How to Level Up Quality Control in Rolled Products Manufacturing

Fractions of a millimeter can mean the difference between flawless end-use and a costly failure in rolled products manufacturing. Slight variations in gauge thickness or surface defects can lead to stopped production, rejected shipments, and financial losses. Quality control in rolled products manufacturing is a battle against inconsistency. Standards define acceptable tolerances, but customer-specific requirements can be even more rigorous. A coil of flat, rolled metal with the slightest deviation might work for one application but cause significant problems in another, especially for industries requiring extreme precision, like aerospace manufacturing. 

Yet, despite advancements in automation and real-time monitoring, many manufacturers still rely on manual inspections and operator intuition as part of their quality control.

Manufacturers that implement modern quality control solutions can reduce defects and improve operational efficiency while ensuring that every product meets their customers’ exacting standards.

Here’s how these rolled product manufacturers can maintain an edge in an industry where precision is everything.

Why Quality Control Matters in Rolled Products Manufacturing

Product imperfections can derail a production run, leading to costly delays, wasted materials, and damaged customer relationships. GoEngineer highlights the significant direct and indirect costs:

• Direct costs
• Scrap costs
• Rework costs
• Inspection costs
• Delays and lost production time
• Warranty, loss of customer trust, and reputational damage

Consider a manufacturer producing metal sheets for high-end architectural projects. If the material isn’t uniform, even within ASTM tolerance limits, it may warp during installation, ruining an entire façade. Similarly, in the automotive sector, inconsistencies in steel or aluminum rolls can lead to structural weaknesses that force costly recalls and foster reputational damage.

The stakes are high, and manufacturers must find ways to ensure that every roll meets exact specifications before it leaves the facility. But this is easier said than done. Quality control in rolled products is not just about checking final outputs; it’s about implementing proactive strategies, leveraging real-time data, and eliminating inefficiencies at every stage. This rigor requires a combination of human expertise, automation, and advanced analytics to catch defects before they become costly problems.

Industry standards such as ASTM A653 for steel in rolled product manufacturing define acceptable tolerances for product specifications. However, customers with specialized applications, such as those requiring flat surfaces for painting, may impose even stricter requirements beyond industry norms.

Ensuring compliance with these standards requires a rigorous approach to material verification. To maintain consistency, manufacturers can benefit from implementing vendor scorecards, measuring incoming materials against ordered specifications, and providing feedback to suppliers.

The Balance Between Human Expertise and Automation

Despite advances in automation, quality control in rolled products still heavily depends on experienced personnel. In some cases, long-tenured employees can detect machine irregularities simply by the sound of the equipment. However, as workforce turnover increases and experienced operators retire, relying solely on human intuition is no longer sustainable.

Manufacturers are increasingly turning to automation and AI-driven quality control methods to fill this gap. High-speed cameras, IoT sensors, and real-time monitoring systems can detect defects and variations more precisely than human inspectors. These technologies enable proactive adjustments to machine settings, reducing material waste and improving overall efficiency.

Traditionally, manufacturers stored QC data in local databases, which limited their ability to analyze trends and make real-time decisions. Cloud computing has changed this by enabling centralized data collection, trend analysis, and predictive maintenance.

Microsoft’s Azure Fabric, for example, is positioned to support manufacturers in collecting and analyzing production data. By integrating data collected with IoT sensors and cloud-based analytics, manufacturers can:

• Detect anomalies in real-time and adjust machine settings accordingly.
• Perform predictive maintenance by identifying performance patterns that precede failures.
• Optimize resource allocation to maximize output while maintaining quality standards.

While some manufacturers remain hesitant to adopt cloud solutions due to concerns over reliability and control, hybrid models that combine local servers with cloud connectivity can offer a balance between real-time operations and advanced analytics.

Case Study: Phifer’s Quality Control Transformation

Phifer Incorporated, the world’s largest producer of aluminum and fiberglass insect screening products, faced significant quality control challenges due to disparate manufacturing divisions operating in silos. Their proprietary MES system had become unmanageable, requiring IT intervention for every product specification change.

To address these inefficiencies, Phifer partnered with Sikich to implement the JUST MES Business Genius® suite. The solution standardized quality control processes across Phifer’s metal screening, fiberglass screening, and specialty product divisions.

Key benefits included:

• Enhanced data accessibility: Real-time data monitoring enabled proactive quality adjustments.
• Seamless ERP integration: One-click synchronization reduced manual data entry and errors.
• Improved preventive maintenance: Machine monitoring minimized downtime and enhanced efficiency.
• Standardized quality checks: Automated reporting and visibility ensured adherence to customer specifications.

Adopting a modern MES solution allowed Phifer to improve quality control processes and enhance its ability to proactively address production challenges. Ultimately, the organization increased operational efficiency, with a significantly positive effect on the bottom line.

Addressing Implementation Challenges

One of the biggest hurdles in upgrading rolled products manufacturing quality control processes is the need to minimize production downtime. Many manufacturers operate on tight schedules with limited opportunities for major overhauls. For example, some companies may have one or two shutdown periods per year, making it difficult to implement new QC systems without disrupting output.

A phased approach to automation and quality control monitoring can help mitigate these challenges. Manufacturers can enhance their processes by gradually integrating IoT sensors, AI-driven analysis, and automated reporting without requiring large-scale operational shutdowns.

Building a Stronger Quality Control Strategy

Traditional methods are no longer enough to stay competitive. Relying solely on human inspection or outdated processes increases the risk of costly quality errors. To meet the challenge head-on, manufacturers need to rethink their approach to quality control by embracing smarter technologies and data-driven decision-making on the shop floor.

Sikich’s data and analytics expertise can help manufacturers build the necessary infrastructure to collect, analyze, and act on production data in real-time. Our unique combination of cutting-edge technology and deep industry knowledge places Sikich at the forefront of an industry ready to make big changes on the shop floor.

Interested in transforming your quality control processes? Contact Sikich to start the conversation.