Manufacturing Analytics: Tools, Framework and Stories (2025)

You can’t talk about manufacturing analytics without talking about Dr. Deming. 

It was 7:59 am, December 4, 1941—a cold, quiet Sunday morning. The woolen winter hung low, the sun was the saddest one, and the air carried the crisp scent of smoke and frost as if winter itself was breathing deeply. 

Dr. W. Edwards Deming, a mathematical advisor at the U.S. Census Bureau, sat by the window of his modest Washington, D.C. home. A steaming cup of coffee rested beside him, and a thick stack of papers sprawled across his desk, demanding his attention. But he had no time for them. The world just changed forever. 

At 8:00 am, Japanese planes attacked the U.S. Naval Base at Pearl Harbor.

The war had officially reached American soil, and the U.S. needed to respond swiftly—not just with soldiers and strategy, but with supplies, weapons, and vehicles on an unprecedented scale. Factories all over the country converted from making everyday items to producing war supplies. The Lionel Toy Train switched over to manufacturing maritime supplies. Ford Motor Company create B-24 liberator bombers. Alcoa, a storied aluminum company, built airplanes. 

The challenge was daunting: the country needed to double, even triple production, with factories that were already stretched thin. And it’s not just more, it was also about better. Errors, inefficiencies, and wasted resources could no longer be tolerated. The answer to this challenge lied with Deming. 

Deming’s work with Statistical Process Control offered a solution to manufacturing chaos. By focusing on reducing variability and building quality into the process from the start, factories could minimize waste and avoid costly rework. Within days after Pearl Habor, Deming was traveling to key industrial hubs, meeting with factory managers and engineers. He taught them how to collect data, analyze production flows, and pinpoint anomalies, and identify the root causes of defects. With the help of Deming, US industry vastly improved production while simultaneously improving quality through statistical process control and statistical sampling. 

Just 3 years later, the Allies emerged victorious. After the war, Soviet leader Joseph Stalin would say “Without American Production, the United Nations could never have won”. 

Mass production won the war. 

The lessons learned on the factory floor would shape the future of manufacturing. This marked the dawn of manufacturing analytics—the systematic use of data to optimize production, improve quality, and sustain growth in peacetime industries. 

In the years following the war, as Europe and Japan lay in ruins, the world faced the daunting task of rebuilding shattered economies. Japan, in particular, struggled to revive its industrial base, which had been devastated by bombings and wartime depletion. Factories were in disarray, resources were scarce, and the country’s reputation for producing low-quality goods made competing in global markets seem almost impossible.

It was during this time that Dr. W. Edwards Deming was invited to Japan. Initially brought in by the Supreme Commander for the Allied Powers (SCAP) to assist with the census, Deming’s expertise in quality control caught the attention of Japanese industrial leaders. In 1950, the Union of Japanese Scientists and Engineers (JUSE) extended a formal invitation for Deming to share his knowledge with executives and engineers, hoping his insights could help rebuild Japan’s economy.

Deming delivered a series of lectures on statistical process control (SPC) and quality management. He emphasized that quality was not the responsibility of workers alone but a systemic issue that required leadership to commit to improving processes. He introduced concepts like reducing variability, focusing on customer needs, and fostering a culture of continuous improvement.

Deming’s teachings resonated deeply with Japanese leaders, who were eager to move beyond the reputation for cheap, unreliable goods. Companies like Toyota, Sony, and Mitsubishi began adopting his principles, embedding quality into their production processes. Over the next two decades, Japan transformed into a global manufacturing powerhouse, renowned for high-quality, innovative products.

At the core of Deming philosophy lies the System of Profound Knowledge, a framework that integrates four interdependent elements:

1. Appreciation for a System: Seeing the bigger picture—how processes interact and influence each other—rather than optimizing parts in isolation.
2. Knowledge of Variation: Understanding the difference between natural fluctuations and genuine problems, allows businesses to focus their efforts effectively.
3. Theory of Knowledge: Learning through experimentation and continuous inquiry, turning data into actionable insights.
4. Psychology: Acknowledging the human side of manufacturing—how motivation, behavior, and collaboration shape outcomes.

