Discovering Cause

Acknowledgements

This book is the work of a collaborative group.

Contributors
Larry Ball (Primary Author)
David Troness
Kartik Ariyur
Jason Huang
Steve Hickman
Petr Krupansky
Larry Miller

Editors
Erika Ball
Larry Ball
David Troness
Paul Dwyer
Robert Lang

Illustrators
Larry Ball
David Troness

Other Authors, Theoreticians, Practitioners Whose Writings or Teachings have Impacted This Work
Genrich Altshuller
Ellen Domb
Roni Horowitz
John Terninko
Alla Zusman
Boris Zlotin
Lev Shulyak
Yuri Salamatov
Victor Fey
Eugene Rivin
Darrell Mann
Sergei Ikovenko
Simon Litvin
Peter Ulan
Lane Desborough
Clayton Christensen
Renee Mauborgne
Kim Chan

Where the Material Comes From

This book draws inspiration from various thought leaders. Initially, the goal was to gather their insights, but this process led to the discovery of new methods. Codifying their ideas required recognizing similar patterns among different tools. Once we identified these patterns, we grouped the tools with crucial decisions. Decisions generate information that informs future choices. This process revealed patterns and gaps, allowing us to introduce experimental methods. The success of these methods hinges on their ability to help problem solvers pinpoint product or process features that will satisfy the market.

Introduction to Discovering Cause

Cause and effect, means and ends, seed and fruit cannot be severed; for the effect already blooms in the cause, the end preexists in the means, and the fruit in the seed.

~ Ralph Waldo Emerson

Prerequisite Knowledge:
If you haven’t read the TRIZ Power Tools Working With Functions, please do so before continuing with this book.

All Solutions Address Causes
How often do we encounter this? You are invited to a “brainstorming session.” Someone mentions a problem and suggests that everyone brainstorm solutions. This is so common that it typically goes unchallenged. Jumping from problem to solutions skips the essential step of figuring out what causes the problem. This shortcuts an important process because all solutions must ultimately address the root causes. Conversely, no solution can exist that doesn’t confront a problem’s cause. Once we truly understand and accept this, we’ll move away from the old habit of “brainstorming solutions.”

Why Conduct Causal Analysis?
A detailed causal analysis can uncover simple solutions that may have been overlooked. This realization can come even after years of grappling with a problem. The goal of causal analysis is to identify as many potential solutions as possible. The more we discover, the more likely we are to find new options.

When we begin a causal analysis, we might not know the exact cause of the problem; we only have theories. Evidence is what confirms whether our theories are valid. Before evidence arises, we need guidance on where to gather information and what tests to perform. A causal analysis diagram effectively records our theories about the causes of the problem. This is crucial for team collaboration.

Conducting causal analysis improves both the quantity and quality of potential solutions. Since most problems have various causes, recognizing and addressing these causes opens up numerous solutions. If we ignore the causes, we risk sticking to familiar ideas, limited by our mental inertia. The curse of knowledge is that once we think we understand something, we often rely on that knowledge repeatedly. For instance, consider an issue with an acid container. If we’re materials engineers, we might quickly conclude that we need to change the container’s material to one less affected by acids or a cheaper option. This seems logical due to our familiarity but narrows our perspective to just one aspect of the problem. By exploring the causes, we’re encouraged to think about a wider range of possibilities.

The benefits of effective causal analysis can carry over many product or service cycles. The insights gained can provide a significant competitive advantage.

Obvious Solutions
Conducting a causal analysis helps problem solvers find solutions that were previously overlooked. A more thorough causal analysis increases the chances of discovering these opportunities. Sometimes, a new solution can be easily implemented without negative effects. This is often the case when a detailed causal analysis is conducted. However, it is essential to note that some individuals approach causal analysis solely to uncover new solutions, missing other possible solutions that could also be explored through learning how to resolve conflicts.

Other solutions that emerge from causal analysis may not focus on easy changes. Understanding the root causes can lead the problem-solving team to quick solutions.

Different Forms of Causal Analysis
There are various types of causal analysis. It’s common to find initiatives promoting different methods. For example, “Six Sigma” generally supports process-oriented (process maps or charts) or model-oriented (statistical models) analysis. “Lean” initiatives often prefer process-centered analysis. Most IR&D efforts advocate some version of model-centric analysis based on physical, chemical, or statistical descriptions. Many TRIZ software providers promote function-centered analysis, where the problem is represented as a series of interactions or functions. “Failure Analysis” initiatives commonly suggest Fault Trees, which reflect attribute-focused forms of analysis.

“Root Cause” initiatives usually prefer Why-Why analysis, Fishbone Diagrams, or Fault Trees, which emphasize attributes. Each type of causal analysis serves its purpose, helping to clarify the problem and highlight its causes.

Combining Different Forms
Since each method provides a unique perspective on the issue, we will use a blend of these techniques. This combined approach will be referred to as a Causal Analysis Diagram. The structure of this analysis will focus on an Attribute-Centric analysis, similar to Fault Trees. This method allows easy updates to illustrate functions and contradictions that are important for understanding the causes of the problem. At each stage of the diagram, we will utilize models from physics and process maps to inform our choices of branching attributes. This combined approach will be clearer and simpler to integrate than applying them separately.

Contradictions
The concept of contradictions and their solutions is a key part of TRIZ. It plays a vital role in creative thinking as it expands the range of possible solutions. Picture needing to fly a complex aircraft with all the controls so tightly secured that you cannot operate them. Many feel this way when they conduct a causal analysis and reveal numerous solutions that would resolve the issue if only they could be executed. Let’s examine a technical problem to explain what a contradiction is.

If we want to increase a vehicle’s carrying capacity, we will likely need to enlarge its volume. However, increasing volume usually creates more aerodynamic drag, leading to higher energy consumption. This increased energy consumption results in higher costs and greater environmental impact. So, we want to boost carrying capacity without raising operational costs or harming the environment. We want to enhance one aspect without negatively affecting another. As mentioned earlier, the parameter we need to adjust to improve the vehicle’s carrying capacity is its volume. Without going into detail about how we arrived here, we can define the contradiction as:

To carry a large amount of cargo, the volume must be large.

To maintain low drag, the volume must be small.

This contradiction helps us identify the parameter requiring opposing values and indicates when a solution is “good enough.”

We might instinctively want to compromise by making the volume bigger but not excessively so. The problem with this reasoning is that it introduces risk. On some occasions, the volume may fall short. If we’re designing the vehicle for commercial sale, some customers may find operational costs too high. Furthermore, we create a risky situation that could persist for generations. Finally, achieving a well-thought-out compromise requires extensive data and analysis, which can be time-consuming.

What we truly seek is a way to resolve the contradiction without settling. Mastering this skill reveals many more problem-solving options. This capability is liberating for problem solvers, revealing that the solution space is much broader than they initially thought.

Adjusting Parameters to Find Contradictions
Generally, we tweak parameters or adjust object attributes to identify contradictions. When a physical parameter is at one extreme, we achieve both beneficial and harmful results. Changing the parameter to the opposite extreme reverses these effects, exchanging desirable outcomes for undesirable ones and vice versa.

There is a natural eagerness to adjust some knobs and reluctance with others. This bias restricts the possible range of solutions. Let’s look at the different types of knobs. In the end, we will treat the resulting contradictions the same way, with the same tools, but it is not possible to solve them if we are unwilling to even consider them in the first place.

