Your quality manager found the deviation. She documented it, filed the corrective action, and closed the record. Six weeks later, the same drift pattern shows up again, this time across three shifts instead of one. By the time it surfaces in your review meeting, you're talking about a hold, not just a trend.
That's a visibility failure. And it's more common than the food and beverage manufacturing industry likes to admit.
Fewer recalls, higher stakes
The recall picture from 2024 should recalibrate how you think about process risk. The FDA recorded 422 food recall events, fewer individual incidents than prior years, but direct recall costs hit $1.92 billion. Hospitalizations tied to foodborne illness outbreaks rose to 487 in 2024, up from 230 in 2023. Deaths increased from 8 to 19, according to FSNS analysis of FDA and USDA data. The pattern is consistent: fewer events, but each one’s worse.
The operational implication is this: the severity isn't increasing because controls are absent. Drift accumulates invisibly until it's large enough to become a recall. By the time enforcement data captures it, the problem has already crossed multiple shifts, batches, and sometimes facilities.
The financial exposure isn't abstract. A single Class I recall averages $10 million in direct costs, before accounting for litigation, retailer chargebacks, or brand damage. For an operations or finance leader, that's a business continuity problem.
Ask yourself these four questions first
Before looking at any software or system, do an honest assessment of your current process visibility:
- When drift begins on a production line, how long before someone with authority to adjust it actually sees the signal?
- If a parameter has been trending toward its upper spec limit for three shifts, does anyone in your facility know that before it crosses the limit?
- Can you pull process performance data for a specific line, shift, or date range in the time it takes an auditor to ask for it?
- When a variance pattern shows up on one line, do supervisors on other lines see it, or does each line operate in informational isolation?
If any of those answers involve paper records, spreadsheet review, or end-of-day summaries, you have a gap. It may not be visible during stable operations, but the 2024 recall data suggests that when it appears, it appears fast and at scale.
The silence between checks is where risk lives
Most facilities have process controls on paper. CCP monitoring schedules, in-process checks, verification procedures. The controls exist. What's missing is the ability to detect slow, incremental drift that happens between checks, the kind that stays within specification limits on any individual reading, but trends consistently in one direction over time.
Statistical process control was built for exactly this. Control charts don't just flag out-of-spec readings. They detect patterns: upward trends, downward trends, runs of consecutive points on one side of the centerline. These patterns signal that a process is no longer stable, even when every single reading looks acceptable. The gap between "in-spec" and "in control" is where most quality failures actually start.
Under 21 CFR 117.145, monitoring must be conducted "with adequate frequency to provide assurance that preventive controls are consistently performed." A program that generates records without detecting drift is creating documentation, but not control. And 117.165 requires verification that your process controls are actually working, not just that records were created. SPC is among the strongest evidence bases for meeting that verification standard, because it shows statistical process behavior over time rather than point-in-time checks. It's a defensible supporting methodcwith a direct logic.
A control chart reviewed at end of week can't catch mid-week drift in time to intervene. And that's the operational gap worth closing.
What cross-facility visibility actually looks like
Westrock Coffee runs four plants, some newly commissioned, some operating for decades. Their challenge wasn't a lack of data. It was that the data was locked in each building. No reliable way to compare performance across facilities, no ability to identify which plant-level variables were driving quality outcomes.
John Schrock, their systems integration manager, described using SPC reporting to evaluate process capability metrics (Cpk and PPK) and determine whether processes were trending toward spec boundaries before they crossed them. That kind of early signal changes the conversation. Instead of "did we catch a failure?" the question becomes "is this process showing early signs of instability?" Only one of those questions prevents a recall.
The supplier management outcome is equally instructive. Westrock's team tracked grind size performance from a coffee supplier and found that inconsistency in grind specs was causing filter clogging and production halts. With the data captured and trended consistently through SafetyChain, they brought specific, evidence-backed findings back to the supplier, which led to specification adjustments and a measurable improvement in production uptime at that facility.
That's supplier quality management working the way it should: not annual audits and scorecards, but continuous incoming material data that surfaces problems before they become your production problem. If supplier-driven variance is a source of drift in your facility, it deserves its own tracking framework, not a footnote in a case study.
