Tag Archives: levels of problem solving

Complexity of the Problem

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“I understand there is a difference in thinking-near-term vs thinking-long-term. Conceptually, I understand. How does that help us, as managers inside a company?” I asked.

“You are familiar with delegation?” Pablo asked, knowing the answer.

“Of course,” I replied.

“You say that so fast, I assume you do NOT understand delegation, except at its surface level,” Pablo stopped. “You understand delegation as a task assignment. What you delegate is not just the task, but the decision making and problem solving that goes with it. Inside any task assignment, as a manager, you must also understand the level of problem solving that goes with it.”

“Near-term vs long-term?” I confirmed.

“Yes, the timespan of the decision will accurately determine the level of problem solving required. If I delegate a step in a process that is due tomorrow, there are decisions that go with it, AND most of the variables are known. To meet a special order for a customer tomorrow, the team can work a little overtime with the materials at hand and we can meet the order. If we have another special order, how do we do that second order?” Pablo asked.

“The same way we did the first special order. Work a little more overtime,” I replied.

“But, what if we get 50 special orders?” Pablo challenged.

“Well, there isn’t enough overtime for 50 special orders, and if we focus on those, what happens to the regular orders that were already in process, it would play hell with our schedule,” I replied.

“You see, that is not such a simple problem. And, you immediately began to think about the impact in the future. Processing 50 special orders, with special setups, depleting our materials on hand, some of which have lead times, delaying our current scheduled commitments to customers with whom we have contracts, the timespan impact of the problem grows. I would submit to you, the complexity of the problem is not just more moving parts.”

“But this is not an unusual problem, companies face this all the time,” I said.

“And, companies figure out the solution all the time. We can accurately measure the complexity of the problem by identifying the timespan impacts of each of the elements of the problem. The timespan impact of each element leads us to the complexity of the solution. Lead times of depleted materials is a clue. If the lead time is six weeks, we don’t have an immediate impact of one delayed order, we have a six week impact on all orders. We cannot solve this problem by working overtime.”

Four Levels of Knowing

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What-we-know is a mental configuration. The way we configure what-we-know extends along our timespan of intention.

Most ideas exist independent of each other. If our timespan of intention is short, it is a perfectly good way of organizing what-we-know. We can rely on what we see, hear, touch, smell. Life is relatively simple. We can choose this idea OR that idea. This is the world of trial and error.

But, we wake up one morning and see ideas that are connected together. Our timespan of intention extends further into the future. What we see, hear, touch and smell is organized by ideas that are connected. This is the world of best practices, connected to our most common problems.

But, we wake up one morning and see ideas that are caused by other ideas. There is not only a connected relationship, but a cause and effect relationship. Our timespan of intention extends even further. Best practices help to solve problems we have seen, but are useless to problems we have never solved. What-we-know comes from root-cause analysis, the basis for creating a single serial system, a series of ideas sitting in a sequence of cause and effect relationships (critical path).

But, we wake up one morning and what-we-know includes more than one system. We see multiple systems sitting side by side. Each internal system has its own constraints, but some of those constraints now sit outside the system. Each system has an output which becomes the input for its neighboring system. Defective output from one system wreaks havoc on its neighboring system. And some systems outstrip the capacity of neighboring systems, crippling overall throughput of the entire enterprise. If our timespan of intention extends this far, our problems exist in the hand-off between systems and in the output capacity of one system to the next. The organization of what-we-know comes from systems analysis.

We can only know (what-we-know) what we are capable of knowing.