Type 1: Easily Adjustable
With this type of parameter, there is complete control over the dependent variable, and nothing worsens when adjusted to the desired level. These are the parameters that most people seek in causal analysis. Adjusting these leads to ideal solutions, but they are often rare in long-standing issues. They appear when someone takes the time to explore the underlying physics and conducts a thorough causal analysis. They may also arise through the use of the Table of Knobs or “Relative To” tools. For example, in our pile driver case, we could increase driving speed by raising the rate at which the pile is struck. To achieve this, we’d likely need to boost the driver’s energy. This can be accomplished without changing the striking momentum; for instance, reducing the interval between strikes enables this increase in energy.

Type 2: Little Effect
Adjusting these knobs has minimal impact. They are generally not important because they do not significantly affect the issue. In this case, the color or temperature of the pile will not notably influence the driving speed.

Type 3: Something Else Gets Worse
Adjusting these factors negatively affects another important aspect. These are typical issues where people feel they must compromise. Making the pile sharper might speed up driving, but it can’t handle the final load as well if driven to the same depth. The pile may need to be driven deeper, which eliminates the speed advantage we thought we gained. The contradiction is clear: the pile needs to be sharp for faster driving but must be blunt to support vertical loads, especially during an earthquake.

Type 4: Difficult to Turn
With these knobs, it is unclear how to operate them. No identifiable physical phenomenon can be observed, or so many knobs must be adjusted simultaneously that it seems impossible. In this scenario, we may realize that the damping of the pile is a crucial factor in driving speed. If the pile damping is significant, a considerable amount of energy is dissipated as heat in the pile during driving, thereby decreasing the energy available for faster driving. While we acknowledge that this is an important aspect of the driving process, we may lack the experience or knowledge required to identify a method for altering this vital parameter. Such expertise might be found in another industry.

Type 5: Only One Flavor or Setting
This knob cannot be turned because it has only one setting. The most common reason this occurs is that an element is simply off-limits for change. Perhaps you are working with a customer part, and the customer has mandated that it remain unchanged to perform well with their processes. Let’s assume, in this case, that there is an artificial restriction placed on the diameter of the pile. When we form the contradiction, we will state that the pile diameter must be small to drive fast, yet it must be large because it only comes in that size. This is not the typical contradiction discussed in mainstream TRIZ literature. The pile diameter causes a problem. Issues regarding changing the diameter are not even considered. The change of diameter is simply not permitted.

Type 6: Highly Variable Knobs
This knob cannot be turned because it is so highly variable that you never know what setting it will be at. In this case we never know from day to day, or in some instances from pile to pile what the ground hardness will be. Most people are reluctant to consider changing this knob or forming a contradiction. You will note that when we come to identifying the contradictions that it is not out of bounds. While it is true that this appears to be more complicated, do not forget that there are a variety of tools at our disposal for solving these types of contradictions.

Type 7: Outcome Knobs
This knob cannot be turned because it is a dependent variable that relies on other knob settings. We will consider adjusting this knob without changing any identified dependent variables. In this case, we will explore changing the driving speed without altering any of the dependent variables that influence it. Initially, this may seem illogical, but the contradiction that follows will help to clarify why we approach this way. The driving speed must be high to save time and costs. Conversely, the driving speed must be low since I am not altering any of the independent variables affecting the speed. Essentially, I am stating that the driving speed must be both slow and fast. As we will see, every box in the causal analysis diagram is a potential solution. We are already evaluating the independent variables in other boxes that contribute to the outcome knob. This enables us to create a contradiction for the outcome knob alone. Novel solutions will emerge when we consider this.

The last four knobs—“something gets worse,” “don’t know how to turn,” “one flavor,” “highly variable,” and “outcome”—are the ones we least frequently adjust, but turning them can lead us to interesting and effective solutions. While not every knob will fix our issue, we shouldn’t ignore a knob just because it doesn’t align with our preferences. We must learn to adjust each knob type.

Why is exposing contradictions important in causal analysis? It’s easy to think a causal analysis is finished when we identify the knobs and settings causing the problem, but that’s not the case. Our understanding is not complete until we recognize why the knobs are hard to turn. The common assumption is that most knobs can’t be adjusted. In truth, they are just challenging to turn.

Additionally, some contradictions can indicate different issues and their underlying causes. Solving these alternative issues can help us “bypass” the original problem.

In short, a causal analysis remains incomplete until we grasp why the knobs are challenging to turn. It is not finished until we comprehend the contradictions.

“Root Cause Analysis”
Some fields refer to this as “Root Cause Analysis[9].” The term “Root Cause” suggests that if we keep asking “why,” we will eventually uncover the root cause. For those following six-sigma principles, it’s crucial to note the distinction between special-cause and common-cause problems. Special cause indicates that a process’s output is outside control limits and is therefore rare. In these instances, we can often pinpoint a single root cause. However, for variations within the control limits, there are multiple interconnected causes. It’s wise to avoid the term “Root Cause Analysis” for understanding problems that aren’t due to special causes.

How Far Should We Go?
We’ve touched on this question, but it’s worth revisiting. When reviewing related requirements, we considered the limits on our solutions, such as the time and budget available, and the number of alternatives we need to generate. This helps us understand how much flexibility we have to tackle the problem.

During problem-solving, causal analysis and implementation require significant time. The time needed to develop solution ideas is usually brief. However, conducting a thorough causal analysis can be time-intensive and may involve experiments that consume considerable time. If we have low urgency or are less invested in the answer, we might conduct a simpler causal analysis. Ultimately, it takes experience to know how far to go, but we can’t be sure if we have gone too far or not far enough without exploring all solution paths.

Solve as You Go
All the tools covered in causal analysis complement each other, and no strict order is necessary. In fact, starting the solving process as soon as we identify a key function or knob that significantly contributes to the causal chain can be beneficial. Addressing the issue often reveals other causes. Even if a problem solver waits until the causal analysis is done to start fixing the issue, implementing solutions will usually bring more information about the causes to light. A causal analysis is never “complete.” There’s always more to learn. Typically, time and resource constraints will prevent further analysis. Therefore, it’s important to be as efficient and effective as possible.

Quality of a Causal Analysis
In summary, by the end of a good causal analysis, you should understand:

— The knobs causing the problem

— How the knobs are interconnected

— How the problem evolves over time

— The contradictions that complicate the problem

— Alternative problems (Address these instead)

— How the evidence supports the theory

Simplified Causal Analysis for Beginners

Simplified Causal Analysis for Beginners (Educational)

This is a simplified method of performing causal analysis, intended to give people an intuitive sense of how it works when we move to the more detailed version. It is not intended to be used in place of the detailed analysis.

Method

Step 1: Determine what you are aiming to improve. This is defined as a knob (object attribute) and a setting.

Step 2: Identify the objects and fields that you believe are involved in the issue.

Step 3: Brainstorm the knobs and settings related to these objects and fields.

Step 4: Add Functions that Contribute to the Issue

Step 5: Adjust the Knobs to Settings that Resolve the Issue

Step 6: Formulate Contradictions

Step 7: Uncover Alternative Problems

Explanation

For simple causal analysis or for beginners, this method of causal analysis can be quite illuminating. While it is not rigorous in determining the chain of causes, it helps the problem solver focus on contradictions and faulty functions. This method is particularly useful during a classical brainstorming session, while relaxing in a chair, or when a colleague seeks help with a problem. For many issues, this straightforward analysis is sufficient. Note that the simple causal analysis involves the consideration of functions and interactions. This may be somewhat advanced for beginners, but it is valuable for advanced users who wish to utilize the simple analysis. It is easier to explain the method with an example, which can then be followed each time we conduct this type of analysis. Let’s take the pile driving issue as an example of how to perform simple causal analysis.

