Combining the Voice of the Customer with the Voice of the Process

Setting product specifications is an iterative and challenging process, combining lab test data, historical data and educated guesses. All too often, the result is a set of product and process specifications that must be changed to meet manufacturing needs and that do not meet all customer requirements. To achieve specifications that are correct and defendable, the engineer must understand how the voice of the customer and the voice of the process interact, and must fully specify product through both specification limits (also called tolerance limits) and production capability, or Cpk.

Voice of the Customer

What the customer expects a product to do—what they are willing to pay for—is known as the “voice of the customer” (VoC). A customer’s expectations may not all be written or explicitly stated, and unwritten or unrecognized needs or wants can be even more important than the written ones. As we design a product, we first translate the VoC to engineering requirements and then flow the requirements down to subcomponents.

For simplicity, I will adopt the convention of referring to the specifications or tolerances as the Target Specification, the Upper Specification Limit (USL) and the Lower Specification Limit (LSL). The target, USL and LSL are the engineering translation of the VoC. I will refer to the measure being specified—length, mass, voltage, etc.—as a characteristic.

Voice of the Process

What we know about the parts actually produced—maximum and minimum, average, standard deviation, outliers, etc.—is known as the “voice of the process” (VoP). The VoP tells us the limits of our manufacturing abilities.

Suppose that we know from the production plant that the typical weight of our product is between 99.2 and 104.2 kg. This is the VoP; it may or may not be acceptable to the customer or fit within the USL and LSL. When we engage in statistical process control (SPC), we are listening to the VoP, but not the VoC.

The engineer must design to the VoC while considering the VoP.

Specification Limits

First, some definitions:

Target Specification
The desired value of the characteristic.
Mean Specification
The average of the upper and lower specification values—the value in the middle of the specification or tolerance range.
Nominal Specification
Used in different ways. May mean the target value of the specification. Sometimes the value printed in marketing literature or on the product, which may be the minimum or some other value.
Upper Specification Limit
The maximum allowed value of the characteristic. Sometimes referred to as the upper tolerance.
Lower Specification Limit
The minimum allowed value of the characteristic. Also referred to the lower tolerance.

Suppose that the customer has said that they want our product to weigh at least 100 kg. Since the customer will always want to pay as little as possible, a customer-specified lower specification limit of 100 kg is equivalent to saying that they are only willing to pay for 100 kg worth of costs; any extra material is added cost that reduces our profit margin.

If the customer does not specify a maximum weight, or upper specification limit, then we can choose the upper limit by the maximum extra material cost we want to bear. For this example, we decide that we are willing to absorb up to 5% additional cost. For our product, material and construction contributes 50% to the total assembled part cost, so the USL on weight is 110 kg.

Defect Rates and Cpk: Where VoC and VoP Intersect

Defect rates are expressed variously as Sigma, Cpk (“process capability”), Ppk (“process performance”), parts per million (ppm), yield (usually as a percent) or defects per million opportunities (DPMO). “Sigma” refers to the number of standard deviations that fit between the tolerance limits and the process mean. A 1-Sigma process has 1 standard deviation (\sigma) between the mean (\mu) and the nearest specification limit. Cpk is a measure of the number of times that three standard deviations (3\sigma) fit between the mean and the nearest specification limit. Cpk is often used by customers, especially automotive OEMs. We can easily convert between Cpk, Sigma yield, ppm and DPMO.

Production tells us that historically, they usually see a range of about 5 kg in assembled part weight, with weights between 99.2 kg and 104.2 kg. With enough data, the range of observed data will cover roughly the range from [mean – 3 standard devations] to [mean + 3 standard devations], so this gives us \mu \approx 101.7 kg and \sigma \approx 0.83 kg. From (\mu-LSL)/\sigma=Sigma, \left(101.7-100\right)/0.83=2.0, we have a 2-Sigma process. With this data, we could estimate the percent of parts that will be below the LSL.

Defect Rate Calculation

We can use this data to estimate the percent of product that will be out of the customer’s specification. We’ll assume that our process produces parts where the weight is normally distributed (having a Gaussian or bell-shaped curve).

Using Minitab, you can do this by opening the “Calc” menu, the “Probability Distributions” submenu, and choosing “Normal….” Then choose “Cumulative probability,” enter “101.7” for the mean and “0.83” for the standard deviation. Select “Input constant” and enter “100.” Click “OK.” The result in the Session Window looks like:

Cumulative Distribution Function

Normal with mean = 101.7 and standard deviation = 0.83

  x  P( X <= x )
100     0.02027

This is read as: the probability of seeing a weight X less than or equal to the given value x = 100 is 0.02, so we can expect about 2% of parts to be out of specification. This can also be done in Excel using NORMDIST(100, 101.7, 0.83, TRUE) or, in Excel 2010 and later, NORM.DIST(100, 101.7, 0.83, TRUE). In R we would use pnorm(100, 101.7, 0.83).

