Narrow Scope Reveals Macro Simplicity

Definition

When a value stream handles high variation — different order types, patient conditions, request categories — mapping teams are told to narrow the current-state scope to one specific, well-defined condition rather than trying to capture every variant at once. Counterintuitively, the book reports this narrow slice almost always turns out to represent 25 percent or less of total volume, yet the future-state design built for it ends up applying to 75 percent or more of the value stream's actual variation.

In the Book

Chapter 2 works through this with two concrete examples. An order-fulfillment value stream may route orders down "extremely different paths depending on the type of order" (Figure 2.3), so the team picks only the variants with thick borders — a defined subset — to build the current-state map. In an outpatient imaging value stream (detailed later in Appendix B), the team chose to map CT scans only, out of MRI, mammogram, and x-ray traffic, selected by criteria like highest volume or most problematic. The book names the reasoning explicitly: at a macro level, "there isn't as much variation as it 'feels' like there is" at the micro level — similar performance issues (batching, handoffs, poor %C&A) tend to recur across superficially different request types, because the barriers to flow live in the shared organizational structure, not in the specifics of any one variant. The authors' advice is to push past the discomfort of narrowing scope: "don't be afraid to narrow your scope beyond your comfort level," because teams that do so consistently find their future-state design applies more broadly than the narrow current state suggested.

Why It Matters

Trying to design one solution that simultaneously handles every variant of a problem usually means either the analysis never finishes or the design becomes too generic to fix anything. Deliberately restricting scope to one representative, well-bounded case is often the fastest way to find the shared root cause underneath surface variation — a bet that a genuinely narrow, deep look at one instance will generalize better than a shallow look at all of them.