Exception-Based Process Review – Letting the Data Tell You Where to Look

This topic is part of the SG Systems Global regulatory & operations glossary.

Updated November 2025 • eBR/BMR | Deviation/NCR | CAPA | SPC | CPV | MES | GxP Data Lake | Data Integrity | Work Order Execution | Batch Variance Investigation
• QA, QA Ops, Manufacturing, Tech Ops, CI, Digital

Exception-based process review is a structured way of reviewing manufacturing and quality data that focuses human effort on exceptions – pre‑defined events, limits, trends or data‑integrity issues that indicate potential risk – instead of manually re‑reading every single “green” data point in a record. In an electronic environment, rule engines across the eBR, MES, LIMS and historians flag anything that deviates from the validated process design, control strategy or data‑integrity expectations. QA and Operations then review and document those exceptions, rather than blindly scanning hundreds of pages of uneventful log entries.

Done well, exception-based process review is not a shortcut or a compliance dodge; it is a risk‑based way to apply brains where they add the most value. Done badly, it’s just a buzzword to justify slashing QA review time without investing in the rules, validation or data quality that make “review by exception” defensible.

“If your ‘review by exception’ is really ‘review by whatever the system happens to flag’, you’re not being efficient – you’re betting compliance on configuration nobody can explain.”

1) What We Mean by Exception-Based Process Review

“Exception-based process review” is a broader concept than the narrow “Review by Exception (RbE) for eBR”, although that’s a big use case.

• Traditional review:
  • QA (and often Operations) read through entire batch records, process charts, lab reports and checklists line by line, looking for anything unusual.
  • This is exhaustive but slow, inconsistent and increasingly unrealistic as the volume of digital data explodes.
• Exception-based review:
  • Defines what “normal” looks like via recipes, specifications, SPC limits, time windows, audit rules and data‑integrity expectations.
  • Uses logic in eBR/MES/LIMS/historians/data lakes to automatically flag when reality departs from those expectations – exceptions.
  • Directs review effort to those exceptions, with targeted assessment and documentation, plus trend analysis across batches and time.

Critically, exception-based review doesn’t mean nobody ever looks at “all the data”. It means the design of the rules, the validation of those rules, periodic spot checks and CPV analytics collectively stand in for rote re‑reading of every single “within spec” entry. If you skip that design and validation work, then “review by exception” is just wishful thinking with a modern UI.

2) Why Exception-Based Review Matters

Manufacturing processes now throw off far more data than any human team can realistically read. That’s the starting point, not an excuse.

• Volume and complexity:
  • Digital plants record every set‑point, measurement, alarm, signature, weighment and line change in real time. Manual full review doesn’t scale.
• Release cycle time:
  • Batch release, shipment release or product disposition can become gated by QA review; exception‑based approaches are one of the few levers that don’t compromise rigour if done properly.
• Consistency and bias:
  • Manual reviewers differ in what they notice and how they document. Well‑designed exception logic applies the same criteria every time; humans then judge impact and context.
• Signal vs noise:
  • The true problems are often buried under a mountain of harmless small deviations, minor OOTs or expected alarms. Exception rules and analytics help separate real signals from noise.
• Trending and systemic issues:
  • Once exceptions are structured data rather than scribbles in a margin, you can trend them by product, line, shift, supplier or equipment – feeding CAPA, CI and investment decisions.

Regulators and customers are not asking you to read everything manually forever. They are asking you to have control. Exception-based process review is one way to demonstrate control in a world where brute‑force reading is no longer plausible.

3) What Counts as an “Exception”?

“Exception” is not just “OOS result”. It’s any event, pattern or data condition that suggests risk, uncertainty or non‑adherence to the intended process. Typical exception types:

• Limits and trends:
  • Values outside defined specification or process limits (CPP/CQA limits, weight control limits, bake profile windows).
  • OOT or unusual trends flagged by SPC rules (for example, drift, cycles, runs, sudden shifts).
• Process and equipment events:
  • Unplanned downtime, alarm bursts, operating outside validated ranges, repeated manual overrides of automated control.
  • Equipment used despite overdue maintenance or calibration.
• Material and genealogy issues:
  • Wrong component, wrong lot, expired or blocked material, missing traceability link, incorrect or unapproved substitute.
  • Unexpected rework usage or scrap reclassification; see Scrap Dough Rework (Bakery Reuse).
• Yield and mass‑balance anomalies:
  • Unexplained losses or gains relative to plan or historical performance; see Yield Variance and Mass Balance.
• Data integrity exceptions:
  • Late or missing signatures, back‑dating, duplicate entries, corrections without reason codes, disabled or bypassed checks, orphan records.
  • Gaps in continuous data where sensors or systems went offline.
• Workflow and timing violations:
  • Operations performed out of sequence, hold times out of limit, skipped mandatory checks, or steps completed too fast to be plausible.

