Tools for Solving Problems


Persistent problems cannot be solved by repeatedly using the same knowledge and insights. Or, as Albert Einstein phrased it, we can’t solve the problems at the same level of thinking with which we created them!

Few decisions have a greater impact on the likelihood of success of an improvement project than the definition of the problem.

Stephen Covey says that the way we see the problem is the

Dr. Don Wetmore of the Productivity Institute says that a problem well defined is at least 50% solved!

However you choose to look at it, the way we define and communicate the problem the team is expected to solve will greatly influence the speed and efficiency with which a team will complete its work, the
degree of satisfaction between the team and the project sponsor, and the efficacy with which an organization prioritizes and sequences the problems to devote resources to.

So the first key step to problem solving is to define the problem. Four key best practices for doing so are:

  1. Write it down and share it
  2. Quantify the waste it is causing
  3. Be specific about the metric you are using to size the problem
  4. Avoid judgments or opinions about root causes

Once a problem is well defined, it is often best to use classic problem-solving tools to examine current reality from a variety of different angles. This will most often require the use of multiple tools to reveal more advanced insights and solutions, as in many cases no one tool will provide all the answers. These tools can include:

  • Pareto Charts to explore ideas about possible causes
  • Process Mapping to spot and quantify the waste and trace it to the primary cause
  • Cause and Effect Diagramming to stretch beyond initial ideas about possible root causes
  • Histograms to provide new insights into the dynamics of process performance
  • Run Charts to understand current process performance and distinguish between random variation and special causes
  • Scatter Diagrams to clarify the importance of possible causal factors on results measurements
  • Affinity Diagrams to find breakthrough ideas and natural relationships among the data
  • Priority Matrices to consider alternatives and identify the right things to work on
  • Interrelationship Digraphs to visually demonstrate the relationship among factors—causal factors (drivers) vs. symptoms