Demand Forecasting Engine
ML-powered forecasting used historical sales, seasonality, and order patterns to improve forecast accuracy from 65% to 88%.
Enterprise / Manufacturing
CP MRP
A cloud-native MRP platform that replaced fragmented spreadsheets with real-time planning, forecasting, procurement, and production visibility.
Year
2023
Team Size
8 engineers
Timeline
9 months
Technology
React, Node.js, PostgreSQL
The Challenge
CP Industries, a mid-sized manufacturing company, was running production planning through a legacy ERP and a growing network of spreadsheets. With 40% year-over-year growth, the old system could not handle demand volatility, SKU expansion, or cross-site coordination.
Production delays had climbed to 15%, inventory accuracy fell to 78%, and finance teams spent more than 60 hours per month reconciling purchase orders, material usage, and production schedules. Leaders had tried point solutions, but each created another disconnected data source.
The company needed a modern MRP system that could integrate with SAP, improve forecast quality, reduce manual planning, and roll out without stopping production.
Client Name
CP Industries
Industry
Manufacturing
Engagement Type
ERP Implementation
Timeline
9 months
Team Size
8 engineers, 1 consultant
Budget Range
$400K-$600K
Status
Live, scaling to 8 sites
Live Users
10,000+ daily users
78% -> 95%
Inventory Accuracy
60 -> 5 hrs
Monthly Manual Work
15% -> 2%
Production Delays
How We Solved It
The Solution
We architected a cloud-native MRP system on AWS, replacing the spreadsheet layer while integrating with the existing SAP core. The platform automates demand forecasting, production planning, procurement triggers, and shop-floor reporting.
Delivery was phased by business unit. The first four months focused on demand planning and SAP integration, while the next five months added scheduling, procurement, analytics, and supervisor mobile workflows.
Production Scheduling
Constraint-based planning optimized machine utilization, setup time, shifts, and resource allocation.
Real-Time Visibility
Live dashboards and mobile alerts gave supervisors instant visibility into bottlenecks and floor status.
Months 1-4
MVP - Demand Planning
Complete
Forecasting, core planning, SAP integration
Forecast accuracy above 80%
Months 5-8
Production Scheduling
Complete
Scheduler, shift optimization, procurement
Machine utilization reached 87%
Month 9
Analytics & Optimization
Complete
BI dashboards, alerts, improvement workflows
Production delays below 2%
Technology
Architecture & Tech Stack
Frontend
React 18
API Gateway
Secure APIs
Backend
Node.js
Data Layer
PostgreSQL
Integrations
Secure APIs
Frontend
- React 18
- TypeScript
- Tailwind CSS
- Recharts
Backend
- Node.js
- Python Flask
- GraphQL API
- REST APIs
Data & Storage
- PostgreSQL
- Redis
- Snowflake
- S3
Infrastructure
- AWS
- EKS
- Docker
- Terraform
Tools & Services
- GitHub
- Jenkins
- Datadog
- SAP adapters
Features
What We Built
ML-Powered Demand Forecasting
Predicts demand 6-12 months ahead using seasonality, sales history, and market signals.
Constraint-Based Scheduling
Optimizes machines, shifts, resources, and setup time across production lines.
Real-Time Production Visibility
Live dashboards surface shop-floor status, bottlenecks, utilization, and alerts.
Supplier Integration
Auto-generates purchase orders and connects procurement to supplier inventory updates.
BI & Predictive Analytics
Executive dashboards track OEE, cost per unit, and production health.
Mobile Shop Floor App
Workers view job cards, update tasks, and report issues from the floor.
API-First Architecture
Integrates with SAP, accounting systems, and third-party planning tools.
Multi-Site Scalability
Supports data isolation and configurable workflows across facilities.
Results
The Impact
CP MRP launched nine months after kickoff with zero production disruption. Within three months, forecast accuracy reached 88%, inventory accuracy hit 95%, and manual planning work dropped from 60 hours to 5 hours per month.
Production delays decreased from 15% to 2%, allowing the business to accept more orders with the same equipment and staffing. The system now supports daily planning across thousands of SKUs and is expanding to additional manufacturing sites.
88%
Forecast Accuracy
up from 65%
95%
Inventory Accuracy
up from 78%
-92%
Manual Work
60 hrs to 5 hrs/month
87%
Machine Utilization
up from 72%
Return on Investment
- Inventory carrying cost reduction: $200K/year.
- Labor cost savings: $180K/year.
- Additional revenue capacity: $800K/year.
- Total annual benefit: $1.18M; payback period: 5 months; year-one ROI: 136%.
DigiteraX did not just build us an MRP system. They transformed how we plan, schedule, and execute production.
Rajesh Sharma, VP Operations, CP Industries
The Team
People Behind the Project
SC
Sarah Chen
Solutions Architect
ERP implementation, demand planning
AG
Amit Gupta
Senior Backend Engineer
Go, microservices, data pipelines
PN
Priya Nair
Frontend Lead
React, real-time dashboards, UX
AH
Ahmed Hassan
DevOps Engineer
Kubernetes, AWS, monitoring
Challenges & Lessons
Overcoming Obstacles
Legacy ERP Integration
SAP data formats were non-standard and several APIs were deprecated.
Built adapters, ETL checks, and a phased migration path.
Always budget extra time for legacy integrations.
Shop Floor Adoption
Factory users were cautious about replacing paper and spreadsheets.
Simplified the mobile UI and provided on-site support.
Change management is as critical as technical excellence.
Real-Time Data Scale
Facilities produced hundreds of sensor readings per minute.
Used edge processing with cloud stream ingestion.
Edge plus cloud is the right pattern for real-time industrial systems.
Evolution
From Launch to Continuous Improvement
Month 9
Launch
Complete
MVP features and 5 sites
88% forecast accuracy
Months 10-12
Optimization
Complete
Performance tuning and analytics
Response time under 500ms
Months 13-18
Expansion
In Progress
Multi-plant rollout and integrations
8 sites planned
Future
Innovation
Planned
AI scheduling and predictive maintenance
15% OEE improvement target
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