“Some people love to make things complicated. The key is to make things simple.”
(As recounted by Michikazu Tanaka in The Birth of Lean)
In my last post, I introduced the concept that the underlying philosophy behind the success of TPS is its ability to simplify complex adaptive systems. But, it is important to have a basic understanding of the foundations and characteristics of complex adaptive systems before we can take a step back and see how that helps explain “why TPS/Lean works” (and “why”, all too often, it “doesn’t work”).
I will start out by quoting some excerpts from a paper by Joseph A. Tainter (Ph.D. Anthropology) titled “Problem Solving: Complexity, History, Sustainability”. This is a fairly long paper and covers broad historical entities (growth and decline of the Roman Empire, the emergence of modern Europe, etc.) but it breaks down the lessons learned into concepts that can be applied to smaller entities (businesses and social organizations for example). The premise under study is how well can mankind solve problems by steadily increasing the complexity of their societal institutions and remain sustainable over time. (Hint: there are both winners and losers in this game).
So what does Tainter have to say?
OK, I know. How can I condense 39 pages of a very complex research paper into 12 bullet points? Well, it helps if you have a very specific focal point as you plow through the paper, i.e., TPS/Lean. And it also helps if you have experienced a long career dealing with complexity but not realizing you were dealing with complexity at the time. The important stuff tends to jump out at you as you relive those experiences. But I welcome input if anyone would like to add or subtract from my attempted summary. Besides, I didn’t think anyone here wanted to relive the rise and fall of the Roman Empire.
My excerpt selections:
- We seem often to be averse to complexity. The reasoning behind sayings like “Keep it simple” is universally understood. …In science, the Principle of Occam’s Razor states that simplicity in explanation is superior to complexity.
- Most of the time complexity works. It is a fundamental problem-solving tool. In its early phases, complexity can generate positive feedback and increasing returns.
- All that is needed for growth of complexity is a problem that requires it. Since problems always arise, complexity seems to grow inexorably.
- The cost of supporting complexity is the energy, labor, time, or money needed to create, maintain, and replace a system that grows to have more and more parts and transactions, to support specialists, to regulate behavior so that the parts of a system all work harmoniously, and to produce and control information. Yet as firms become larger there are diminishing returns to scale. Transaction costs increase as information channels become congested, waste increases, and the cost of organizing further internal transactions grows.
- Because of the problems of transaction costs, rational, omniscient decision makers will reduce internal transactions when the cost of an extra internal transaction equals the cost of an external one. The problem is that decision makers are typically not omniscient, and cannot foretell the future. Thus they inevitably make decisions that inadvertently increase costs.
- Problem-solving, whether involving resources or information, commonly evolves along a path of increasing complexity and positive returns, then higher costs and diminishing returns.
- Complexity has unintended consequences over the long term in part because it is cumulative. Each increment of complexity builds on what has gone before, so that complexity seems to grow exponentially.
- This is the central problem: diminishing returns to complexity. Carried far enough it brings on economic stagnation and ineffective problem solving.
- “Every active force produces more than one change—every cause produces more than one effect” – Herbert Spencer
- “Decision-makers rarely foresee the full consequences of their actions.” – Peter Senge
- Decision making in a complex system may be surrounded by such confusion as to make the linkage between problem and solution tenuous.
- Historical case studies illustrate different outcomes to long-term development of complexity in problem solving. These cases clarify future options for contemporary societies: collapse, simplification, or increasing complexity based on increasing energy subsidies.
We’ve all seen this growth in complexity in our workplaces, right? Or have we? Did we even notice? Complexity doesn’t happen overnight. Besides, complexity usually solves some type of problem we are having. Put in some complexity and the problem goes away – we are happy and relieved. And we go to the next problem. That’s what we are paid to do. We are paid to solve problems and if we don’t have a solution ready at hand, we are paid to learn to cope with the problem. Either way, we expend more money and/or energy than we did before. That’s what we do. We survive through increased complexity.
And the scary part is, we grow due to this increasing complexity. At least at first. We add more part numbers in manufacturing and we sell those part numbers and we grow. Of course, part of that growth is adding more staff, systems, floor space, etc. to handle those extra part numbers. But we feel good – we are growing. Sales are up. Profits are up. Let’s add some more part numbers! Grow, grow, grow!
But how about profits as a percentage of sales? How about profits as a percentage of cash invested? That’s where the real story is. Are those metrics growing also? In the early part of the growth curve, they probably are. If not, somebody will say, “Don’t worry, we’ll grow our way out of it! – Add some more part numbers!”.
At some point in the growth curve, we all know what happens. Profits as a percent of sales or investment starts declining. And then (or later), somebody notices (usually higher up). We also know what happens next. Somebody says (also usually higher up), “We’ve got to cut costs!”. So we lay off staff, we squeeze suppliers, we look for cheaper everything.
But do we reduce complexity? Do we reduce our part numbers and downsize? Do we simplify? Absolutely not! “We can’t give up what we’ve worked so hard to achieve! We will just have to work a little harder to keep what we’ve got! It’ll all work out OK if we just keep chugging along” goes the mantra.
Well, this story is a rather simplified version of normal reality, but it does reflect the gist of what Tainter is expressing. The last Tainter bullet point gives us our remaining options. Collapse, simplify or find another source of energy (money). The latter option is usually a slower road to the first option.
But Lean/TPS gives us a path to Tainter’s second option – simplification! In fact, simplification, early and often, may allow us to avoid ever facing those other two options.
I’ll begin to outline this path a little more clearly in the next post.