Take Control

By Douglas C. Wood and W. Frazier Pruitt · Originally published in Quality Progress, October 2026.

From this exclusive book excerpt, understand how to use cost of quality to maintain control and sustain gains

Keywords

• Cost of quality,
• Cost of poor quality,
• Trend analysis,
• Quality tools

EDITOR’S NOTE

The following is an excerpt from Principles of Quality Costs, fifth edition, a Quality Press book published earlier this year. Authored by Douglas C. Wood and W. Frazier Pruitt, the 296-page book clarifies often-overlooked waste drivers (such as delays, rework, miscommunication and hidden costs) and provides a modern, practical framework for reducing them. For more details on this recent release, visit bit.ly/4xSQ8eS.

AUTHORS’ NOTE

Control may be one of the most conventional parts of quality management, but it is where improvement either becomes the new way of working or gradually gives way to drift. This excerpt from Principles of Quality Costs, fifth edition (Quality Press, 2026) addresses that essential discipline within the broader cycle of planning, control and improvement explored throughout the book.

The fifth edition substantially reorganizes, reframes and expands the material around how organizations use cost of quality (COQ) today. It brings the concepts into more contemporary, accessible language, extends their application into areas from construction to software and strengthens the connections among quality, operations, finance and leadership.

The journey continues from building a practical COQ system through advanced financial concepts and culminates in “Leading With COQ: From Metrics to Management”—equipping readers with the tools, language and economic insight to move beyond reporting results, helping to shape investment decisions and lead strategically.

Just the Facts

• In their new book, Principles of Quality Costs, fifth edition (Quality Press, 2026), the authors return to the fundamentals by framing cost of quality through Joseph M. Juran’s trilogy of planning, control and improvement. This excerpt focuses on the control component and the importance of sustaining process efficiencies and cost savings.
• Without a plan to monitor, measure and maintain control of operations and processes, even the most successful corrective actions risk being lost to drift or regression.
• A cost of quality system provides the data needed to establish a baseline, detect drift early, justify corrective action with sound economics and keep teams aligned on what matters long after the “project” ends.

Ever wonder why your hard-won improvements fade after several months? Often, the answer lies in control, and quality cost data are your best ally. This excerpt shows how to use cost of quality (COQ) not just for improvement, but also for sustaining gains through effective control.

Joseph M. Juran’s quality trilogy highlights three essential components: planning, control and improvement. Earlier in the book, we focused on planning—specifically, on establishing a COQ system as the structural foundation for tracking, managing and reducing quality costs. After a system is in place, organizations naturally turn to improvement. Too often, however, they skip over control—the quieter, behind-the-scenes discipline that ensures gains are maintained.

Control is rarely ignored entirely, but like cost avoidance, often it is undervalued. Without it, even the most successful corrective actions risk being lost to drift or regression. “Why spend on control if there are no pressing issues?” is a short-sighted viewpoint.

Control: How you keep the gains

In Juran’s model, stabilization precedes and follows every change. Control is what secures the gains of improvement. A COQ system plays a key role in this function—it monitors for drift, provides early warning signals and supports data-driven decisions when performance strays off course.

Think of it this way: Improvement is acceleration; control is steering and braking. Without control, speed becomes a risk.

In practice, control enables organizations to establish and maintain a baseline. Without knowing what “normal” looks like before intervention, it’s impossible to measure the success—or stability—of an improvement. COQ data help establish the baseline and confirm whether changes deliver lasting results.

Fix it once: Corrective action that pays back

Control is not just about avoiding chaos—it also enables more intelligent decisions. When a COQ system identifies a costly issue, it supports targeted corrective actions. These actions must be justified, of course. A $5,000 fix for a $200 weekly rework issue may seem excessive—until it becomes clear that doing nothing costs $10,000 annually.

Some problems are resolved: updating a tool, tightening a spec or clarifying a work instruction, for example. Others are harder to diagnose: design flaws, vague responsibilities or marginal process conditions. Without structured, ongoing cost metrics, these issues often are dismissed as the “cost of doing business.”

A manufacturer observes rising customer complaints, for example, and identifies a common thread in packaging failures across shipments. At first glance, there are many incorrect labels on shipments. A sweep is made of customer databases and other label information data sets. Many mistakes are found and fixed.

After six months, other problems surface: cheaper packaging materials that failed to withstand shipping in subzero temperatures, incorrect power cords on shipments to distant countries and instructions too small for customers to read that fail to convey critical start-up information. As these issues are found, timing-adjusted COQ data show that the retirements of three essential employees was the source of the problems. The company realized it had no effective knowledge management system, and key knowledge was being lost.