Rather than a set of abstract ideas, this framework became a practical roadmap for transforming industries. It laid the foundation for modern manufacturing analytics tools and practices.

Among these tools, the Xmr chart stands out as one of Deming’s most enduring contributions. Designed to monitor and analyze process variation, the XMR chart transforms raw data into knowledge by revealing patterns and enabling predictions about business outcomes. Deming believed that the purpose of data is knowledge - defined as models or theories that allow businesses to predict outcomes and adapt to change.

Today, the principles Deming championed, like SPC and the XMR chart, are at the heart of modern manufacturing analytics. They enable businesses to monitor processes, optimize performance, and drive continuous improvement. Deming’s insights remain as relevant as ever, guiding industries toward smarter, data-driven decision-making.

Deming’s influence is vast, and there’s no way to cover it all here. This is just a starting point for you to explore the world of manufacturing analytics and becoming more data-driven. I encourage you to check out CommonCog, where our friend Cedric has written extensively about Deming’s teachings and data-driven practices. He also introduced us to the xmr chart. 

In this post, however, we’ll simply talk about manufacturing analytics tools. 

You’ll learn how to evaluate their features, understand their value, and discover 6 of the best manufacturing analytics tools. Whether you’re an engineer working on the production floor or a CTO making high-stakes decisions, this guide will help you navigate the complex world of manufacturing analytics. 

Evaluate Manufacturing Analytics Tools: Key Criteria

Evaluating manufacturing analytics tools requires a structured approach to ensure they meet your operational needs and drive meaningful improvements. 

1. Integration Capabilities

Manufacturing involves multiple systems, such as ERP, MES, and IoT devices. A tool that integrates seamlessly with existing systems ensures a unified view of data and avoids silos.

How to Evaluate: Check for API availability, compatibility with your current tech stack, and support for widely used data formats. Conduct integration tests during a trial to ensure smooth data flow.

2. Customizability and Flexibility

Every manufacturing process is unique. A one-size-fits-all tool may not meet specific operational needs, especially when dealing with specialized metrics or workflows.

How to evaluate: Look for tools that allow you to create custom dashboards, metrics, and visualizations. Test its ability to adapt to your manufacturing processes during a trial period or demo.

3. Custom Visualizations 

Manufacturing data can be complex, and clear, tailored visualizations help teams quickly interpret metrics like production rates, defect percentages, and cycle times.

How to Evaluate: Assess the tool’s ability to create charts and graphs that fit your specific needs, such as XMR charts, which help filter out routine variability and pinpoint outliers that demand attention. For instance, if production efficiency drops by 5%, an XmR chart helps you quickly determine if the deviation is within normal limits or indicative of a deeper problem. Without this level of granularity, you risk either chasing false alarms or ignoring critical issues.

3. Self-Service Experience 

A tool that’s difficult to navigate slows down adoption and hinders team efficiency. A user-friendly interface empowers both technical and non-technical users to leverage its full potential.

How to evaluate: Test the interface for ease of use, clarity of visualizations, and intuitive navigation. Check if it includes drag-and-drop functionality or low-code customization options.

4. Collaboration and Data Sharing

Manufacturing analytics often involves cross-functional teams. Role-based access and collaborative workflows ensure everyone has the right information to make informed decisions.

How to evaluate: Look for features like data sharing, version control, and role-based access. Cloud-based solutions should offer secure access from multiple locations.

5. Advanced Analytics and Predictive Capabilities

Predictive analytics enable proactive decision-making, helping manufacturers anticipate issues like equipment failures or supply chain disruptions.

metricsHow to Evaluate: Look for features like machine learning, anomaly detection, and forecasting models. Evaluate how well the tool supports actionable insights.

6. Single Source Of Truth 

Manufacturing processes are interconnected systems where changes in one area can affect the entire operation. Deming’s Appreciation for a System emphasizes the importance of understanding these interactions. Your manufacturing analytics tool should allow you to consolidate data into a single source of truth and manage metrics centrally, ensuring decisions are based on accurate, consistent information.