Exception rules should be deliberate, documented and risk‑based. If the only exceptions you flag are outright OOS results, you’re leaving a lot of early warning signals on the table and pushing problems downstream into complaints and recalls.

4) Where Exception-Based Review Is Applied

Exception-based process review isn’t a single workflow; it shows up in multiple layers of the operation:

• Batch or lot record review:
  • Review by exception for eBR: the classic use case, where QA reviews system‑generated exceptions instead of re‑reading every “within limit” record entry for a batch.
• Work order and shift review:
  • Supervisors review a shift or day by focusing on exceptions: failed start‑ups, repeated alarms, out‑of‑target temperatures, weight failures, missed checks.
  • See Work Order Execution.
• CPV and annual reviews:
  • CPV and PQR/APR teams review trends in exception rates and types across products, lines and campaigns, rather than just narrative summaries.
• Maintenance and reliability:
  • Exception rules trigger review when alarm patterns, downtime or quality hits cluster around certain assets – feeding CMMS decisions.
• Supply chain and service:
  • Exception views of service failures, capacity bottlenecks and schedule breaks (“missed slots”, “late campaigns”) focus S&OP and CI teams on structural issues instead of anecdotes.

You can think of exception-based review as the review philosophy behind a lot of modern dashboards: don’t show everything; show what broke, what’s drifting and what changed – and let experts dig into those spots in depth.

5) Regulatory Expectations – What’s Acceptable and What Isn’t

Most regulators now accept exception-based review in principle, especially for electronic batch records, but with conditions.

• Risk-based, not cost‑based:
  • The driver must be risk and process understanding, not just “we need faster release”. If you can’t show a risk‑based rationale for the rule set, expect pushback.
• Documented rule design:
  • Exception logic must be documented, linked to the control strategy, and understandable to QA and inspectors – not buried in vendor config fields nobody can interpret.
• Validation of the rule engine:
  • The systems that detect and present exceptions must be validated, with tests showing that relevant events are caught and correctly classified, and that critical events cannot be silently suppressed.
• QA accountability remains:
  • QA still owns release decisions and must review all exceptions properly. You can’t say, “the system said there were no exceptions, so we didn’t really look.”
• Periodic verification and spot checks:
  • Organisations are expected to periodically cross‑check a sample of batches with full review vs exception‑based review to confirm the rule set is still adequate.
• Change control on rules:
  • Changes to exception rules go through change control, with impact assessment, QA approval and, where needed, re‑validation.

“We trust the vendor’s defaults” is not a defence. If you can’t explain to an inspector which events are exceptions, why, and how you know the system is catching them, then you haven’t really implemented exception-based review – you’ve just abdicated part of your QA function to a black box.

6) Designing Exception Rules – Where the Real Work Lives

Most of the value – and risk – in exception-based review lives in the rule design. That design should come from process knowledge, not from IT convenience.

• Start from the control strategy:
  • Map CPPs, CQAs, key process parameters, critical workflows and data‑integrity risks. Exceptions should concentrate on these, not on trivial admin details.
• Use QRM to prioritise:
  • Combine failure modes, severity, detectability and historical issues to decide which events absolutely must be flagged and which can be trended or sampled.
  • See Risk Management (QRM).
• Define exception categories:
  • For example: Critical (must trigger deviation and QA review), Major (requires documented justification), Minor (logged and trended but may not block release), Informational (for CPV only).
• Minimise “free text only” exceptions:
  • Where possible, use structured codes and flags, with free text as supporting context, so exceptions can be trended and analysed.
• Beware alert fatigue:
  • Over‑aggressive limits generate noise; people stop paying attention. Under‑aggressive limits miss risk. Expect to tune thresholds using real data and CPV analytics.
• Include absence of data:
  • “Expected but missing” should be treated as an exception: missing readings, missing signatures, missing attachments, missing checks.

