Vernaio BLOG
Mariia Ruzova

AI-powered RCA in Manufacturing: 4 Crucial Elements to Streamline Production Processes - Part 2

Key Takeaways
  • Success in integrating AI-based RCA depends on managing four key factors: data quality, AI software, IT infrastructure, and corporate culture.
  • High-quality, well-structured, and consistent data are foundational to AI's ability to provide valuable insights into manufacturing processes.
  • Three essential characteristics of the right AI software: predictive capabilities and real-time alerts, high scalability, and easy-to-understand and actionable insights.
  • Robust IT infrastructure, including IIoT technologies and strong cybersecurity measures, forms the backbone of an efficient AI-powered RCA process.

Did you know that manufacturing companies lose, on average, 25 hours of productivity per month due to unexpected monthly downtimes (according to a study by Siemens)? Even more alarming: the tremendous losses caused by such downtime activity range from $39,000 to over $2 million per hour, depending on the industry! This highlights the urgent need for manufacturing companies to identify and understand the root causes of such machine downtimes and equipment breakdowns to mitigate the risk of production disruptions. In other words, manufacturing companies must find an effective way to protect production in real time.

One highly effective method to pinpoint the root causes of disruptions is Root Cause Analysis (RCA). Enhanced with the superpowers of Artificial Intelligence (AI), RCA is designed to bulletproof your production processes for good. However, the sole knowledge about the benefits of AI-driven RCA doesn't help you reap its benefits if you don't know how to implement it effectively into your existing manufacturing processes. Therefore, this article explores how to effectively implement AI-driven RCA on the shopfloor to live-protect production processes from unexpected downtime. On this journey, we will cover 4 crucial elements of AI-driven RCA, serving as a robust framework for successful implementation:  

  1. Data quality
  2. AI software
  3. IT Infrastructure
  4. Company culture

4 Pillars of AI-driven Root Cause Analysis for successful implementation and maximal profits in manufacturing.

Element 1: Data Quality, Data Management, and Data Strategies

Data is the lifeblood of any AI-driven process. Without it, artificial intelligence fails to provide insights. However, data quality is the biggest challenge manufacturers face when implementing AI technology: a survey by NewVantage Partners found that 95% of businesses cite poor data quality or data inconsistency as a major obstacle to digital transformation and enhancing customer experience.

Manufacturers can tackle the data quality challenge by implementing a comprehensive data collection strategy that aligns with industry-specific standards. Regardless of the industry, a well-structured data collection plan that specifies data sources and frequency is essential.

For instance, in the pharmaceutical industry, regulatory compliance and traceability are paramount, while the automotive sector prioritizes the precision and accuracy of the data.  

Another important aspect is data storage and processing. This includes selecting suitable hardware and software systems, implementing regular system updates, performing timely backups, and utilizing data validation techniques to prevent data corruption.

The Data Lifecycle explaining the AI implementation journey in manufacturing from data collection to productive application.

Element 2: AI Software

After ensuring a solid data foundation, it is about implementing AI-powered software. Artificial Intelligence has been a game-changer for many industries, and manufacturing is no exception. AI software uses machine learning algorithms, statistical models, and other AI models to analyze data for RCA.

AI-powered RCA software can quickly process vast amounts of data, identifying relationships and patterns in real-time and providing manufacturers with actionable insights. When choosing an AI for RCA, manufacturing companies should favor AI solutions that include:

  1. High-speed Data Processing: In the era of Industry 4.0, manufacturing operations generate massive volumes of data, often reaching terabytes, at an unprecedented pace. The chosen AI tool must be able to process this data to identify issues in real-time swiftly. Real-time data processing and analysis are crucial for monitoring production performance in real-time and protecting machines and equipment against malfunctioning.
  2. Real-time Alerts: Given the pace of manufacturing processes, delays in identifying issues can lead to significant losses. AI tools must therefore offer real-time alert systems that notify relevant personnel as soon as an impending risk scenario is detected, ideally even before it happens. It would be even more helpful if the AI provided recommendations for the optimal countermeasures to counteract the malfunction effectively.  
  3. Scalability: A resilient production process can quickly adapt to significant changes based on production volumes due to demand, product changes, or expansion. The same is true for AI technology, which needs to handle increasing production volumes and different use cases flexibly. This requires the AI used for RCA to seamlessly handle growing data volumes without compromising performance, accommodating additional production machines or production lines, and incorporating new use cases or different production setups to ensure efficient responses to evolving needs and enables continuous improvement.
  4. User-friendly Interface: Considering the diverse range of roles in manufacturing, from shop floor operators to management, the AI tool should be user-friendly and accessible to all levels of technical expertise. For instance, an intuitive dashboard that presents RCA results in a digestible format can aid in quick decision-making. Customizing the dashboard for managers, engineers, and operators is crucial. It ensures that each user sees the information relevant to their tasks, enabling them to make the most efficient use of the AI in root cause analysis.
  5. Easy Integration: Manufacturing systems often comprise machinery, software, and data sources. The selected AI tool must be able to integrate seamlessly into the existing IT infrastructure without massive integration efforts. Moreover, the AI needs to be able to cope with data from different data environments, i.e., it should be able to consolidate data from disparate sources like machine sensors, ERP systems, and quality control systems.
  6. Problem Identification: Manufacturing processes can be complex with interconnected dependencies. The AI tool should be capable of defining the core problems and identifying hidden dependencies in the process data. For example, recognizing how a temperature fluctuation in one process could impact the quality of the final product.
  7. Predictive Capabilities: Predictive capabilities are vital in manufacturing to proactively address potential issues, meaning prediction and handling even before they occur. A tool that can predict possible machine failures or quality issues in advance, and suggest countermeasures, can significantly reduce downtime and improve efficiency.