A functioning control process surfaces these hidden issues early when they’re still manageable and the cost of action is still lower than the cost of delay. The principle remains timeless: You pay once for corrective action, but you pay indefinitely for inaction.

Shared ownership is what makes control work: It’s easy to assume that quality failures begin on the shop floor. In reality, many originate from other areas such as engineering, planning, tooling and documentation. Even well-intentioned upstream decisions can create costly downstream issues.

Quality costs in office environments may differ from those in production areas, but errors in the office can lead to expensive problems in production. Control extends visibility across functions. A well-structured COQ system helps shift accountability from individuals to systems, replacing blame with cross-functional problem solving.

COQ should not be viewed as a tool strictly for quality professionals. It belongs in broader management conversations involving finance, operations, engineering and even HR, where training and procedural adherence are key.

As researchers have noted, quality cost includes “the costs incurred in the design, implementation, operation and maintenance of a quality management system[,] … the cost of resources committed to continuous improvement, the costs of system, product and service failures, as well as all other necessary costs and nonvalue-added activities required to achieve a quality product or service.”^1,2

In this context, control is not merely about preventing regression—it’s also about coordinated action across the entire value stream. Managing a value stream requires more than budgetary adherence when cross-functional issues arise. It’s not an academic exercise; real process management cost data—including investments in monitoring and waste—are required.

Making quality costs visible to management: Control also plays a critical role in management reporting. For managers, COQ metrics aren’t just reports—they’re also decision tools. They show where to invest, hold steady and intervene fast. They allow leadership to identify persistent issues and evaluate which areas are improving. We are talking about data for decision making, as well as current and future process control.

These metrics, however, must be interpreted wisely. Not all quality costs are comparable. A new, complex product line, for example, naturally will carry higher costs than a mature, optimized process. The more useful perspective is directional:

• Are costs decreasing over time?
• Are improvements holding?
• Are we maintaining performance after interventions?
• Is additional monitoring required to determine performance trends?
• What are the correlations between investments for quality and waste or downstream consequences (DC)?

When used correctly, COQ reporting keeps the organization focused on sustainability, control and long-term progress.

Trends will tell the story

There are two types of quality cost trend analyses:

Long-range analysis typically views total quality costs over quarters or years. It is used primarily for strategic planning and monitoring overall progress.

Short-range trend charts are prepared for each company area in which individual quality cost improvement goals must be established. One approach is to assign a short-range target to each general operational area simply. Alternatively, an initiative can become as detailed and sophisticated as the quality management team and system will support.

Figure 1 is a composite example of a long-range quality cost trend chart for a typical organization with sales of $100 million to $200 million. It shows the total COQ as a percentage of net sales over 10 years. In addition, it separately reports investment, monitoring, waste and waste plus DC costs as a percentage of sales (DC costs are shown as the difference between waste and waste plus DC costs).

FIGURE 1
COQ TREND ANALYSIS

The first two years show quality cost history without any knowledge of or emphasis on its reduction. The third year is the start of quality cost measurement and use. Years four through nine show actual progress accomplished. Year 10 is a projection of the expected continued progress. Note that a short-term drop in sales occurred in year four, effectively hiding improved quality cost performance as a percentage of sales.

A minimum-quality measurement system should include summary monitoring results for all key operational areas. Typically, these will be receiving inspection, fabrication inspection, final assembly inspection and test, as well as field monitoring reports. Usually, the summaries are presented as trend charts to indicate and make people aware of current quality performance levels. Quality cost trend charts, when correlated, will supplement these performance charts with viable cost data to support the improvement effort. This is the essence of their use together. (See Figure 2 for a rework cost example.)

FIGURE 2
ASSEMBLY AREA QUALITY PERFORMANCE

Usually, there is a time lag between basic quality measurement data and quality cost data. Quality measurement data always are current (typically, daily), whereas quality cost data are accumulated retrospectively in cost accounting reports. It is crucial, therefore, to understand that quality costs are used to support before-the-fact improvement and verify its after-the-fact accomplishment. Still, actual improvement stems from using current quality measurement data to identify root causes and implement corrective actions.

There also is a time lag between cause and effect. (Quality improvements do not show immediate reductions in quality costs because of the time lag between the cause and its impact.) This lag is evident in a quality cost trend chart. For this purpose, it may be desirable to indicate on the chart when quality improvements were made.

Figure 3 is a simplified quality cost trend chart marked to indicate the start of a quality improvement activity. The note explains the steady progress during the last five months: In April, training programs were initiated.

