Agree on starting points and ranges before changing anything. Choose SMART targets grounded in historical distributions and capacity constraints. When anyone asks why a goal exists, the answer should reference data and trade-offs, not hope. Clarity builds trust and protects teams from unrealistic, demoralizing demands.
Propose a hypothesis, define success metrics, decide sample sizes, and precommit to analysis methods. Pilot with a slice of traffic or a single workcell. When results arrive, accept them. Evidence-based iteration accelerates progress, reduces politics, and teaches everyone that learning beats certainty in complex operations.
Hold weekly reviews that inspect trends, test assumptions, and celebrate wins. Publish briefs summarizing context, actions, and outcomes. Invite questions from frontline staff and customers. This transparency tightens feedback cycles, uncovers blind spots, and strengthens belonging, encouraging subscriptions, comments, and voluntary contributions to shared measurement repositories.