The Improve phase stands as one of the most critical stages in the DMAIC (Define, Measure, Analyze, Improve, Control) methodology of Six Sigma. For professionals preparing for their Six Sigma certification exam, mastering the concepts within the Improve phase is essential for both passing the examination and applying these principles effectively in real-world business scenarios. This comprehensive guide explores the fundamental concepts, methodologies, and question types you are likely to encounter on your certification exam.
Understanding the Improve Phase in Six Sigma
The Improve phase is where theory transforms into action. After defining problems, measuring current performance, and analyzing root causes, practitioners enter the Improve phase to develop, test, and implement solutions. This phase requires a deep understanding of various statistical tools, creative problem-solving techniques, and practical implementation strategies. You might also enjoy reading about Scaling Solutions from Pilot to Full Implementation: Key Considerations for Success.
During your certification exam, questions related to the Improve phase will test your ability to select appropriate improvement solutions, design experiments, manage risks, and implement changes effectively. The questions typically assess both theoretical knowledge and practical application skills, often presenting real-world scenarios that require analytical thinking and strategic decision-making. You might also enjoy reading about Poka-Yoke in Six Sigma: Error-Proofing Your Process Improvements for Quality Excellence.
Key Concepts in the Improve Phase
Design of Experiments (DOE)
Design of Experiments represents one of the most powerful statistical tools in the Six Sigma toolkit. DOE allows practitioners to systematically investigate the relationship between multiple input variables and output responses, identifying optimal settings that maximize process performance. You might also enjoy reading about Changeover Reduction: Minimizing Downtime Between Different Products for Maximum Efficiency.
Consider a manufacturing scenario where a company produces plastic bottles and wants to improve bottle strength. The team identifies three potential factors: temperature (Factor A), pressure (Factor B), and cooling time (Factor C). A full factorial design with two levels for each factor would require eight experimental runs.
Here is an example of a 2^3 factorial design:
- Run 1: Temperature Low (150°C), Pressure Low (100 PSI), Cooling Time Low (30 seconds) = Strength 45 PSI
- Run 2: Temperature High (170°C), Pressure Low (100 PSI), Cooling Time Low (30 seconds) = Strength 52 PSI
- Run 3: Temperature Low (150°C), Pressure High (120 PSI), Cooling Time Low (30 seconds) = Strength 58 PSI
- Run 4: Temperature High (170°C), Pressure High (120 PSI), Cooling Time Low (30 seconds) = Strength 67 PSI
- Run 5: Temperature Low (150°C), Pressure Low (100 PSI), Cooling Time High (45 seconds) = Strength 49 PSI
- Run 6: Temperature High (170°C), Pressure Low (100 PSI), Cooling Time High (45 seconds) = Strength 56 PSI
- Run 7: Temperature Low (150°C), Pressure High (120 PSI), Cooling Time High (45 seconds) = Strength 63 PSI
- Run 8: Temperature High (170°C), Pressure High (120 PSI), Cooling Time High (45 seconds) = Strength 72 PSI
Certification questions might ask you to calculate main effects, interaction effects, or determine which factors significantly impact the response variable. Understanding how to interpret these results and make recommendations based on experimental data is crucial for exam success.
Solution Selection and Prioritization
Not all potential solutions deserve equal attention or resources. The Improve phase emphasizes using structured methodologies to evaluate and prioritize improvement ideas. Common tools include the Pugh Matrix, impact-effort matrix, and cost-benefit analysis.
Imagine a healthcare clinic experiencing long patient wait times. The improvement team generates five potential solutions:
- Solution A: Implement electronic check-in system (Cost: $15,000, Expected reduction: 8 minutes)
- Solution B: Add one additional staff member (Cost: $45,000 annually, Expected reduction: 12 minutes)
- Solution C: Reorganize patient flow process (Cost: $2,000, Expected reduction: 10 minutes)
- Solution D: Extend clinic hours (Cost: $30,000 annually, Expected reduction: 5 minutes)
- Solution E: Implement appointment reminder system (Cost: $5,000, Expected reduction: 6 minutes)
Using a cost-benefit ratio (minutes reduced per $1,000 invested), we can calculate:
- Solution A: 8/15 = 0.53 minutes per $1,000
- Solution B: 12/45 = 0.27 minutes per $1,000
- Solution C: 10/2 = 5.0 minutes per $1,000
- Solution D: 5/30 = 0.17 minutes per $1,000
- Solution E: 6/5 = 1.2 minutes per $1,000
Based on this analysis, Solution C provides the best return on investment, followed by Solution E. Exam questions frequently present similar scenarios requiring candidates to apply decision-making frameworks and justify their recommendations.