Example—Pile Driving Speed

The driving speed of piles is notably slow. Expensive equipment and personnel often have to wait while the driving progresses. How can we enhance the driving speed?

Step 1: Determine what you are trying to improve. This is expressed as a knob (object attribute) and a setting.

In this case, the driving speed is slow. Identify the main issue as a knob and a setting.

Step 2: Identify the objects and fields that you believe are involved in the problem.

The objects include the driver, the pile, and the soil.

Step 3: Brainstorm the knobs and settings related to these objects and fields.

List these as independent variables of the attribute you are trying to improve. Each new attribute is also noted as a knob and setting. The driving speed is slow due to the large pile diameter, low driver mass, hard ground, and flexible pile, among other factors. Note that most of these are design parameters.

Step 4: Add Functions That Cause the Problem

Up to this point, we have only considered the knob settings or attributes of the driver, the pile, and the ground. There are also functions that cause the problem. One such action in this instance is that the ground is pushing back on the pile. We can treat these functions in the same way that we treated knob settings. They may or may not lead to a contradiction.

The inclusion of functions is somewhat advanced for most beginners since functions are not typically taught in school. A beginner may choose to skip this step until they become more experienced with the use of functions.

Step 5: Turn the Knobs to Settings that Resolve the Problem

These settings are the direct opposites. Write this as a column alongside the attributes that cause the issue. We must temporarily disregard what worsens when we adjust the knob. Turn it far enough in our mind’s eye to resolve the specified problem (slow driving speed) for several product generations.

Step 6: Form Contradictions

We can now allow our critical self to consider what we have been resisting all along. We need to identify what worsens when we adjust the main parameter we are trying to improve. Write down what gets worse in an additional column.

Note that the two middle columns provide parameters that must have two different settings. The pile diameter must be both large and small; the driver mass must be both high and low; the ground hardness must be both soft and hard; and the pile must be both flexible and stiff.

The last column indicates what deteriorates. While improving driving speed, breakage or depth of driving becomes worse. As driving speed increases, breakage worsens. Additionally, as driving speed increases, the driving depth becomes deeper. Lastly, with increased driving speed, the costs of the pile also rise.

When contradictions arise in this manner, it appears natural. Remember to always start with a parameter that you wish to improve, then identify the attributes that influence it. This approach works on both familiar and unfamiliar problems.

Note that some knobs may be adjusted without causing any issues. This is a positive outcome, and one should not conclude that they have made a mistake. For instance, what if we increase the driver’s speed to lift the mass back more quickly? In this case, nothing worsens, and the driving speed significantly increases. While this type of solution is rare, a thorough causal analysis often uncovers these types of knobs.

We might be tempted to immediately start resolving contradictions. However, there is another tactic that we will explore next.

Step 7: Discover Alternative Problems

Alternative problems[10] are identified by permanently adjusting any one of the knobs to the settings that resolve the main problem. These knob settings can be found in the second column from the right. If we begin with these permanent settings and do not attempt to address the contradiction by adjusting the knob to both settings, then we introduce an alternative problem to the original one. We must find a way to compensate for what we have changed. For example, let’s assume we have addressed the primary issue of driving speed by using a small diameter pile. Now we have two alternative problems to tackle. How can we reduce breakage and how can we enhance the support of the pile given its small diameter? Having two alternative problems to solve is not ideal. If we chose instead to increase the mass of the pile driver to solve the issue of slow driving speed, then we have one alternative problem to address: how can we reduce breakage of the pile given a heavy driving mass? We will later consider how we might solve this problem through compensation. In other words, we resolve one issue by adjusting a knob, and then find another knob to adjust to fix the problem we just addressed.

The simplified Causal Analysis is now complete. Note that we have identified several knobs and settings that are contributing to the problem as well as the reasons why these knobs are challenging to adjust. We have also pinpointed a detrimental interaction or function that occurs during pile driving. In subsequent sections, we will seek solutions to the contradictions, compensate for the alternative problems, and idealize the interactions that are causing issues.

Exercise—Corrosion of Acid Container

Cubes are placed in warm acid to investigate the effects of various acids on the cubes. Unfortunately, the container holding the acid and cubes is corroded. The container is made of gold and is very expensive to replace. Because the acid is highly reactive and the tests are performed frequently, the pan must be replaced often. Using your knowledge of corrosion, perform a simple causal analysis to identify some of the knobs, contradictions, and alternative problems. Recall that “Cost of Replacement Is High” is the base problem.

Exercise—Garden Rake

Let us consider the situation of a common garden rake. When the rake is used to collect loose debris such as rocks and weeds over an uneven surface, a problem arises: the rake “leaks” some of the debris underneath its tines, requiring several strokes to fully collect it. Using what you know about raking, perform a simple causal analysis to identify some of the knobs, contradictions, and alternative problems. Recall that “Debris Leakage Is High” is the base problem.

Exercise—Year End Review

The yearly performance review process is very time-consuming, especially when managing a large number of direct reports. Using your knowledge about performance reviews, perform a simple causal analysis to identify some of the knobs, contradictions, and alternative problems. Recall that “Review Cycle Time Is High” is the base problem.

Detailed Causal Analysis

Method

Step 1: Display the Base Problem as the Starting Box on the Causal Analysis Diagram

Rule: Every Box Displays a Knob and a Setting—Every Setting is “Bad”

Rule: Quantify the Current Setting (If Known)

Step 2: Identify the Causes and connect them together.

Rule: Think in Terms of Equations or Models Y = f (X1, X2, X3…).

Rule: All Causes are Assumed to be at the Worst Setting

Rule: If You Cannot Use an Equation, Think in Terms of Y = f (X1, X2, X3…)

Rule: Highlight Important Branches and Disregard Branches of the Diagram that Have Little Impact

Suggestion: Consider Including Models in the Diagram

Step 3: Identify Contradictions

Rule: Adjust the Knobs as You Proceed

Rule: Turn the Knobs Enough to Resolve the Main Problem

Suggestion: Consider Extreme or Unusual Settings from the Knob Table

Step 4: Some Requirements Are Not Caused by Anything—Develop Alternative Problem Paths

Step 5: Add Functions.

Rule: Functions are added by asking which dependent variables are changing or controlled over time. These elements would typically not be design parameters or parameters that are fixed or constant (unless they are controlled). They are altered or regulated by something else. If a dependent variable is changing over time or is a measure of change with time, then a function is involved.

Rule: We introduce the function by mentally shifting the dependent variable downward, thus creating a space for the function. The function is then placed in the area that the dependent variable occupied.

Suggestion: Illustrate ideas in the boxes

Explanation

The causal analysis diagram is a crucial tool for determining the factors and settings that contribute to the disadvantage. They assist us in understanding the relative significance of each cause. They reveal underlying contradictions and help us identify alternative problems to solve instead of the one we initially address. In summary, they enable the problem solver to consider all perspectives of the issue.

An additional benefit of causal analysis diagrams is that they facilitate teamwork, even in emotional or contentious situations. Once a team reaches a consensus on the underlying cause of the problem, the course of action becomes clear. Consequently, the team is more likely to unite. Furthermore, the diagram will help identify areas where team knowledge is insufficient and where data needs to be gathered or tests conducted. They also ensure that everyone has a voice in understanding the problem, preventing more vocal members from dominating the discussion.

