Diagnostic Analytics Dashboard | Bismart

Process efficiency is the key to success in the manufacturing sector. Companies that optimize their operations and minimize errors can remain competitive in an increasingly demanding market. However, most businesses in the industry face significant challenges that hinder productivity and affect product quality.

In this article, we explore how a manufacturing company identified the root causes of production delays, detected machinery failures, and reduced material losses using a diagnostic analytics dashboard. Discover this success story!

Context and Relevance of Diagnostic Data Analytics in the Manufacturing Sector

The manufacturing industry relies heavily on precision and process efficiency. However, companies in this sector frequently face challenges such as production line failures, unexpected downtime, and material waste.

For this reason, diagnostic analytics dashboards play a crucial role in manufacturing. Unlike traditional reporting, diagnostic analytics not only measure outcomes but also uncover the root causes of problems. Through advanced techniques like cause-and-effect analysis, manufacturers can detect patterns in machine performance, identify bottlenecks in production lines, and make informed decisions based on in-depth analysis rather than surface-level insights.

What Is Diagnostic Data Analytics and Why Is It Essential?

Diagnostic analytics is a type of data analytics that focuses on understanding the why behind events. Its primary objective is to uncover the reasons behind things within an organization.

Unlike descriptive analytics, which simply reports what has happened, diagnostic analytics seeks to explain why certain phenomena occur within a company.

Types of Diagnostic Data Analytics

Diagnostic analytics leverages advanced techniques to identify the root causes of events. The most prominent methods include:

• Correlation Analysis: Identifies relationships between variables and helps determine which factors influence outcomes.
• Cause-and-Effect Analysis: Uses multidimensional models and drill-down analysis to understand how specific factors impact business results.
• Correlation vs. Causation: Recognizes that while correlation indicates a relationship between variables, it does not necessarily imply causation.
• Slice-and-Dice & Drill-Down Analysis: Breaks down large datasets to detect anomalies and performance variations.
• Statistical Modeling & Machine Learning: Employs regression models, clustering, and time series analysis to identify patterns and generate predictions.
• Diagnostic Regression Analysis: Examines relationships between variables to anticipate future impacts.

PrecisoDiagnostic Analytics Dashboard: The Key to Accurate Business Insights

A Diagnostic Analytics Dashboard is an essential tool for organizations seeking to thoroughly evaluate and understand their operational and strategic performance. This type of dashboard focuses on identifying strengths, weaknesses, opportunities, and threats, enabling informed decision-making.

To implement a diagnostic-specific dashboard, it is crucial to define key performance indicators (KPIs) that reflect the most critical aspects of the business. These KPIs must align with strategic objectives and provide valuable insights for organizational diagnostics.

Power BI is a powerful tool for creating tailored diagnostic analytics reports. With Power BI, organizations can design interactive reports that integrate data from multiple sources, providing a comprehensive view of the company's status. Additionally, Power BI simplifies the updating and distribution of reports, ensuring decision-makers always have access to up-to-date information.

A well-designed diagnostic dashboard should be intuitive and customizable, allowing users to explore data according to their specific needs. Interactivity and drill-down capabilities are key features that enhance the value of diagnostic analytics, offering in-depth insights into the factors affecting business performance.

In summary, implementing a diagnostic analytics dashboard powered by tools like Power BI enables organizations to monitor and assess their performance effectively, facilitating the identification of improvement areas and supporting data-driven strategic decision-making.

Success Story: Optimizing the Production Chain with Diagnostic Data Analytics in Manufacturing

A leading manufacturing company was facing inefficiencies in its production chain, including equipment failures, delivery delays, and difficulties in identifying the root causes of these issues—despite having access to vast amounts of operational data.

Through its collaboration with Bismart and the implementation of a diagnostic analytics dashboard, the company was able to uncover the true causes of downtime and enhance its production processes. By leveraging cause-and-effect analysis, the company successfully anticipated machinery failures, optimized preventive maintenance, and improved operational stability.

