Control Six Sigma Variation for Superior Process Efficiency

Six SIgma Variation

“Uncontrolled variation is the enemy of quality,” according to statistician Dr. W. Edwards Deming. However, process inconsistency plagues even the most well-oiled organizations. In Six Sigma methodology, this inconsistency is referred to as ‘variation.’ Variation encompasses any deviations from a desired process output, impacting quality, quantity, timing, or any other measurable metric.

Variation is inevitable; from machine wear and tear to minor differences in materials, every business will deal with some form of variation. We can’t avoid it, but what we can do is evaluate the variation in a process, understand it, and, most importantly, control it. By controlling variation, organizations can achieve consistent performance, reduce waste, and meet customer expectations more reliably. Understanding process variation and controlling it is fundamental to process improvement, a core principle of the Lean and Six Sigma process.

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Understanding Types of Variation

Every repetitive process experiences some level of variation. Six Sigma categorizes variation into two distinct types: Common Cause and Special Cause.

Common Cause Variation: Common Cause variation is the variation that is inevitable in any repetitive process. It may stem from machine wear and tear, subtle differences in materials, or environmental fluctuations. We can control or minimize this variation, but it cannot be eliminated entirely.

An easier way to think about this is with an analogy, like baking cookies; even when using the same recipe, ingredients, and equipment, there will be subtle variations between batches. One baker’s meticulous process may minimize this variation compared to another baker’s, but some variation will always be present. This inherent variability is an example of Common Cause variation.

Special Cause Variation: This type of variation arises from specific, identifiable events or circumstances. Inconsistent materials, improper machine settings, or deviations from established procedures can all contribute to Special Cause variation. Unlike Common Cause variation, Special Cause variation can be completely eliminated by addressing the root cause of the issue.

The ultimate goal of Six Sigma is to achieve a stable, “in control” process. This state is achieved by diligently eliminating all Special Cause variation and minimizing Common Cause variation to the lowest cost-justified amount.

Tools and Techniques for Managing Variation

Six Sigma provides the toolkit to control and reduce variation, resulting in considerably better results and process stability. The table below compares Statistical Process Control (SPC) tools and techniques:

TechniqueFunctionAdvantage
Control ChartsMonitor process data & identify Special Cause variation.Proactive issue detection for consistent quality.
Capability AnalysisAssess process capability compared to customer specifications.Identify processes at risk of failing to meet customer requirements.
Poka-Yoke DevicesImplement physical or digital safeguards to prevent defects.Eliminate root causes of variation, minimizing rework and enhancing efficiency.
Visual ControlsUtilize visual indicators to highlight variation on the frontline.Facilitate immediate identification and response to process deviations.
Standardized WorkDocument and continuously improve standardized procedures.Mitigate variation introduced by individual operators through adherence to established best practices.

Implementing Six Sigma Variation Reduction

Six Sigma works because it eliminates sources of variation and makes process performance consistent. The DMAIC method is the course map for Lean Six Sigma and Six Sigma improvement projects.

Here’s how a Six Sigma project translates theory into action using this methodology:

Define: Clearly define the problem, project goals, and customer needs. Identify where variation is negatively impacting quality, efficiency, or customer satisfaction.

Measure: Gather and analyze data to establish a baseline for the current process performance, focusing on metrics that directly link to sources of variation. This data will be used to track progress toward reducing variation during the improvement phase.

Analyze: Identify the root cause of variation and inefficiency within the process. Utilize various tools and techniques like process mapping, cause-and-effect diagrams, and statistical analysis.

Improve: Develop and implement solutions specifically designed to address the root causes identified in the Analyze phase and minimize variation. This may involve process redesign, new technologies, or employee training.

Control: Measure and sustain changes to ensure that long-term process control is maintained – and that variation never returns. This entails control plans, standardization, and committing to continuous improvement.

DMAIC Process

Case Studies: Real-World Applications

Let’s look at some real-world examples of using Six Sigma to reduce Common Cause and Special Cause variation to improve processes:

Case Study 1: Reducing On-Time Delivery Issues in a Delivery Service

A package delivery service experienced a high rate of missed on-time deliveries, caused by the fact that delivery routes were not always optimal and packages were sometimes not sorted in the most efficient way. The company used Six Sigma’s DMAIC methodology to identify the root causes:

  • Special Cause Variation: Misplacement of packages at the sorting facilities caused delivery delays.
  • Common Cause Variation: Traffic accidents and inefficient route planning led to longer delivery times for some routes.

Results: Increasing the efficiency of routes led to an optimized time for deliveries and gave room in the delivery window for issues with traffic. Implementation of a standardized system to sort packages and visual controls reduced the number of misplaced packages. Also, training the drivers gave them a thorough understanding of how to navigate routes efficiently and manage their time.

Outcome: These improvements led to an increased number of on-time deliveries, which in turn resulted in happier customers and a better reputation for the brand.

Case Study 2: Minimizing Defects in a Pharmaceutical Manufacturing Plant

A pharmaceutical company was experiencing a high number of product defects as a result of inconsistencies in the mixing process. Six Sigma techniques helped to identify the root causes:

  • Special Cause Variation: Faulty calibration of mixing equipment led to inaccurate ingredient ratios.
  • Common Cause Variation: It is determined that even once calibrated, the out of date mixing equipment lacked the accuracy to make some modern drugs and needed to be replaced with modern, more sensitive equipment.

Results: An interval preventative maintenance program ensured that quality checks were performed after regular intervals, avoiding discrepancies stemming from faulty machinery. They also put in place Poka-Yoke devices, designed to physically prevent measurement of the wrong ingredient – the other major source of variation. Finally, clear operating procedures describing explicitly what operators should do also helped reduce the variation due to human error.

Outcome: These transformations brought down defect rates, ensuring uniform product quality and, even more importantly, patient safety. The reduction in defects brought along another welcome side effect; lower production costs. Production costs decreased due to less rework and reprocessing of the materials, which otherwise would have been wasted.

Challenges and Best Practices

Among the usual pitfalls are: too little data, inappropriate (or nonexistent) analytical tools, insufficient skills in using the tools, and management support. These can be mitigated by:

  • Educating Decision Makers: Show them the return on investment (ROI) of Six Sigma improvements. Demonstrate how the reduction in variation and increase in process stability will pay forward into the future.
  • Investing in Training and Tools: Providing employees with the necessary training and tools to collect and analyze data effectively is essential. This includes both the technical knowledge and the practical skills required to implement Six Sigma strategies successfully.
  • Ensuring Management Support: To gain and maintain management support, key Six Sigma successes need to be communicated regularly so that there is a strong justification for continuing investments in quality improvement.

Conclusion: Lowering Variance Through Process Improvement

In any business process, variation is inevitable. Variation, in and of itself, should never be viewed as an impediment to excellence. Variation in Six Sigma processes is controlled thanks to a multitude of established tools and methodologies. The tenets of Six Sigma methodologies involve lowering process variation to the absolute minimum and, in doing so, improving quality, operational efficiency, and customer satisfaction. If the organization is willing to put in the work, Six Sigma techniques will give an excellent return and lead to a low-level variance and long-term success.


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