Minitab can also display this graphically. Open the “Graph” menu, select “Probability Distribution Plot…” Select “View Probability” (the right-most option) and click “OK,” On the “Distribution” tab, select “Normal” from the distribution menu and enter “101.7” for the mean and “0.83” for the standard deviation. Then switch to the “Shaded Area” tab, select “X Value,” “Left Tail” and enter “100” for the “X value.” Click on “OK” to obtain a plot like that below.

Probability density distribution showing cumulative probability below a target value of 100 shaded in red.

Probability density distribution showing cumulative probability below a target value of 100 shaded in red.

Of course, not all processes produce parts that are normally distributed, and you can use a distribution that fits your data. However, a normal distribution will give you a good approximation in most circumstances.


The calculation for Cpk is:

where Cpu and Cpl are defined as:

If \mu is centered between the USL and the LSL, then by simple algebra we have

which is often a convenient form to use to estimate the “best case” process capability, even when we do not know what \mu is.

The combination of VoC-derived specification limits (USL, LSL and T) with process data (\mu and \sigma) ties together the VoC and the VoP, and allows us to predict future performance against the requirements.

Multiplying Cp or Cpk by \sqrt{1+\left(\left(\mu-T\right)/\sigma\right)^{2}} produces a measure of the process capability that includes centering on the target value, T, referred to as Cpm or Cpkm, respectively. These versions are more informative but much less commonly used than Cp and Cpk.

In the example, \mu-LSL=101.7-100=1.7=2\times0.83=2\sigma. We can then calculate the Cpk:

Acceptable values of Cpk are usually 1.33, 1.67 or 2.0. Automotive OEMs typically require Cpk = 1.33 for non-critical or new production processes, and Cpk = 1.67 or 2.0 for regular production. In safety-critical systems, a Cpk should be 6 or higher. The “Six Sigma” improvement methodology and Design For Six Sigma refers to reducing process variation until six standard deviations of variation fit between the mean and the nearest tolerance (i.e. Cpk = 2), achieving a defect rate of less than 3.4 per million opportunities. Some typical Cpk, and corresponding process sigma and process yield are provided in table [tblCpkSigmaYield].

Cpk Sigma Yield (max) Yield (likely)
0.33 1 85.% 30.%
1.00 3 99.9% 90.%
1.33 4 99.997% 99.%
1.67 5 99.99997% 99.98%
2.00 6 99.9999999% 99.9997%

In the table, “Yield (max)” assumes that the process is perfectly stable, such that parts produced today and parts produced weeks from now all exhibit the same mean and variance. Since no manufacturing process is perfectly stable, “Yield (likely)” assumes additional sources of variation that shift the process by 1.5\sigma (e.g. seasonal effects, or differences in setups across days or shifts). This 1.5\sigma shift is a standard assumption in such calculations when we do not have real data about the long-term process stability of our processes.

Specification Limits and Cost

When parts are out of specification—that is, they may be identified prior to shipment as defective or nonconforming—they they can have four possible impacts:

  • they must be reworked;
  • they must be scrapped;
  • they come back as warranty claims; or
  • they result in lost customers (reduced revenue without reducing “fixed” costs).

For example, underweight or damaged injection-molded plastics might be melted and reprocessed, but this adds cost in capital for the added equipment to proces the parts and cost for extra electricity and labor to move, sort and remelt. Later in production, defective parts will have to be scrapped.

We can see, then, that a cost function can be associated with each end of a specification range. The specification limits must be derived from the VoC, but the VoP imposes the cost function. The figures below illustrate this for both one-sided and two-sided specifications.

Percent of target production costs given an average production weight and four different process capabilities.

Percent of target production costs given an average production weight and four different process capabilities.

Percent of target production costs given an average production weight and four different process capabilities.

Percent of target production costs given an average production weight and four different process capabilities.

The minimum of the cost function is a good place to set our target specification, unless we have some strong need to set a different target.

Variance of Components and Cpk

When a part characteristic is the sum of the part’s components, as with weight, then the variation in the part characteristic is likewise due to the variation in the individual components. However, while the weight adds as the sum of the components,

the variance in weight, \sigma^{2}, adds as the sum of squares

Since the given \sigma is the maximum allowed for the assembled part to meet the desired Cpk, this means that the component variances, \sigma_{i}^{2}, are an estimate for the maximum allowed component variance. Manufacturing can produce parts better than this specification, but any greater variance will drive the parent part out of specification.