For many beginners, constructing a causal analysis diagram can be somewhat perplexing. The confusion arises from applying unfamiliar rules and guidelines, which are intended to prevent problem solvers from falling into known traps. Nevertheless, creating a causal analysis diagram often requires considerable thought during the initial attempts. With each subsequent use, it becomes more intuitive and ultimately accounts for a smaller portion of the total causal analysis time. Over time, most of the effort will be spent on studying, analyzing, conducting experiments, quantifying variables, observing, and other related activities.

Not only do individuals become accustomed to the rules, but many will eventually personalize these diagrams to suit their needs. The formats and software tools used to create them can vary widely, which is to be expected and encouraged. However, the fundamental rules should likely not be disregarded.

Chains of Cause—Decomposing Causes
A straightforward approach to identifying causes is to brainstorm based on our experiences. (As a reminder, we should focus on brainstorming causes rather than solutions). We can do this by considering the dependent variable that we aim to improve as a mathematical function of several

independent variables. Remember your high school algebra: y = f(x1, x2, x3…). Let’s apply this functional thinking to the corrosion of the acid container. We can brainstorm the independent variables as illustrated on the right. Though this may seem simplistic, this humble beginning can lead to effective solutions, as will be demonstrated later.

Note that some of the independent variables of the mathematical function are dependent on one another. For instance, the cost of the container relies on the cost of the material. We can express the function in a manner that considers these interdependencies. Refer to the diagram on the right; the dependencies are now clearly visible. This type of analysis produces a cause-and-effect chain of dependencies that relate back to the original issue.

A further refinement occurs when we recognize that each knob is linked to an interaction or function that contributes to the problem.

The diagram below shows a basic cause-and-effect chain. Each box represents a dial setting or function that leads to the issue we want to fix. Causal analysis diagrams can display the main problem at the bottom, top, or sides. The arrows show the direction of cause and effect.

This is where we identify causes. What factors and settings lead to the problem? This is similar to our earlier causal analysis. Now, we will organize these factors into boxes and create a chain of causes. We will carefully construct the diagram, focusing on each box. Every “cause” is the “effect” of something else. Ultimately, all Xs will be seen as Ys. This could, in theory, continue indefinitely. However, we will stop when we reach causes that we can hardly control.

More Than Just the Causal Analysis Diagram
Creating a causal analysis involves more than just making a diagram. Most of the time will be spent away from the diagram, developing models, pondering the root of the problem, understanding the underlying principles, and conducting tests. Our findings eventually feed back into the diagram.

Value of Precision and Consistency
A final note on making these diagrams: it’s important to ensure that the logic is precise. You should be able to review the diagram and feel confident that no branches are missing. Overlooking a branch limits potential solutions. Consistent logic fosters trust within the team, assuring them that they are tackling the most crucial parts of the problem. Incorporating equations and values helps ensure the analysis is “self-consistent,” minimizing the chance of self-deception.

Beyond the Basic Template
So far, our work with causal analysis diagrams has focused on how attributes or settings can help us understand contradictions and alternative issues. This is valuable but somewhat limited. Concentrating solely on attributes doesn’t encourage us to explore other systems that use different objects for their main functions. In other words, the solutions tend to improve the current system. This may be restrictive, especially if the team can consider more significant changes to the system. Here, we’ll explore ways to enhance our basic causal analysis diagrams to enable larger system modifications.

We will also introduce new elements to help less experienced team members grasp these diagrams.

Introducing Functions
Most problems originate from useful objects that either fail to function properly or cause additional issues. What are these objects? The causal analysis diagram can highlight these objects, their functions, and the parameters connecting them. We can think of this as linking flawed functions through their attributes.

Functions help us understand how parameters change over time. For example, one might ask, “Why is maintaining this oven so costly?” The answer could be, “The pan is corroded by acid and must be replaced.” The pan’s corrosion occurs over time.

This answer outlines two functions. The acid is eating away at the pan, which is then being replaced. Notice that neither of these two “causes” appears in the diagram yet. So far, we have only suggested that a function is involved.

Another reason to include functions is that it helps us focus on the objects and physical phenomena at play and why they are necessary. This is crucial when trying to identify the variables that affect the dependent variable. Each object in the function comes with features or “knobs” that influence the dependent variable being studied. It’s easy to overlook these knobs if we forget that the related objects are part of the situation.

Later, we’ll examine these functions one by one as we work on solving the problem. We will “idealize” them, meaning we will question what each element of our system does. We may consider removing or altering elements or allowing them to serve different functions. The overall goal is to solve the problem while simplifying the system.

Drawing Pictures
The author has found that teams untrained in causal analysis diagrams can quickly get up to speed and contribute effectively if they engage with the process. Creating a causal analysis diagram fosters a shared understanding of a problem and enhances team effectiveness.

While this step isn’t strictly necessary, it is very useful when collaborating with teams. Adding visuals to the diagram encourages group members to participate and understand it better. Without visuals, untrained readers may lose interest before the end. Drawing pictures helps team members grasp what is happening, even if they lack a deep understanding of the underlying physics.

Example—Corrosion of Acid & Cube Container

Step 1: Show the Base Problem as the Starting Box on the Causal Analysis Diagram

The starting box can be positioned anywhere on the diagram. Some prefer to start at the top, like in “Fault Trees.” The author prefers to begin at the bottom. Choose what feels best for you. Sometimes, we discover problems that are more fundamental than we initially thought. That’s okay—just place it lower in the chain.

As we have already stated, the main issue is the high cost of replacing the corroded boxes. In our case, this will be located at the bottom.

Rule: Every Box Shows a Knob and a Setting—Every Setting is “Bad”

At the top of each box is a knob and setting (object attribute and the level of the attribute). Every box displayed has a “bad” setting. In this case, the knob is “Cost of Replacement.” The setting is “High.” Note that this is a bad setting from the perspective of the problem solver. Some problem solvers become confused and start putting in good settings to indicate solutions. The solutions can be placed elsewhere on the diagram.

Further confusion can arise when people identify knob settings that they have always perceived as nominal. They don’t view these settings as “bad.” However, if the knob could be turned, could you make the base problem better or worse? If you could, then assume that the current nominal setting is “bad.”

The knob is the cost of replacement for the container. The setting is High.

Rule: Quantify the Current Setting (If It is Known)

We do this so that later, we can determine the relative importance of each parameter and to establish the discipline that makes the evidence self-consistent.

In this case, we see that the cost of replacement is $5,000 per year.

Step 2: Determine the Causes and connect them together.

Rule: Think in Terms of Equations or Models Y = f(X1, X2, X3…).

The “effect” is the dependent variable (Y) and the “causes” are the independent variables (X1, X2, X3…).

Referring to the diagram, the effect titled “cost of replacement” (Ct) is the dependent variable. We model what is happening with the equation Ct = F × Cr. This indicates that the cost of replacement is a function of the “frequency of replacement” (F) multiplied by the “cost per replacement” (Cr). The causes are placed in their own separate boxes. Note the direction of the arrows from the cause boxes to the effect box. The direction of the arrow indicates which boxes are the “causes” and which one is the “effect.” As we progress upward, each cause will then be considered as an effect that has its own input causes.