Discover the full process and the results achieved in the downloadable success story!

Context and Challenges in the Manufacturing Industry

When the company turned to Bismart, it was struggling with critical inefficiencies in its production chain, affecting operational efficiency and delivery timelines. The main challenges included:

• Production delays: Despite having a real-time data dashboard, the company was unable to determine why certain batches took longer to complete, causing delivery delays and disrupting planning.
• Recurring machinery failures: Unexpected breakdowns frequently halted production, leading to downtime, reduced operational capacity, and higher reactive maintenance costs.
• Limited data analysis: Although the company had real-time operational data, its analytics were purely descriptive, providing no insights into the underlying causes of delays and failures. Without clear efficiency metrics, process optimization was challenging.

These issues not only affected profitability, but also introduced uncertainty into operational planning and undermined customer confidence by compromising on-time delivery commitments.

The Solution: Cause-and-Effect Analysis

To address these challenges, Bismart designed a strategy based on cause-and-effect analysis, with the primary goal of identifying the root causes of unplanned downtime.

• Downtime Cause Analysis: A custom analytical algorithm was developed to identify the factors behind unplanned production halts and assess their impact on operational efficiency.
• Real-Time Data Integration: IoT sensors were integrated into the machinery to collect real-time operational data, enabling immediate anomaly detection and preventive action.
• Power BI Diagnostic Analytics Report: A custom diagnostic analytics dashboard was developed, providing not only key metrics but also insights into patterns and correlations, helping to optimize production.
• Automated Alerts: Automated notifications were configured to warn of potential machinery failures before they occurred, minimizing downtime and improving maintenance planning.
• Optimized Preventive Maintenance: Based on diagnostic analytics, proactive maintenance strategies were implemented to reduce interruptions and extend machinery lifespan.

Impact and Results Achieved

Gracias a la estrategia implementada y a meses de trabajo del equipo Bismart, la empresa logró reducir sus tiempos de inactividad en más de 12 horas semanales.

Al comprender las causas de los retrasos, la empresa pudo ajustar su programación para garantizar una producción más estable y cumplir con los plazos de entrega de manera más consistente.

Thanks to the implemented strategy and months of collaboration with Bismart’s team, the company successfully reduced downtime by more than 12 hours per week.

By understanding the root causes of delays, the company was able to adjust its scheduling, ensuring a more stable production flow and consistently meeting delivery deadlines.

Discover all the detailed results in the downloadable success story and get inspired by a real case with promising outcomes!

Identifying the Root Causes of a Problem with Cause-and-Effect Analysis

Diagnostic analytics for identifying the root cause of a problem or trend is typically divided into three main phases:

• Anomaly Identification: Trends or anomalies detected through descriptive analysis may require further investigation if their cause is not immediately clear. In many cases, the high natural variability of data makes it difficult to determine whether a pattern represents a new trend or merely normal fluctuations. Statistical analysis helps assess whether the results indicate a significant deviation from the norm.
• Data Discovery: Once anomalies are identified, the next step is to gather data that explains their origin. This may involve collecting external data and analyzing internal records. For example, external data can reveal factors such as supply chain disruptions, new regulatory requirements, competitive market shifts, or weather patterns that influence the observed results.
• Determining Causal Relationships: Identifying associations between variables is not enough to establish causation. While two events may be correlated, this does not necessarily mean that one causes the other. A deeper analysis of data related to a specific trend—such as an increase in sales—helps determine which factors had the greatest impact and whether a real causal relationship exists.

Conclusion

This success story highlights how a diagnostic analytics dashboard can transform operational efficiency in the manufacturing industry.

Through its collaboration with Bismart, the company significantly reduced downtime, optimized preventive maintenance, and stabilized production. As a result, it improved planning, minimized disruptions, and ensured more precise delivery timelines—all without compromising product quality.

If your company faces similar challenges, data analytics can help optimize production and enhance operational efficiency. Contact us to explore data-driven solutions tailored to your needs.