Understanding Data Gaps in Food Manufacturing

Key Takeaways

• Data gaps are not merely “missing data” but often involve inconsistent or difficult-to-use data that hampers decision-making in operations, quality, and supply chain.
• Recent research indicates that organizations identify integration complexity and data issues as primary reasons for technology investments falling short, highlighting the importance of data readiness as a leadership priority.
• Substantial strategic benefits can be realized by establishing a few critical data flows that are reliable, role-relevant, and repeatable, affecting all levels from the shop floor to the executive teams.

The Impact of Technology Investments

According to a survey, 92% of U.S. operations and supply chain leaders report that their technology investments have not produced the expected results, mainly due to integration complexity (47%) and data issues (44%).

In the food and grocery sector, a 2025 supply chain integrity survey found that although most organizations have the necessary equipment for accurate visibility, only one-third achieve 360-degree, real-time inventory visibility.

Why Data Gaps Matter

Data is essential for leaders in the food and beverage industry to maintain product safety, meet customer expectations, manage margins, and plan efficiently. Data informs them about production status, changes, quality checks, inventory, and more.

Types of Data Gaps

A data gap manifests in several ways:

• Missing: Data not captured.
• Delayed: Data received too late to influence decisions.
• Inconsistent: Different teams track the same data inconsistently.
• Inaccessible: Data trapped in systems not usable by stakeholders.

Operational Data Gaps

Most common data gaps in food manufacturing occur in operational areas:

Production and Throughput

• Inconsistent capture of line performance and downtime reasons.
• Changeover and sanitation times tracked but not categorized for improvement.
• Post-event recording of scrap and rework leading to slower root-cause analysis.

Quality and Food Safety

• Disparate storage of quality check results hinder analysis.
• Documented deviations challenging to track trends across shifts or sites.

Inventory and Genealogy

• Raw material and finished goods counts often misaligned.
• Manual steps in lot traceability hinder reliable compliance.

Cost and Yield

• Yield loss often evaluated after finance close instead of in the moment.
• Material variability not correlated to performance outcomes effectively.

The Benefits of Closing Data Gaps

Addressing data gaps can yield significant enhancements in:

• Faster Decisions: Improved visibility reduces time spent reconciling numbers, allowing teams to focus on actionable outcomes.
• Predictable Execution: Reliable data leads to steadier planning, especially around labor and scheduling.
• Cross-Functional Alignment: Shared definitions between departments minimize friction and duplicate work.
• Focused Continuous Improvement: Teams can prioritize issues based on measurable impacts.

Designing Usable Data Flows

It’s crucial to design data flows that align with the needs of users. Catherine Tardif of Worximity emphasizes that:

“Real-time monitoring serves operators on the shop floor, enabling them to act on immediate data.”

The objective is to ensure operators receive actionable signals and leaders can analyze trended insights effectively.

Strategies to Address Data Gaps

1. Identify Critical Data Flows

Focus initially on data that drives frequent decisions, like:

• Production status and downtime
• Quality holds
• Inventory availability
• Order constraints

2. Standardize Definitions

Standardize a few key definitions to facilitate performance comparison among sites.

3. Minimize Manual Entry

Reducing manual transcription minimizes delays and inaccuracies.

4. Manage Master Data Effectively

Assign clear ownership for critical data categories to reduce confusion.

5. Establish Feedback Loops

Encouraging teams to quickly flag issues enhances data accuracy over time.

Measuring Success: How to Assess Data Improvement

Look for signals indicating enhanced reliability in data:

• Fewer meetings for reconciling data
• Faster resolution of quality deviations
• Reduced frequency of supply shortages
• More stable production schedules

FAQs for Food Manufacturing Leaders

What common data gaps exist in food manufacturing?
Data gaps often arise in tracking downtime, scrap and rework, quality documentation, and inventory precision.

How do ERP and MES differ?
An ERP manages business processes, while MES focuses on plant floor operations. Data gaps frequently emerge at their connection points.

Is real-time data necessary everywhere?
Not necessarily; tactical improvements in critical areas can yield significant gains.

How do data gaps affect food safety?
Data gaps complicate tracing incidents and compliance documentation.

What is a reasonable first step if we are resource-constrained?
Identify one high-cost pain point and map existing gaps in data management.

How can we minimize additional work for frontline teams?
Capture data where it is created, ensuring it is instantly useful to the personnel involved.