definitions, decentralized reporting practices, and localized analytics setups resulted in data integrity issues, reduced confidence in reported metrics, and negatively impacted leadership-level decision-making.
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Edge to Cloud to Enterprise Integration Gaps
Operational data flowing from systems such as WMS through multiple handoffs introduced latency, duplication, and integration friction, weakening interoperability between edge systems, cloud platforms, enterprise analytics layers, and downstream reporting consumers.
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Extended Turnaround Time for Analytics and Reporting Changes
Changes to KPIs, dashboards, or reporting structures required multiple coordination steps across teams, increasing turnaround time for analytics updates and slowing response to evolving operational requirements.
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Architecture Constraints Limiting Scalability
Rigid, tool-driven reporting architectures constrained the scalability of analytics pipelines, limited the adaptability of dashboards to evolving business needs, and hindered rapid rollout across new sites and operational environments.
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Analytics Agility and Engineering Throughput Bottlenecks
Changes to KPIs, dashboards, or reporting structures required extensive cross-team coordination, increasing turnaround times, slowing responsiveness to operational needs, and constraining engineering throughput due to manual rework and repeated customization efforts.
Solution
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Unified Enterprise Analytics Backbone
APPWRK implemented ByDashboards as a single enterprise analytics backbone, consolidating reporting across factories, warehouses, and logistics into a consistent, scalable platform.
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Connected Factories and Warehouses Through a Single Analytics Layer
ByDashboards enabled connected factory and warehouse visibility by allowing users to select sites dynamically and visualize standardized KPIs across locations. This supported cross-site benchmarking, operational comparisons, and consolidated performance views without localized reporting setups.
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Standardized KPI and Data Governance Model
The platform introduced a governed KPI framework with centralized definitions, ensuring manufacturing and operations data integrity while allowing controlled flexibility for site-level analytics. This improved confidence in reported metrics and enabled consistent decision-making across leadership and operational teams.
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Edge to Cloud to Enterprise Integration Enablement
ByDashboards integrated directly with operational systems such as the Blue Yonder Warehouse Management System and operated on a unified enterprise database. This streamlined data flow from edge systems to cloud and enterprise layers, reducing latency, duplication, and integration friction.
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Self-Service, Configurable Analytics Framework
The solution delivered real-time, configurable dashboards allowing business users to modify sites, KPIs, and visualizations without engineering intervention.
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Scalable, Interoperable, Metadata-Driven Architecture
APPWRK built an API-driven, metadata-based architecture to support interoperability, rapid onboarding of new sites, and extensible analytics pipelines.
Outcome
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Reduced Data Friction and Dependency Risks Across Systems
The implementation of a unified analytics backbone significantly reduced data friction between operational systems, analytics layers, and reporting consumers, enabling smoother data flow and more reliable insights.
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Improved Turnaround Time for Analytics and Reporting Changes
Self-service dashboard configuration and governed KPI selection reduced turnaround time for analytics updates, allowing business teams to respond faster to operational changes without extended coordination cycles.
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Consistent Enterprise KPI Governance
Centralized KPI definitions and standardized performance models improved data integrity and ensured consistent measurement across factories, warehouses, and logistics operations.
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Connected, Cross-Site Operational Visibility
Leadership and operational teams gained connected, cross-site visibility into performance metrics, supporting benchmarking, comparative analysis, and more informed decision-making across regions.
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Scalable Analytics Adoption and Engineering Acceleration
The platform enabled rapid onboarding of new factories and warehouses into the analytics ecosystem, supporting scalable adoption without re-architecting reporting pipelines. By moving analytics configuration to a platform-driven model, engineering teams reduced repetitive customization work and increased throughput for higher-value platform enhancements.