A good sanity test: take a few historical bad batches or serious deviations and replay them through your proposed rules. If the rule engine doesn’t flag anything meaningful, you’ve designed a comfort blanket, not a safety net.

7) Data Foundations – Without Structure, Exceptions Are Guesswork

Exception-based review depends on structured, trustworthy data. If your core data is messy, you’re just automating the mess.

• Structured eBR/MES design:
  • Critical information must be captured in structured fields, not free‑form comments or paper attachments. That includes set‑points, actuals, results, reasons, sign‑offs and statuses.
• Time alignment across systems:
  • Historian, MES, LIMS and WMS timestamps should be aligned well enough to reconstruct cause/effect and apply time‑based rules (for example, hold durations, oven dwell times).
• Master data consistency:
  • Specifications, limits, recipes, routing and cleaning rules must match across systems. If each silo of data has its own “truth”, exception logic quickly becomes inconsistent.
• Event and reason code taxonomies:
  • Downtime codes, deviation categories, alarm types and failure reasons should be standardised so exceptions can be grouped meaningfully.
• Data integrity controls:
  • Audit trails, access controls and electronic signatures must be in place; otherwise an exception engine is just analysing potentially manipulated data.
  • See Data Integrity.

Many sites jump to “AI that highlights exceptions” before they’ve standardised how operators record a simple line stoppage. Don’t skip steps: without data discipline, your exception logic will be flaky and impossible to defend.

8) The Human Workflow Around Exceptions

Exception-based review is still a human process; the system just decides where humans look. A typical workflow:

• 1. Detection:
  • During or after execution, the system flags exceptions and groups them by batch, order, line, day or campaign.
• 2. Triage:
  • Operations or QA Ops quickly classify exceptions: what type, which area, likely impact, any obvious false positives or duplicates.
• 3. Investigation:
  • For each meaningful exception, someone with appropriate expertise reviews context: trends, related alarms, material lots, equipment status, similar past events.
  • Formal RCA may be triggered for significant clusters or repeated issues.
• 4. Disposition and documentation:
  • Decide on impact (product acceptable, conditional, rejected), record justification, and link any required deviations or CAPAs.
• 5. Review and approval:
  • QA reviews exception handling, challenges weak rationales and either approves or escalates prior to batch or order release.
• 6. Trending and feedback:
  • On a weekly or monthly cadence, exception data is reviewed for patterns, feeding CPV, CI projects, training and rule‑set refinement.

Tools can help by providing dashboards, drill‑downs and guided workflows. But the hard part is discipline: if people routinely mass‑close exceptions with boilerplate comments, you haven’t reduced risk – you’ve just moved the box‑ticking into a different screen.

9) Linking Exceptions to Deviation, CAPA and the QMS

Exception-based review doesn’t replace your QMS; it feeds it.

• Triggering deviations/NCRs:
  • Certain exception categories – critical limit breaches, unplanned changes, data‑integrity concerns – should automatically trigger a deviation or NCR, not just an informal note.
• Linking to CAPAs:
  • Repeated exceptions of the same type or area are classic triggers for CAPA. Automated clustering of exception data can highlight where “local fixes” aren’t working.
• Risk register integration:
  • Exception trends should feed back into your risk register and QRM assessments – either confirming controls are effective or revealing new failure modes.
• Management review and PQR/APR:
  • Exception statistics (rates, types, time‑to‑closure) are far more informative in management review than generic statements like “no major issues”.
  • See Product Quality Review (PQR/APR).

When exception-based review is mature, you can answer awkward questions like “How many times did this parameter exceed its control limit in the last year, and what happened each time?” with data, not with shrugs and archive searches.

10) Analytics, CPV and Advanced Detection

Once you have a structured exception layer, you can get more ambitious – carefully.

