Manufacturing OS | Operations Automation

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Operations

Inventory & Production OS for Manufacturing

Real-time inventory tracking, production forecasting, and compliance automation for multi-plant manufacturing.

Operations automation case study featuring workflow automation and ai implementation.

78% → 99%

Inventory Accuracy

-40%

Production Delays

-85%

Compliance Reporting Time

Overview

A manufacturing firm operating multiple plants needed real-time inventory tracking, production forecasting, and compliance automation to replace outdated ERP modules and spreadsheet-driven processes. ERP modernization was achieved without replacing the ERP.

Business Context

The manufacturing firm was experiencing significant operational friction due to disconnected systems across their four production facilities. Their legacy ERP system, while containing critical historical data, lacked modern API capabilities and couldn't provide the real-time visibility needed for efficient operations. Inventory discrepancies between plants led to frequent stockouts, emergency orders at premium prices, and production line stoppages that cost thousands of dollars per hour. The executive team had evaluated full ERP replacement options, but the $2-4 million price tag and 18-month implementation timeline was prohibitive. They needed a solution that could modernize their operations without the risk and disruption of replacing their core ERP system.

How We Built It

We designed a microservices architecture using Python that wraps around the legacy ERP, extracting data through custom connectors that work with the ERP's database export capabilities and file-based integrations. The PostgreSQL data layer serves as the operational hub, receiving synchronized data from all four plants and maintaining a unified view of inventory across the organization. Redis provides a high-performance caching layer that enables sub-second queries for real-time dashboards, even when underlying ERP queries would take minutes. We built an automated ingestion pipeline that runs on Azure Functions, polling the ERP for changes and pushing updates to the central database in near real-time. The real-time inventory engine tracks items at the unit level, maintaining complete chain of custody from receiving through production to shipping. Automated reorder triggers incorporate FIFO and FEFO compliance requirements, generating purchase orders when inventory falls below calculated safety stock levels. For production forecasting, we implemented AI models that analyze historical production data, seasonal patterns, and current order backlogs to predict capacity needs and recommend scheduling adjustments. The custom Next.js application provides production managers with an intuitive scheduling interface that visualizes capacity across all plants simultaneously.

Challenges

Legacy ERP with no API

Inventory inconsistencies between plants

Manual production forecasting

Compliance reporting requiring manual consolidation

No real-time visibility into raw materials or capacity

What We Delivered

Python microservices with PostgreSQL data layer

Automated ingestion from ERP through custom connectors

Real-time inventory engine with item-level tracking

Automated reorder triggers with FIFO/FEFO compliance

AI production forecasting with scenario simulations

Custom Next.js application for production scheduling

Multi-plant operational visibility dashboard

Tech Stack

Python, PostgreSQL, Redis, Next.js, Azure Functions, Azure Blob Storage

Results

78% → 99%

Inventory Accuracy

-40%

Production Delays

-85%

Compliance Reporting Time

Strategic Impact

The transformation from 78% to 99% inventory accuracy fundamentally changed how the organization operates. Production delays decreased by 40% as the right materials are now consistently available when needed, eliminating the scramble for emergency orders and the production line stoppages that previously plagued operations. Compliance reporting time dropped by 85% because data is now centralized and automatically validated — what previously required a team spending days consolidating spreadsheets from multiple plants now generates automatically with complete audit trails. The operations team gained true real-time visibility into raw materials and production capacity for the first time, enabling proactive decision-making rather than reactive firefighting. Plant managers can now see cross-facility inventory and coordinate transfers to optimize stock levels across the organization. The AI-powered forecasting has reduced both stockouts and excess inventory, improving working capital efficiency significantly. Emergency expedited shipping costs dropped dramatically as proactive reordering replaced last-minute scrambles for critical components. The cross-plant visibility has also enabled strategic inventory pooling, reducing overall safety stock requirements while maintaining service levels. Supplier relationships have improved as the organization now provides more consistent order patterns rather than erratic emergency requests. Perhaps most importantly, the organization achieved ERP modernization without ERP replacement — proving that strategic integration architecture can extend the life of legacy systems while delivering modern capabilities. The platform provides a foundation for continued automation expansion, with planned additions including predictive maintenance integration and quality management automation.