Rule: All Causes are Assumed to be at the Worst Setting

This may not be intuitive, but note that according to any equation, the outcome could be improved by changing the value of any variable, regardless of the values of the other variables. In the acid bath problem, making the frequency of replacement high or the cost per replacement high drives the total cost to its worst possible condition.

Note that, in the previous diagram, we have continued to follow the previous rules by using a knob and a setting and placing the level or setting of each knob at the bottom of the box. While it is not possible to display the entire diagram for this problem on this page, we continue in the diagram below with this process to show how these rules are applied to extend the causal analysis.

The value of thinking in terms of equations or models cannot be overstated. This approach makes it possible to discover causes that nobody has considered before. When we identify a cause, we have planted the seeds for a solution. One significant class of solutions arises by adjusting the knobs and discovering that nothing gets worse. It is crucial to find as many knobs as possible, and thinking in terms of equations will help identify many options that otherwise would not be considered.

Rule: If You Cannot Use an Equation, Think in Terms of Y = f(X1, X2, X3…)

To write an equation, one must understand the “physics” of the problem. This skill is often not innate for most people but needs to be cultivated through practice and experience. Models and equations are second nature to most scientists and engineers due to their extensive training in “analysis.” For others, it can feel awkward or even impossible. While helping individuals think in terms of mathematical models goes beyond the scope of this book, it is possible to utilize causal analysis through intuition. Even for engineers and scientists, writing an equation is not always feasible. Sometimes, the model may involve a complex simulation or the relationships may be ambiguous. In either case, it is sufficient to “intuitively” identify the variables we believe are involved and their values. In other words: “If I could write an equation, it would include these variables…” However, a pitfall exists in doing this. It is easy (even for engineers and scientists) to revert to brainstorming variables instead of seeking equations. If this happens, important parameters may be overlooked. Make it a habit to develop models and simple equations whenever possible.

Rule: Highlight Important Branches and Abandon Branches of the Diagram that Have Little Effect

If we know the values of the object attributes (parameter levels or knob settings) and document these values within the boxes, we can then compare various legs of the causal analysis diagram to discern which legs may not be worthwhile to pursue. For example, the effect of acid on the container may not be significantly influenced by the cost of labor when compared to the actual material costs of the container.

If we do not know the values of the object attributes, we may need to seek data or conduct screening tests where only one variable is altered at a time. These tests will further aid in understanding the relative importance of each knob.

Note from the above diagram that reducing labor costs below $15 will have minimal effect compared to the $985 material costs. There is no need to continue developing this leg of the diagram; it is indicated with a cross-out.

Important branches can be highlighted in some way to signify where to focus. I prefer to use thick arrows. If we could always identify an unimportant branch from the outset, there would be no need to highlight any particular branch. Usually, this determination occurs at some point further up the branch, so we need a method to identify the important branches. In the acid container diagram, all branches are significant except the one that is crossed out. Personalize the diagrams in ways that help you distinguish the important branches.

Suggestion: Consider Incorporating Models into the Diagram

For convenience, larger models can sometimes be integrated into the tools containing the diagrams. It is possible to transform Microsoft Excel into a more robust flowcharting tool by creating boxes with additional attachment points and utilizing the connectors from the AutoShapes toolbar. This allows for mathematical models to be directly included within the sheets. It is even feasible to have the results from the calculations automatically update the values in the boxes of the causal analysis. Another feature in Excel is the ability to divide the diagram across different sheets and create links between the sheets for navigation. The author believes that using commonly available software tools such as Microsoft Excel is preferable to flowcharting tools because they are accessible to everyone. Anyone can open such a file to view or modify it, making it much more portable. This can assist in implementing TRIZ in larger companies.

Step 3: Discover Contradictions

Now we arrive at one of the most potent aspects of causal analysis diagrams, discovering the contradictions that hinder system development. Many individuals inquire about how to uncover contradictions within their systems. This method is particularly effective and efficient, especially when the problem is complex or “tangled,” or if it is a novel issue.

A thorough causal analysis is not complete without a solid understanding of contradictions. We will demonstrate that there is a relatively straightforward systematic approach to achieve this.

Rule: Turn the Knobs as You Go

When we identify a new box with a knob and setting, we should repeatedly ask ourselves, “What happens if I turn the knob to the opposite setting? Does something else worsen?” We accomplish this by mentally placing a box alongside the box we are considering and indicating the opposite setting within that box. If something worsens, we draw an arrow from the box to a new box depicting what deteriorates.

We could have performed this as we constructed the diagram, starting with the very first box. We could have asked, “If the replacement cost is low, does something else worsen?” In this scenario, nothing worsens, and we proceed without physically drawing the box we contemplated. Just because nothing worsens when we adjust a knob does not imply the absence of contradiction; it merely indicates that the contradiction is implicit. Every box has an implied contradiction. The reason these contradictions are not depicted is that the diagram remains more compact without them.

Here is what the diagram would look like if the implied knobs were turned and we retained the boxes (minus the equations and a few boxes for brevity).

Notice that only two of the contradictions indicate something getting worse. When the amount of material is low, the time it takes to consume the material is also low. Additionally, if the hours of use are low, then we must process fewer cubes. The rest are implied contradictions that we may want to resolve. While contradictions where nothing deteriorates may not be familiar, they present an opportunity to develop solution concepts. “The cost of replacement is both high and low.” We will not know until we try. When we get to the section on contradictions, we will examine the different types of contradictions in more depth.

Rule: Turn the Knobs Far Enough to Fix the Main Problem

Some objections prevent people from turning knobs as far as they should. Most would prefer to turn the knob partially and settle for a compromise. Unfortunately, compromise guarantees risk and leaves the problem to be addressed later. We must turn the knob far enough to resolve the primary issue. So, if a little medicine is beneficial, then shouldn’t a lot of medicine be even better? We ask, “why not be even more extreme?” While extreme thinking can be beneficial, it can also lead one into trouble at times. It is possible to overperform a function and suffer other problems as a result. The solution is to turn the knob sufficiently to address the problem for several product generations. While this may not be an entirely satisfactory answer, it gets to the core of the issue of system evolution. Systems evolve because their needs change. What represented excellent performance five generations ago may now be considered poor performance. We want to avoid addressing this problem again for several product generations.

Suggestion: Consider Extreme or Unusual Settings from the eBook Mobilizing Function Resources.

On the other hand, our knob-turning skills may not be as proficient as we believe. There is often more than one way to turn a knob. We might have overlooked some knob-turning possibilities. The Table of Knobs (Attributes) provides a variety of extreme or unusual knob settings to consider.

Visit the Table of Knobs (Object Parameters) and think about extreme conditions for the object attributes you picked. For example, when considering pile driving and checking Mobilizing Function Resources, we might look at using voids. The extreme version of this involves multiple voids that help with driving, but as the soil settles, it locks with the pile, providing strong load support.

Step 4: Some Requirements Are Not Caused By Anything—Develop Alternative Problem Paths.

While setting up a causal analysis diagram, we will ultimately encounter object attributes (knobs) that are not “caused” by anything. This may be a design parameter, such as the length of something, or it may be that this attribute comes in only one “flavor.” A design parameter does not have a cause. The reason for the knob setting is simply because we selected it. There may be very good reasons for the settings we have chosen. In many cases, the design parameter is necessary because if it does not have that setting, something else becomes worse. We can consider this setting as a “requirement.” Neither design parameters nor requirements have causes. It makes no sense to search for a “cause” for these variables. We chose the setting because we realized that without it, something else would deteriorate. Unfortunately, this setting also contributes to our main problem. In this type of contradiction, when we improve the main problem by adjusting the knob, something else worsens. We can illustrate the issue that worsens by introducing an adjacent box showing the setting that would resolve the original problem. This new box then causes an alternate problem[1].

