Manufacturers can’t control tariffs, supply chain volatility, labor shortages, or geopolitical instability. But they can manage operational efficiency.

Operational excellence is one of the few factors that organizations can fully control. In challenging economic times, quality is an increasingly important competitive advantage.

Executive leadership should prioritize investment in quality and process improvement as strategic growth drivers, not merely as compliance requirements or cost centers. The growing adoption of design of experiments demonstrates this shift. Design of experiments (DOE) is a scientific method that helps you see how different inputs, or factors, influence an outcome by planning and running multiple controlled tests. Rather than changing just one thing at a time, DOE changes several factors together, efficiently. This approach shows which inputs are most important, how they interact, and how to achieve the best results. The main aim is to collect reliable data to build predictive models and improve products or processes more quickly and with fewer tests.

Many organizations still view DOE as a technical tool for engineers. In fact, it’s a disciplined approach to managing risk, improving efficiency, and accelerating learning in complex systems. It’s up to executive leadership to decide whether tools like experimentation remain niche or become core business capabilities.

Competitiveness in complex systems

Modern manufacturing processes are highly interconnected. Adjusting one variable usually affects multiple outcomes, including yield, cycle time, strength, compliance, durability, and cost.

Traditional trial-and-error or one-factor-at-a-time testing can’t keep pace with this complexity. Without understanding variable interactions, teams rely on educated guesses—and guessing can be costly.

Advanced DOE provides clarity by enabling organizations to isolate cause and effect and quantify trade-offs, and build predictive models that minimize surprises. Rather than reacting to variation, teams can design structured experiments to explain, predict, and prevent it.

Experimentation as enterprise risk management

Quality professionals already recognize the value of experimentation. The key question is whether executive leadership sees it as strategic, since experimentation is a form of risk management.

When senior leaders support structured experimentation, they reduce enterprise risk, shorten development cycles, prevent late-stage redesigns, minimize scrap and rework, and improve compliance.

Organizations with sustained executive sponsorship of quality initiatives consistently outperform those where quality is siloed. When leadership aligns KPIs with process capability and continuous improvement, and allocates resources accordingly, experimentation drives decision-making.

Without executive support, even the most advanced statistical tools are underused.

The connected factory needs insights, not just data, to transform

Industry often discusses connected factories and the need for real-time analytics, sensors, and dashboards. Minitab recently added real-time machine and OEE monitoring to our platform to meet this demand. But connectivity alone doesn’t generate insight.

Real-time data improve visibility, but experimentation provides the insights needed to realize data’s full value. A dashboard might show process drift, while DOE identifies causes and determines optimal settings to stabilize it.

Customers in pharmaceutical, semiconductor, chemical, and other industries requested advanced experimentation capabilities integrated into operational workflows. This need led Minitab to acquire Effex’s advanced DOE design catalog and optimal design solutions. This advanced DOE solution introduces advanced optimal design methods, including OMARS (orthogonal minimally aliased response surface) designs, allowing teams to gain more information from fewer experimental runs. In environments with costly downtime and materials, this efficiency is essential.

This reflects a broader industry shift. Experimentation is no longer limited to R&D labs but is now central to digital manufacturing strategy. The modern improvement chain is clear: Measure. Hypothesize. Experiment. Optimize. Monitor. Improve. Without experimentation, a connected factory remains incomplete.

Design of experiments in action

The value of structured experimentation is evident in measurable outcomes.

In one pharmaceutical tablet production setting, a team needed to optimize compression parameters to meet strict quality specifications while minimizing material waste. Traditional methods required lengthy trial runs and significant amounts of expensive raw material.

By using advanced DOE designs, the team reduced total testing time to about four months and saved approximately 10 kg of high-value raw ingredients while maintaining regulatory compliance and achieving required quality standards.

This result was achieved through disciplined experimentation supported by leadership and aligned with business priorities.

When approached strategically, experimentation delivers measurable results.

Why consolidation is accelerating

Another clear trend is analytics consolidation. Many manufacturers have accumulated separate tools for dashboards, statistics, experimentation, and reporting. Although each addressed specific needs, together they often created friction, such as inconsistent data definitions, manual handoffs, and duplicated effort.

Executives recognize that fragmentation slows improvement. A unified analytics environment creates a common language in quality, engineering, and operations. It improves governance, shortens the time from insight to action, and enables best practices to scale across locations.

From a leadership perspective, consolidation isn’t only about software efficiency. It also reinforces discipline and consistent decision-making throughout the enterprise. For this reason, Minitab continues to invest in its end-to-end solutions platform for manufacturers.

A leadership imperative and a practical question

For executives, the question is whether experimentation is embedded in the organization’s decision-making or relies on isolated champions.

When the economy slows down, many companies cut research and development budgets, which can hurt innovation and long-term success. In tough financial times, advanced DOE helps R&D teams learn more with fewer experiments and less money. This approach provides valuable insights and faster, lower-cost process improvements.

Quality professionals may have a different question: How do I get leadership to invest in DOE?

Leaders don’t fund experiments solely to improve models. They support projects that protect revenue, increase margins, and drive growth. Demonstrate the value of DOE in terms that leadership cares about, such as reduced scrap, faster time-to-market, and improved compliance. The main goal is to connect experimentation to clear financial results.

When experimentation is positioned as a lever for efficiency, resilience, and growth, especially in uncertain markets, leadership is more likely to see it as a strategic investment.

In today’s environment, manufacturers can’t control every external pressure. But they can control how intelligently they learn and improve.

Organizations that learn the fastest will compete most effectively.