The Importance of Information Architecture in Manufacturing: A Key Step Towards AI Success

AI requires machine learning, machine learning requires analytics, and analytics requires the right data and information architecture.

-Anand Mahurkar, CEO of Findability Sciences

The manufacturing industry needs to find new ways to make more money, reduce risks and mistakes, and improve how efficiently things are produced. Artificial intelligence (AI) can help with these goals by using a lot of data from manufacturing to automate complicated tasks, optimize processes, and make decisions on its own.

In 2022, AI was worth $2.3 billion in the manufacturing market, and experts predict it will reach $16.3 billion by 2027. After the COVID-19 pandemic, the demand for AI has increased not only in manufacturing but also in other industries. Many businesses now understand that they need AI to keep up with the current business environment. According to a recent report by PwC, AI technology could add up to $15.7 trillion to the global economy by 2030 and continue to improve productivity.

AI in manufacturing allows for accurate predictions, forecasting of resource needs, and predicting energy and commodity prices. However, there are challenges in implementing AI effectively. One important challenge is having the right structure and organization for information, which is called information architecture (IA). AI depends on machine learning, which relies on analytics, and analytics depends on having the right data and a good information structure. In simpler terms, AI can’t work without IA as a strong foundation.

7 key steps to establish the right data and information architecture

1. 1. Understand Your Data Landscape: To establish the right data and information architecture, manufacturers must first gain a comprehensive understanding of their data landscape. This involves identifying and categorizing data sources, both internal and external, structured and unstructured. By mapping out the data landscape, manufacturers can identify data silos, gaps, and quality issues that need to be addressed.
   2. Establish Data Governance: Data governance plays a crucial role in ensuring data accuracy, integrity, and security. Manufacturers need to define data governance policies and processes that outline data ownership, data quality standards, data access controls, and data lifecycle management. By implementing robust data governance practices, manufacturers can establish a solid foundation for reliable and trustworthy data.
   3. Data Integration and Cleansing: Integrating data from disparate sources is often a significant challenge in manufacturing. It requires implementing data integration technologies and techniques to bring together data from various systems, such as enterprise resource planning (ERP), manufacturing execution systems (MES), sensors, and IoT devices. Additionally, data cleansing processes should be employed to eliminate inconsistencies, duplicates, and errors, ensuring the data is accurate and reliable.
   4. Implement Scalable Data Storage Solutions: Manufacturers deal with vast amounts of data generated from production processes, supply chains, and equipment. It is crucial to have a scalable and reliable data storage infrastructure that can handle the volume, velocity, and variety of data. This may involve adopting cloud-based solutions, leveraging big data technologies, or utilizing data lakes or data warehouses to efficiently store and manage data.
   5. Embrace Advanced Analytics: Analytics plays a vital role in extracting insights and generating actionable intelligence from manufacturing data. By leveraging advanced analytics techniques, such as machine learning and predictive analytics, manufacturers can uncover hidden patterns, detect anomalies, optimize processes, and make data-driven decisions. It is essential to invest in analytics tools and platforms that can handle the complexity and scale of manufacturing data.
   6. Ensure Data Security and Privacy: Protecting sensitive manufacturing data is paramount. Manufacturers must implement robust security measures, including encryption, access controls, authentication mechanisms, and intrusion detection systems, to safeguard data from unauthorized access or breaches. Compliance with data privacy regulations, such as GDPR or CCPA, is also critical for maintaining customer trust and avoiding legal consequences.
   7. Collaborate with Technology Partners: Building the right data and information architecture can be a complex and daunting task. Collaborating with technology partners who specialize in data management, analytics, and AI solutions can significantly simplify the process. Companies like Rockwell Automation, Stratus Technologies, and Servo Dynamics provide comprehensive solutions for manufacturing, including hardware, software, and consulting services, to help manufacturers establish robust IA foundations and successfully embark on their AI journey.

In the era of Industry 4.0, AI holds immense potential for revolutionizing the manufacturing industry. However, manufacturers must recognize that the path to successful AI implementation begins with establishing a solid information architecture. By understanding their data landscape, implementing effective data governance, integrating and cleansing data, adopting scalable storage solutions, leveraging advanced analytics, ensuring data security, and collaborating with technology partners, manufacturers can lay the groundwork for AI success. Don’t overlook the importance of IA—embrace it as a vital stepping stone towards realizing the full potential of AI in manufacturing. Contact Servo Dynamics and our trusted partners to discuss your specific requirements and unlock the transformative power of AI in your manufacturing operations.