Exception Handling Process Template
Three documents and three sheets that score every recurring exception on frequency and approval rate, so the ones acting like policy get written into it.
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A return that arrives 38 days after purchase gets argued the same way every time, because nobody has counted how often it happens or how often it gets approved. Vantage Outfitters, a 40-person apparel retailer, logged six months of return exceptions instead of guessing. Final-sale returns came in more than once a week, the highest volume of any type, and looked like the policy gap. Only 26.5 percent got approved. It was working as intended, argued fresh every single time.
Frequency alone ranks that pair backwards. Price match requests, honored within 7 days of purchase, came in less than once a week, well below final-sale's 1.31. But 89.5 percent got approved, meaning the store was already saying yes almost every time it was asked. Clearing a frequency bar and an approval-rate bar together is not a judgment call being made fresh each time. It is a rule the business already follows in practice, just never written down, which is why price match qualifies for promotion and final-sale returns do not.
The same split appears in statistical process control. NIST's own handbook defines a common cause as a source of variation affecting every instance, one that is difficult to eliminate by reacting case by case. A special cause, by contrast, is intermittent and does call for individual judgment. A frequent, consistently-approved exception behaves like the first kind, and belongs next to the SOP library as a written rule instead of a recurring argument. One that fails the bar stays the second kind, kept as a named category in the Decision Tree rather than re-argued from scratch.
What's in the pack
Exception Policy
What counts as an exception, the frequency and approval-rate bars a recurring one has to clear, and what happens next.
Decision Tree
The order to work a request, so a known category gets the same ruling no matter who is on duty.
Approval Note
The write-up format for one ruling, short enough for the ticket and specific enough for the next similar case.
Exception Log
Every request logged, approved and denied both, because a denial rate only means something measured against what actually got refused.
Frequency Analysis
Each exception type scored on how often it comes up and how often it gets granted, ranked on both together.
Standardization Candidates
The types that cleared both bars, with the specific rule change proposed, ready to announce and roll out.
How it works
- 1
Send the history
Tickets, emails or a spreadsheet covering the last two or three months of exception requests, approved and denied both.
- 2
Every request gets logged
Each one typed into the Exception Log with its type and ruling, building the real denominator the frequency analysis needs per type.
- 3
Types get scored
The Frequency Analysis ranks each type on how often it comes up and how often it gets approved, together rather than as two separate readings.
- 4
Qualifying types become policy
Standardization Candidates lists what clears both bars with a proposed rule change, and the Decision Tree updates so the next request is answered consistently.
Frequently asked questions
What counts as an exception versus a normal request?
A request to deviate from a documented standard process, made by someone who already knows the standard exists. If nothing is written down for the situation at all, that is a documentation gap, which the SOP gap audit finds, not an exception to log here.
Why log the denied requests, not just the approved ones?
Because a denial rate only means something measured against what got refused. A log of approvals alone cannot tell a type granted 90 percent of the time from one granted 100 percent, and that gap is exactly what decides whether it becomes policy.
What stops two approvers from ruling differently on the same type?
The Decision Tree, which sends a known category to the criteria the prior cases were judged against instead of a fresh read. Consistency across whoever is on duty is the entire reason a category gets written down at all.
How many exception requests do I need before this works?
A few months of history is enough to see which types clear the bars. Fewer than that still runs, it just means one unusual week carries more weight in the count, so treat an early result as provisional.
What happens to a type that almost qualifies?
It stays in the Decision Tree as its own category with its own criteria, reviewed again next quarter rather than promoted or dismissed on one pass. Policies drift, and so does what counts as routine.
What format are the downloaded files?
Word (.docx) for the three documents, CSV (.csv) for the three sheets, zipped together and needing no conversion. Excel, Numbers and Google Sheets open the sheets directly, and the format documents open in Word or Pages.
What does 'Edit with AI' actually do?
It creates a private workspace with this exact pack installed, then asks for your exception history: tickets, emails or a spreadsheet, approved and denied both. River logs each one, scores the types, and proposes which ones to promote.
Find out which exceptions already are your policy
Send the exception requests from the last few months, approved and denied both, and see which types clear the bar to become a written rule.
Edit with AI