Today, manufacturing is largely shaped by supply chain volatility, complex labor dynamics, and—like most global industries—the rise of AI. Adopting AI technologies on the shop floor can help manufacturers minimize operational costs, mitigate risk, and optimize processes, which drives efficiency and improved quality standards. However, successful AI adoption in manufacturing first depends on the consistent collection of high-quality, reliable data.

Jason Chester, director of product management at Advantive, shares his perspective on modern manufacturing, including how innovative software solutions, such as statistical process control (SPC) and manufacturing execution systems (MES), alongside strategic AI, can advance manufacturers’ data capabilities and prepare them for the industry’s digital future.

Quality Digest: Rising ESG expectations, labor shortages, and growing supply chain complexities are placing greater demand on today’s manufacturers when it comes to quality standards. What proactive measures can manufacturers take not only to meet but to exceed quality standards in this volatile environment?

Jason Chester: Manufacturers can adopt a proactive approach to quality by using advanced data collection and statistical process control (SPC) tools. Features such as real-time data capture and predictive analysis enable manufacturers to identify and address quality issues before they escalate.

As environmental and social governance (ESG) continues to shift from voluntary to essential, integrating sustainability into quality processes is crucial. This includes reducing waste, optimizing resource use, and maintaining compliance with evolving regulations. Labor dynamics, such as the rise of Gen Z in the workforce, also demand a focus on automation and digital tools to fill gaps and maintain high standards.

In volatile supply chain environments, manufacturers can’t rely on historical trends. Adopting agile, data-driven quality processes that adapt to current conditions will ensure that customer expectations are consistently met.

QD: The desire to digitize operations often outpaces the actual implementation. How can today’s manufacturing leaders effectively bridge this gap to ensure a successful digital future?

JC: Bridging the gap between the desire to digitalize and actual implementation requires manufacturing leaders first to identify the cause-and-effect relationships within their operations. This means understanding what they want to achieve with digital transformation and focusing on the capabilities technology can provide, rather than the technology itself.

A successful digital future requires cultural shifts driven by leaders who foster a mindset that embraces change and innovation. Proactive planning will help organizations move away from outdated processes and resistance to new methods. Still, manufacturers should start small by piloting new technologies and scaling based on reliable data insights.

Aligning technology investments with clear operational goals ensures cross-functional collaboration and enables the creation of a digital transformation road map that is both strategic and sustainable.

QD: Simple data collection has been a staple in manufacturing for years, yet only 25% of manufacturers today have high confidence in the data they collect. How can manufacturing data collection be optimized for AI readiness?

JC: Optimizing data collection for AI readiness begins with auditing existing data sources to ensure accuracy and relevance. Manufacturers can modernize shop floors with IoT-enabled sensors, human-machine interfaces, and programmable logic controllers to facilitate uninterrupted, high-quality data collection.

Standardizing data across systems is crucial for eliminating discrepancies and establishing trust in the information collected. Investing in analytics platforms allows manufacturers to visualize key performance indicators in real time, making it easier to extract actionable insights.

Finally, cultivating a data-driven culture equips teams to interpret and act on insights, laying the groundwork for scalable AI adoption. Clean, reliable, and consistent intelligence is the foundation for leveraging AI to drive improvements in manufacturing operations.

QD: What are the biggest challenges that today’s manufacturing leaders face as they look to modernize processes on the shop floor?

JC: Manufacturers face a multitude of barriers to modernization. The most prevalent struggles are around data siloes and inconsistencies from fragmented systems that hinder real-time visibility. Many are also dealing with outdated processes, lack of capital investments, and cultural inertia—i.e., overcoming that change-resistant mindset.

To overcome those and other challenges, it’s important for manufacturing leaders to adopt innovative software solutions such as manufacturing execution systems (MES) and SPC, which streamline operations and provide a unified view of processes. The tools enable industry leaders to identify inefficiencies, optimize workflows, and make data-driven decisions to boost agility and efficiency in shop floor environments.

QD: How are modern manufacturing solutions (like MES and SPC) helping manufacturers manage costs while still meeting high quality standards? Can you explore the link between cost savings and quality control?

JC: Those solutions serve as resources that support manufacturers’ efforts to offer the best possible products while improving their bottom line. SPC tools use predictive analysis to identify trends within process variables, revealing previously unseen opportunities for improvement. These insights help to optimize production, reduce waste, and prevent costly defects.

MES solutions provide visibility into current operations, equipping manufacturers with the analytics needed to inform changes that will streamline workflows and allocate resources more effectively. The tools’ ability to integrate quality control into every stage of production can help organizations balance cost efficiency and product excellence.

The link between cost savings and quality control stems from proactively addressing issues before they escalate, and ensuring consistent output while minimizing downtime and resource expenditures. It’s a reliable approach for driving profitability while simultaneously strengthening customer satisfaction and brand loyalty.