Overview

A mid sized engine reconditioning and machining company serving the automotive, agricultural, and construction vehicle sectors, approached AEM Consultancy (Analyze . Eliminate . Maximize) after experiencing a sharp increase in customer complaints related to engine performance.

Despite having capable machinery and experience technicians, the organization was struggling with post rebuild exhaust smoke, low compression, and warranty rework, causing financial loss and threatening long term customer contracts.

AEM was engaged to diagnose the true causes using data and engineering science, not assumptions, and to implement a sustainable, plant wide solution

The Challenge

In the last six months client experienced a sharp rise in post reconditioning customer complaints, specifically realted to black and white exhaust smoke within 1000km of engine rebuild, leading to warranty rework, customer dissatisfaction, and a significant financial impact.

Customer complaint rate increased from 4% to 15%.
Monthly warranty rework jumped from 6 engines to 23 engines.
Average 3.86 lakh rupees per month lost due to rework, downtime, and indirect customer impact.
TRotal estimated loss over 6 months exceeded 23 lakh rupees.
Risk of losing a 60 lakh rupees annual fleet contract renewal.

Previous corrective actions, such as technician retraining, new measurement tools, and mandatory test runs failed to produce results becasue they were not supported by statistical analysis, root cause validation, or process controls.

Management Needed:

A fact based diagnosis, not assumptions
A time bound solution less than 3 Months
Assurance that the issue would not reoccur

AEM's Investigation & Root Cause Discovery

AEM applied its Analyze . Eliminate . Maximize methodology with a strong emphasis on data, not opinion.

Step 1: Define & Measure

Collected 6 months of customer complaint data, machining records, assembly data, injector calibration results, and test benchoutcomes.
Conducted VOC analysis, SIPOC mapping, detailed process mapping, and shop floor Gemba walks.
Established baseline KPIs across machining, assembly, and fuel system calibration.

Step 2: Analyze (Statistical & Engineering Analysis)

Performed Process Capability Analysis (Cp/Cpk) on critical machining parameters
Conducted Regression Analyssi and Design of Experiemnts (DOE) to identify smoke causing variables.
Seperated statistically significant causes from perceived causes.

Finding: Idle-time exposure >10 minutes correlated with 62% of all defects. Inconsistent handling and queue buildup were the biggest contributors.

Key Findings (Data Backed)

Significant Contributors to Smoke

Cross hatch angle instability (severly incapble process)
Poor bore geometry (Cylindricity & out of round variation)
Incorrect piston ring end gap contro
Injector flow imbalance causing incomplete combustion

Non Significant Contributors

Average bore diameter (highly capable process, Cp/Cpk > 2)
Surface roughness (Ra)
Piston to bore clearance

Conclusion: Smoke was driven by geometry, surface orientation, ring sealing, and fuel atomization; not bore size, contrary to common belief

The AEM Solution

AEM Implemented a dual track solution addressing both technical root causes and systemic process weaknesses

1. Data Driven Technical Optimization

Using DOE and confiramtion runs, AEM established and locked the optimum process window:

Cross hatch angle standardized at 42° +- 2°
Cylindricity and out of round controlled within ≤ 0.01 mm
Ring end gaps redefined using manufacturer‑based calculations, not tribal knowledge
Injector flow variation reduced to ≤ +- 1.5%
Mandatory torque‑plate honing for real‑world geometry accuracy

2. Process & Governance Improvements

Introduced standardized SOPs for boring, honing, assembly, inspection, and testing
Implemented operator certification for honing and ring assembly
Rolled out digital job travelers and inspection sign‑offs
Established SPC charts for critical machining parameters
Implemented TPM‑based preventive maintenance

3. Pilot & Scale‑Up

Ran a 4‑week pilot on 25 engines in the boring & honing line
Validated results through compression tests, emission checks, and 1000‑km field feedback
Scaled improvements plant‑wide after pilot success

Results & Impact

✓

Operational Results

Complaint Rate

Before

15%

After AEM

<4.5%

Rework Engines

Before

23 / month

After AEM

6-7 / month

70% reduction in rework engines

Monthly Rework Cost

Before

₹4.14 lakh

After AEM

₹1.4-1.6 lakh

⚙

Quality Performance

Engine Compression Variation

Before

High

After AEM

Within ±3%

Achieved within 3 months

Visible Smoke (Opacity)

Before

Inconsistent

After AEM

< 10% consistently

Fleet Contract Risk

Successfully Mitigated

Renewal risk eliminated

Financial Impact

Monthly Savings

₹2.7 lakh/month

Reduction in rework and indirect losses

₹2.7 lakh monthly savings achieved

Annualized Savings

Exceeding ₹30 lakh

Sustained cost reduction

Contract Renewal

Risk Mitigated

Fleet contract successfully secured

Outcome Summary

Permanent elimination of post‑rebuild smoke issues
Data‑driven, standardized engine rebuild process
Improved customer confidence and repeat business
Shift from reactive firefighting to process‑controlled operations
Real‑time visibility into quality and performance metrics

AEM didnʼt just fix a problem, we enabled our client to sustain quality excellence

Eliminating Post-Rebuild Engine Smoke & Warranty Escalations

Overview

The Challenge

AEM's Investigation & Root Cause Discovery

Step 1: Define & Measure

Step 2: Analyze (Statistical & Engineering Analysis)

Key Findings (Data Backed)

Non Significant Contributors

The AEM Solution

1. Data Driven Technical Optimization

2. Process & Governance Improvements

3. Pilot & Scale‑Up

Results & Impact

Operational Results

Quality Performance

Financial Impact

Outcome Summary

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