Watch the Westrock Coffee case study to see how cross-facility SPC dashboards work in practice:
How operators become your earliest warning system
A baked goods manufacturer’s weight monitoring problem is a clean example of what changes when visibility reaches the production floor. Before implementing real-time monitoring with corrective action workflows, the facility was placing roughly 2,500 pallets of product on hold annually, primarily underweight product. After implementation, with real-time weight monitoring, triggered corrective action workflows, and full traceability, annual holds dropped to approximately 150 pallets. That's a 94% reduction, and more than $30,000 in annual product disposal costs recovered.
The mechanism matters here: holds didn't drop because inspectors caught more problems, they dropped because operators saw the trend before it produced a nonconforming pallet.
When a control chart is visible to the person running the line, the adjustment happens at the point of drift, and not at the point of failure.
Death Wish Coffee saw a version of this with roast shade correlation. Paper-based scrap tracking couldn't connect roast shade records to machinery temperature readings. Once data capture was consistent and trended digitally, that process relationship became visible. Real-time adjustments replaced estimates. The food safety implication is the same: you can't act on a connection you can't see.
This is also where CAPA management earns its keep. An alert firing is only half the system. The other half is a structured corrective and preventive action process that captures what happened, what was done, and whether it worked. Without that closed loop, the same drift pattern reappears on the next shift, which is exactly the scenario this article opened with.
The infrastructure that makes detection reliable
Deploying control charts without the right underlying infrastructure produces unreliable signals and operator distrust. A few conditions have to be in place first.
Data integrity at the source. SPC is only as accurate as the data feeding it. Paper checks entered into a system hours later introduce transcription errors, timing gaps, and incomplete records that corrupt the statistical signal. Digital forms with required fields and timestamp enforcement eliminate these problems before they reach the chart.
Machine data is the harder version of this problem. When process parameters come from equipment directly, manual re-entry is the weakest link. Integrating machine data directly through an OPC-compliant connection removes the human transcription step entirely. Control charts then reflect what the equipment is actually doing, not what an operator logged 20 minutes later.
Alerts that reach people who can act. Threshold-based alerts and automated workflows route issues for review or escalation when defined conditions are met. This removes the dependency on someone manually reviewing a chart at the right moment.
Role-appropriate views. A simplified view for a line operator, current sample averages against spec limits, focused on the last two hours, is operationally different from the capability analysis a quality manager needs for root cause work. Both are legitimate needs. Only one belongs on a tablet at the production line.
Cross-shift and cross-line comparison. Many quality failures aren't visible on a single line or shift in isolation. The Westrock Coffee story is the clearest example: process signals only became actionable when data from four plants could be compared at the network level. For an operations leader at a multi-site company, that governance question, who sees what, and how does variance at Plant 2 surface to the network quality team, is the one worth asking before you deploy anything.
Audit readiness as a byproduct, not a sprint
A pork manufacturer reduced end-of-shift documentation time from 1.5 hours to 0.5 hours per shift by moving to digital record capture, recovering approximately 624 hours of overtime annually. That's a real labor cost recovered; but the more durable value is that records built continuously through the production cycle are already organized, timestamped, and searchable when an auditor or customer requests them.
That matters for FDA inspections and GFSI audits alike. 21 CFR 117.165 requires verification activities to be documented in records subject to review. When those records exist as structured digital data rather than handwritten logs, retrieval is fast, completeness can be validated, and the documentation trail required to demonstrate that process controls were consistently applied is immediately accessible.
Westrock Coffee's team described it directly: "I don't have to wait for an audit. We're auditing ourselves every day." That's an operational description of what continuous record capture produces. No pre-audit assembly sprint. No 10 PM Friday documentation scramble. The evidence exists because it was built in real time.
If you're evaluating how your current quality assurance program measures up against audit-readiness standards, this is where the gap usually lives: in the accessibility of proof that those controls ran correctly.
What changes when continuous monitoring is in place
When your shift supervisor can see a control chart trending toward a spec limit at 2 AM, the adjustment happens before the nonconforming product exists. When incoming material data is trended over time, supplier-driven variance gets caught before it causes production downtime. When corrective actions are structured and tracked, the same deviation doesn't appear three audits in a row.
The audit trail stops being something you build before an inspection. It becomes a record of how well your processes actually ran.
If your honest answers to the four questions earlier in this article pointed to gaps, in shift-to-shift visibility, cross-line comparison, or documentation accessibility, those are the specific places to start. The technology to close them exists. The question worth asking now is how long you can afford to operate without it.
See how SafetyChain's SPC and process control capabilities work in your environment. Start a conversation today.
