Case Study: 35% Less QC Rejection for Forging Unit

Case Study: Forging Unit Reduces QC Rejection Rate by 35% Using Odoo Quality Control

For forging manufacturers, maintaining consistent product quality is critical for customer trust, operational efficiency, and long-term profitability. However, many forging units still depend on manual records and disconnected quality processes, making it difficult to identify the true causes of rejection or provide evidence during customer quality disputes.

This case study explains how a Rajkot-based closed-die forging manufacturer transformed its quality management system by implementing a forging-specific Odoo ERP solution with digital job cards, heat lot tracking, in-process quality control, and die life management.

Within six months of implementation, the company reduced its average QC rejection rate from 8.2% to 5.3%, improved production traceability, successfully defended customer rejection claims, and created a data-driven quality improvement process.

At a Glance

• Industry: Closed-Die Forging (Automotive & Industrial Components)
• Location: Rajkot, Gujarat
• Production Volume: 40–60 tonnes per month across 200+ SKUs
• Pre-Implementation Rejection Rate: 8.2% average across products (some SKUs reaching 12%)
• Challenge: High rejection rates with no reliable production traceability
• Solution: Odoo ERP with Digital Job Cards, Heat Lot Tracking, Quality Control, and Die Life Management
• Implementation Timeline: 12 Weeks
• Key Result: 35% reduction in overall QC rejection rate

Client Overview

The client is an established closed-die forging manufacturer based in Rajkot, Gujarat, supplying precision automotive and industrial components to Tier-1 and Tier-2 customers across Gujarat and Maharashtra.

With nearly two decades of manufacturing experience, the company had developed strong engineering capabilities and maintained long-term relationships with major industrial customers.

However, increasing production volumes and growing customer quality expectations exposed limitations in their existing paper-based quality management approach.

The manufacturing team had the technical expertise to produce quality components, but the company lacked accurate production data required to investigate defects, identify recurring problems, and provide evidence during customer quality discussions.

The Challenge: High Rejection with No Way to Find the Cause

The forging unit had experienced a gradual increase in rejection rates over two years. While production teams understood forging processes well, management struggled to identify exactly why certain batches were failing.

The core issue was not manufacturing knowledge—it was the absence of connected production and quality data. When components failed internal inspection or customer incoming inspection, the company had no systematic method to trace the issue back to the exact heat, die condition, machine, operator, or production stage.

No Complete Production Traceability

Rejected components could not be quickly traced back to the original heat lot, forging die, operator, or machine used during production. Important production information was scattered across multiple records and manual registers.

Paper-Based Job Cards

Production details were recorded manually on paper job cards. These records were difficult to retrieve, sometimes incomplete, and unavailable when quality investigations required immediate information.

Quality Checks Happened Too Late

Most quality checks were performed during final inspection. This allowed defects created during forging or trimming operations to continue through multiple production stages before being detected.

No Die Performance Visibility

Forging dies were operated until visible failure occurred because systematic die life monitoring was not available. Dimensional variation increased as dies approached the end of their usable life.

Customer Rejections Were Difficult to Defend

When customers reported quality issues, the company lacked digital evidence to verify whether the problem originated during manufacturing or after dispatch.

As a result, some customer claims were accepted without sufficient production data to support investigation or corrective action.

The Solution: Odoo Quality Control and Job Card Integration

Sunray Datalinks implemented a forging-specific Odoo ERP solution covering production, quality control, inventory, and maintenance processes. The objective was to create complete visibility from raw material receipt to final customer dispatch.

The implementation was designed around the company's actual shop-floor workflow instead of forcing operators to follow generic ERP processes. Every important manufacturing activity was digitally connected inside one integrated system.

1. Digital Job Cards with Operator and Machine Capture

Every production order automatically generated digital work orders for each manufacturing stage including furnace, forging press, trimming, shot blasting, and final inspection.

Operators completed their assigned work orders through shared shop-floor terminals by recording their employee ID, machine used, production start and finish time, output quantity, and rejection details with reasons.

Each production stage had to be completed digitally before material moved to the next operation. This eliminated missing paperwork, incomplete records, and end-of-shift data reconstruction.