FIGURE 3
QUALITY COST TREND

Control in context: Small business

Control is especially critical in small businesses and supplier quality management in which the cost of repeated errors can be amplified.

In small businesses, limited resources make it imperative to avoid low-value fixes and prevent recurring problems. A COQ system, paired with consistent control practices, helps teams focus on high-impact areas—avoiding wasted effort and ensuring each fix sticks.

In supplier management, control ensures that quality does not begin at your receiving dock. COQ data enable you to measure supplier performance, detect early signs of drift and pursue corrective action based on shared, objective data—not assumptions or blame. It builds trust, transparency and accountability into supplier relationships.

There is no better way for the quality management function to put its stake in the ground than to develop a strategic quality plan substantiated by quality costs and have this plan committed to in the organization’s business plan.

Problem-solving tools turn COQ into action

We have mentioned several tools that may be used to provide meaningful interpretations of COQ. It may be helpful here to review those tools and understand how their use significantly affects an organization’s benefits.

Pareto analysis—Finding the vital few costs: The Pareto principle is named after Vilfredo Pareto, an Italian economist and sociologist from the late 19^th century. Looking at land ownership, he found that 20% of individuals owned 80% of the land. He made a second discovery in his garden: 20% of the plants produced 80% of the fruit. And while the Pareto principle is named after him, he did not develop the Pareto chart as we know it today. The chart was created and popularized by Juran in the 1940s as he recognized the principle’s generality. Let’s build a simple chart to illustrate:

• A table of data (Table 1) is accumulated, perhaps of defects by category.
• Categories are arranged in descending order in the vertical bar chart in Figure 4.
• To make the final Pareto chart in Figure 5, create a cumulated percentage of the counts and plot a line over the bar chart with a secondary axis on the right side.

TABLE 1
DEFECTS BY CATEGORY

FIGURE 4
INCIPIENT PARETO CHART

FIGURE 5
FINAL TOP-LEVEL PARETO CHART

In Figure 5, the first two bars (“missing parts” and “out of spec”) account for 80% of the defects, as indicated by the point on the line above “out of spec” on the right-hand axis.

Further analysis can decompose the data and plot additional Pareto charts. For example, missing parts may come from several suppliers, as seen in Table 2 and used for the drill-down Pareto chart in Figure 6.

TABLE 2
MISSING PARTS BY SUPPLIER

FIGURE 6
DRILL-DOWN PARETO CHART

Cause and effect analysis—Understanding why the costs exist: Brainstorming is a good approach for collecting potential causes of problems when the team is experienced. Sources of team focus are established earlier with the cause and effect chart, also known as an Ishikawa diagram or fishbone chart. Popularized by Kaoru Ishikawa in the 1960s, this brainstorming framework (Figure 7) helps a team identify potential causes of a problem.

1. It starts with describing the problem so that everyone understands the scope. Write this in a box in front of the group at the right side and draw a box around it. Leave most of the space blank.
2. Draw an arrow left to right ending at the problem box.
3. Choose broad categories of causes that make sense to the group. There are several standard sets of categories:
   • Eight M’s: manpower, machine, material, method, measurement, Mother Nature (environment), management and maintenance.
   • Five S’s for service: surroundings, suppliers, systems, skill and safety.
   • Four P’s: product, place, people and performance.
4. Draw and label boxes for the categories above and below the main arrow and place diagonal arrows pointing to the main arrow.
5. The group brainstorms one category at a time, leaving the “people” or “manpower” one for last, to avoid blaming.

FIGURE 7
CAUSE AND EFFECT CHART

Relations diagram—Seeing how causes interact: After causes are identified, they may need to be sorted. One helpful way to sort causes is to determine which ones depend on others. The relations diagram (also called the interrelationship digraph) in Figure 8 is an example.

1. Causes are arranged in a circle, leaving space in the middle.
2. Considering each cause, draw arrows from that cause to any and all others that are driven by it.
3. Count arrows going into each cause and arrows going out from each cause.
4. Create a proportion of ins and outs.
5. Sort causes in order of their proportions. No arrows going in? It is a final cause. All arrows going out? It is a trigger cause.

FIGURE 8
RELATIONS DIAGRAM

In this relations diagram, Table 3 shows in-out proportions.

TABLE 3
RELATIONS DIAGRAM IN-OUT PROPORTIONS

What tool when: Tool selection

After you have a set of problem-solving tools on the table, the next question is obvious: Do we use all of them or just one? In control work, the answer almost always is “Only what is needed.” The tools just covered are not the only options, but they deliver a substantial impact for the effort. They are meant to help you move from a drift signal to a decision without turning every issue into a full-scale project. The flow begins with a drift trigger and moves through three phases.