How to evaluate: Look for features like a semantic layer that organizes data into business-friendly terms, making it easier for teams to align on metrics and drive system-wide improvements.

7. Maintainability/Easy To Maintain

Deming emphasized the importance of constancy of purpose, which includes creating systems that are sustainable and easy to manage over time. A maintainable tool minimizes administrative burden, reduces errors, and ensures long-term usability.

How to evaluate: Features like analytics as code allow you to define, reuse, and manage analytics components centrally, making bulk updates and refactoring more efficient and reducing the risk of inconsistencies.  

8 best analytics tools for Manufacturing Analytics 

The right analytics tool can revolutionize how manufacturing teams monitor, optimize, and enhance their processes.

These tools empower businesses to make data-driven decisions, reduce inefficiencies, and embrace continuous improvement.

Below, we explore six top tools, highlighting their strengths, limitations, and alignment with the Deming-inspired criteria above. 

1. Holistics

Holistics is a cloud-based self-service BI platform that allows everyone to quickly get answers to data questions without learning SQL.

With Holistics, your data team can define reusable metrics and manage them centrally in a code-based modeling layer. Business users can then build reports and get accurate analytics in a curated environment. Dashboards and data logic can also be serialized into code and checked into the Git version control repository. 

One of Holistics’ standout features is its programmable approach to analytics, which allows manufacturers to define reusable metrics and customize dashboards using code.

This makes it easier to: 

• Build a single source of truth for the entire organization so that everyone can use consistent metrics. 
• Build custom charts (like xmr Charts) and track specialized KPIs and metrics, such as cycle times, defect rates, or production bottlenecks, aligning the tool with unique manufacturing workflows. 

Holistics also excels in creating clear, tailored visualizations. While non-technical users may require onboarding to leverage its full potential, its flexibility ensures that teams can visualize complex metrics effectively.

Holistics also offers robust scheduling and automated alert features, allowing manufacturers to monitor changes and react to manufacturing anomalies promptly. 

However, its predictive capabilities are limited compared to tools with built-in machine learning or AI features. Users can supplement predictive needs by integrating Holistics with external advanced analytics platforms.

2. Power BI

Power BI offers powerful visualization capabilities, seamless integration with Office 365, and AI-driven insights to identify trends and anomalies. Power BI seamlessly integrates with ERP, MES, IoT devices, and cloud platforms like Azure, ensuring manufacturers can centralize data from diverse sources. Its real-time data connectivity enables teams to monitor and optimize production processes without delays.

A standout feature of Power BI is its advanced visualization options, which allow manufacturers to create tailored dashboards and reports.

From tracking defect rates to analyzing production efficiency, Power BI's drag-and-drop functionality and dynamic visuals empower users to explore complex metrics intuitively. Additionally, its predictive analytics capabilities, powered by machine learning models, help manufacturers anticipate issues like equipment failures or supply chain disruptions.

However, its approach to managing a single source of truth is less robust than tools designed specifically for centralized data governance. Power BI’s data modeling is project-limited and workbook-scoped, using visual interfaces and DAX for defining relationships and calculations, with limited global reusability unless datasets are explicitly shared. If multiple users or teams independently create these without a shared data model or governance, the same metric (e.g., "Production Efficiency") may be calculated differently across reports, leading to inconsistencies.

Maintainability is another area where Power BI shows limitations. Power BI doesn’t support native Git-based version control, so managing analytics components at scale can become cumbersome, especially when bulk updates or changes tracking are required. 

3. Looker

Looker integrates seamlessly with ERP systems, MES, IoT devices, and modern data warehouses like Snowflake and BigQuery, enabling manufacturers to unify data across diverse sources. Its robust API support and direct query capabilities provide real-time insights into production processes, enhancing operational efficiency.

