Embedding Quality in Manufacturing to Eliminate Mislabelling Errors

Mislabeling errors in building materials manufacturing are more common and more disruptive than many teams expect. A single incorrect label can trigger rework, customer dissatisfaction, or costly returns, even when the product itself meets specifications. In many organizations, labeling problems surface as one of several quality escapes that point to deeper execution challenges on the shop floor.

At Operations Calling, Pranav Panakkal, Manager of Operational Technology at VEKA shared how mislabeling became the issue that forced closer examination of their quality system. The takeaway was that visible errors often expose hidden gaps between physical production, digital systems, and manual quality processes that can’t keep pace with real-time operations.

This work led to VEKA not only solving mislabeling and reducing customer returns by 60%, but also continuously improving quality across their operations and gradually dropping quality by 88% in 2 years.

In this blog, we’ll look more broadly at how in-process digital quality helps building materials manufacturers prevent quality escapes of all kinds. Using lessons drawn from real-world manufacturing experience, we’ll show how embedding quality checks, controls, and traceability directly into line execution reduces risk, improves decision-making, and creates a scalable foundation for consistent quality.

Most mislabeling errors don’t start at shipping or final inspection. They begin during live production, when digital systems update faster than physical reality.

Consider a common scenario: an operator scraps a handful of parts mid-run and keeps producing to make up the difference. Meanwhile, a supervisor who is unaware of the scrap digitally activates the next order. The system advances, the label printer follows suit, and labels are generated for the new order while the line is still producing the previous one.

From the system’s perspective, everything is correct. On the floor, it isn’t. Inspection may catch the issue later, but by then the error has already been created. Preventing mislabeling requires quality controls embedded into production itself, where these mismatches first occur.

Knowing why quality escapes happen isn’t enough. Preventing them requires changing how quality is executed during production.

The steps below outline a practical way to embed in-process digital quality at the line, so issues are prevented as work happens, not reviewed after the fact.

Step 1:Identify where digital and physical production diverge

Start by identifying moments to uncover these gaps. Teams need to map when a process changes digitally versus when it actually changes on the floor. That includes identifying who has the authority to advance orders, how scrap or rework is communicated in the moment, and which system is treated as the source of truth during production.

Without this visibility, operators and supervisors are left making reasonable decisions based on partial information. With it, manufacturers can pinpoint the exact handoffs where controls are needed e.g., before labels print, material moves, or errors propagate downstream.

Step 2: Standardize quality checks as “one test, one action”

Once divergence points are clear, the next step is removing ambiguity from quality execution.

Each quality check should have a single purpose and a defined outcome. One test. One action. Operators shouldn’t have to interpret what a result means or decide what to do next.

By standardizing what data is captured, when it’s captured, and what happens if results are in or out of tolerance, manufacturers reduce variation and prevent workarounds. Quality stops being subjective and becomes part of how the process runs every time.

Step 3: Embed quality decisions directly into line workflows

Quality checks only prevent errors when they influence what happens next.

In-process digital quality works by turning checks into gates. If a required quality step is missed or a result falls out of tolerance, downstream actions like printing labels, advancing orders, or moving material are automatically blocked.

This shifts quality from after-the-fact documentation to real-time control. Decisions are enforced by the process itself, reducing reliance on memory, judgment calls, or cleanup later in the shift.

Step 4: Connect in-process quality to ERP for objective outcomes

To eliminate judgment-based decisions, in-process quality must connect to enterprise systems.

When quality and work order data flow into ERP, teams can compare what’s happening on the line with what the system expects by making material status clear. Nonconforming products can be automatically held, flagged for deviation, or scrapped based on defined rules and not assumptions.

This connection ensures downstream actions reflect actual quality status in real time, reducing risk and shortening the path from issue detection to resolution.

Step 5: Design for operator ownership and adoption

Operator adoption doesn’t come from mandates or training alone. It comes when technology is clearly positioned as a pain-point solver for the people doing the work.

In effective in-process quality programs, teams start by understanding what makes an operator’s day harder than it needs to be. Common issues include repetitive data entry, unclear expectations during order changes, and lack of visibility into whether quality issues will resurface later in the shift. When digital quality tools are designed to eliminate those frustrations, operators are far more willing to engage.

The goal is simple: make the correct action obvious and the extra work disappear. When quality steps are fast, embedded into normal workflows, and clearly tied to preventing downstream problems, adoption follows naturally.

“If I didn’t get yelled at today, then I know I had a good day.” - Shop floor operator to Pranav Panakkal, Manager of Operational Technology, VEKA

That sentiment highlights why ownership matters. When operators can see how quality tools reduce uncertainty and prevent blame, quality becomes something they rely on and not something they work around.

“Administratively, we rolled it out in June of 2023, where we were saying, you know, please use this application. If you don't, it's okay.” In 2024 is when we actually released it [the production tracking application with required quality tests], where you had to have quality tests assigned to it.” - Pranav Panakkal, Manager of Operational Technology, VEKA

Step 6: Scale from a pilot to multiple sites

Scaling in-process digital quality works best when it’s treated as a progression.

Most manufacturers start with a lighthouse site, a single plant where new quality workflows can be tested in real production. At this stage, the goal isn’t enforcement. It’s learning. Teams observe how workflows perform under real constraints, gather operator feedback, and refine execution before expanding further.

Once workflows are proven, rollout typically happens in phases. Quality steps may be introduced as optional at first, allowing teams to build confidence and consistency. Over time, those same steps become mandatory, enforced directly by the system, so material can’t move forward if requirements aren’t met.

As solutions scale, it’s critical to standardize the logic, not the entire experience. Core elements like quality checks, thresholds, and escalation rules remain consistent across sites. Interface details, data visibility, and workflow context can adapt locally to reflect how each plant operates.

This approach allows manufacturers to scale quickly without sacrificing adoption, trust, or control, turning a successful pilot into an enterprise-wide quality foundation.

Tulip supports in-process digital quality by embedding quality checks, controls, and traceability directly into frontline workflows, without requiring manufacturers to replace existing MES or ERP systems.

With Tulip, teams can define and standardize quality logic such as required checks, tolerance thresholds, and escalation paths, then apply that logic consistently across lines and sites. At the same time, workflows can be configured to reflect local context, allowing each plant to surface the information operators and supervisors need most.

By integrating with equipment, inspection tools, and enterprise systems, Tulip enables real-time quality decisions at the line. Nonconforming material can be automatically flagged, held, or routed for review based on defined rules rather than manual judgment. The result is faster response, clearer accountability, and fewer quality escapes, all supported by tools designed for the people who use them every day.