Exploring the Potential Value of Generative AI Throughout Manufacturing
Generative AI could be the next rung on the ladder in the quest toward digital transformation and enhanced performance across manufacturers’ businesses.
TAKEAWAYS:
● Generative AI may be an important next step in the manufacturing industry’s digital transformation journey.
● Generative AI is expected to offer immense potential in areas such as product design, talent development, aftermarket services, and supply chain management.
● Manufacturers seem to be investigating the possibilities of generative AI through several use cases across the business that could help to add significant value to their operations.
Technology is poised to play an even greater role in supporting manufacturers as they tackle current and future challenges. With a persistent search for efficiency and focus on building resilience across their organizations, many manufacturers look to continue to pursue their digital transformation objectives—even as some may be considering pausing investments because of the challenging business environment. Most companies surveyed in the 2023 Deloitte and MLC industrial metaverse study1 have made significant investments and are generally either implementing technologies like data analytics, cloud computing, artificial intelligence (AI), 5G, and Internet of Things across multiple projects and processes, or they are currently experimenting with one-off projects (Figure 1). The same is true for digital twins, 3D modeling, and 3D scanning. Companies also seem to be embracing a smart factory approach, exploring the industrial metaverse, and investigating the possibilities of generative AI as tools to add value to their operations.2
Figure 1: Surveyed manufacturers have invested in the technology foundation for the industrial metaverse
The potential benefits of smart factories are vast—ranging from gains in asset efficiency, labor productivity, and product quality to substantial cost reduction, along with the advancement of the cause of safety and sustainability.3 According to the 2023 Deloitte and MLC industrial metaverse study,1 92 percent of surveyed manufacturers are already experimenting with or implementing at least one metaverse-related use case. Executives surveyed anticipate an increase of 12 percent or more in several key performance indicators, including sales, quality, throughput, and labor productivity because of industrial metaverse initiatives.1
One of the latest additions to this digital transformation drive is generative AI, which is expected to hold immense potential in areas such as product design, aftermarket services, and supply chain management.2 It could lead to reduced costs across manufacturing organizations and potentially serve as another tool for navigating a challenging labor market.
Generative AI can be considered a new frontier in digital transformation in manufacturing. Given the immense scope of this technology, we highlight several generative AI use cases throughout manufacturing.
Developing Employee Training
Companies can use generative AI to customize training materials based on specific job roles, site conditions, or regulatory requirements. It can analyze large volumes of data, such as incident reports, occupational health and safety (OHS) guidelines, or compliance standards, and generate tailored content. Combined with virtual reality (VR), generative AI can be used to develop virtual training environments that replicate operational conditions, helping trainees navigate hazardous situations, and improve their OHS awareness and response capabilities in a safe setting.4 The flexibility in technology-facilitated training can also enable individuals to upskill at their convenience, helping to foster a more dynamic and efficient learning environment.
Optimizing Product, Process, and Facility Design
Leveraging technologies like computer vision, generative AI, drones, and digital twins enables data-driven optimization of manufacturing processes and product design. These solutions can optimize production lines and streamline product design, leading to faster time-to-market and reduced costs. For instance, simulating hurricane winds on a wind turbine’s digital twin lets engineers adjust its design for stability under extreme conditions.5 Generative design can enable product development teams to create and visualize multiple alternatives of a new 3D product design based on input constraints such as weight, performance requirements, strength, material, cost, etc. The benefits can include optimized products, cost savings, and accelerated product innovation.2
The number of manufacturing establishments in the US grew by more than 11 percent between the first quarter of 2019 and the second quarter of 2023, approaching 393,000 by the end of the period.6 Construction spending in manufacturing—that is, dollars invested to build new or expand existing manufacturing facilities—has more than tripled since June 2020, reaching a new record high of US $228 billion in April 2024.7 As companies expand and build new manufacturing facilities, generative AI can automate certain aspects of the site design process, provide designers with a multitude of design options, and help reduce time, cost, and emissions during construction.8
Enhancing Supply Chain Management
Generative AI could help identify and simulate potential disruptions or risks in the supply chain from publicly available data and supplier data. By assessing port congestion, shipment routes, and tier supplier mapping, generative AI could predict potential risks and their corresponding impact on operations, and recommend actions such as rerouting shipments, adjusting maintenance plans, or triggering stock transfer. It could allow supply chain managers to proactively implement mitigation strategies, develop contingency plans, and help improve overall resilience.4
Enriching Aftermarket Services
A strong aftermarket presence can serve as a significant source of revenue, signal a commitment to long-term product reliability, and increase customer satisfaction. The lockdowns following the pandemic highlighted some of the challenges, costs, and inefficiencies of dispatching field service technicians to address customers’ critical repair and maintenance needs to maintain product uptime and optimal operation. Digital technologies that enable remote assistance can become important in ensuring business continuity.8
For example, a generative AI-enabled virtual field assistant can serve as a reference tool and provide quick access to a vast amount of technical information. When encountering issues or challenges in the field, for example, with an in-service product, engineers or technicians can describe the problem to a virtual field assistant and it will return appropriate questions to identify the cause or provide step-by-step guidance for resolution.4
Conclusion
The rise of generative AI could mark a pivotal moment in the manufacturing industry’s digital transformation. Companies are actively exploring generative AI’s potential for enhancing efficiencies and are working on solutions to harness this technology to suit their business needs. Manufacturers could improve their business outcomes by experimenting with generative AI use cases throughout the organization.
This article contains general information only and Deloitte is not, by means of this article, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This article is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte shall not be responsible for any loss sustained by any person who relies on this article. M
References
1. Paul Wellener, John Coykendall, Kate Hardin, John Morehouse, and David Brousell. “Exploring the industrial metaverse.” Deloitte, September 13, 2023.
2. John Coykendall, Kate Hardin, and John Morehouse. “2024 manufacturing industry outlook.” Deloitte, October 30, 2023.
3. Deloitte. “Smart Factory for Smart Manufacturing.” 2024.
4. Deloitte AI Institute. The Generative AI Dossier. 2024.
5. Stanley Porter, Animesh Arora, Jean-Louis Rassineux, Kate Hardin, and Anshu Mittal. “Boosting industrial manufacturing capacity for the energy transition.” Deloitte, May 13, 2024.
6. Deloitte analysis of data from US Bureau of Labor Statistics. “Quarterly Census of Employment and Wages.” accessed March 21, 2024.
7. Deloitte analysis of “Construction Spending – Data” from the U.S. Census Bureau, accessed June 14, 2024.
8. Paul Wellener, Kerry Millar, Oliver Bendig, and Aijaz Hussain. “Aftermarket services.” Deloitte Insights, May 14, 2020.
About the authors:
John Coykendall is vice chair, Deloitte LLP.
Kate Hardin is the executive director of Deloitte’s Research Center for Energy and Industrials.
John Morehouse is the research leader for industrial products manufacturing in the Deloitte Research Center for Energy & Industrials.
Kruttika Dwivedi is a manager for the Deloitte Research Center for Energy and Industrials.