• Exception trending and heat maps:
  • Visualise where exceptions cluster: by product, line, shift, equipment, supplier or time of day. Focus CI and engineering effort where it obviously hurts.
• CPV integration:
  • Use CPV models not just to detect trends but to dynamically adjust what counts as an exception (for example, control limits vs spec limits, multi‑variate patterns).
• Predictive models:
  • Machine‑learning models can flag unusual combinations of parameters or early warning signs of exceptions (for example, oven temperature profile drift that precedes crust colour issues).
• False positive and false negative tuning:
  • Analyse which exceptions turned out to be non‑issues and which serious problems weren’t caught in time. Use that to tune rules and models.
• Data lake context:
  • A GxP data lake can combine exceptions with raw time‑series, maintenance, supplier and complaint data to build a fuller picture of cause and effect.

Don’t be seduced by “AI detects everything” marketing. Use advanced analytics to augment structured, rule‑based exceptions – not to replace clear thinking about what “good” looks like in your process.

11) Common Failure Modes and Audit Findings

When exception-based review goes wrong, the issues are usually predictable:

• Undefined or undocumented rule sets:
  • Config grew organically over time; nobody can produce a coherent description of what the system flags or why. Auditors smell this instantly.
• Critical events not flagged:
  • Major deviations, late sign‑offs or spec hits never appear as exceptions because rules were poorly designed or turned off. QA only finds out via complaints or luck.
• Excessive noise:
  • Hundreds of low‑risk exceptions per batch because thresholds were set at “anything slightly unusual”. Reviewers stop paying attention and start blanket‑closing.
• Rule drift with no change control:
  • IT “tidies up” rules, vendors push updates, product ranges change; nobody re‑runs validation or updates documentation. The system you validated three years ago is not the one you’re using now.
• Over‑reliance on system outputs:
  • QA assumes that “no exceptions” means “no issues”, without considering what the rules actually cover, or whether the underlying data is complete.
• Disconnection from QMS:
  • Exceptions get worked informally (“we fixed it”) but never feed into deviations, CAPA or risk reviews. The same problems repeat, just with more slick dashboards.

If an inspector reviews a few bad batches and finds critical problems that never surfaced as exceptions, they’ll quite fairly ask why you trust this system for other decisions. If you can’t answer, expect a finding and a lot of remediation work.

12) Design Choices That Matter

Beyond the basic rules, a few design decisions have outsized impact:

• Granularity:
  • Do you flag exceptions per individual measurement, per operation, per batch, per campaign? Coarser granularity is easier to review but hides detail; finer granularity can drown you in flags.
• Gating vs non‑gating exceptions:
  • Some exceptions should automatically block batch release until resolved; others can be resolved post‑release as part of trend analysis. Blurring this distinction leads to inconsistent decisions.
• Real‑time vs end‑of‑batch:
  • Will you surface exceptions to operators in real time, or only at review? Real‑time visibility supports proactive correction but can cause alarm fatigue if badly designed.
• User interface:
  • How exceptions are presented matters. Priority, grouping, drill‑down and context views determine whether reviewers can process them effectively or get lost in lists.
• Scope creep:
  • As people see the power of exception views, they will want to dump every conceivable metric into it. Be ruthless about what belongs in exception-based review vs general analytics.

Good design makes exception-based review feel like a sharp, focused spotlight. Bad design turns it into an endlessly scrolling list of nagging messages nobody really owns.

13) Implementation Roadmap – Moving to Exception-Based Review

Shifting from manual to exception-based process review is not a flip of a switch. A pragmatic path:

• 1. Baseline the current state:
  • How long does review take now? What do reviewers actually look at? Where have issues been missed historically? Use this to set targets and identify obvious high‑risk areas.
• 2. Map data and systems:
  • Identify which data lives where (eBR, MES, historians, LIMS, WMS) and how it can be accessed and correlated. Fix glaring data‑integrity or structure gaps first.
• 3. Pilot a narrow scope:
  • Pick a product family, line or process step with decent data and meaningful risk (for example, mixing and baking for a key product). Define and implement exception rules just for that pilot.
• 4. Run dual review:
  • For the pilot scope, run both traditional and exception-based review for a defined period. Compare what each method finds; adjust rules and logic accordingly.
• 5. Formalise governance:
  • Define who owns rule design, validation, change control and periodic reassessment. Document procedures, training and responsibilities.
• 6. Scale, but carefully:
  • Extend to more products, lines and exception types in waves, not all at once. Use lessons learned to avoid repeating mistakes at larger scale.

Trying to “turn on exception-based review” plant‑wide in one shot usually ends with QA buried in noise and turning half the rules off again. Treat it as a programme, not a toggle.

