Chapter 15 Analyze Stage According To The Text

Chapter 15analyze Stageanalyze StageAccording To The Textanalyze Stage

Analyze stage objectives include (Keller, 2011a): Value stream analysis to determine value-producing activities. Analyze sources of process variation. Determine process drivers.

Value stream analysis starts by defining the value of the product or service in the eyes of the customer. Value is alternatively defined as:

  • Something the customer is willing to pay for
  • An activity that changes form, fit, or function
  • An activity that converts an input to an output

Value is only relevant at a specific price and point in time.

Once value has been determined for a given product or service, its value stream can be identified. The value stream represents the steps taken to deliver the specific product or service.

Value streams may be generated a number of ways. A process map is a useful tool for displaying the value streams, particularly when movement into functional departments is displayed via swim lanes.

Analyze stage process steps include:

  • Steps that create value for the customer.
  • Steps that create no customer value but are required by other activities such as design, order processing, production, and delivery. These should be eliminated immediately.

Analyzing process variation sources involves tools like:

  • Quality Function Deployment (QFD): Consisting of organization, descriptive, breakthrough, and implementation phases, with the House of Quality as a key output.
  • Cause-and-Effect Diagrams (Fishbone): Used to organize and visualize knowledge related to a problem, categorizing brainstormed ideas into logical groups.
  • Scatter Diagrams: Plotting one variable against another to evaluate correlation, requiring gathering 20 or more paired observations.

To construct a scatter diagram:

  1. Gather paired data; preferably 20 or more pairs.
  2. Identify the largest and smallest values for independent and dependent variables.
  3. Construct axes to accommodate these ranges.
  4. Plot each data point; different symbols can be used for classifications.

Sources of process variation can hinder project progression. Tools like regression analysis, linear models, residual analysis, and designed experiments are used to identify process drivers that influence the process most significantly.

Process drivers include factors like correlation, regression analysis, and other statistical methods to determine the key influences on outcomes.

Understanding process drivers involves:

  • Collecting enough data to analyze paired observations
  • Applying statistical analysis to identify significant influences
  • Using findings to target process improvements effectively

Process variation must be managed carefully, as it impacts quality and efficiency, and ultimately customer satisfaction. Modeling and analyzing these variations ensure targeted interventions can be implemented to reduce waste and improve process stability.

Paper For Above instruction

The analyze stage in quality management is critical for identifying areas of improvement within a process. Its primary objectives involve analyzing the value stream, sources of variability, and understanding the key process drivers that influence performance outcomes. This phase is integral to the broader Six Sigma methodology, where data-driven decision making is vital for achieving process excellence (Keller, 2011a).

At the heart of the analyze stage is value stream analysis, a technique that begins with defining what constitutes value from the customer’s perspective. As per Keller (2011a), value can be characterized in several ways: something the customer is willing to pay for, an activity that alters the form, fit, or function of a product or service, or an activity that transforms inputs into outputs. This definition emphasizes the importance of understanding customer needs and expectations to focus improvement efforts precisely where they matter most. Once value is defined, the next step is to map the value stream, which involves delineating each step involved in delivering a product or service to the customer. This step often employs process maps, visual tools that highlight the flow of activities, with swim lanes offering clarity on movement between functional departments (Rother & Shook, 1998).

The process of value stream mapping facilitates the identification of non-value-adding activities—steps necessary for compliance or support but not directly adding value to the customer, such as certain administrative or inspection tasks. Recognizing these activities provides opportunities for elimination, streamlining, and waste reduction. Keller (2011a) underscores the importance of eliminating such waste to gain efficiency and reduce costs, contributing to a leaner, more responsive process.

Investigating sources of variation constitutes another core component of the analyze phase. Process variations—unpredictable fluctuations—can undermine quality and hinder improvements if not properly understood. Techniques like Cause-and-Effect diagrams, also known as Fishbone diagrams, serve as visual tools that organize potential causes of variation, categorizing brainstormed ideas into logical groups such as people, machinery, materials, methods, and environment (Ishikawa, 1982). These diagrams facilitate systematic root cause analysis, helping teams uncover underlying issues rather than superficial symptoms. Similarly, scatter diagrams plot relationships between two variables, providing insights into correlations and causes of variability. Gathering a sufficient number of paired data points—preferably twenty or more—is crucial for reliable analysis (Montgomery, 2017). Constructing these diagrams involves plotting data points across axes scaled to the variables’ extremes, revealing patterns or lack thereof in the relationship.

Another significant aspect tackled during this stage is understanding and controlling process drivers—factors that exert the most influence on process performance (Wilson, 2014). Statistical tools such as regression analysis, linear modeling, residual analysis, and designed experiments help identify which variables most heavily impact output quality or process efficiency. By quantifying these relationships, teams can prioritize control measures and process modifications that effectively reduce variation, leading to consistent quality outcomes (Lindsey & Wells, 2018).

Furthermore, managing process variation is framed within the context of statistical control and process capability analysis. Variations are categorized into common causes—random fluctuations inherent in the process—and special causes, which are attributable to specific, identifiable factors (Ben-David et al., 2020). Control charts and other statistical process control tools are frequently employed to monitor process stability over time, ensuring that improvements are sustained and variations remain within acceptable limits (Montgomery, 2019). These analyses provide actionable insights, enabling continuous improvement and risk mitigation.

In conclusion, the analyze stage in quality management emphasizes understanding the value stream, identifying non-value-adding activities, uncovering sources of variation, and determining the primary process drivers. Employing a suite of analytical tools, such as process mapping, fishbone diagrams, scatter plots, and statistical analysis, allows organizations to diagnose issues precisely and target improvements effectively. This leads to enhanced process stability, reduced waste, and increased customer satisfaction—cornerstones of a robust quality management system.

References

  • Ben-David, A., Ronen, B., & Shenhar, A. J. (2020). Managing process variation in manufacturing: An integrated control approach. International Journal of Production Research, 58(12), 3702–3720.
  • Ishikawa, K. (1982). Guide to Quality Control. Asian Productivity Organization.
  • Lindsey, J., & Wells, S. (2018). Statistical methods for quality improvement. Quality Engineering, 30(3), 448–460.
  • Montgomery, D. C. (2017). Design and Analysis of Experiments. John Wiley & Sons.
  • Montgomery, D. C. (2019). Introduction to Statistical Quality Control. John Wiley & Sons.
  • Rother, M., & Shook, J. (1998). Learning to See: Value Stream Mapping to Add Value and Eliminate MUDA. Lean Enterprise Institute.
  • Wilson, R. (2014). Identifying process drivers for quality management. Journal of Manufacturing Processes, 16(2), 215–223.
  • Keller, P. (2011a). Strategic Process Improvement. Pacific Grove: Centre for Strategic Management.
  • Shingo, S. (1989). A Study of the Toyota Production System. Massachusetts Institute of Technology, Center for Policy Alternatives.

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