Specifying Characteristics

Very often, only specification ranges (or tolerances)—the USL and LSL—are provided by engineering when designing parts. Almost as often, these ranges are based on a target value (derived from the VoC) with some “allowed” tolerance based on what manufacturing says they can achieve on the existing equipment and processes (the VoP). It should be clear from the above that a minimally-adequate part specification includes USL and LSL derived from the VoC and the minimum acceptable Cpk derived from both the VoC and the VoP. The inclusion of the minimum process capability is the only way to ensure that the parts are made within the target costs and at the target quality level.

The next challenge is to flow these requirements down to the components. Having laid the groundwork for the basis of requirements flow-down, I will look at a way to do this in a future post.


All graphs created in R using ggplot2.

  • R Core Team (2014). R: A language and environment for statistical computing. R Foundation for
    Statistical Computing, Vienna, Austria. URL
  • H. Wickham. ggplot2: elegant graphics for data analysis. Springer New York, 2009.

Combining Expert Judgements

Both product development and project planning often require making educated guesses. There are two models that I’ve seen for doing this. The first is to have a designated subject matter expert (SME) who provides The Answer. The second is to get a group of SMEs together to discuss and arrive at a consensus answer.

Under most circumstances, I’m not a fan of the first approach. Individuals are simply too fallible, too easily swayed by anecdotes rather than real data or too busy to consider a problem fully and in depth. The exception is when you are estimating work; then the SME is the person who will actually have to do the work, and their estimate is better than anyone else’s. The second solution often suffers from several other problems. Too many people working a problem can take a long time, answers can be driven by the most vocal or the most risk-averse members of the group and groups sometimes deliver bad answers due to diffusion of responsibility or other “group think” effects.

One solution to the problems of group decisions is to use Affinity Diagramming and the Analytical Hierarchy Process (AHP) to structure the problem, gather individual judgements of the alternatives, and then determine their relative values or importance. Affinity diagramming followed by AHP is incredibly powerful, and does a great job taking the individual bias out of the equation. I’ve used it with teams to rank the importance of product requirements, as a training exercise to accurately estimate the relative area of geometric shapes and personally to decide which car to buy. It works.

Unfortunately, AHP takes time, and for any but the simplest assessments, you really need custom software to support the process. Affinity diagramming can be done very effectively with Post-It notes, but the calculations of AHP cannot be easily set up in a program like Excel. It also requires discrete alternatives to choose from. For estimates of a single variable, such as lifetime, or other performance characteristics, I have had to develop a different approach.

The technique below works when you want to create a point estimate of a continuous variable. For instance, you might need to estimate product lifetime and establish a warrant period, or you might need to estimate a performance level that can be communicated to customers (implying a performance guarantee), or you might need to estimate the duration of a set of project activities. We can easily implement the calculations in Excel, R, or a similar tool.


What we’re going to do is get the SMEs to individually estimate a range of possible results (low, most likely, high). Then we’ll generate triangular probability distributions from each estimate and finally combine those estimates. To get a robust result, we will account for the SME’s estimation of their own accuracy, and treat their different estimates discrete distributions when combining.

The below is the data entered by the SMEs.

Subject Matter Experts data entries for estimating a continuous variable

Using the above data, we can calculate triangular probability distributions for each, then combine them by treating them as discrete distributions. This produces the sequence of distributions, below.

Combining estimation distribution from Subject Matter Experts

We can then summarize the combined distribution with some useful values:

most likely: 7087
likely low: 4360
likely high: 10711
50% between 5162 and 8370

The process

  1. Bring a few SMEs together. While the technique will work with any number, usually five to six is more than sufficient, and ten should be considered the maximum for useful input. We’ll call this number N so we can refer to it later.
  2. Define the problem. Everyone should agree precisely what you are estimating.
  3. Agree on a basis for the evaluation. This might be an agreed conceptual model of how the product behaves or a set of criteria or goals to evaluate against.
  4. Collect three estimates—low, most likely, high—from each SMEs for the variable of interest (call this variable X).

Collecting a range is important; we need to be honest with ourselves that we don’t know what the value will actually be: there is uncertainty in the SME judgement—if we had the data to be more precise then we’d use that—and there’s variation to consider. Ranges allow us to derive probabilistic estimates that represent both the limits of our SME’s knowledge and the natural variation.