The company gained complete visibility into who produced each batch, which machine was used, how long the operation took, and where rejection occurred.

2. Heat Lot Tracking from Furnace to Dispatch

Each furnace charge was registered as a unique heat lot before heating began. Material grade, furnace identification, target temperature, and soaking duration were recorded directly inside Odoo ERP.

When components moved from forging to subsequent production stages, every batch remained linked with its original heat lot number, creating complete material traceability.

The heat lot number was also included in dispatch documentation, allowing the company to trace any customer complaint back to the exact furnace batch.

If a customer raised a rejection several weeks after delivery, the quality team could immediately identify the manufacturing conditions, review furnace parameters, and check other deliveries produced from the same heat.

3. In-Process Quality Control Points

Previously, most inspections happened only after production completion. To prevent defects from moving through multiple operations, Sunray configured quality checkpoints at critical production stages.

Three major inspection points were introduced:

• Post-Press Inspection: Dimensional checks performed on sample forged components.
• Post-Trim Inspection: Visual inspection, weight verification, and process compliance checks.
• Final Inspection: Complete dimensional verification, hardness testing, and customer-specific quality requirements.

Each quality checkpoint was digitally recorded inside Odoo with inspector identification, measurement values, inspection results, and pass/fail status.

Production could not proceed until mandatory quality checks were completed, turning quality control from a final inspection activity into an active production control system.

4. Die Life Tracking and Maintenance Scheduling

Forging dies were registered inside Odoo with their expected production shot life. Each completed production order automatically updated the die usage count.

When a die reached approximately 85% of its expected operating life, the system generated a maintenance notification for review and refurbishment planning.

This preventive approach helped the toolroom team schedule maintenance before excessive wear affected component dimensions and product quality.

Previously, dies were often used until failure occurred. With die life monitoring, the company moved from reactive replacement to preventive maintenance.

Implementation: 12 Weeks from Decision to Go-Live

The implementation followed a phased approach to ensure minimal disruption to production while helping operators, supervisors, and quality teams gradually adopt the new digital workflow.

Instead of changing every process simultaneously, Sunray introduced the system step by step, allowing each department to become comfortable before moving to the next phase.

Weeks 1–3: Process Mapping and ERP Configuration

The project started with detailed process mapping of production routing, material masters, product structures, and high-volume SKUs. Odoo workflows were configured according to the company's actual forging operations.

Weeks 4–7: Digital Job Card Rollout

Digital job cards were introduced at forging press and trimming stations. Furnace heat lot registration was integrated into the production process to establish complete batch traceability.

Weeks 8–9: Quality Control Implementation

Quality checkpoints were configured at required production stages. Inspectors were trained to record measurements, inspection results, and non-conformance information directly inside Odoo.

Weeks 10–12: Die Tracking and Full Deployment

Die life monitoring, maintenance alerts, and complete production workflows were activated across the facility. User adoption was monitored closely and improvements were made based on shop-floor feedback.

Driving Shop-Floor Adoption

The biggest implementation challenge was not technology—it was changing the daily working habits of operators who had been following manual production practices for years.

Operators at forging press stations were used to completing production without any additional recording activity between heats. Any digital system that added complexity or slowed production would have created resistance.

To solve this challenge, Sunray designed a simple barcode-based workflow. Operators only needed to scan the heat lot, scan their employee badge, enter production quantity, and record rejection details.

The complete recording process took less than 45 seconds, ensuring production speed was not affected while maintaining accurate digital records.

Results: Six Months After Go-Live

Six months after implementing Odoo ERP, the forging unit achieved significant improvements in quality performance, production visibility, and customer confidence.

The biggest change was the ability to understand exactly why rejection occurred and take corrective action based on real production data instead of assumptions.

Quality Improvement Results

• 35% reduction in overall QC rejection rate — average rejection reduced from 8.2% to 5.3%.
• Three high-rejection SKUs improved below 3% rejection after die condition was identified as the root cause.
• Improved production consistency through early detection of process variations.
• Faster corrective actions using rejection data captured at every production stage.