Phase one is a quick reality check and category trending, just enough information to separate real change from accounting noise, timing effects or mix shifts, as well as to see how the COQ categories actually are behaving.

Phase two is where you choose a tool. Name the pattern (global shift, category trade-off or localized spike) and pick the lightest tool that fits.

Phase three closes the loop. Fest what you think is driving the change, verify stability on the trends with lag in mind, and lock the gain into standard work and routine management. You may use a different system in your organization, and that’s fine. The utility is not the exact sequence; it is ensuring that drift turns into action and that action turns into sustained control.

Trigger: Detect the drift.

• The drift flag originates from a COQ trend move or a leading quality signal (with COQ confirmation to follow).

Phase one: Initial analysis (quick + trend).

• Quick reality check.

• Trend the categories (not just total).

Phase two: Classify the pattern (name the problem).

• Global shift (most categories move together).

• Category trade-off (up while another is down).

• Localized spike (one bucket/area jumps; others stable).

Phase three: Close the loop (test + verify + lock in).

• Test the likely drivers.

• Verify on trends (expect lag).

• Lock in the gain.

Control into the way the work runs

Improvement gets the applause. Control keeps the trophy on the shelf.

A COQ system gives you the numbers to spot drift early, justify corrective action with real economics and keep teams aligned on what matters after the “project” ends. When you pair day-to-day quality measures with trend-based COQ reporting, you get speed and stability: current signals to act now and cost confirmation to prove the gains are sticking.

The organizations that sustain results are not the ones that fix the most problems. They are the ones that build control into the way work runs.

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REFERENCES

1. Barrie G. Dale and J.J. Plunkett, Quality Costing, third edition, Routledge, 1999
2. Andrea Schiffauerova and Vince Thomson, “A Review of Research on Cost of Quality Models and Best Practices,” International Journal of Quality and Reliability Management, Vol. 23, No. 6, pp. 647-669

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Steps to Implement Control Using COQ

Choose the control subject: Select the target category, department or process.

Define measurement methods: Define and document the measure, source and update cadence.

Set performance standards: Similar to control limits, set a baseline range and drift trigger.

Measure actual performance: Review the trend routinely according to the established method.

Act on deviation: Execute the reaction plan primarily by acting on special cause deviations.

—D.C.W. and W.F.P.

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Cost of Quality-Based Control Checklist

• Is the control target clear? What outcome must not drift and where in the value stream?
• Do we have a stable baseline? What does “normal” look like for total cost of quality (COQ) and for each COQ category?
• Are we sure the signal is real? Any changes in coding rules, costing rates, ledger timing, product mix or recosting that could explain it?
• Do we understand the time lag? Which leading operational measures should move before COQ moves, and are they changing too?
• Is drift global, a category shift, or localized? Total up COQ everywhere vs. trade-off between categories versus one bucket/area spiking.
• Have we trended each category separately over time? Not just totals—each category’s trend line, with intervention dates marked.
• Have we stratified to find where it lives? By product family, line/shift, plant, supplier, customer segment or region—whatever reveals “where.”
• Do we have trigger rules and owners? What threshold counts as drift, who is notified and who must respond?
• Is there a defined reaction plan? Containment → cause analysis → corrective action → verification → lock-in.
• Is the economics test explicit? Cost of action versus cost of inaction/payback, using COQ.
• Did we verify stability after the fix? Evidence of “back in baseline band” for a sustained period, considering lag.
• Did we lock it in? Standard work, training, checks/audits and clear accountability are updated to ensure the gain is sustained.  —D.C.W. and W.F.P.

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Key Takeaways

• Control is the missing link. Without it, improvements regress and costs quietly creep back in.
• Cost of quality (COQ) makes drift visible. Baselines, thresholds and routine trend reviews turn surprises into early warnings.
• Corrective action should pay back. Use COQ to compare the one-time cost of a fix to the ongoing cost of doing nothing.
• Ownership must be cross functional. Many quality costs originate upstream, so control must span engineering, operations, documentation, finance and HR.
• Trend charts tell the truth over time. Use long-range trends for strategy and short-range trends for local control and accountability.
• Expect time lags. Quality metrics are current; cost data arrives later. Use both and annotate charts with intervention dates.
• Use the right tools to turn cost into action. Pareto identifies the vital few; fishbone and relationship diagrams organize causes; and FMEA adds risk through a financial lens.
• COQ strengthens suppliers and small businesses. Control reduces repeat errors when resources are tight, and supplier drift can become expensive quickly.  —D.C.W. and W.F.P.

Original publication: Take Control, Quality Progress, October 2026.