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ARC Advisory Group has embarked on a mission to simplify the complex, identify relevant breakthroughs, and cut through the hype surrounding industrial AI and explore its use cases. In The Industrial AI (R)Evolution we cut through the Generative AI hype, dispels myths and summarizes the latest developments and trends in the field.
Industrial AI, a subset of the broader field of artificial intelligence (AI), refers to the application of AI technologies (including Generative AI) in industrial settings to augment the workforce in pursuit of growth, profitability, more sustainable products and production processes, enhanced customer service, and business outcomes. Industrial AI leverages machine learning, deep learning, neural networks, and other approaches. Some of these techniques have been used for decades to build AI systems using data from various sources within an industrial environment, such as sensors, machinery, industrial engineers, and frontline workers.
ARC Advisory Group has identified 25 high value applications of Industrial AI. These use cases are a great starting point for industrial organizations to leverage the diverse AI tools and technologies available, including the innovative advancements in Generative AI. The aim is to ensure these tools can be effectively applied across the complete life cycle of sustainable products. From the preliminary stages of product and supply chain design, through the implementation phase of manufacturing and supply chain execution, to customer sales, service, and ensuring safe end-of-life disposal and recycling, these AI applications promise to deliver significant business value.
Product Development and Design
Artificial Intelligence (AI) significantly augments product development and design, offering data-driven insights to enhance efficiency, promote sustainability, and improve user experience.
Top 5 Use Cases:
Generative Design: AI algorithms propose optimal design solutions based on specific criteria.
Materials Science: AI predicts the best materials for specific applications, enhancing product durability, performance, and circular economy impact.
Simulation and Testing: AI accelerates the testing process by simulating real-world conditions and forecasting product performance.
Sustainability Analysis: AI evaluates product, process and circular supply chain designs to minimize environmental impact.
Cost Optimization: AI guides designers in creating cost-effective products without compromising quality or performance.
Supply Chain Planning and Execution
AI is instrumental in optimizing supply chain operations, predicting and aligning demand with supply constraints to balance customer service, cost to serve, and circular economy goals.
Top 5 Use Cases:
Demand Forecasting: AI analyzes historical and real-time data to predict future demand.
Inventory Optimization: AI adjusts inventory levels to decrease storage costs and prevent stockouts.
Supply Chain Design and Risk Management: AI anticipates potential disruptions and suggests preventive measures for end-to-end circular supply chain operations.
Logistics Optimization: AI optimizes transportation routes and schedules to reduce costs, improve delivery times, and minimize carbon footprint.
Supplier Selection: AI evaluates suppliers based on various criteria to ensure reliable and cost-effective sourcing.
Production Operations
AI optimizes production operations, balancing customer scheduling and quality needs, energy and carbon constraints, resource availability, and other real-world production constraints.
Top 5 Use Cases:
Production Scheduling: AI optimizes production schedules to meet customer demands while minimizing costs.
Quality Control: AI-powered visual inspection systems detect defects and enhance product quality.
Predictive Maintenance: AI predicts equipment failures and schedules maintenance to minimize downtime.
Process Optimization/Waste Reduction: AI identifies inefficiencies and recommends changes to minimize waste, increase efficiency, and optimizing energy and water usage in production processes.
Digital Twin/Industrial Metaverse: Augmented reality provides local and remote frontline workers and engineers with real time access, AI powered insights, and operations advice via digital twins.
Sales and Services
AI aids in predicting customer demand, personalizing marketing strategies, and enhancing post-sale customer service.
Top 5 Use Cases:
Customer Segmentation: AI groups customers based on behavior and preferences for targeted marketing.
Sales Forecasting: AI predicts future sales trends based on historical and real-time data.
Chatbots: AI provides automated customer service, freeing up human agents for complex issues.
Product Recommendations: AI suggests products based on customer browsing history and purchases.
Sentiment Analysis: AI gauges customer sentiment from reviews and social media posts to improve products and services.
Workforce Management
AI enhances the skills of engineers and frontline workers, promotes collaboration, and boosts productivity.
Top 5 Use Cases:
Skills Gap Analysis: AI identifies areas where employees need training.
Recruitment: AI screens candidates and predicts their fit for a role.
Employee Retention: AI predicts which employees are at risk of leaving and suggests interventions.
Collaboration Tools: AI enhances teamwork across geographical boundaries.
Productivity Analysis: AI identifies patterns in employee behavior to suggest ways to increase productivity.
Industrial AI's Role in Achieving Sustainability Goals
Artificial Intelligence (AI) holds immense potential for transforming various facets of industrial organizations, particularly in achieving sustainability goals. By prioritizing select Industrial AI use cases, industries can not only optimize their operations but also make significant strides towards a sustainable future.
In the realm of product development and design, AI serves as a catalyst for innovation. It facilitates generative design, materials science, simulation and testing, sustainability analysis, and cost optimization. With its ability to evaluate and minimize environmental impact, AI is at the forefront of driving sustainability in product creation.
AI's role in enhancing supply chain planning and execution is equally crucial. It aids in demand forecasting, inventory optimization, supply chain design, risk management, logistics optimization, and supplier selection. By optimizing logistics, AI can significantly reduce fuel consumption and emissions, thus promoting sustainable supply chain operations.
Production operations can also benefit from AI's transformative impact. Through production scheduling, quality control, predictive maintenance, process optimization, and waste reduction, AI ensures efficient and sustainable production operations. It helps minimize carbon emissions and water consumption, underlining its pivotal role in sustainability.
Sales and services are another area where AI has made significant inroads. It aids in customer segmentation, sales forecasting, chatbots, product recommendations, and sentiment analysis. AI-powered sales forecasting can help reduce overproduction and waste, contributing to more sustainable business practices.
Lastly, when it comes to workforce management, AI plays a critical role. It aids in skills gap analysis, recruitment, employee retention, collaboration tools, and productivity analysis. By ensuring effective reskilling and upskilling, AI promotes workplace sustainability and reduces the need for external hiring.
In conclusion, by prioritizing specific Industrial AI use cases, industrial organizations can leverage AI's potential to drive operational efficiency and achieve their sustainability goals. The journey towards a sustainable future is challenging, but with AI as an ally, it becomes an achievable endeavor.
For more information or to contribute to Industrial AI research, please contact Colin Masson at cmasson@arcweb.com .