November 20, 2024
Digital transformation in manufacturing integrates digital technologies into all areas of a manufacturing business to improve efficiency, increase flexibility, drive innovation, and boost profitability. This includes replacing paper-based or manual processes with digital tools and automation technologies in production, supply chain management, and customer interactions.
Artificial intelligence (AI) enriches digital transformation through algorithms that mine and analyze large amounts of data acquired through current and past interactions and transactions. AI uses insights from this data to create predictive and prescriptive analytics that help drive automation, optimize decisions, and predict future events.
This article explains how each technology below can benefit manufacturers and offers step-by-step guidance on developing an AI strategy and implementation process.
Advanced data analysis
Computer vision quality control (CV-QC)
Machine learning (ML)
Robotics
IoT data sensors
How AI-Driven Technology Benefits Manufacturers
Digital transformation can significantly improve every aspect of manufacturing, from product design to shop floor efficiency and operational cost savings to forecasting market changes. Some of the ways manufacturers are leveraging artificial intelligence to transform operations include:
Advanced data analysis: Made possible by combining AI and the processing speed of today's high-performance computing (HPC) systems, advanced data analysis allows product designers to explore thousands more product design changes in the same timeframe as the traditional engineering approach. AI simulations, pattern recognition, and optimization help refine designs and elevate product safety and reliability.
Computer vision tools: Such tools enable computers to interpret and analyze visual data, such as videos and camera images, to identify product patterns, defects, or anomalies. Manufacturers can use these tools to advance quality control practices, including inspecting products, automating processes, and optimizing productivity on production lines, making quality control a constant and ever-improving process.
Reliable and efficient quality control processes boost operational efficiency, reduce costs, accelerate time to market, and improve customer experiences. AI-powered computer vision can also help predict maintenance issues and guide robots in assembly processes.
Machine learning (ML): ML empowers machines to learn using AI algorithms and statistical models created from raw process data. One of the most common uses of ML is to help manufacturers avoid costly machine downtime by predicting potential problems before they occur.
Machine learning models analyze historical data to identify trends and predict future events. Demand forecasting, financial market analysis, and energy consumption forecasting are just a few areas where more accurate analyses made possible by ML can make a critical difference in predicting and responding quickly to dynamic market environments.
IoT sensors: These hardware components that monitor machine performance and environmental variables such as chemical levels, temperatures, and proximity. When paired with AI, IoT sensors enable predictive analytics to optimize performance, reduce downtime, and predict failures.
Robotic systems: While not inherently "AI," unless coupled with cognitive technologies like machine learning, robotic systems can play a transformational role in almost every manufacturing sub-industry. However, the type of robot used in the automotive industry for everything from welding and painting to assembly and inspection will look quite different from those used in the electronics industry to pick and place tiny electronic components or in the food and beverage industries, where robots perform multiple tasks, such as food prep, cooking, and packaging. In every sector, robots can streamline production processes, enhance precision, enhance quality, and reduce labor costs.
Robotic process automation (RPA): RPA automates administrative tasks and back-office processes using software robots. These robots, or “bots,” as they are often called, automate high-volume, rules-based, and trigger-driven tasks without human intervention. Examples include copy-paste tasks or moving computer files from one location to another. RPA is not associated with physical robotics. The role of RPA is to boost efficiency and free up human resources for more value-added work.
Steps to Achieving AI-Driven Digital Transformation in Manufacturing
Achieving AI-driven digital transformation requires a structured, multi-step approach to ensure effective implementation and sustainable results.
1. Assess Current Progress
The journey begins with an assessment of your current progress in adopting digital technologies. Evaluate existing business applications to determine if they can serve as a solid foundation for an AI strategy or if new applications are necessary. Examine your current data infrastructure to gauge its readiness for handling AI workloads. From there, define near-, mid-, and long-term strategies while identifying use cases across operations. Select pilot projects that align with expected business outcomes and are feasible to implement.
2. Establish Processes
Next, establish reliable data acquisition, management, and governance processes. Data is the bedrock of any AI initiative and essential for effectively applying AI algorithms to business operations. To unlock AI’s potential, companies must break down data silos and ensure data quality, creating a robust framework for analytics.
3. Identify Infrastructure & Tools
The third step is to identify technology infrastructure and tools. This includes systems for data gathering, storage, and processing, as well as analytics and visualization tools. Pre-built models and templates, like those available to Infor CloudSuite Industrial customers, can accelerate AI adoption by enabling rapid use case setup, quick access to insights, and faster returns on investment.
4. Develop a Skilled Workforce
The fourth step involves building a skilled workforce to manage and utilize new technologies and data resources. Companies can upskill existing employees or hire specialists such as data scientists, data engineers, and analytics translators. Alternatively, they can partner with experienced providers like Guide Technologies to expedite time to value. It’s also critical to foster a collaborative environment where these experts can work seamlessly alongside traditional engineers, ensuring alignment between technical and operational expertise.
5. Define Governance Model
Step five emphasizes the importance of establishing AI governance and processes. Define clear protocols for data ownership, access, and security and set performance criteria for AI models. Cross-departmental collaboration is essential, as is addressing ethical considerations such as transparency, accountability, and fairness.
6. Cultivate an AI-Driven Culture
Finally, organizations must work to cultivate a data- and AI-driven culture. Build trust by providing continuous education to counter fears of AI replacing jobs. Increase employee retention by establishing transparent KPIs and metrics. Empower employees with self-service reporting tools, enabling them to derive insights independently and contribute to ongoing improvements through feedback loops.
This comprehensive, step-by-step approach ensures that AI-driven digital transformation not only meets immediate goals but also creates a sustainable and scalable framework for future innovation.
AI-Driven Digital Evolution is Not Optional
Companies that do not take advantage of AI-driven technologies' transformational power will become less competitive. Guide Technologies and Infor have the technology and expertise to ensure improvements to your manufacturing systems and processes to help you stay ahead of the competition. Infor’s manufacturing-specific ERP, CloudSuite Industrial, comes with analytics capabilities that make it easier to integrate AI models and start getting insights quickly.
Contact Guide Technologies today to learn more.