14) Exception-Based Review in Bakery and Food Operations

In bakeries and similar food plants, the concepts are the same; the exceptions look different.

• Dough prep and proofing:
  • Exceptions for dough temperature and absorption outside target, proof times out of window, preferment age limits breached, wrong flour silo or inclusion used.
  • See Target Dough Temperature Control and Dough Absorption Control.
• Bake profile and yield:
  • Exceptions for oven profile deviations, under‑ or over‑bake signals, moisture loss or bake yield outside limits; see Bake Profile Verification and Moisture Loss and Bake Yield Testing.
• Weights and labelling:
  • Exceptions for under‑weight packs, label errors, wrong allergen declarations, missing date codes; often driven by integrated checkweighers and vision systems.
• Allergen and cleaning controls:
  • Exceptions when allergen changeover validations fail, cleaning intervals exceeded or sequences violate allergen ladders; see Allergen Changeover Verification (Bakery).
• Inventory and flow:
  • Exceptions for trolley dwell time in proofing, freezer inventory mis‑matches, dough ball age exceeding limits, or missing pan/tin tracking events.
  • See Dough Ball Freezer Inventory Management, Bakery Trolley Flow Control and Pan, Tin and Sheet Asset Tracking.
• Sensory and crumb quality:
  • Exceptions when crust colour, crumb structure, texture profile or sensory scores fall outside expected ranges; see Crust Color Uniformity Testing and Texture Profile Analysis (Bakery Crumb Quality).

In high‑volume bakeries, exception-based review is often the only realistic way to keep up with the volume of SKUs, batches and data. The same rule applies though: if you’re using it to hide sloppy process control or poor data capture, it will eventually show – in complaints, rejected lots or a very long audit close‑out letter.

15) FAQ

Q1. Is exception-based process review really acceptable to regulators?
Yes – with conditions. Regulators generally accept exception-based review for electronic records and process monitoring where rules are risk‑based, clearly documented, validated and kept under change control, and where QA still reviews and documents all exceptions. They do not accept “we didn’t look because the system didn’t flag anything” when the rule set is incomplete, undocumented or clearly misses critical events.

Q2. Does exception-based review mean QA never looks at “green” data?
No. Exception-based review shifts routine attention to flagged events, but QA still needs to understand the overall data landscape. That means participating in rule design, periodically performing full reviews on sampled batches, assessing CPV trends, and auditing that the rule set remains adequate. Exceptions tell you where to start, not the only places you’re allowed to look.

Q3. How is exception-based review different from having alarms or SPC charts?
Alarms and SPC charts are detection tools at the time of operation; they warn operators when something is going wrong. Exception-based review is a structured review process that decides which of those events, plus other data conditions, must be examined and documented as part of batch, shift or periodic review. It includes workflow, documentation, trending and integration with deviations and CAPA – not just flashing lights in the control room.

Q4. How many exceptions are “too many”?
There’s no universal magic number, but if every batch or shift generates dozens of exceptions, something is wrong. Either your rules are far too sensitive, generating noise, or your process is genuinely unstable. In both cases you should investigate. A healthy system produces a manageable number of meaningful exceptions that drive action and learning – not pages of flags that everyone mechanically closes without thought.

Q5. Where is the best place to start with exception-based process review?
Start where you have decent data and clear pain: a product family or line that is quality‑critical or QA‑bottlenecked, and where eBR/MES/LIMS data is already structured. Design a limited rule set around known CPPs, CQAs and data‑integrity risks, run dual review for a while, tune the rules and demonstrate impact on review time and issue detection. Use those results to justify and guide broader deployment. Don’t start by trying to solve every exception problem in the plant at once.

Related Reading
• Batch & Record Control: BMR | eBR | Work Order Execution | Materials Consumption Recording
• Quality, Risk & Investigations: Deviation / NCR | CAPA | RCA | QRM | Data Integrity | Batch Variance Investigation
• Monitoring & Analytics: SPC | CPV | GxP Data Lake & Analytics Platform | Yield Variance
• Bakery & Process Control: Target Dough Temperature Control | Dough Absorption Control | Bake Profile Verification | Moisture Loss & Bake Yield Testing | Crust Color Uniformity Testing | Texture Profile Analysis (Bakery Crumb Quality)