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Module 4Process & automation 18 min

Lab: Map & improve the returns process

Map Harbor Lane's returns process from fragments, localize the root-cause gap, score every step for automation, and write the to-be recommendation.

The final piece of the investigation: the fix, installed in the process. Below is the returns-flow description assembled from your interviews and the support system's docs — incomplete and slightly contradictory, like every process source you'll ever get.

returns flow — assembled fragmentstext
- Customer requests return via web form or in store; web requests create
  a support ticket with a free-text reason field (agents 'usually' pick a
  reason tag from a dropdown as well - support lead: 'when there's time').
- Agent approves or denies based on policy doc (last updated 14 months
  ago); approved web returns get a prepaid label emailed.
- Item arrives at warehouse; inspection 'when volume allows' (fulfillment:
  'day or two'; support tickets suggest longer); inspector marks item
  restockable / damaged / discard. Damaged items: no photo required.
- Finance issues refund after inspection for items over $50; under $50,
  refund goes out at label-scan to keep customers happy (policy exception
  added during a past holiday rush, never revisited).
- Refund reason in the finance system is copied from the ticket tag IF
  present; otherwise logged as 'other'.
- Packaging: warehouse packs orders per the pick list; items flagged
  fragile get the rigid box + fill; everything else ships in standard
  (since April: lightweight eco) mailers. Fragile flags come from the
  product catalog; no owner listed for catalog fields.
  1. 1Draft the swimlane with the [UNKNOWN]-marking prompt (lanes: customer / support / warehouse / finance / systems). Expect 4-6 legitimate UNKNOWNs — e.g., what happens to denied requests? who audits the under-$50 fast path? Keep them visible; two of them go in your recommendation as open risks.
  2. 2Annotate with what you know: reason-tag coverage (the 'other' leak — quantify it from extract 3's other share), the inspection wait discrepancy, the never-revisited $50 exception. Mark the root-cause gap in red: fragile flags, unowned, stale — the process explanation for everything Module 2 found.
  3. 3Score every step with the frequency / rules-vs-judgment / error-cost rubric, and assign AI seats: reason-tagging (decide — and note it would have flagged the damage spike in April, not June), refund decisions (draft), inspection-photo triage (assist), fragile-flag review (not automation: ownership + quarterly checklist). Defend any step you deliberately keep fully human.
  4. 4Write the to-be recommendation (one page, answer-first — Module 3 muscle): the two process fixes (flag ownership + review cadence; mandatory reason tags with the dropdown-first form change), the two AI installations (tag automation with sampled audits; refund-draft with agent approval), sequenced with the cheap-and-reversible steps first, each with owner + success metric ('damage-reason share back under 25% by September; other under 10%').
  5. 5Stress-test it: run the three-persona Q&A (COO, CFO, support lead whose team gets the new mandatory field). The support lead's objection is the real one — you're adding a click to their busiest workflow. Your answer should include what you're removing (the free-text field's ambiguity, the re-tagging) — process changes that only add are process changes that quietly die.
Problem set 4 — and the arc, complete

The problem set maps a different process (invoice approvals) with a planted judgment-step-disguised-as-rules trap. But step back: interviews found the clue (April, mailers, stale flags), data confirmed and sized it, the story sold it, and the process work just installed the fix and the early-warning system. That end-to-end arc — not any single skill — is what the certification attests, and it's exactly what you'll now run solo in the capstone.