This alternative problem path is depicted below by the added boxes. Whenever we see the double boxes, we know that we have a contradiction and an alternative problem path. The alternative problem indicates that if we choose the knob setting that resolves the base problem, we are then left with the alternative problem. We can address this alternative problem in various ways, including compensation.

For example, the strong reactivity of the acid is one reason for corrosion. If the reactivity is lower, the cubes take longer to corrode. “Strong acid reactivity is necessary.” We illustrate this with a second box showing the opposite value. This opposite box leads to an “alternate problem” path. If the acid’s reactivity were low (to aid corrosion), we would face a new issue related to the cubes corroding slowly. Alternate problems are an important part of all causal analysis diagrams. They show what deteriorates when we try to improve something. While we speed up the corrosion of the pan, the cubes corrode more slowly. The acid needs to have both low and high reactivity. This contradiction can be explained: the acid must be low in reactivity to stop the pan from corroding and high to make sure the cubes corrode.

When we start with something that needs fixing and find out what it relies on, changing the factor leads to another issue. This is similar to the approach used in the basic causal analysis. When we changed the factor, another problem arose, highlighting the contradiction.

Below is the diagram showing several new causes. Three contradictions and alternate problem paths are visible, with the top one highlighted. Each alternate problem path starts with a contradiction. (There’s a contradiction about the pan being made of gold. It could have been made from an even more expensive corrosion-resistant material, which we’ll skip for now to keep it brief).

Further investigation of the highlighted alternative problem would look at all the causes that slow the corrosion rate of the cubes. The alternate problem path should be examined just like the original problem, with equal care. When the diagram is done, it might look like we’re trying to fix five different “main” problems at once. This is fine because there’s a good chance that solving one contradiction can resolve multiple issues. These contradictions are “lynchpins.” When they are addressed, everything shifts. Understanding the alternate problem paths and the contradictions leading to them helps us see different angles of the issue.

The diagram above includes three contradictions or alternative problem paths that we haven’t discussed. If we keep the hours of use low, we must address the alternative issue of how to improve productivity of corroding the cubes. If we minimize the pan material to cut costs when it wears down, we then have to tackle the problem of the pan corroding quickly.

Practice and familiarity will prepare you for the more advanced diagrams that follow. It’s recommended to create and utilize several diagrams. This will make the next section more relevant.

Step 5: Add Functions.

Rule: Functions are introduced by identifying which dependent variables are changing or controlled over time. These elements usually aren’t design parameters or fixed values (unless they are managed). They change or are regulated by something else. If a dependent variable changes over time or indicates a change over time, then a function is involved.

In the above diagram, we see that the rates of corrosion for the pan and cubes are indicators of change. This indicates we need two functions. The other parameters are mostly constant, so we won’t need functions for them.

Rule: We insert the function by mentally sliding the dependent variable downward, thereby creating space for the function. The function is then placed in the space that the dependent variable occupied.

Let’s consider the function related to the rate of corrosion. First, we slide down the box associated with the high rate of corrosion.

We then insert the function linked to the dependent variable and its corresponding independent variables.

Let’s include relevant functions in our analysis of the acid and cubes problem. Here’s what it looks like (some parts of the diagram have been adjusted for clarity).

Suggestion: Draw Images in the Boxes.

Below is the outcome.

In this case, we can say that the key issues are the corrosion of the pan by the acid or the mild corrosion of the cubes from a diluted acid. We have already mentioned the controls (knobs) that manage how objects interact. We have also pointed out some of the main contradictions.

Note that in this example we did not illustrate the methods used in determining why elements are required. Since the existence of objects serves as parameters or knobs for every function, we could have arrived at the same conclusion as we did in the book TRIZ Power Tools – Skill #7 Determining Why Targeted Objects are Required. However, as stated in this book, this is a causal analysis shortcut that can lead us to the core of the problem more quickly, often without necessitating an understanding of the physics that full causal analysis entails.

Exercise—Pile Driving Speed

The driving speed of piles is exceedingly slow. Often, costly equipment such as cranes or barges is rented to execute the work. Personnel must be present in case anything goes awry. All of this contributes to significant expenses associated with pile driving. None of this is required for the primary function of the piles. Using your knowledge about driving stakes, create a causal analysis diagram to identify some of the knobs, contradictions, and alternative problems. Remember that “Pile Driving Speed Is Slow” is the base problem.

Exercise—Garden Rake

Let us consider the situation of a typical garden rake. When the rake is utilized to gather loose debris such as rocks and stray weeds over an uneven surface, a problem occurs: the rake “leaks” some of the debris that is meant to be collected beneath the tines, necessitating several strokes to completely gather the debris. Employing what you know about raking, create a causal analysis diagram to identify some of the knobs, contradictions, and alternative problems. Remember that “Debris Leakage Is High” is the base problem.

Exercise—Year-End Review

The annual performance review process is very time-consuming, particularly when managing a large number of direct reports. Using your knowledge about performance reviews, create a causal analysis diagram to identify some of the knobs, contradictions, and alternative problems. Remember that “Review Cycle Time Is High” is the base problem.

Discovering the Causes

Explanation of Discovering the Causes

Now that we have a good system for organizing our causal analysis, it’s time to discuss the actual investigative work. These tasks typically happen away from the causal analysis diagram and occupy most of the time spent on causal analysis. Having the diagram is not the main goal; the real aim is to think deeply and truly understand the causes of the problem.

If a subject matter expert is unavailable and the problem isn’t well defined, discovering the cause-and-effect relationships can take a lot of time. Be ready to explore the details and conduct necessary experiments.

At first, we might think we understand what’s causing the problem, or we might feel completely lost. Believing we know the cause can be as risky as having no idea at all. Assumptions can lead to psychological inertia. We need to let the situation reveal itself and listen carefully. As we proceed, we should develop hypotheses to guide our questions and experiments, but we must keep an open mind and not get too attached to any one hypothesis.

As we gather evidence and consult with experts, we will naturally start forming a hypothesis about what is happening. The best problem solvers tend to be patient and willing to observe for long stretches. Some may even seem extreme in their observations. It’s crucial to do this, as each new insight can create loose ends that need to be resolved to establish a consistent analysis. Being consistent doesn’t mean we “know” what’s going on; rather, our beliefs will strengthen as things come together.

Eventually, we reach the stage of “verifying” the solution. We do this by controlling the variables and switching the problem on and off. This doesn’t guarantee that we fully understand the situation, but it means we can predict the outcome accurately enough to make the problem disappear.

Performing a good causal analysis has many challenges. It takes time and experience to become skilled investigators. Here are some tips for conducting a thoughtful and thorough causal analysis.

Method: Models and Resulting Equations

Method

Step 1: Develop models to illustrate the physics.

Step 2: Derive equations based on these models.

Step 3: Utilize the variables in causal diagrams.

Step 4: Alternatively, consult the internet, books, or artificial intelligence.

Explanation

We have already covered this topic. Equations come from physics models. It’s often easiest to look this up online, in books, or with artificial intelligence.

