System Improvement Process (SIP)

The System Improvement Process is a simple, generic, analytical process designed to apply to all types of complex social system problems, especially difficult ones. SIP has these four main steps:

1. Problem Definition - Identify the problem in terms of the present and goal states of a particular system. The goal state is the desired state that a solution will move the system to.

2. System Understanding - Analyze the problem/system until its key cause and effect relationships are understood. This step is also called analysis.

3. Solution Convergence - Use that knowledge and experimentation to converge on a solution.

4. Implementation - Implement the solution.

The first step is to formally define the problem to be solved. This greatly focuses all subsequent problem solving effort. The second step is to understand why the system behaves the way it does so well that its low and high leverage points become obvious. This causes the third step, converging on the solution, to be relatively straightforward. The fourth step implements the solution.

Let's take a deeper look at SIP and see why it is so powerful.

The System Improvement Process works its analytical magic by decomposing a problem into three carefully engineered, much smaller subproblems:

A. How to overcome systemic change resistance. This is refusal to adopt workable solutions and move away from the status quo. Once systemic change resistance is overcome, the system will "want" to move to the goal state. This is a key principle.

B. How to achieve proper coupling. This occurs when the behavior of one system affects the behavior of other systems properly, using the appropriate feedback loops, so the systems work together in harmony in accordance with design objectives. For example, presently the human system is improperly coupled to the greater system it lives within, the environment. Once proper coupling is achieved, the system is in the goal state.

C. How to avoid excessive model drift, so the problem doesn't recur, due to solution obsolescence. Model drift occurs when a solution model drifts so far from what's needed to keep a system in the goal state that problem symptoms reappear, and the system is no longer sufficiently properly coupled. Since complex social systems tend to continuously evolve, model drift is always present, unless avoiding it is built into the solution. This is also known as self-managing solutions.

Each of these subproblem is much easier to solve. For a difficult complex system problem, this has the effect of taking a giant Gordian knot of incomprehensible complexity and deftly turning it into three much simpler and therefore potentially solvable problems. In practice this decomposition is so powerful it can transform a problem from insolvable to solvable.

The subproblems are interrelated. The first must be overcome so that the solution to the second can be implemented. The third must be solved to prevent overall problem recurrence. All three must be solved to solve the overall problem.

The goal state of the system occurs when problem symptoms are reduced to acceptable levels. If the system is in the goal state or is moving there by a predetermined deadline with a sufficiently high probability, the problem is considered solved.

The System Improvement Process gains it analytical power through three key strategies: (1) Decomposition of the problem into the three subproblems of change resistance, proper coupling, and model drift. (2) The use of the System Understanding Step to understand the system so well that its low and high leverage points become obvious. (3) The organization of the process into a total of 22 steps. Each step is a clearly defined, much smaller problem to solve in itself. Which would you rather solve: One big impossible problem or 22 little easy ones?

The use of explicitly defined steps makes this a repeatable and improvable process. Below are the main steps. Notice how they form a reusable template for asking The Right Question at each step in the process:

1. Problem Definition – What is the problem? This is defined in terms of the goal state versus the present state of the system with the problem.

2. System Understanding – Why are the three subproblems occurring? For each subproblem:
2.1 Why is there such strong change resistance?
2.2 Assuming 2.1 is solved, why is the system not
automatically moving to the goal state?
2.3 Assuming 2.2 is solved, why is the system not staying in the goal state?

3. Solution Convergence – How can the three subproblems be solved? For each subproblem:
3.1 How can change resistance be overcome?
3.2 Once 3.1 is solved, how can we move the system to the goal state?
3.3 Once 3.2 is solved, how can we keep the system in the goal state?

4. Implementation – Once solutions to the three subproblems are found, the three subproblems are solved by these three sequential substeps:
4.1 Overcome change resistance to adopting the solution.
4.2 Move from the present state to the goal state.
4.3 Stay in the goal state indefinitely.

(1) The first step is Problem Definition. (2) The second step is System Understanding. It is where problem solvers should spend about 80% of their time. If the all important second step is done well, problem solvers (and anyone else, including decision makers) will understand the system with the problem so deeply and correctly that the third step, Solution Convergence, is almost trivial. Problem solvers will understand the dynamic structure of the system so completely that they can predict, within a broad range, how it will respond when low, medium, and high leverage points are pushed on. (3) Solution Convergence then becomes a simple matter of selecting a reasonably straightforward way to push on the high leverage points. Because the correct points will be used, almost any form of pushing on them will do. A seemingly trivial solution is the payoff for using the right problem solving process.

The System Understanding (Analysis) Substeps

The System Understanding step is also called analysis, since it's where the main analysis occurs. The analysis step is so crucial it has its own subprocess:

A. Find the feedback loops that are currently dominant.
B. Find the root cause of why they are dominant.
C. Find the low leverage points and symptomatic solutions.
D. Find the feedback loops that should be dominant.
E. Find the high leverage points to make them go dominant.

System Understanding is all about feedback loop dominance. Feedback loops control the behavior of social systems. If you don’t understand a system’s feedback loops, then you don’t understand the system.