  1. Ask each SME for their assessment of three probabilities on a scale of 0% to 100%:
    1. How likely are we to see the real data fall within this range if your reasoning or model is correct? This will usually be very high, like 95% or higher.
    2. How likely are we to see the real data fall within this range even if your reasoning has some flaw? This may also be high, but you can use 50% if the answer is “I don’t know.”
    3. How likely is it that the reasoning is correct? Again, this will usually be pretty high. That’s why they’re SMEs.

At this point, you have all the information that you need from the SMEs and can proceed to the calculations.

  1. Using the three probabilities of the argument accuracy, calculate the values of X at end points for a triangular probability distribution.
  2. For each SME’s guesstimates, use the triangle distributions to generate a large number of probabilities across the total range of possible Xs.
  3. For each value of X used in the generation of the probability distributions, randomly choose a probability from one of the SMEs. For each value of X, we now have N probabilities that we treat as part of a discrete distribution.
  4. Use the resulting combined distribution to determine values of interest, e.g. the median, fifth percentile, ninety-fifth percentile and so on.

From a diverse set of estimates, you now have a single, robust estimation for a variable. The resulting probability distribution will not be smooth, but you will be able to pull out single values that are meaningful and robust, such as the ninety-fifth percentile for duration estimates.

A few tips

How you ask for the estimates matters. Just asking for “low,” “middle” and “high” estimates will get you very inconsistent results. Likewise, asking for “worst-case” or “best-case” will often get you some pretty wild estimates. You want to ask questions like “how long would this take if many things went right,” “how long would the product last in more severe operating conditions” or “what is the most likely power available?” You don’t want the “middle” estimate, but a “most likely” estimate; you’re not shooting for half-way between the low and high estimates, but for a high-probability point estimate.

When some SMEs know more about the variable being estimated than others, you could also weight their judgements. This weighting is used in the second to last step by adjusting the probability of randomly selecting a value from each SME’s probability distribution.

Issue Logs and Risk Registers

Every product development project includes uncertainty over what will happen. The uncertainty—each assumption or best guess—reduces our chances of project success. The job of the project manager and team members is to ensure success by managing risk.

When something goes wrong—deviates from the plan—it stops being a risk and becomes an issue that must be addressed to ensure success. Issues are those conditions that are having a negative impact on your ability to execute the project plan. You can easily identify them because they directly cause schedule slippage and extra work.

There are two simple tools that can—and should—be used on every project to manage risks and issues to prevent disaster. One is the risk register; the other is the issue log. In my experience, these two documents are often conflated, but they are distinct documents that should contain different information and drive different actions.

The risk register is a means of capturing risks that we want to monitor over the life of the project so that we can take action before they have a negative impact on the project. These are conditions that you have decided not to explicitly work into the plan, but don’t want to let “slip under the radar” to create big issues for you later.

The issue log is where you record any problems that were not accounted for in the plan and that threaten to delay the project, push it off budget or reduce the scope (e.g. reduce product performance).

Issue Log Risk Register
Description of the issue Description of the risk
Underlying problem or cause of the issue Risk profile—sources of uncertainty and the potential impact
Action plan Potential actions
Priority or scheduling Monitoring plan
Who is responsible for assuring this issue is resolved Who is responsible for monitoring
Date opened and date resolved, sometimes a tracking number or other ID Date last updated, tracking ID

Issue Log

The issue log is fundamentally about corrective actions. The project has deviated from the plan, and now we need to get back on course to complete the project on time, on budget and with the agreed goals. The issue log is used to capture this information.

While the cause of the problem is often obvious, it is always a good idea to probe for deeper, systemic causes that could lead to further delays. Asking “why?” five times in order to permanently and irrevocably fix a problem doesn’t take very long compared to the total delays that a project can experience.

Risk Register

The hard part of a risk register is the risk profile. Different people respond differently to risk, and some are more comfortable with thinking about uncertain outcomes than others. These differences between people lead to a lot of variation and debate in identifying risks; a good strategy for making risk registers easy is to standardize. The best practices are to focus on the causes of the risk and the probable impacts and to standardize the process.

There has been a lot written about risk management. Some of the best, in my opinion, is the work by De Meyer, Loch and Pich, which was first brought to my attention by Glenn Alleman over at the Herding Cats blog. In their excellent book, Managing the Unknown: A New Approach to Managing High Uncertainty and Risk in Projects, they break down risk into two major components: relationship complexity and task complexity.

When the relationships of stakeholders or partners are complex—groups aren’t aligned—then you can expect disagreements and conflict. Successful strategies for dealing with relationship complexity include increased communication and more rigidly defined relationships.

When tasks are complex—there are many links between tasks, so that changing one task can affect many, or there is a high degree of uncertainty in what needs to be done—then the successful strategies range from critical path management to an entrepreneurial approach of working multiple solutions in parallel (see also De Meyer 2001).