A standout feature of Looker is its LookML data modeling layer, which allows teams to define metrics centrally, ensuring a single source of truth. Manufacturers can customize dashboards to monitor key metrics such as defect rates, production efficiency, and downtime with confidence in the consistency of data across reports.

Collaboration is another strength, with built-in sharing, role-based access, and the ability to embed dashboards directly into operational systems. This fosters alignment across teams and simplifies the decision-making process.

However, Looker’s steep learning curve for LookML can pose a high learning curve. Additionally, the upfront cost of deployment, estimated to be just north of $145,000 annually, may be a concern for smaller manufacturers. 

Looker’s support for custom visualizations is also limited. Many users find its visual styling and dashboard customization options restrictive, which can hinder the creation of highly tailored manufacturing dashboards.

Custom visualizations, such as XmR charts or unique layouts, often require external tools or workarounds, adding complexity.

4. Grafana

Grafana is an open-source platform designed for real-time monitoring and flexible dashboards. It excels in visualizing time-series data, making it a versatile choice for tracking financial performance.

Grafana’s standout feature is its real-time monitoring capabilities. It offers dynamic, interactive dashboards that allow users to track manufacturing KPIs such as machine performance, defect rates, and production bottlenecks. Its support for custom plugins adds further flexibility, enabling tailored visualizations to suit specific manufacturing needs.

However, Grafana’s native visualization capabilities, while extensive, can lack the polish and ease of use found in other tools. Creating highly detailed or specialized visualizations, such as XmR charts, often requires additional plugins or scripting, which may not be user-friendly for non-technical teams.

5. DataPARC

DataPARC is a manufacturing-focused analytics tool designed to provide real-time process monitoring and robust data integration capabilities. Its focus on operational insights makes it an excellent choice for manufacturers prioritizing efficiency and process optimization.

DataPARC integrates seamlessly with various manufacturing systems, including MES, SCADA, and IoT devices, enabling users to unify operational data across the shop floor. Its ability to handle time-series data ensures accurate real-time monitoring, making it particularly valuable for tracking equipment performance and production metrics.

A standout feature of DataPARC is its native support for SPC analysis, enabling manufacturers to monitor process stability and quality control directly within the platform. Tools like trend charts, scatter plots and control charts allow teams to identify variations and take proactive corrective actions.

However, its customization options for dashboards and visualizations are less extensive compared to modern tools, potentially limiting its adaptability for unique or highly specific KPIs.

While DataPARC offers strong operational analytics, its predictive capabilities are limited, requiring external tools or integrations to forecast equipment failures or supply chain issues effectively.

6. SAS

SAS is a powerful analytics platform renowned for its advanced statistical and predictive modeling capabilities, making it a strong choice for manufacturers focused on data-driven decision-making and process optimization.

SAS integrates effectively with ERP, MES, IoT devices, and a wide range of databases, providing a unified view of operational data. Its robust ETL (Extract, Transform, Load) processes ensure seamless data preparation, enabling manufacturers to consolidate and clean data for accurate analysis.

One of SAS's standout features is its advanced analytics suite, which includes machine learning, predictive modeling, and statistical process control (SPC) tools. These capabilities allow manufacturers to anticipate equipment failures, optimize production schedules, and improve quality control by identifying patterns and anomalies in real-time.

While SAS excels in analytics depth, its interface is less intuitive compared to modern platforms. Non-technical users may find the system complex, requiring significant training to navigate its features effectively. Additionally, SAS lacks flexibility in dashboard customization and visualizations, which may limit its adaptability for specific manufacturing KPIs.

Wrap-Up

The tools we’ve covered—each with its own strengths—can address specific financial challenges, from actionable insights to real-time monitoring. Yet, as Deming’s principles remind us, the perfect tool isn’t just about what it offers; it’s about how well it aligns with your processes, supports continuous improvement, and empowers your team to make smarter decisions.

Ultimately, the best analytics tool is one that aligns with your organization’s unique needs while addressing potential limitations. By evaluating tools through the lens of system interconnectivity, actionable insights, and usability, finance teams can ensure their choice not only meets today’s demands but grows with them into the future.