Thanks to the evolution of the global quality movement, we now have an extraordinary collection of tools for analyzing problems and measuring and managing complex processes.
Here are the ones I use on virtually every innovation engineering assignment.
Decision Tree / Driver Tree Diagrams
You can use this valuable decision-making tool to translate a high-level strategic objective into increasingly detailed tactics and action steps. In addition, you can use decision trees to prioritize tasks based on their impact on the budget or timeline…identify Key Drivers using information from other sources…and help your team keep track of its progress on key initiatives.
Used widely in management circles, business schools, the investment community, and other organizations, decision trees have another strong selling point: They help you make smart decisions even when you have to rely on expert opinion and logical assumptions instead of cold, hard facts.
Many managers look at performance from three isolated moments in time: this month, last month, and this month a year ago. But that limited viewpoint can give you a very distorted perception.
That’s why Run Charts are a great tool for managing high-priority processes. They reveal performance trends over a longer timeframe. They also help you identify process variations, abnormalities and factors that may be interfering with optimal consistency, because they often include a line that represents the average or mean performance.
One of the big decisions every manager faces is when to intervene. So you need a tool—based on objective analysis, not fallible subjectivity—that will guide you in making the big decision about jumping in or staying on the sidelines.
With a control chart—which graphically tracks performance data over a defined timeframe—you can establish a visible “comfort zone” with upper and lower limits that help you evaluate trends.
When your data points fall within the zone, you can rest easy (at least for awhile).
If your data fall outside the zone, a significant process change has occurred. And you need to investigate right away to see if there is a significant problem.
Control Charts also give you a strong clue about the future. If the trend line is wandering up and down in the comfort zone, the process will probably stay under control for the foreseeable future. If the trend line is getting ready to break out of the zone, the process will probably be out of control sooner or later.
But here’s something to remember. In innovation engineering, you’re trying to achieve results that are “out of control” by previous standards. So there are times when you want to see trend lines break out of the comfort zone. If you don’t see that happening, you’re innovations may not be working as effectively as you expected.
Like many of the tools I’ve discussed here, the Control Chart has an interesting history. It was developed at Bell Labs in the 1920s by Walter Shewhart and later used to improve munitions manufacturing processes in the U.S. during World War II.
Edwards Deming, founder of the modern quality management movement, adopted it shortly after its invention. Mr. Deming later showed Japanese manufacturers how to use Control Charts, sparking an industrial transformation that turned Japan into the international poster child for manufacturing efficiency and quality.
You know about one Law of Averages. Let me propose another: If you make business decisions based on averages, you are making a mistake.
The simple fact is, averages conceal variations in data that often hold the key to insightful analysis. Example in point: A man with his feet in the oven and head in the refrigerator is, on average, pretty darn comfortable. But does that “average case” give the man any information he can use to improve his situation?
The histogram, on the other hand, offers a much more insightful view of the frequency distribution of data. Similar to a bar chart in appearance, it groups data points into different classes of information along a continuous range. And it reveals important patterns that may be impossible to detect by looking at the raw data.
Bottom line? Histograms are a great way to display variations in process quality and output. They also provide the detailed, data-driven insights you need for effective decision-making.
A personal aside: I once used a histogram to assess the performance of my employees in a factory where everyone was rated on a scale from one to ten. The distribution curve that showed up in the histogram had a big impact on shaping my views on individual productivity and employee engagement.
One of the most important ideas to come out of the quality movement is the principle of “the vital few.” It’s often called the “Pareto Principle” in honor of the Italian economist Vilfredo Pareto who noted that that eighty percent of the land in his country was owned by twenty percent of the people.
Pareto Charts provide an easy way for people to sort data based on relevant importance. So when you complete a chart—if you do it correctly—you should be able to see the “vital few” performance factors in virtually any analysis. You should also end up with an 80-20 distribution.
Innovation engineers use Pareto Charts to identify the Key Drivers and Sub-level Drivers of performance. I also compare “before-and-after” Pareto Charts to assess performance factors and validate the accuracy of the original Key Driver analysis after an innovation has been deployed.
The Five Whys
It’s amazing how quickly you can determine the root cause of a problem or process breakdown simply by asking a series of questions based on the “Why did this happen? Why did that happen?” theme.
In the hands of an experienced facilitator, this technique—which was invented by Sakichi Toyoda, the founder of Toyota—probes beyond the obvious to help teams identify controllable causes of specific effects without requiring time-consuming data collection and analysis.
I really like the Five Whys, because they help you see that most problems are ultimately the result of controllable process breakdowns, not hard-to-control human error. As a result, you can take action to improve the process and prevent similar problems in the future.
Innovation engineers also use this disciplined questioning process to identify Key Drivers and generate ideas for Key Driver-based innovations. In addition, the Five Whys are often used in combination with Root Cause or Fishbone diagrams, which represent the next Power Tool in this discussion.
Root Cause or Fishbone Diagrams
If there was a Mt. Rushmore for quality, Kaoru Ishikawa would be a finalist for inclusion. A pivotal figure in developing modern management techniques, he invented the Fishbone Diagram in the 1960s to help analyze the causes of defects.
This graphical display—which is also called a Cause and Effect or Ishikawa diagram—is a great tool for brainstorming, because it gives you a disciplined way to consider all of the inputs of a process and all of the potential causes of a problem or defect.
If you switch your focus from a defect to a desired effect, these diagrams can also help you design innovative new products, services and processes. So they have a variety of applications in innovation engineering.
Flow Charts help you analyze complex operational processes from input to output, because they translate workflows into a series of sequential steps that anyone can recognize and understand.
Once you develop a Flow Chart for a particular process, you can identify problems, spot bottlenecks, and highlight inflection points that mark a deviation from the norm. All of that information will help you take fast action to improve process quality and efficiency.
In terms of innovation engineering, there are two very important applications for Flow Charts. You can use them to:
- Optimize the efficiency of existing processes in your organization. So you can free up the extra process capacity you need to make a breakthrough.
- Design efficient processes for newly developed innovations prior to deployment.
As you might expect, Flow Charts have a long history in terms of engineering and quality management. According to Wikipedia, the first Flow Chart was introduced to an organization of mechanical engineers nearly a century ago in a presentation called “Process Charts—First Steps in Finding the One Best Way.”
I love that last phrase. It’s a great alternative to “process optimization.”
Check Sheets give you an easy way to collect real-time performance data right on the spot. Once you customize the Check Sheet form for a specific application, all you have to do it put an X or checkmark in the appropriate box. Then you just add up the tallies.
Because of the simplicity of the form and the sharp focus on a very specific set of data, Check Sheets are easy to create, use, understand and interpret. The downside? They can sometimes be subject to bias.
There are a lot of applications for Check Sheets in innovation engineering. For example, I once used them to track the number of daily prospect appointments made each day by the sales reps on my team. I started doing that right after we determined that the number of daily appointments was a Key Driver of sales. Quality control experts also use them to count and categorize defects.