87.5% Faster Production Scheduling: 6-Week Low-Code Implementation for 27-Machine Manufacturer

Quick Facts


Industry	Paper Product Manufacturing	Company Size	$60M revenue, 116 operators
Challenge	8 hours/week manual Excel scheduling across 27 machines	Solution Type	Low-code Process Automation with ERP Integration
Timeline	6 weeks	Key Outcome	87.5% time reduction (8hrs → 1hr/week)
Scale Indicators	27 machines, 116 operators, multiple shifts	Integration	ERP system, HR availability tracking

Problem

A $60M paper product manufacturer in Texas faced significant operational bottlenecks in their production scheduling process. With 27 machines operating across multiple shifts and 116 operators, the production manager spent 8 hours every week manually creating production schedules in Excel.

The scheduling process involved:

Reviewing customer orders and production requirements from the ERP system
Manually assigning jobs to machines based on capacity and operator availability
Tracking machine downtimes and maintenance schedules
Calculating changeover times between different product runs
Adjusting schedules when rush orders came in
Distributing updated schedules to shift supervisors via email
Managing conflicts when operators called out sick or machines went down unexpectedly

This manual Excel-based approach limited the production manager’s ability to optimize capacity utilization and respond quickly to changing production demands. The lack of real-time visibility into shop floor status meant schedules were often outdated by the time they reached shift supervisors.

Solution

Low-Code Production Management System

We built a custom production scheduling system using low-code tools that automated the entire scheduling workflow while maintaining the flexibility needed for the manufacturing environment. The system integrated with existing ERP and HR systems to pull live data, applied rule-based scheduling logic, and delivered real-time updates to all stakeholders.

Implementation Timeline

The project was completed in 6 weeks with the following phases:

Week 1-2: Requirements gathering, process mapping, and system design with production team
Week 3-4: Core system development, ERP integration, and automated scheduling engine configuration
Week 5: User interface development, mobile views for shift supervisors, and user training
Week 6: Production deployment, parallel run validation, and schedule optimization refinement

Core System Architecture

1. Data Integration Layer

Real-time connection to existing ERP system for order data and production requirements
Integration with HR system for operator availability and shift assignments
Automated machine status monitoring for downtime tracking
Maintenance schedule import for proactive capacity planning

2. Automated Scheduling Engine

Rule-based job assignment algorithm optimizing for machine capacity and operator skills
Changeover time calculation based on product type transitions
Priority handling for rush orders with automatic schedule adjustment
Capacity optimization across all 27 machines considering shift constraints
Conflict detection and resolution suggestions for operator or machine unavailability

3. User Interface & Dashboard

Drag-and-drop schedule adjustment capability for manual overrides
Visual capacity planning dashboard showing utilization by machine and shift
Color-coded status indicators for schedule conflicts and bottlenecks
Mobile-friendly views for shift supervisors accessing schedules on shop floor
Real-time OEE (Overall Equipment Effectiveness) tracking

4. Distribution & Notifications

Automated schedule distribution via email and SMS to shift supervisors
Real-time alerts for schedule changes pushed to mobile devices
Shift handoff reports with production status and next-shift priorities
Daily capacity utilization summaries for production management

Performance Optimization

The system was optimized to handle the facility’s scale, processing schedules for 27 machines and 116 operators in under 2 minutes. The automated scheduling engine reduced manual intervention from 8 hours to 1 hour per week, with the remaining hour used for reviewing automated suggestions and making strategic adjustments rather than manual data entry.

Users

The system serves three primary user groups across the organization:

Production Manager: Creates and adjusts weekly schedules, reviews capacity utilization, responds to rush orders
Shift Supervisors: View daily schedules on mobile devices, report machine status changes, access shift handoff information
Operators: Receive their daily assignments via mobile notifications, see upcoming job assignments

Impact

Before & After

Metric	Before Automation	After Automation	Improvement
Weekly scheduling time	8 hours	1 hour	87.5% reduction
Schedule generation time	6-8 hours manual	<2 minutes automated	99% faster
Schedule conflicts/errors	2-3 per week	Near-zero	95%+ reduction
Response time to rush orders	24+ hours (next schedule cycle)	<2 hours (immediate reoptimization)	90% faster
Operator utilization visibility	Manual tracking, day-old data	Real-time dashboard	N/A (new capability)
Capacity planning accuracy	Estimated, often incorrect	Data-driven, 95%+ accurate	N/A (new capability)

Time Savings

Eliminated 7 hours per week of manual Excel data entry and schedule calculations
Freed up production manager to focus on strategic capacity planning and continuous improvement initiatives
Reduced schedule distribution time from 30+ minutes to instant automated delivery
Eliminated emergency schedule revisions that previously required 1-2 hours each

Quality Improvements

Real-time machine status integration prevented scheduling jobs on machines already down for maintenance
Automated conflict detection eliminated scheduling errors (double-bookings, operator unavailability)
Improved operator utilization through better capacity planning and skills-based job assignment
Enhanced shift communication through automated handoff reports

Business Outcomes

Faster response time to customer rush orders improved customer satisfaction
Better visibility into production capacity enabled more accurate delivery commitments
Improved on-time delivery rates through proactive bottleneck identification
Reduced overtime costs through optimized operator scheduling
Enabled production manager to focus on Lean manufacturing initiatives and process improvements

System Performance

Schedule generation: Under 2 minutes for 27 machines and 116 operators
99.9% uptime since deployment (6 months in production)
Real-time updates across all user devices with <5 second sync latency
Mobile access enabling shop floor schedule visibility without returning to office

Technical Highlights

Low-code platform deployment enabling rapid 6-week implementation without extensive custom development, reducing project cost by 60% compared to traditional development approach
ERP system integration through REST API middleware connecting legacy manufacturing systems with modern low-code platform, handling real-time order data synchronization
Rule-based scheduling algorithm optimized for paper manufacturing constraints including changeover times between product types, operator skill requirements, and multi-shift coordination
Mobile-responsive interface for shop floor accessibility without dedicated hardware investment
Automated notification system reducing communication overhead and eliminating “email got lost” schedule distribution failures
Real-time capacity visualization dashboard tracking OEE (Overall Equipment Effectiveness) metrics across all 27 machines
Drag-and-drop interface for manual schedule adjustments when automated suggestions need production manager override
Multi-shift coordination with automated handoff reports improving shift transition communication by 80%
Integration with HR system for operator availability preventing schedule conflicts from sick days or vacation
Changeover time optimization reducing machine downtime between product runs by 15% through intelligent job sequencing

Key Learnings

Low-code platforms enabled rapid deployment without custom development overhead, critical for 6-week timeline
User involvement during design phase was critical for adoption - production manager and shift supervisors tested prototypes weekly
Mobile accessibility was essential for shift supervisors and operators who spend 90% of time on shop floor, not at desks
Automated distribution eliminated the “I never got the email” problem that plagued previous manual scheduling process
Visual dashboards improved capacity planning beyond just time savings - production manager now identifies bottlenecks 2-3 days ahead
Parallel run strategy (automated system running alongside manual Excel process for 2 weeks) built confidence and caught edge cases before full transition
Real-time machine status integration prevented 80% of scheduling errors that occurred when schedules didn’t account for unplanned downtime