Method: Observe the Situation Firsthand

Method

Visit the place where the issue and effects are happening and observe it directly. Don’t just rely on pictures or your imagination. Visuals miss a lot of important details. They lack the depth of real-life experiences. Interact with the equipment.

Explanation

The causal analysis process starts with a close look at the system. While this may seem obvious to some, it’s surprising how often the author has skipped this step, only to be embarrassed later when someone else notices an important, yet obvious detail about the situation.

Method: Catch it in the Act

Method

Step 1: Create a simple experiment to observe interactions. You might want to use slow motion.

Step 2: Refer to the chapter on Idealizing Informing Functions for insights on what is happening (like copies, etc.)

Explanation

Many causes are hidden because they happen very quickly or by the time we see the results, the signs of what occurred are erased by other effects. Parts can break down and then damage each other, hiding how they failed. Sometimes, failures happen so fast that we can’t observe them. Often, the key part of an investigation is finding a way to catch something happening live.

Method: Statistical Methods

Method

Step 1: Use Statistical Analysis to organize the data.

Step 2: Use statistical tools to link known attributes to the issue and assess their impact.

Step 3: Compare your results with the expected model outcomes.

Step 4: Identify any outliers.

Step 5: Decide if these outliers are due to measurement errors or if they provide valuable insights.

Explanation

Statistical data helps us understand what’s going on. Key techniques include Weibull analysis, regression analysis, and run charts. These methods can effectively find factors that cause issues, especially when the underlying reasons are unclear. However, remember that these statistical methods work well for common cause problems but not for special cause problems. In other words, a data point that is far from other points may have unique causes.

Method: Negative Evidence

Method

Step 1: What didn’t happen that usually does?

Step 2: What continues to work normally that shouldn’t?

Step 3: Where doesn’t this issue occur in my system…and why?

Step 4: What is missing that we normally expect?

Step 5: What is different between where it occurs and where it doesn’t?

Explanation

Negative evidence[13] is noticing what did not happen and figuring out where the problem doesn’t show up. It’s important to consider this when we expect certain things to occur. For example, cancer usually spreads to nearby organs. However, it rarely affects the heart or muscles. So, what prevents it from spreading to those organs? Asking these negative questions can lead to insights we might not think about otherwise.

Method: Crime Scene Analysis

Method

Step 1: Carefully examine all items using a microscope or magnifying glass. Look for signs of what happened. Study them closely.

Step 2: Take pictures and/or draw what you see. Drawing helps you notice more details. Compare your drawing to the actual object and look for differences. Use good drawing techniques like shading and perspective.

Step 3: If you can, gather many objects with the same issue. They may show the problem at different stages. Compare them for differences and identify any patterns.

Step 4: Confirm your observations by discussing with others.

Step 5: Consider measuring properties like resistance, density, and hardness. Look for differences.

Step 6: Try to connect everything into a consistent narrative.

Explanation

A good “whodunit” mystery relies on a clear analysis of the evidence. In everything we do, we aim to uncover evidence and connect it to create a consistent understanding. There should be no loose ends. Much of our work involves looking closely at details. Sometimes, the smallest bits of evidence, which we might miss, can change everything. How we observe can be our best tool.

Method: Problem History

Method

Step 1: Check the problem’s history, especially if you have test data.

Step 2: Identify any changes that happened around that time that could relate to the problem.

Explanation

How does this evidence compare to things you’ve seen before or to similar situations? This usually comes from experience, but there may be someone nearby who can point out what is unusual if you’re unsure. Looking at the history of a problem can help you know where to focus your efforts and where to avoid. It can save a lot of time.

With enough experience, you can often find out when a problem started and connect it to a change in objects or their attributes.

Method: Subject Matter Experts

Method

Step 1: Learn from experts:

—-Look up books, articles, online resources

—-Speak with experts directly

Explanation

Typically, there is a subject matter expert involved. If this is a long-standing issue, this expert may not have been able to find a solution. Ideally, the person solving the problem should also be the expert. All the information about the issue exists in the mind of this expert. Organizing this information using a causal analysis diagram is a key step in solving the problem.

Based on our observations, we may need to adjust or reinforce our expectations. Our expectations are shaped by our understanding of how the world works. This understanding can range from being vague to having a thorough grasp of the problem’s underlying principles. Understanding the core principles of a problem is crucial. It’s important to identify the interactions between objects and what influences these interactions. Sometimes, it can be challenging to pinpoint the cause of a problem.

Method: Process Maps and Story Boards

Method

Step 1: Describe each step of the process simply. We start by “walking through the process” over time, as a series of functions.

Step 2: Outline the process as a map or storyboard.

Step 3: For better clarity on key steps, break down the process into more detailed parts.

Step 4: Identify any functional issues that you may have overlooked.

Step 5: Think about presenting the earlier steps visually with a storyboard.

Explanation

When describing a process or product, you’re explaining what happens over time. Products consist of items that work in time. The value of a process map[14] lies in its ability to break down a process into simpler steps. A process map provides a snapshot of the sequence of functions with limited focus on causes and might not cover all elements of the system. Sometimes, creating a “storyboard[15]” can be more effective due to its visual appeal.

Example—Dispensing Tape

Step 1: Describe each step of the process simply. We begin by “walking through the process” over time, as a series of functions.

Step 2: Outline the process as a map or storyboard.

Start with “person positions base,” then move to “person positions tape,” and keep going.

Step 3: To understand the key steps better, break the process into simpler parts.

Here, we can detail “person/blade cuts tape” into smaller steps.

Step 4: Look for any problems you haven’t noticed before.

We might not have realized that a person typically twists the tape to start cutting.

Step 5: Try drawing the previous steps using a storyboard.

Method: Empathy

Method

Step 1: Imagine yourself as the objects you are studying

Step 2: Look at the process from start to finish

Method: Subversion Analysis

Method

Step 1: Identify the problem you want to create.

Step 2: Pretend to be a Saboteur. How would you create the issue within the current system? How could you prevent it from being noticed? Find a physical effect that can help create or conceal the problem. Use the Effects Database for assistance. (The full effects database is too large to include here. You can find more complete versions in paid software. A simplified version is available at function.creax.com)

Step 3: Identify the resources needed for the effect to work. For example, if the effect involves mechanical strain, look for objects in the environment that might apply pressure. If those resources aren’t available, think about how they could temporarily be created, perhaps through chemical reactions. Also, consider if these harmful resources could exist in small amounts naturally. If no solution exists, the effect may not be achievable.

Step 4: If the resources are available, then Increase the effect until it creates a problem.

–Identify all the parameters (factors) of the components involved that worsen the situation. This means either increasing the influence of what is acting or making the object being affected weaker.

–Adjust these parameters so that the effect is strong enough to cause damage.

Step 5: Repeat all steps for each possible way the system might fail.

Explanation

Subversion analysis[16] [17] or Anticipatory Failure Analysis [18] [19] helps overcome mental blocks to find new ways a system can fail. This approach was first used to identify problems. We effectively ask: If you were a Saboteur, how would you create the issue?

Typically, Subversion Analysis is used to predict unknown problems before they arise. This is crucial in many fields and deserves extensive discussion. We include it here because it’s also valuable in causal analysis.

