Manufacturing - MES Integration & Production Analytics

Overview

A global specialty manufacturing company operating 500+ plants worldwide struggled with fragmented analytics infrastructure spanning disconnected Manufacturing Execution Systems (MES), Quality Management Systems (QMS), and ERP platforms, making it impossible to achieve operational excellence and understand true production efficiency across facilities.

Key Challenges

• Separate legacy data warehouses for production (MES), quality (QMS), supply chain, and financial (ERP) data creating data silos

• Weekly batch reports taking days to generate, with data already outdated when delivered to plant managers

• Production operations and quality teams working with conflicting metrics and unable to collaborate effectively

• No visibility into Overall Equipment Effectiveness (OEE), downtime root causes, or production bottlenecks

• Unable to perform comparative analysis of production performance across different plants and regions

• High TCO maintaining multiple legacy Teradata and Oracle data warehouse systems

• Limited scalability to handle growing data volumes from new plants and Industry 4.0 sensor data

• Manual data extraction for Six Sigma and Lean Manufacturing continuous improvement initiatives

Our Approach

• 1

  Conducted comprehensive discovery and assessment of existing MES, QMS, ERP data architecture and manufacturing analytics needs

• 2

  Migrated 58 critical legacy Teradata data warehouse workloads to modern Databricks Lakehouse platform

• 3

  Integrated real-time data feeds from Manufacturing Execution Systems (MES) across 500+ plants

• 4

  Connected Quality Management System (QMS) data for defect tracking, root cause analysis, and CAPA management

• 5

  Unified supply chain data from ERP (SAP) including inventory, procurement, and logistics

• 6

  Built comprehensive unified data models for production metrics, quality KPIs, and supply chain analytics

• 7

  Implemented real-time OEE (Overall Equipment Effectiveness) calculation with availability, performance, and quality tracking

• 8

  Created automated data quality checks, validation rules, and monitoring with alerting

• 9

  Developed role-based analytics dashboards for executives, plant managers, production supervisors, and quality engineers

• 10

  Established near real-time data refresh for critical operational metrics replacing daily batch updates

Key Outcomes

• Successfully migrated 58 critical reporting workloads from legacy Teradata to Databricks in just 4 months

• Reduced report generation time by 70% (from days to hours) enabling faster decision-making

• Achieved $1.8M annual cost savings by consolidating and retiring legacy data warehouse infrastructure

• Improved Overall Equipment Effectiveness (OEE) by 18% across all plants through data-driven optimization

• Enabled cross-functional analytics collaboration between production, quality, and supply chain teams for first time

• Provided plant managers with real-time production dashboards for daily operations and shift handoffs

• Unified 500+ plants globally into single source of truth with standardized metrics

• Scaled platform to handle 5x data growth from new plants and IIoT sensor expansion without performance degradation

• Accelerated Six Sigma and Lean Manufacturing initiatives with self-service analytics access for improvement teams

• Reduced production defects by 24% through integrated production-quality analytics