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Data Consulting Group

Solutions & Projects

Delivery-focused project case studies

Each program combines strategy, implementation, and adoption to deliver measurable value. The case studies below show the challenge, execution pattern, and business outcomes.

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Case study 01 · Consumer Retail

Retail AI: Demand Forecasting & Dynamic Pricing

A production-ready pricing and demand engine for fresh goods, designed to cut waste while preserving margin and sell-through.

Business context

Challenge. Store teams were balancing short shelf life products with volatile demand, while planning decisions were still based on fragmented spreadsheets and delayed reporting.

Approach. We built context-aware forecasting models, near-expiry pricing automation, and store-level replenishment guidance with event and demographic signals in the loop.

Scope delivered

  • Demand and markdown optimization models
  • Store segmentation and local event signal pipelines
  • Ops dashboard for planners, category teams, and buyers
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Case study 02 · Industrial Operations

Knowledge Graphs & Digital Twins

A graph-native operational model that unifies assets, events, and relationships, enabling richer diagnostics and AI-ready context retrieval.

Business context

Challenge. Operational knowledge lived across disconnected systems, making root-cause analysis slow and reducing confidence in predictive maintenance decisions.

Approach. We designed entity-relationship ontologies, linked cross-system semantics, and exposed digital twin views with graph-backed context for RAG workflows.

Scope delivered

  • Domain ontology and entity mapping framework
  • Knowledge graph ingestion and governance layer
  • Digital twin views for operational monitoring
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Case study 03 · Enterprise Platform

Cloud Migration & Data Platform Modernization

A secure cloud data foundation that replaced a brittle legacy estate and accelerated analytics and AI product delivery.

Business context

Challenge. Legacy systems were creating delays in data availability, inconsistent governance, and high friction between engineering and business analytics teams.

Approach. We delivered a modern lakehouse architecture, real-time ingestion, and governed access layers designed for both BI and AI workloads.

Scope delivered

  • Cloud-native lakehouse and ingestion framework
  • Role-based data access and governance model
  • Monitoring, lineage, and cost-optimization controls
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Case study 04 · Manufacturing

Smart Manufacturing: Predictive Maintenance & Optimization

A plant-scale optimization program combining machine telemetry, quality signals, and AI recommendations to reduce downtime and stabilize throughput.

Business context

Challenge. Production teams were facing unplanned line stoppages, variable product quality, and fragmented visibility across maintenance, process, and quality systems.

Approach. We unified sensor and MES data, deployed failure-risk and yield models, and introduced operator-facing guidance for maintenance windows and process parameter tuning.

Scope delivered

  • Unified manufacturing data model across OT and IT systems
  • Predictive maintenance and yield optimization model suite
  • Plant operations cockpit for engineering and maintenance teams
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