Method: Catch Missing Knobs from Mobilizing Function Resources

Method

Step 1: Consider if any of the knobs in Mobilizing Function Resources can apply to your case: Existence, Number of objects, Location, Movement, Structure, Surface Properties, Bulk Properties, Direction, Field Structure, Adding or Superimposing Fields, Conductivity, Adjustability, Timing, Time Variation

Explanation

In the first look at the cause-effect chain, we will spot the more obvious knobs. However, there may also be subtle knobs that are less apparent. Since these knobs might not have been explored by others, adjusting them can sometimes be done without negative impacts.

Mobilizing Function Resources reinterprets the “Inventive Standards[20]” and describes common practices for solving typical invention problems. Many of these standards give hints about object and field parameters (knobs). While the term knob may sound trivial, it serves a useful purpose. It suggests that objects and fields have characteristics that can change. Once we grasp the structure, we can tweak the measurable parameters, similar to adjusting knobs on a device. We apologize if the term “knobs” seems to lessen the significance of object and field parameters.

Using Mobilizing Function Resources can assist problem solvers in finding new ways to manage the function.

Example—Acid Bath

New knobs identified: (1) Acid contact area. (2) Surface area of the pan.

Method: Relative To

Method

Step 1: Let us pick a specific box.

Step 2: What is this parameter being compared to? The answer to this question lies in the new knob.

Step 3: Review all of the boxes to determine if there are more “relative to” knobs.

Explanation

Here is yet another way to identify knobs. One might wonder why we need so many knobs. The discovery of new knobs is quite significant. Occasionally, we can uncover knobs that no one else has found. When we adjust knobs, we uncover new solutions. It’s understandable that a newcomer may feel overwhelmed by the number of independent variables in each situation. Having more options is advantageous. You can take solace in knowing that, in the end, we will likely only adjust a few knobs to find the solution. Here, we are still exploring what is available.

Every knob is measured relative to something. Consider changing that “something” instead.

Example—Acid Bath

By this point, we recognize that there are a number of factors involved in the acid bath problem.

Step 1: Let us select one specific box.

Answer: The hours of usage are high: 1500 hours.

Step 2: What is this parameter in relation to? The answer to this question introduces a new factor.

Answer: Number of cubes and the total oven time. These are new factors.

Step 3: Look through all the boxes to determine if there are additional “relative to” knobs.

A completed causal analysis diagram is shown below. The implied contradictions are included to emphasize that they are also valid contradictions. Typically, these diagrams would be presented without the implied contradictions for the sake of brevity.

Verifying the Causes (And Maybe the Solutions)

Verify the Causes (and Maybe the Solutions)

Method

Step 1: Test to confirm you can turn the problem off and on by adjusting the system settings.

Step 2: If nothing worsens, you likely have a solution.

Step 3: Ensure there are no “loose ends.” All evidence should fit together and support the same conclusion.

Explanation

If we can switch the problem off and on by adjusting settings, we have a stronger case for a solution. Some might think we’ve found the solution, but not really. Often, we identify a setting that fixes the issue, but can cause another problem to worsen.

Alternatively, we might discover simple settings that solve the problem without creating new issues. If this is the case and you don’t need more solutions, you can move on to implementation. In situations with tight deadlines, this might be the best choice. However, if we need multiple solution options or want to simplify the system further, we should move to the next step in the process and tackle the problem more thoroughly.

References

[1] Frank Gilbreth, “Process Charts—First Steps in Finding the One Best Way” American Society of Mechanical Engineers (ASME) in 1921

[2] Ericson, Clifton (1999). “Fault Tree Analysis – A History”. Proceedings of the 17th International Systems Safety Conference.

[3] Rechard, Robert P. (1999). “Historical Relationship Between Performance Assessment for Radioactive Waste Disposal and Other Types of Risk Assessment in the United States”. Risk Analysis (Springer Netherlands) 19 (5): 763–807.

[4] Winter, Mathias (1995). “Software Fault Tree Analysis of an Automated Control System Device Written in ADA” . Master’s Thesis (Monterey, CA: Naval Postgraduate School).

[5] Benner, Ludwig (1975). “Accident Theory and Accident Investigation”. Proceedings of the Society of Air Safety Investigators Annual Seminar.

[6] Also know as a Five Whys analysis, based on a Japanese quality technique and its description by quality consultant Peter Scholtes. See Peter Senge’s ”The Fifth Discipline Fieldbook.”

[7] Also known as Ishikawa diagrams were proposed by Kaoru Ishikawa in the 1960s, who pioneered quality management processes in the Kawasaki shipyards. Hankins, Judy (2001). Infusion Therapy in Clinical Practice. pp. 42.

[8] First paper published by G.S Altshuller and Rafael Shapiro was “Psychology and Inventive Creativity” which was published in the Journal Voprosi Psichologii— (Problems of Psychology)

[9] The origins of Root Cause Analysis can be traced to the field of Total Quality Management (TQM) It is believed that it has been in use in the fields of engineering since the early 1980s. (Andersen & Fagerhaug, 2006, p. 12).

[10] The concept of “alternative problem” was used by G.S. Altshuller in most versions of the Algorithm for the Solution of Inventive Problems (ARIZ) . The intent was to identify an alternative problem that could be solved and compare it to the original problem. It was recognized that the alternative problem might be easier or more obvious to solve. For an example of this see step 1-2 on page 111 of The Innovation Algorithm by G.S. Altshuller.

[11] There are a number of ways to construct causal analysis diagrams. The method that is used in this book is an adaptation of the methods described by John Terninko, Alla Zusman and Boris Zlotin in Systematic Innovation An introduction to TRIZ, St. Lucie Press, pages 47-63.

[12] The concept of “alternative problem” was used by G.S. Altshuller in most versions of the Algorithm for the Solution of Inventive Problems (ARIZ) . The intent was to identify an alternative problem that could be solved and compare it to the original problem. It was recognized that the alternative problem might be easier or more obvious to solve. For an example of this see step 1-2 on page 111 of The Innovation Algorithm by G.S. Altshuller.

[13] Don Rossi from personal conversation and email during 2009. Don pointed out that identifying where something did not occur could be as important as identifying where it did occur and gave an example of cancer. He asked: What organs of the body seem unaffected by cancer? Why should they be spared? What are the differences between unaffected organs and organs that are more easily affected?

[14] The first structured process maps were made by Frank Gilbreth and presented to members of ASME in 1921 as the presentation “Process Charts—First Steps in Finding the One Best Way”.

[15] The first story boards were created at Disney studios by Webb Smith in the early 1930s. It originated from cartoon panels that were pinned up on a wall to tell a story. The idea of story boards then spread to other studios. The idea is easily adapted to problem solving.

[16] Genrich Altshuller, Boris Zlotin, Alla Zusman, Vitalii Filatov. “Searching for New Ideas.” Kishniev: Kartya Moldovenyaska Publishing House, 1989.

[17] Boris Zlotin, Alla Zusman, “Solving All Scientific Problems” Kishniev: Kartya Moldovenyaska Publishing House, 1989.

[18] Stan Kaplan, “Finding Failures before They Find Us: An Introduction to The Theory of Scenario Structuring and the Method of Anticipatory Failure Determination.” Proceedings of the 9th Symposium on Quality Function Deployment, June, l997. http://www.qfdi.org

[19] Kaplan, S., Visnepolschi, S.,Zlotin, B., Zusman, A., ‘New Tools for Failure and Risk Analysis; Anticipatory Failure Determination (TM), Ideation International Inc, Detroit, 1999.

[20] The Inventive Standards can be found in a variety of texts including Yuri Salamatov, TRIZ: The Right Solution at the Right Time by INSYTEC pages 226-244

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