Data Management Modernization for CPG
Data Management Modernization for CPG
DURATION
9 Months
DURATION
9 Months
CLIENT
Confidential
CLIENT
Confidential
Digital Transformation
Digital Transformation
System Integration
System Integration
Data Engineering
Data Engineering
API & Microservices Development
API & Microservices Development
PROJECT OVERVIEW
PROJECT OVERVIEW
The client, a multinational consumer goods enterprise, needed to unify fragmented data sources across regions, channels, and SKUs. Their legacy reporting processes were slow, error-prone, and dependent on manual effort, limiting their ability to plan production, forecast accurately, and respond to market shifts. TechnePlus was engaged to modernize the entire data ecosystem, making information accessible, reliable, and ready for AI-powered decisioning across the product value chain.
The client, a multinational consumer goods enterprise, needed to unify fragmented data sources across regions, channels, and SKUs. Their legacy reporting processes were slow, error-prone, and dependent on manual effort, limiting their ability to plan production, forecast accurately, and respond to market shifts. TechnePlus was engaged to modernize the entire data ecosystem, making information accessible, reliable, and ready for AI-powered decisioning across the product value chain.
The Challenge
The Challenge
The organization operated across diverse markets with siloed systems that stored data in incompatible formats. This created delays in financial planning, SKU-level forecasting, and daily operational reporting. The client required a future-ready data architecture capable of: Consolidating product, sales, inventory, and financial datasets Automating FP&A reporting Enabling SKU-level demand forecasting Supporting proactive, AI-driven supply chain decisions Migrating legacy systems without interrupting ongoing business The transformation needed to be executed without disrupting the company’s active manufacturing and distribution operations.
The organization operated across diverse markets with siloed systems that stored data in incompatible formats. This created delays in financial planning, SKU-level forecasting, and daily operational reporting. The client required a future-ready data architecture capable of: Consolidating product, sales, inventory, and financial datasets Automating FP&A reporting Enabling SKU-level demand forecasting Supporting proactive, AI-driven supply chain decisions Migrating legacy systems without interrupting ongoing business The transformation needed to be executed without disrupting the company’s active manufacturing and distribution operations.
WHAT WE DID
WHAT WE DID
TechnePlus delivered a unified, scalable data ecosystem using Azure Data Lake, Power BI, and Azure ML. Key initiatives included: 1. Phased Migration to Azure Cloud We transitioned critical datasets to Azure Data Lake using a staged approach—ensuring no disruption to daily supply-chain or financial reporting. High-priority data was migrated first, followed by incremental consolidation of SKU, sales, and market-level datasets. 2. AI-Driven Product Affinity & Forecasting Models Using Azure ML, TechnePlus built affinity mapping to uncover SKU relationships, customer buying patterns, seasonal demand shifts, and market trends. The insights improved forecasting accuracy and enabled proactive ordering and inventory planning. 3. Automated FP&A & Operational Dashboards TechnePlus delivered 80+ dashboards across functions, sales, finance, supply chain, SKU performance, and production planning. These dashboards replaced manual spreadsheets with real-time visual intelligence, improving decision speed across the organization. 4. Empowering SKU-Level Decisioning With integrated datasets and predictive models, SKUs could now be planned at a granular level. Teams gained visibility into run-rates, stock depletion, wastage patterns, and price-performance relationships, unlocking faster and more reliable market-level planning.
TechnePlus delivered a unified, scalable data ecosystem using Azure Data Lake, Power BI, and Azure ML. Key initiatives included: 1. Phased Migration to Azure Cloud We transitioned critical datasets to Azure Data Lake using a staged approach—ensuring no disruption to daily supply-chain or financial reporting. High-priority data was migrated first, followed by incremental consolidation of SKU, sales, and market-level datasets. 2. AI-Driven Product Affinity & Forecasting Models Using Azure ML, TechnePlus built affinity mapping to uncover SKU relationships, customer buying patterns, seasonal demand shifts, and market trends. The insights improved forecasting accuracy and enabled proactive ordering and inventory planning. 3. Automated FP&A & Operational Dashboards TechnePlus delivered 80+ dashboards across functions, sales, finance, supply chain, SKU performance, and production planning. These dashboards replaced manual spreadsheets with real-time visual intelligence, improving decision speed across the organization. 4. Empowering SKU-Level Decisioning With integrated datasets and predictive models, SKUs could now be planned at a granular level. Teams gained visibility into run-rates, stock depletion, wastage patterns, and price-performance relationships, unlocking faster and more reliable market-level planning.
Technology Stack
Technology Stack
Azure Data Lake, Azure ML, Power BI, SQL, Python, Azure Cloud
Azure Data Lake, Azure ML, Power BI, SQL, Python, Azure Cloud
