Prіnt Gеek is a high-volume print-on-demand manufacturer supporting e-commerce brands with customized apparel and merchandise. The business relied on a legacy system built by an external vendor. The platform had become slow, fragile, and difficult to maintain, with certain production tasks requiring hours to complete during heavier workloads. There was no internal engineering capability to manage or extend the system, and the vendor had become largely unresponsive while planning to drop support.
Prіnt Gеek required an assessment of whether the existing platform could be stabilized or whether a full replacement was necessary. The long-term goal was to support automated high-volume order ingestion, custom manufacturing workflows, and efficient production processes.
The Challenge
Prіnt Gеek faced several operational and technical constraints:
System Performance and Stability
- Critical production jobs required hours to run during heavier volumes.
- The system showed increasing instability and performance degradation.
- No path existed to scale throughput without introducing additional risk.
Lack of Technical Ownership
- No internal engineering team was available to maintain or understand the system.
- The external vendor provided minimal or delayed responses and was planning to discontinue support.
- Knowledge of system internals, edge cases, and production logic was not documented.
Architectural Limitations
- Client-specific logic was embedded throughout the codebase, creating inconsistent behavior and making changes risky.
- The system lacked a generalized ingestion pipeline and required per-client code paths.
- Off-the-shelf ERP/MRP systems could not support Prіnt Gеek’s custom manufacturing steps or artwork-per-order requirements.
Operational Constraints
- Order ingestion and processing included manual steps that increased the risk of delays or errors.
- Production teams lacked consistent visibility due to system latency and inconsistent status updates.
- Address-related issues and image variations created additional manual workload.
The combination of performance limitations, lack of ownership, and architectural fragility made the existing system unsuitable for long-term scale.
The Approach
Assessment and Interim Stabilization
A technical assessment evaluated whether the existing platform could be partially repaired or extended. Findings indicated that repairs would only provide short-term stability and would not resolve architectural limitations.
To maintain operations during the evaluation and buildout, a developer was brought in to handle maintenance, apply necessary patches, and mitigate operational risk associated with the legacy system.
Platform Evaluation
Multiple paths were examined:
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Repairing the legacy platform
- Identified as high-risk due to limited vendor support and fragmented architecture.
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Building a fully custom replacement
- Offered full control but carried significant long-term maintenance and cost implications.
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Implementing a commercial ERP/MRP platform (e.g., Netsuite)
- Provided structure but limited customization control and would require customizations not owned by Prіnt Gеek.
-
Implementing an open-source ERP/MRP platform
- Provided full control of customizations with lower cost and greater flexibility.
Evaluation criteria included:
- Long-term ownership
- Ability to support Prіnt Gеek’s unique workflow
- Cost of implementation and maintenance
- Support for high-volume automated order ingestion
- Ability to integrate with printing, binning, shipping, and QC processes
- Scalability and maintainability
Odoo was selected based on its modular architecture, open-source extensibility, and fit with Prіnt Gеek’s operational needs.
System Buildout and Integration
Custom Workflow Extensions
The Odoo implementation required substantial customization to meet operational requirements:
- Support for artwork ingestion and customization at the item level
- Custom printing applications to drive manufacturing stations and equipment
- Binning and routing applications to support production flow
- Integrated QC functionality to ensure validation during production
- Custom shipping modules to leverage multiple carriers and manage label workflows
Automated Order Ingestion Architecture
A new generalized ingestion framework replaced the fragmented legacy ingestion process.
Key elements:
- A preprocessing queue for validating and preparing orders
- Image preprocessing to standardize artwork and fit production pallet sizes
- Address validation and correction to reduce downstream issues
- A generalized payload normalization system driven by configuration files, not custom code
- Automated creation of manufacturing orders within Odoo
- Status callbacks to clients based on workflow progression
This ensured consistent behavior across clients and eliminated the need for per-client custom code paths.
Data and System Integration
Additional work included:
- Integrating the new workflow into Odoo’s manufacturing, inventory, and shipping modules
- Building status update mechanisms to support real-time reporting
- Ensuring that workflows remained maintainable and standardized across operational areas
Results
Operational Improvements
- Faster order throughput as legacy bottlenecks were removed.
- More reliable processing, with system performance stabilizing significantly after key workflows were migrated.
- Predictable production timelines due to consistent workflow execution and reduced long-running processes.
- Improved visibility across order processing, production, QC, and shipping, enabling better planning and coordination.
Integration Efficiency
- New e-commerce clients could be onboarded much faster because payload normalization eliminated most custom engineering work.
- Integration timelines shortened materially due to standardized ingestion logic and configuration-driven mappings.
- The system could accommodate higher-volume, more complex client payloads without introducing bottlenecks.
System Maintainability
- Generalized workflows allowed changes and extensions without destabilizing the platform.
- Client-specific code paths were removed, reducing long-term maintenance overhead.
- Full ownership of the codebase eliminated dependency on external vendors and reduced operational risk.
- The architecture now supports long-term scalability as order volume and product complexity increase.
Summary
Prіnt Gеek required a stable, scalable system to support high-volume print-on-demand operations. The legacy platform created operational risk due to performance issues, architectural fragmentation, and lack of support. By transitioning to a customizable, open-source ERP/MRP platform and implementing a generalized ingestion architecture, Prіnt Gеek gained a maintainable foundation capable of supporting growth, automation, and a wide range of client integrations.