Key takeaways:
- Data gaps usually aren’t “missing data” as much as late, inconsistent, or hard-to-use data that slows decisions across operations, quality, and supply chain.
- Recent research shows many organizations see integration complexity and data issues as core reasons technology investments don’t deliver full value, making data readiness a practical leadership priority.
- The biggest strategic lift often comes from making a few critical data flows reliable, role-relevant, and repeatable, from the shop floor to the C-suite.
Most operations and supply chain leaders in the U.S. (92%) say their operations technology investments haven’t fully delivered expected results. The most commonly cited reasons were integration complexity (47%) and data issues (44%).
And according to a 2025 supply chain integrity survey for the food and grocery sector, the vast majority of organizations possess the necessary equipment to achieve accurate visibility. However, only one-third consistently achieve 360-degree, real-time inventory visibility. They have some tools and some data, but the value gets diluted when data capture and accuracy aren’t dependable end to end.
Technology offers the most value for food manufacturers if it can get the right data flowing to the right people in time to act. So let’s take a closer look at where data gaps tend to show up in food and beverage operations, how to close visibility gaps, and how to ensure data delivers true strategic value.
Why data gaps matter in food manufacturing
Data is the everyday information food and beverage leaders rely on to keep product safe, meet customer expectations, manage margins, and plan confidently. It tells them what ran, what stopped, what changed, what was scrapped, what passed quality checks, what inventory is actually available, and what lots went where.
A data gap happens when that information is:
- Missing (it was never captured)
- Delayed (it exists, but it arrives too late to influence the decision)
- Inconsistent (two teams track the same thing differently)
- Inaccessible (it’s trapped in a system, spreadsheet, or inbox that others can’t use)
These gaps reflect how complex modern food operations have become, especially when growth, cost pressure, workforce constraints, and customer demands collide.
Most data gaps in food manufacturing are operational:
Production and throughput data:
- Line performance and downtime reasons are captured inconsistently (or not at all).
- Changeover and sanitation time is tracked, but not categorized in a way that supports improvement.
- Scrap and rework are recorded after the fact, so root-cause learning is slower.
Quality and food safety data:
- Checks are done, but results are stored in disconnected places (like paper, spreadsheets, or emails).
- Deviations and corrective actions are documented, but trends are hard to see across shifts or sites.
Inventory and genealogy:
- Raw material, work-in-process, and finished goods counts don’t match what teams feel is available.
- Lot traceability relies on manual steps that work well, until volume spikes or staff changes.
Cost and yield:
- Yield loss is visible in finance after the close, but harder to pinpoint operationally in the moment.
- Material variability (by supplier, season, or spec range) isn’t consistently connected to performance outcomes.
Better data = better planning, decisions, and collaboration
When data gaps close, strategic value shows up in several simple, tangible ways:
- Faster decisions with fewer meetings: When inventory, production status, and constraints are visible and trusted, teams spend less time reconciling numbers and more time choosing actions.
- More predictable execution: Reliable data supports steadier planning, especially around labor, scheduling, sanitation windows, allergen changeovers, and high-cost ingredients.
- Better cross-functional alignment: Operations, quality, maintenance, and supply chain can work from shared definitions (not competing spreadsheets), which reduces friction and duplicate effort.
- More targeted continuous improvement: Instead of guessing where losses occur, teams can prioritize the highest-impact problems based on repeatable measurement.
Data should usable for people, not just systems
One of the most practical ways to reduce costly data gaps is to design data flows around who needs to act and when.
“Real-time monitoring is more for the operators on the shop floor and the supervisors, so they can act on the data,” Catherine Tardif of Worximity describes.
Operators and supervisors typically need immediate, actionable signals (what’s happening right now), while leaders often need trended insight (like what to fix systemically, where to invest, or what’s repeatable across sites).
If production is the primary goal and data capture adds friction to that mission, it tends to become inconsistent, creating gaps that ripple upward. When it’s lightweight and clearly useful, data quality often improves naturally.
How to approach data gaps
While there’s no single blueprint for every manufacturer, many teams may find it helpful to focus on a few repeatable patterns.
1. Start with decision-critical data flows
Instead of trying to fix everything all at once, many organizations focus first on the data that drives high-frequency decisions, such as:
- Production status and downtime
- Quality holds and releases
- Inventory availability by lot
- Order commitments and constraints
2. Normalize definitions before adding complexity
If two sites track downtime differently, comparing performance is challenging, even with good dashboards. Standardizing a small set of definitions (e.g., downtime categories, scrap reasons, yield calculation method) can unlock value without major system changes.
3. Reduce double entry wherever possible
Manual transcription is a common source of delay and inconsistency. Even small changes, like capturing data once at the point of work and reusing it downstream, can reduce gaps.
4. Treat master data like a product
Product codes, supplier IDs, units of measure, and specs are often the overlooked root causes of reporting mismatches. It’s well worth assigning clear ownership for:
- Item master / product master data
- Supplier master data
- Location and equipment naming conventions
This tends to be less about bureaucracy and more about eliminating recurring confusion.
5. Build feedback loops so data gets better over time
When teams can see how their data is used and when they can flag issues quickly, accuracy improves. This can be as simple as a weekly review of:
- Top unknown downtime reasons
- Missing lot scans
- Repeated quality documentation gaps
- Inventory adjustments by category
How to tell if you’re closing gaps
Watch for operational signals that data is becoming more reliable:
- Fewer meetings spent reconciling numbers
- Faster root-cause analysis after a quality deviation
- Lower frequency of surprise shortages
- More stable scheduling and fewer last-minute changeovers
- Clearer linkage between yield loss and specific causes (e.g., materials, equipment, process, training)
If you want more concrete metrics, consider tracking changes in:
- Scrap rate and rework rate
- Changeover time variability
- Unplanned downtime frequency
- Inventory adjustment rate (by reason)
- Time to compile audit or customer documentation
FAQ for food manufacturing leaders
Q: What are the most common data gaps in food manufacturing?
A: Common gaps appear in downtime reasons, scrap and rework tracking, lot genealogy, quality documentation, and inventory accuracy, especially when data moves between disconnected systems or manual processes.
Q: What’s the difference between ERP and MES, and why does it matter for data?
An enterprise resource planning (ERP) system typically manages business processes like purchasing, inventory, orders, and finance. A manufacturing execution system (MES) typically manages what happens on the plant floor, including production events, work orders, and execution details. Data gaps often happen at the handoff between these layers, especially when definitions or timing don’t align.
Q: Do we need real-time data everywhere to get value?
A: Not necessarily. Organizations can often see meaningful gains by making a few high-impact areas more timely, like downtime, quality holds, and inventory availability, while leaving other reporting on a daily or weekly cadence.
Q: How do data gaps affect food safety and traceability?
A: Data gaps can make it harder to quickly confirm what happened, when it happened, and which lots were involved. Even when food safety programs are strong, fragmented data can increase the effort required to assemble documentation, investigate deviations, or respond to customer questions.
Q: What’s a reasonable first step if we’re already busy and resource-constrained?
A: Some teams start by identifying one recurring, high-cost pain point like untracked downtime, inventory mismatches, or slow quality release, then mapping where data is missing, delayed, or inconsistent. That tends to surface a small number of fixable gaps without launching a major multi-year initiative.
Q: How can we keep data initiatives from becoming extra work for frontline teams?
A: Capture data where it’s created, and make it immediately useful to the person capturing it. Role-appropriate dashboards, simpler inputs, and fewer duplicate entries can improve adoption and accuracy over time.
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