Statistical Process Control in the Improve Phase
Control Charts and Process Capability
While control charts are prominent in the Control phase, understanding how improvements affect process capability is essential during the Improve phase. Certification exams test your ability to calculate process capability indices and interpret their significance.
Consider a call center where the specification limits for call handling time are set at 3 to 9 minutes (target of 6 minutes). Before improvement initiatives, the process showed:
- Process Mean: 7.2 minutes
- Process Standard Deviation: 1.5 minutes
- Upper Specification Limit (USL): 9 minutes
- Lower Specification Limit (LSL): 3 minutes
The process capability index Cp would be calculated as: Cp = (USL – LSL) / (6 × Standard Deviation) = (9 – 3) / (6 × 1.5) = 6/9 = 0.67
The Cpk calculation considers process centering: Cpk = minimum of [(USL – Mean)/(3 × SD), (Mean – LSL)/(3 × SD)]
Cpk = minimum of [(9 – 7.2)/(3 × 1.5), (7.2 – 3)/(3 × 1.5)] = minimum of [0.4, 0.93] = 0.4
After implementing improvements (better training and streamlined procedures), the new metrics show:
- Process Mean: 6.1 minutes
- Process Standard Deviation: 0.8 minutes
New Cp = (9 – 3) / (6 × 0.8) = 6/4.8 = 1.25
New Cpk = minimum of [(9 – 6.1)/(3 × 0.8), (6.1 – 3)/(3 × 0.8)] = minimum of [1.21, 1.29] = 1.21
This improvement demonstrates how the process moved from incapable (Cpk less than 1.0) to capable (Cpk greater than 1.0). Exam questions often require similar calculations and interpretation of results.
Creative Thinking and Innovation Tools
Theory of Inventive Problem Solving (TRIZ)
TRIZ provides systematic approaches to innovation by identifying and resolving contradictions. Certification exams may present scenarios where candidates must recognize contradictions and apply TRIZ principles to generate solutions.
For example, a smartphone manufacturer faces a contradiction: customers want longer battery life (requiring larger batteries) but also want thinner phones. TRIZ principles suggest several innovative directions:
- Segmentation: Use flexible, distributed battery cells throughout the device
- Asymmetry: Create varying thickness across the phone, with battery concentrated in areas that do not affect perceived thinness
- Prior action: Develop quick-charging technology to reduce the need for larger capacity
- Composite materials: Utilize new battery chemistries that provide more power in smaller packages
Understanding these creative problem-solving frameworks helps practitioners generate innovative solutions rather than settling for obvious compromises.
Pilot Testing and Implementation Planning
Pilot Studies and Small-Scale Testing
Before full-scale implementation, Six Sigma methodology emphasizes pilot testing to validate solutions and identify unforeseen issues. Exam questions frequently address proper pilot study design, success criteria definition, and result interpretation.
Imagine a retail company planning to implement a new checkout process designed to reduce transaction time. A proper pilot study would include:
Pilot Design Parameters:
- Scope: Three stores representing different customer demographics and traffic patterns
- Duration: Four weeks to capture various shopping periods including weekends and promotional events
- Sample Size: Minimum 200 transactions per store (600 total)
- Baseline Comparison: Same stores measured for four weeks prior to implementation
- Success Criteria: 20% reduction in average transaction time with no increase in errors
Baseline Data (Average Transaction Time in Seconds):
- Store A: 186 seconds (Standard Deviation: 34 seconds)
- Store B: 192 seconds (Standard Deviation: 38 seconds)
- Store C: 179 seconds (Standard Deviation: 31 seconds)
- Overall Average: 186 seconds
Pilot Results (Average Transaction Time in Seconds):
- Store A: 142 seconds (Standard Deviation: 28 seconds) – 23.7% improvement
- Store B: 151 seconds (Standard Deviation: 32 seconds) – 21.4% improvement
- Store C: 138 seconds (Standard Deviation: 26 seconds) – 22.9% improvement
- Overall Average: 144 seconds – 22.6% improvement
This pilot study demonstrates achievement of the success criteria across all test locations. Certification questions might ask you to determine whether results are statistically significant, whether the pilot was properly designed, or what additional data should be collected before full rollout.
