Case Study: “20% Faster, 100% Smarter: A Six Sigma Success Story in Throughput Reduction”

1. Introduction

In today’s competitive manufacturing environment, operational efficiency and reduced cycle time are critical for improving profitability and customer satisfaction. This case study presents a Six Sigma Green Belt project executed at a pharmaceutical manufacturing unit aimed at reducing throughput time across the production lifecycle.

Throughput time, defined as the total time taken from raw material receipt to final product dispatch, was not formally measured or controlled. This resulted in high work-in-progress (WIP), delayed deliveries, poor visibility of bottlenecks, and inefficient resource utilization.

The project leveraged the DMAIC (Define–Measure–Analyze–Improve–Control) methodology to identify inefficiencies, reduce variation, and improve process capability.

2. Define Phase

Problem Statement

Throughput time measurement was not established, and its impact on business performance was not visible. Significant delays existed due to waiting time between processes.

Project Goal

• Reduce throughput time by 20%
• Minimize variation in process cycle time
• Improve process capability (Cpk > 1.2)
• Increase sigma level from ~2.5 to ~3.5

Business Impact

• Reduction in working capital blockage
• Improved production output
• Reduced inventory holding costs
• Enhanced internal and external customer satisfaction

3. Measure Phase

Data Collection Strategy

A structured data collection plan was implemented to capture:

• Waiting time (Non-value-added)
• Processing time (Value-added)
• Total throughput time

Tools Used

• SIPOC Diagram
• Spaghetti Diagram
• Control Charts
• Process Capability Analysis (Cp, Cpk)

Baseline Findings

• High variation in throughput time
• Process instability (out-of-control conditions)
• Low capability (Cpk ≈ 0.8)
• Excessive waiting time between sampling, QA approvals, and dispatch

4. Analyze Phase

Root Cause Identification

Using tools such as Cause & Effect Diagram, Why-Why Analysis, and brainstorming, the following root causes were identified:

• Hidden Factory: Majority of time lost in waiting rather than processing
• Sampling Delays: Unplanned queues in QA/QC testing
• Poor Coordination: Lack of synchronization between departments
• Batch Processing Approach: Large batch sizes causing delays
• Inefficient Material Movement: Excessive transportation and handling

Validation

Statistical validation and data analysis confirmed that waiting time contributed to more than 60% of total throughput time.

5. Improve Phase

Improvement Strategy

The team implemented the following solutions:

1. Flow-Based Processing

• Shift from batch processing to continuous flow
• Reduced waiting between operations

2. Standardization

• Defined SOPs for inter-department handoffs
• Reduced ambiguity and delays

3. Scheduling Optimization

• Alignment between production, QA, and dispatch
• Priority-based sampling and testing

4. Layout Optimization

• Reduced unnecessary movement using spaghetti analysis

5. Visual Management

• Introduced tracking dashboards for throughput time

Pilot Results

• Significant reduction in waiting time
• Improved process stability
• Faster material flow across departments

6. Control Phase

Control Mechanisms Implemented

• Daily KPI tracking of throughput time
• Control charts to monitor process stability
• Audit plans and checklists
• Monthly Gemba walks
• Standard operating procedures (SOPs)

Sustainability Actions

• Horizontal deployment across other product lines
• Continuous monitoring of critical process parameters
• Regular cross-functional review meetings

7. Results & Impact

Metric	Before	After	Improvement
Throughput Time	High & Variable	Standardized	↓ 20%
Sigma Level	~2.5	~3.5	Significant
Process Capability (Cpk)	~0.8	>1.2	Improved
WIP Inventory	High	Optimized	↓ 15–25%
Production Output	Moderate	Increased	↑ 10–15%