Element 3: IT Infrastructure

Now that you have a robust data management system and the right AI tool, it is time to implement a proper IT Infrastructure, which is the backbone of any AI-powered RCA process. At this stage, it's essential to highlight the significance of leveraging IIoT technologies in the infrastructure and ensure robust cybersecurity measures.

Imagine a bustling city where manufacturing infrastructure represents the cityscape. In this city, machines' Programmable Logic Controllers (PLCs) and sensors function like streetlights, picking up data about the operational conditions. At the same time, the IoT Gateway acts as the city's management center, collecting and processing this information. The insights derived from this data are essential to perform AI-driven RCA and uncover the root causes of any issues.

In some cases, additional sensors are installed to supplement the existing PLCs, much like adding extra lighting in dim areas of a city. These sensors provide more detailed data, increasing visibility into the production process and enhancing the accuracy of AI-driven RCA. They help create a fuller picture of the manufacturing landscape, leading to more precise issue detection and resolution.

However, just like any vibrant city, security is paramount. Robust cybersecurity measures form a digital shield, protecting the interconnected IIoT ecosystem from potential threats. The manufacturing industry witnessed a 156% increase in cyber-attacks in 2020, as reported by Deloitte. By implementing robust network protocols, access controls, encryption techniques, and regular security assessments, manufacturers fortify the confidentiality of data within the manufacturing infrastructure. This also mitigates the risk of unauthorized access and data breaches, ensuring the reliability and trustworthiness of the AI-powered RCA process.

Smart interconnected city where manufacturing infrastructure represents the cityscape.

Element 4: Company culture

Lastly, company culture is pivotal in fostering a thriving environment for Root Cause Analysis. In a continuous improvement mindset, RCA thrives through systematic data collection, analysis, and action. To cultivate this mindset in a manufacturing setting, it is essential to promote an open-door policy where employees feel encouraged to raise production problems and recognize the significance of diligent data recording. Moreover, encouraging cross-departmental collaboration, such as between IT and production, fosters that all employees understand and appreciate the value of RCA. Manufacturers can further enhance this culture by providing training and development programs to empower employees with data management and RCA techniques. This helps create a data-centric culture where RCA becomes an integral part of the manufacturing process, and data is valued as an asset that drives productive outcomes.

1. Data Quality & Data Management.  

We dealt with vast raw production data from five production lines, totaling 442 GB. The data consisted of telemetry data from the shop floor and categorical data indicating machine and resource status. The intelligence behind our AI solution Process Booster, the aivis® engine, specializes in processing raw, unformatted data, therefore eliminating the need for additional data labeling, cleansing, and formatting.

3. IT Infrastructure.

To address the client's critical issues, we integrated data from the shop floor and the Manufacturing Execution System (MES). Using data connectivity software and IoT uplink technology, we seamlessly transported the data from the shop floor to a private cloud server.

4. Company culture.

During the value-proof phase, we collaborated closely with our client, who previously relied on manual RCA searches for critical parameters or configurations that could be the root causes of issues. To ensure the successful adoption of AI-driven RCA, we maintained frequent communication and worked with internal experts to interpret and verify our findings at every stage. This collaborative approach led to invaluable insights that our client had not been able to obtain before, contributing to the project's success.

Unlocking Success with AI-driven RCA: The 4 Elements for Optimized Manufacturing Operations  

AI-powered Root Cause Analysis is a game-changer for manufacturers willing to optimize production processes and safeguard business performance. By focusing on four crucial elements - data quality and management, AI software, IT infrastructure, and company culture: manufacturers can effectively harness the potential of AI-driven RCA to bulletproof their manufacturing operations.

Through accurate data collection, integration, and management, manufacturers can ensure the reliability and accuracy of insights generated by AI-driven RCA. Advanced AI software enables faster time to insights, greater accuracy, and improved reliability, enhancing process transparency and knowledge transfer. Building a robust IT infrastructure, leveraging IIoT technologies, and implementing strong cybersecurity measures are essential for seamless data gathering, aggregation, and protection. Lastly, fostering a data-centric company culture promotes systematic data collection, analysis, and action, driving continuous improvement.

The four elements represent a critical aspect of AI-driven RCA's success and effectiveness. Together, they form a solid foundation that enables manufacturers to identify, analyze, and address the root causes of production issues more efficiently and accurately. By understanding and implementing these four elements, manufacturers can leverage the power of AI to enhance their RCA capabilities, improve operational efficiency, minimize disruptions, and drive continuous improvement in their manufacturing processes.

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