DMAIC five phase model - Six Sigma - DFSS: Design For Six Sigma

 The DMAIC model is a structured, five-phase process used in Six Sigma to improve existing business processes that are not meeting customer specifications. DMAIC is an acronym for the five phases: Define, Measure, Analyze, Improve, and Control. Each phase is a sequential step toward identifying the root cause of a problem and implementing a sustainable, data-driven solution. 

The acronym DFSSS is not a standard, universally recognized methodology in Six Sigma.

It is highly likely that you are referring to DFSS, which stands for Design For Six Sigma.

DFSS: Design For Six Sigma

DFSS is a systematic methodology used to design new products, services, or processes to meet customer 

needs and achieve Six Sigma quality levels (3.4 defects per million opportunities) from the outset.

It is the proactive approach to Six Sigma, in contrast to the traditional DMAIC 

(Define, Measure, Analyze, Improve, Control) methodology, which is used

 to improve existing processes.

1. Define 

In the "Define" phase, the project team clearly outlines the problem, the project goals, and the scope of the improvement initiative. The objective is to get a shared understanding of what needs to be fixed and to define what success looks like from the customer's perspective. 

Key activities: 

• Create a Project Charter: A document that includes the problem statement, goals, scope, and team members.
• Map the process: Use high-level maps like a SIPOC (Suppliers, Inputs, Process, Outputs, Customers) diagram to visualize the process from beginning to end.
• Identify customer requirements: Use tools like Voice of the Customer (VoC) to understand what is critical to the customer's satisfaction. 

2. Measure 

The "Measure" phase focuses on collecting data to establish a baseline for the current process performance. This data provides quantitative evidence of the problem's magnitude and a benchmark against which to measure future improvements. 

Key activities: 

• Develop data collection plan: Determine what data is needed, where it will come from, and how it will be collected reliably.
• Establish baseline performance: Collect current data on defects, cycle time, or other relevant metrics to quantify the current state.
• Validate the measurement system: Ensure that the data being collected is accurate and reliable through a Measurement System Analysis (MSA). 

3. Analyze 

The "Analyze" phase is where the project team uses the collected data to investigate the root causes of the problem. This phase moves from understanding the symptoms to identifying the "why" behind the process variations and defects. 

Key activities: 

• Perform root cause analysis: Use techniques like the "5 Whys" or a fishbone (cause-and-effect) diagram to brainstorm and verify the underlying causes.
• Identify gaps: Compare the current process performance (from the "Measure" phase) to the desired performance to quantify the improvement opportunity.
• Analyze data statistically: Use charts (like Pareto charts and histograms) and statistical tests to determine the inputs (
  

  $X$
  ) that have the biggest impact on the output (
  

  $Y$
  ). 

4. Improve 

In the "Improve" phase, the team develops, tests, and implements solutions to address the root causes identified in the "Analyze" phase. The solutions are designed to eliminate the causes of defects and improve the process. 

Key activities: 

• Generate potential solutions: Brainstorm creative ideas for improvement.
• Select and test the best solution: Use pilot studies or simulations to evaluate the effectiveness of proposed solutions.
• Implement the solution: Roll out the selected changes on a larger scale. 

5. Control 

The final "Control" phase ensures that the improvements are sustained over the long term. The new and improved process is standardized, and a monitoring plan is put in place to prevent the process from reverting to its old, inefficient state. 

Key activities: 

• Create a Control Plan: Document procedures for monitoring the process, defining who is responsible, and what steps to take if performance dips.
• Standardize procedures: Update Standard Operating Procedures (SOPs) and provide training to ensure all team members follow the new process consistently.
• Monitor performance: Use tools like control charts to track the process and ensure it remains stable and continues to deliver the desired results.

Case study: Reducing rubber weather strip defects in a manufacturing company

This case study illustrates how a manufacturing company used the DMAIC methodology to reduce the high defect rate of rubber weather strips for cars. 

Define

Problem: The company was experiencing a high rejection rate of 5.5% for rubber weather strips due to various defects, which was leading to significant financial losses and customer dissatisfaction. 

Goal: The project team aimed to reduce the rejection rate from 5.5% to below 2% and increase the sigma level of the manufacturing process. 

Scope: The project focused specifically on the production process of the rubber weather strips for a particular product line. 

Measure

The team collected data over seven months to understand the extent of the problem and establish a baseline. They recorded the types of defects, the frequency of their occurrence, and the cost associated with the rejected parts. 

Key findings:

• Winding department bottleneck: A Pareto analysis of the data showed that the winding department, the final stage of the manufacturing process, had the highest percentage of defects. It was identified as the critical bottleneck.
• Key defect areas: The Pareto analysis revealed that 20% of the activities in the winding department were causing 80% of the defects. The main causes of downtime were related to product changeover time, cycle time deviation, and dressing operations. 

Analyze

The team performed a root cause analysis using tools like the "5 Whys" and fishbone diagrams to pinpoint the factors contributing to the defects. 

Root causes identified:

• Product changeover time: The time taken to change over the machine for different product types was excessive due to a lack of standard procedures.
• Cycle time deviation: The time required to produce a specific quantity of rings deviated significantly from the standard, leading to production delays.
• Dressing operation: Inefficiencies in the dressing operation (which prepares the grinding wheel) were found to be a major source of process downtime. 

Improve

Based on the analysis, the team implemented several solutions to address the root causes and reduce the defect rate. 

Solutions implemented:

• Optimized machine parameters: Using techniques like Taguchi's Design of Experiments, the team determined the optimal settings for machine parameters such as feeding rate and grinding wheel velocity.
• Standardized procedures: New, standardized operating procedures were created and documented for product changeovers and the dressing operation to minimize variability and reduce downtime.
• Employee training: Operators were given training on the new standard operating procedures to ensure consistent application of the improved process. 

Control

To ensure the improvements were sustained, the team established a control plan. 

Control measures:

• Monitoring system: Weekly performance indicators were created to track the rejection rate and other key metrics.
• Regular audits: Monthly cross-functional audits were scheduled to ensure adherence to the new standard operating procedures.
• Statistical process control: Control charts were implemented to continuously monitor the stability of the improved processes and detect any performance deviations. 

Results:
The DMAIC project was highly successful, leading to a significant improvement in performance. 

• The average rejection rate was reduced from 5.5% to 3.08%, with a target of 2% in sight.
• This translated to a financial saving of Rs. 15,249 per month from reduced material waste.
• The company's sigma level improved from 3.9 to 4.45.