Risk Analysis and Mitigation Strategies
Failure Mode and Effects Analysis (FMEA)
FMEA represents a critical tool for proactively identifying and addressing potential failure modes before they occur. The Improve phase requires conducting FMEA on proposed solutions to anticipate implementation risks.
Consider a hospital implementing a new medication dispensing system. An FMEA analysis might identify:
Failure Mode 1: System database contains incorrect medication information
- Potential Effects: Wrong medication dispensed, patient harm
- Severity Rating: 10 (catastrophic)
- Occurrence Rating: 3 (low probability with proper database validation)
- Detection Rating: 4 (likely to detect through pharmacist verification)
- Risk Priority Number (RPN): 10 × 3 × 4 = 120
Failure Mode 2: Staff insufficiently trained on new system
- Potential Effects: Processing delays, workarounds, system abandonment
- Severity Rating: 6 (moderate impact on operations)
- Occurrence Rating: 7 (likely without comprehensive training)
- Detection Rating: 5 (moderate detection capability)
- Risk Priority Number (RPN): 6 × 7 × 5 = 210
Failure Mode 3: Integration failure with existing electronic health records
- Potential Effects: Incomplete patient information, duplicate data entry
- Severity Rating: 7 (significant operational disruption)
- Occurrence Rating: 5 (moderate probability)
- Detection Rating: 3 (high detection through testing)
- Risk Priority Number (RPN): 7 × 5 × 3 = 105
Based on RPN scores, the team should prioritize mitigation strategies for staff training (RPN 210), followed by database accuracy (RPN 120) and integration issues (RPN 105). Exam questions often require calculating RPN scores, prioritizing risks, and recommending appropriate mitigation actions.
Change Management and Stakeholder Engagement
Overcoming Resistance to Change
Technical excellence alone does not guarantee successful improvement implementation. Understanding change management principles and stakeholder engagement strategies is essential for Six Sigma practitioners and is frequently tested in certification exams.
The ADKAR model (Awareness, Desire, Knowledge, Ability, Reinforcement) provides a framework for managing organizational change. Consider a manufacturing facility implementing automated quality inspection:
Awareness: Communicate why change is necessary, sharing data on current defect rates (currently 3.2% with manual inspection) and competitive pressures requiring improvement to less than 0.5% defect rate.
Desire: Address employee concerns about job security by clarifying that automation will handle repetitive inspection tasks while employees transition to higher-value quality analysis roles with corresponding pay increases.
Knowledge: Provide comprehensive training on new automated systems, quality data analysis software, and advanced problem-solving techniques over a six-week period.
Ability: Support employees during transition with coaching, readily available technical support, and gradual responsibility transfer as competency develops.
Reinforcement: Recognize and reward employees who effectively utilize new systems, share success stories, and demonstrate sustained improvement in quality metrics.
Certification questions may present change management scenarios and ask candidates to identify which ADKAR element is missing or recommend specific strategies to address resistance.
Financial Analysis and Cost-Benefit Assessment
Calculating Return on Investment
Six Sigma projects must deliver measurable financial benefits. The Improve phase requires rigorous financial analysis to justify implementation costs and demonstrate value creation. Exam questions frequently test your ability to perform various financial calculations.
Consider a logistics company implementing route optimization software:
Implementation Costs:
- Software License: $75,000 (one-time)
- Hardware Upgrades: $25,000 (one-time)
- Training: $15,000 (one-time)
- Annual Maintenance: $12,000 (recurring)
- Total First Year Cost: $127,000
Expected Annual Benefits:
- Fuel Cost Reduction: $85,000 (15% reduction from current $567,000 annual fuel costs)
- Vehicle Maintenance Reduction: $23,000 (reduced mileage)
- Overtime Reduction: $31,000 (more efficient routes reduce driver hours)
- Total Annual Benefits: $139,000
Financial Metrics:
Year 1 Net Benefit: $139,000 – $127,000 = $12,000
Year 2 Net Benefit: $139,000 – $12,000 = $