The goal of this step is to understand the system’s structure so well that its leverage points become obvious. This will cause two supreme insights about the problem to emerge: The first is that because the system’s low leverage points are now so clearly revealed, it becomes perfectly obvious why we have been failing to solve the problem. The second is that because we can now see where the root causes and high leverage points are, how to solve the problem becomes a relatively simple matter of determining how to best push on the correct high leverage points.

This is why if we do a good job of analysis then the remaining steps, Solution Convergence and Implementation, are relatively easy. That’s why this step is also called analysis. This strategy forms the very heart of why the System Improvement Process is so productive. To execute this strategy, problem solvers should spend approximately 80% of their time in the System Understanding step.

The Process Grid

The process can be better conceptualized by using the grid shown below. (Click on it for the larger version) The grid has been filled in with the results of executing the process on the sustainability problem. The grid is from near the end of the Cracking the Mystery of the Progressive Paradox film.

Note how columns A and C are generic. They apply to any problem whose solution would benefit the common good, not just the sustainability problem. This illustrates how deep the analysis has gone, so deep we are resolving root causes, instead of going with intuitively derived symptomatic solutions, which only address intermediate causes. That of course is why intuitive approaches fail. They have fallen into the Intuitive Process Trap.

When the five substeps for cells 2A, 2B and 2C are added, the grid looks like this:

The problem is defined once, in the problem definition step. Each of the three columns has 7 steps. This gives the process grid a total of 22 steps.

Compare the System Improvement Process to Classic Activism, which has only four steps:

1. Identify the problem to be solved.

2. Find the proper practices, if they are not yet known

3. Tell the people the truth about the problem and the proper practices.

4. If that fails, exhort and inspire the people to support the proper practices.

Classic Activism is the process used by most social problem solvers. It works fine for problems where change resistance is low, where efficient proper coupling solutions are relatively obvious, and where the solution model tends not to drift into obsolescence over time. These are the characteristics that make a problem easy. But if change resistance is high, or efficient proper coupling solutions are obscure, or rapid system evolution causes the solution model to rapidly drift into obsolescence, then you have a difficult problem. Classic Activism has historically failed to solve difficult problems proactively, especially those where change resistance is high.

It's not the 22 versus 4 steps that makes the difference. Any process can have lots of steps. It's the way those 22 steps are organized, the strategies they employ, and the right questions they tend to cause analysts to ask at each step in the long, treacherous road from problem discovery to solution.

Comments

In problem solving jargon, the System Improvement Process provides an extremely efficient means of “searching” a large and unknown “solution space” for a solution that will work. The reduction of millions of possible solutions to one or more that will actually work is known as Solution Convergence, which must be preceded by System Understanding so that convergence happens quickly and correctly.

SIP is an example of a best practice. For a further introduction see Part One of Process Is Everything. For a look at a similar, highly successful process, see the Nature Conservancy's Conservation by Design.

However, please be aware that as good as it is, Conservation by Design has only allowed the Conservancy to achieve its own objectives better. It has not led to solution of the sustainability problem, as explained in the chapter on An Assessment of Process Maturity. This argues that:

The mission of The Nature Conservancy is not to solve the environmental sustainability problem. Instead, it is a conservation organization:

"The mission of The Nature Conservancy is to preserve the plants, animals and natural communities that represent the diversity of life on earth by protecting the lands and waters they need to survive."

The bolding is theirs and is a nice capsule summary of their mission. It points to the goal of preserving enough of the biosphere to save the diversity of life on earth, while ignoring the rest. This is a save-the-representative-ecosystems strategy, and is nearly identical to the first form of environmentalism: the conservation movement of the 19th and early 20th centuries. The idea was that if we set aside enough areas of the world as protected parks or managed renewable natural resources, such as forests, then that would keep enough of the earth in pristine condition for the average condition of the earth to be acceptable. This did not work, however, because it had no effect on pollution and environmental degradation elsewhere. It was a naive solution.

Still, The Nature Conservancy is doing a superb job of conservation. As of 2005 they have protected an impressive 17 million acres in the US and 117 million acres in other countries. They have achieved high mission success.

Or so it seems. But there is a dark cloud hanging over every protected acre. It is the brutal fact that if the rest of the biosphere is not protected, it will soon degrade to the point where the human system collapses. That in turn will cause the islands of conservation that The Nature Conservancy has so lovingly set aside to be impossible to maintain, and they too will fall to the irresistible forces of collapse.

One may object that "But problems solvers can't spend 80% of their time in the analysis step! Much and usually most of their time needs to go into implementation."

Yes. But in large difficult social problems, problem solvers rarely implement solutions. That is the responsibility of politicians and governments. But someone needs to keep an eye on implementation results, so the analysis and solution recommendations can be interatively fine tuned. Ideally this is an inherent feature of the model drift part of the solution.

How does SIP address the "Wicked Problem" aspects of the sustainability problem? See this thread on the forum. You may want to first read this Wikipedia entry.