Customer Quality Improvements

• Customer rejection investigation time reduced from 48 hours to under 2 hours.
• Two customer rejection claims were successfully defended using Odoo-generated evidence including heat records, QC results, and dispatch documentation.
• A major automotive customer restored the company's standard supplier status after a quality audit.
• Customer confidence improved due to complete production traceability.

Operational Improvements

• 100% digital job card adoption achieved across production operations.
• Complete traceability from raw material heat to finished component dispatch.
• Manual paperwork and production record searching were significantly reduced.
• Management gained real-time visibility into rejection trends and production performance.

The Quality Manager's Perspective

What Made the Difference

In-Process Quality Control Instead of Only Final Inspection

The biggest improvement came from shifting quality control earlier in the manufacturing process. Detecting dimensional issues immediately after forging prevented large quantities of defective components from reaching final inspection.

Early detection reduced scrap costs, shortened corrective action cycles, and improved overall production efficiency.

Die Tracking Revealed the Actual Root Cause

Analysis of rejection data revealed that three products with consistently high rejection rates were being manufactured using dies that had exceeded their recommended shot life.

Since die condition had never been tracked previously, the relationship between die wear and quality variation remained hidden.

After implementing preventive die refurbishment, rejection rates for those products dropped from above 10% to below 3% within two production cycles.

Rejection Data Became a Diagnostic Tool

Previously, rejection was measured only as a monthly percentage. Odoo changed this by capturing rejection reasons, production stages, operators, machines, and product details.

The production team could now identify patterns and take targeted corrective actions.

For example, when underfill rejection increased during Monday morning production runs, analysis showed that furnace warm-up inconsistency was the cause. A process improvement was introduced to eliminate the issue.

Key Lessons from the Project

Quality Problems Are Often Data Problems

Many forging manufacturers already have experienced engineers, skilled operators, and established production processes. However, without accurate production data, identifying the real causes behind quality issues becomes extremely difficult.

When heat records, machine details, operator information, die condition, and inspection results are stored separately, quality improvement becomes dependent on assumptions rather than facts.

By connecting all production information inside Odoo ERP, the company gained the visibility required to identify problems quickly and implement effective corrective actions.

Traceability Builds Customer Confidence

Automotive customers increasingly require suppliers to demonstrate complete traceability during quality audits and rejection investigations.

With heat lot tracking and digital quality records, the company could provide detailed evidence showing material history, production parameters, inspection results, and dispatch information for every batch.

This transformed customer discussions from uncertain investigations into data-backed quality conversations.

Preventive Maintenance Improves Product Quality

Die wear was previously managed through experience and visual inspection. While experienced toolroom teams can identify many issues, gradual dimensional variation can remain unnoticed until rejection rates increase.

By monitoring die usage automatically inside Odoo, maintenance activities could be scheduled before quality problems occurred.

Integrated ERP Creates Better Decision-Making

When production, quality, inventory, and maintenance information operate in separate systems, teams spend valuable time collecting and comparing data.

An integrated ERP platform eliminates duplicate records and provides management with a single source of truth for operational decisions.

High Rejection Rates Are a Data Problem as Much as a Process Problem

Most forging units experiencing repeated quality issues are not producing poor-quality components because of a lack of technical capability. The real challenge is often the absence of reliable data needed to understand where problems originate.

Without digital traceability, manufacturers cannot easily connect rejection with heat condition, die life, machine performance, operator activity, or production stage.

When all these factors are connected inside a single ERP system, the root cause of rejection becomes visible. And once problems become visible, they can be systematically corrected.

Sunray Datalinks: Odoo ERP Solutions for Forging Manufacturers

Sunray Datalinks develops industry-specific Odoo ERP solutions for forging manufacturers across Rajkot and India. Our solutions are designed around actual shop-floor operations, helping manufacturers improve quality, traceability, and production efficiency.

From digital job cards and heat lot tracking to in-process quality control, die life monitoring, inventory management, production planning, and maintenance automation, we build ERP workflows that match the requirements of modern forging companies.

Our focus is not simply implementing software. We help manufacturers create data-driven production systems that reduce rejection rates, improve customer confidence, and support scalable growth.