By implementing these pairings of source of risk with management strategy in a risk register template, we can greatly simplify the process and drive more consistent risk management results. Adding in a simple analysis of the impact can help us with prioritization (where do we spend our resources monitoring) and monitoring frequency.

Monitoring is all about how you will know when to do something about the risk. i.e. You want to decide in advance what condition will trigger you to transition this risk to the issue log. Measures should be relevant to the risk, quantitative where possible and the method of measurement should be clearly defined (you don’t want people disagreeing over the project plan just because they measure something differently). Set up measurement intervals that make sense by asking yourself how long you can go without knowing that you have a problem. Plot the results as a time series or on a control chart to allow you to distinguish between normal variation in the measurement and a condition that requires action.


  • Loch, Christoph H, Arnoud De Meyer, and Michael T Pich. Managing the Unknown. Hoboken, New Jersey: John Wiley & Sons, 2006. Print.
  • De Meyer, Arnoud, Christoph H Loch, and Michael T Pich. “Uncertainty and Project Management: Beyond the Critical Path Mentality.” 2001 : 1–23. Print.

Apple-Google Wage Fixing and Systems Thinking

It seems that some of the most successful people in the world, at some of the world’s largest and most respected companies, engaged in an apparently wide-spread and illegal effort to limit employees’ job opportunities and wages.

This was stupid. It was stupid for the obvious reason: it was illegal. It was stupid for a more insidious reason, though: it will backfire on the company. We can explore why if we use a couple of system archetypes to think about the situation. These archetypes will come in handy in a wide range of situations.

The problem was retaining talent, who could be easily enticed away by more attractive compensation packages or to work at more exciting companies. Either it was easier to get an attractive compensation packages at a competitor, or the work did not stay sufficiently interesting or engaging. Simply put: employees were not happy enough.

The fix was to limit recruitment among competing companies.

In systems thinking, this is a classic “shifting the burden” dynamic. In shifting the burden, pictured below, you have two types of solutions to the symptoms of a problem: the fundamental solution—a corrective action for the root cause—and the symptomatic solution. The symptomatic solution reduces the symptom, but also creates a side effect that has a negative impact on the fundamental problem. Symptomatic solutions only have a temporary benefit before things get worse.

The classic shifting the burden system archetype describes wage-fixing practices as solutions to employee turnover.

The classic shifting the burden system archetype describes wage-fixing practices as solutions to employee turnover.

The green links, labelled “+,” indicate that the two conditions at either end of the tail increase and decrease together. The application of more symptomatic solution causes an increase in the side effect; reducing the use of symptomatic solutions causes a decrease in the side effect. The red links, labelled “–,” indicate that the two conditions at either end work in opposite directions. An increase in the side effect causes a decrease in the effectiveness of the fundamental solution.

There are three cycles, or loops, in this diagram. Two of them are “balancing loops;” over time, one factor tends to balance out the other, and the situation stabilizes. The third loop is a “self-reinforcing loop;” such loops will “snow-ball” or continue increasing over time:

  1. Applying a symptomatic solution
  2. increases the side effect
  3. which reduces the effectiveness of the fundamental solution
  4. which increases the symptom
  5. which drives more application of the symptomatic solution
  6. and increases the side effect.

The solution is to focus on fundamental solutions—get at the root cause—and avoid or limit reliance on symptomatic solutions. Symptomatic solutions are always temporary and usually make things worse in the long term; fundamental solutions are permanent and don’t have negative side effects.

Why employees leave is also explained, in broad strokes, by another system archetype: the limits to growth.

The classic limits to growth system archetype describes why it’s hard to keep employees engaged and hold down turnover.

Here we have a self-reinforcing dynamic created by successes and interesting work with good compensation. Over time, employees should generate more success and have more interesting work and better compensation. However, this is coupled to a balancing loop that becomes stronger over time, and slows down the reinforcing loop, like the brakes in a car. Employees burn out, or generally stop producing as much. This balancing loop is driven by some limiting condition, which makes the slowing action—burnout or disengagement—stronger over time. While this simple version looks like it will lead to a steady state, more realistic versions often result in a crash, where the results not only level off, but actually decrease.

The solution to a limits to growth system is to attack the limiting condition. If employees get bored doing the same thing over time, then you have to find a way for them to be engaged with enough new, interesting work. If they can earn substantially better compensation packages at competitors, then you have to (approximately) match those packages.

If you don’t fix the limiting condition, you might see a temporary improvement via the dynamics of shifting the burden (or the closely-related archetype, fixes that fail), but in the long term the problem will only get worse.