Supercharging Six Sigma: How Querri Enhances Quality and Efficiency in Manufacturing

In the fast-paced world of manufacturing, precision and timing are crucial. Six Sigma's structured approach and Querri’s AI-powered insights can help companies achieve unmatched quality and efficiency.

“I need it yesterday.”

In the manufacturing world, the mantra “I need it yesterday” often comes to mind. Consistency is not just a goal—it's a necessity for any modern manufacturing company. This is key to any company with just-in-time and just-in-sequence production methodologies, where any hiccup can cause problems.

Six Sigma

That’s why so many companies have adopted Six Sigma, a methodology that has revolutionized the quality of parts produced by companies embracing its principles. Central to Six Sigma is the DMAIC framework—Define, Measure, Analyze, Improve, Control—which provides a structured approach to problem-solving and process improvement.

Six Sigma reduces process variability, helping ensure that products meet strict quality standards with minimal deviations. This consistency is crucial in high-precision manufacturing environments.

The Measure Phase & Querri

The Measure phase focuses on gathering data to create a comprehensive picture of current processes and performance. For many manufacturers, this data is captured across a wide range of systems—from legacy databases holding historical data to newer systems monitoring real-time performance metrics. Production line data may come from PLCs (Programmable Logic Controllers) that record details of machine operation, while quality control data is often tracked in separate systems, sometimes even on paper.

Bringing all this data together can be daunting. Equipment sensors, maintenance logs, operator records, and ERP systems each store valuable information, but the lack of interoperability often means these insights remain siloed. Extracting and joining this data for analysis is typically labor-intensive and error-prone, hindering timely insights.

Querri simplifies this process by providing a powerful yet intuitive platform for integrating data from diverse sources. Without extensive data wrangling, manufacturers can join data from different systems and begin analyzing it immediately. This seamless integration allows teams to access a unified view of their processes, uncovering meaningful patterns and trends that would otherwise be buried in isolated systems.

The Analyze Phase & Querri

Querri really shines in the Analyze phase. It is an AI-powered data analysis tool that streamlines this phase by quickly processing large datasets and uncovering actionable insights. You upload your data and start asking questions.

Let's look at some data from a cable manufacturer (see the data set used here).  Using Querri, the company can efficiently analyze its data to determine which operator-machine pairs are associated with the most downtime.

Here, Querri reveals that certain operators working with specific machines are experiencing higher failure rates. Delving deeper, Querri can assess downtime occurrences across different shifts.

It may identify that the A shift has more downtime than the B shift. However, rather than attributing this to operator error, Querri enables you to delve deeper just by asking questions.

Machines Two and Eight have significant spikes in downtime compared to others. This insight opens the door to exploring predictive maintenance, another area where Querri excels by forecasting potential machine failures before they disrupt production.

Going further, Querri can support a wide range of analyses that can drive organizational efficiencies. Here are just a few examples:

Root Cause Analysis
Prompt: “Analyze defect rates by machine and operator to identify which combinations are producing the most quality issues.”
This prompt allows Querri to identify patterns in defects related to particular machine-operator combinations, narrowing down possible root causes for targeted improvements.

Trend Analysis
Prompt: “Show a trend line for average production time per batch over the past six months, broken down by shift.”
This enables you to visualize any time-based trends, such as shifts that consistently perform faster or slower, helping to identify bottlenecks or efficiency variances.

Pareto Analysis
Prompt: “List the top five defect types contributing to total quality issues, highlighting their contribution percentages.”
Using Pareto analysis, this prompt allows Querri to highlight which defect types account for the majority of quality issues, focusing efforts on the most impactful improvements.

Correlation Analysis
Prompt: “Check the correlation between material temperature and defect rate for product batches over the past year.”
Querri will examine the relationship between material temperature and defect rates, helping to reveal whether a change in one variable may affect quality and guide optimal temperature settings.

Failure Mode and Effects Analysis (FMEA)
Prompt: “List the most frequent equipment issues in the past year, sorted by frequency, and highlight any associated downtime.”
This specific approach to FMEA pinpoints frequent failure points, enabling teams to prioritize interventions for the most common issues. Thus, downtime is reduced, and reliability is enhanced.

Capability Analysis
Prompt: “Compare current production process specifications to target limits for defect rate and variation.”
Querri will evaluate whether current processes meet desired specifications and highlight areas where performance may need adjustment to align with Six Sigma standards.

Predictive Maintenance Analysis
Prompt: “Identify machines with a rising trend in repair frequency or downtime.”
This prompt enables Querri to detect machines showing early signs of failure, providing actionable insights on when maintenance should be scheduled to prevent disruptions. In this way, Querri enables not only predictive but also proactive maintenance strategies, supporting Six Sigma’s goal of minimizing disruptions and maintaining consistent output quality.

Keeping the Lean in Six Sigma

Querri cuts down on the manual hours typically spent wrestling with Excel formulas by letting you ask questions in plain language and automating repetitive tasks. It’s designed to streamline every phase of the DMAIC process, making Six Sigma simpler and faster:

• Save Time and Reduce Complexity (Define & Measure): Skip the formulas and data-cleaning steps. With Querri, complex analyses are just a question away, freeing up hours for more impactful work.

• Automate Repeatable Analyses (Analyze & Control): Once you’ve set up an analysis, Querri’s automation tools make it easy to run again. Schedule workflows to keep your insights fresh without lifting a finger.

• Build Dashboards for Continuous Improvement (Improve & Control): Querri lets you create custom dashboards that track KPIs, trends, and metrics in real-time. The dashboards are easy to refresh, so your team always has the latest data for ongoing improvements.

• Unify Messy, Scattered Data (Measure): Querri pulls together data from legacy systems, sensors, and other databases, making it easy to work with all of it in one place—no extra cleanup needed.

• Gain Insight and Visibility (Analyze): From predictive maintenance alerts to root cause analysis, Querri reveals insights that are tough to spot in Excel, helping teams make proactive, data-driven decisions.

With Querri, every step of DMAIC is simplified, letting you keep your processes lean and focused on continuous improvement without the usual hassle.

If you want to learn more about predictive maintenance, mastering inventory management, or demand forecasting, check out our blog page.