AGING POPULATION
2.1B
People aged 60+ by 2050
GEN Z PERCEPTIONS OF INDUSTRIAL WORK
20%
Pays poorly
25%
Unsafe
- Shrinking Labor Pool Due to Aging Population: The global population is aging rapidly due to increased longevity and declining fertility, with the number of people aged 60+ expected to double to 2.1 billion by 2050. This shift increases the median age and shrinks the working-age population. In the U.S., the 65+ population reached 55.8 million in 2020, a 38.6% increase over ten years. A declining working-age ratio (18–64) is expected to aggravate labor shortages.
- Reducing Interest Among Young People in Factory Jobs: Many young people view manufacturing as an unstimulating and dirty work environment. Approximately 20% of Gen Z believes industrial work pays poorly, and 25% view it as unsafe. Moreover, manufacturing often lacks flexible hours, remote options, or work-life balance, with demanding shifts that are unattractive to younger workers. Therefore, young people are avoiding factory jobs, leading to a significant labor shortage in the manufacturing sector.
- Increasing Rate of New Product Introductions: The rate of new product introductions is increasing, driven by evolving consumer preferences and the need to stay ahead of competition. While new products offer opportunities for growth, they simultaneously introduce challenges that can hinder factory efficiency and impact quality. Constant product updates can lead to unplanned downtime, as traditional factory models often struggle to adapt quickly to rapid changes caused by new products. Modifying processes to meet changing demands can create bottlenecks, leading to missed deadlines and lost business opportunities. Rapid shifts in production also increase the risk of quality defects and product inconsistencies. Integrating new technologies to manage these shifts can further lead to quality issues.
- Increasing Product Complexity: Increasing product complexity, driven by customization, smart technologies, and sustainability demands, poses challenges for factories by causing production bottlenecks, increased operational costs, and workforce shortages.. Factories need to undergo constant changes to deal with increasing product complexity. Increasing product complexity leads to more complex factory operations, often requiring major investments in new physical and digital technologies. This is leading to many operational challenges such as increasing backlogs and costs, and eroding profit margins.
- Increasing Geopolitical Pressure to Reduce Reliance on Imports: Geopolitical pressure to reduce import reliance is driving a global shift toward supply chain de-risking and near-shoring, with governments prioritizing national security over economic efficiency. Diversifying away from low-cost import sources often leads to higher procurement costs and inflationary pressures. Moreover, setting up new factories takes significant lead time. Manufacturers are feeling pressure to quickly create new factories to accelerate near-shoring and lowering costs to remain competitive.
- Increasing Social Pressure to Mitigate Climate Issues: There is widespread recognition of the impact of climate change on disruption to human life. There appears to be increasing social pressure to mitigate climate issues caused by human activities. Factory operations drive climate change primarily through energy consumption and releasing greenhouse gases. Factories are considered a significant pollution source, with manufacturing emissions projected to increase by 17% between 2024 and 2050. Mitigating climate issues caused by factory operations will require improving operational efficiency by enhancing energy efficiency, adopting circular economy principles, and reducing waste.
Factories are feeling the consequences of the above trends and reporting the following operational challenges:
- Delays and backlogs due to labor shortages.
- Inconsistent quality due to manual work leading to high manufacturing and warranty costs.
- Long lead-times to add capacity or reconfigure factories due to change in demand.
- Significant wastage of resources and inefficiencies due the lack of the right information being available at the right time during the production.
- Production disruptions due to unplanned downtime caused by breakdown of equipment.
Traditional factories need to undergo dramatic transformation to overcome the above described challenges. The Factory of the Future will need to be a flexible, autonomous, fully connected, and self-optimizing manufacturing environment. It will need to employ adaptive, closed-loop systems that self-adjust to variabilities in real-time and capture information to maintain an unbroken digital thread for complete traceability.
The advent of physical AI has the potential to completely transform factories. Advances in multi-modal sensing, data collection, computing hardware, and system architecture have finally converged to achieve a new level of sophistication in physical AI, delivering autonomous robots in a wide variety of manufacturing applications. AI-powered autonomous robots can address labor shortages and deliver consistent quality. Robotic operations automatically preserve digital threads and provide complete traceability. Data generated by robotics operations can be used to further optimize processes and achieve performance that far exceeds manual operation. The ability to continuously improve, adapt, and function autonomously creates a new capability that will enable factories to deal with increasing complexity. Moreover, AI-based tools can be used to automatically design factories and speed up deployment and rapidly reconfigure factories. AI can also be used to optimize processes and eliminate waste. Finally, AI can be used to perform prognostics and health management functions in the factories to prevent breakdown and production interruptions.
AI will be the backbone of the factories of the future. However, successfully deploying AI-powered solutions in factories will require significant time and effort. Realizing the vision of AI-powered factories requires committed long-term champions, carefully planning, and a detailed roadmap.
There is a lot of talk about near-shoring. However, these initiatives without resources, incentives, and the right kind of talent to build AI-powered factories will just create inefficient domestic factories that can’t compete in the global market place. Adequate resources and realistic timelines will be needed to ensure success of these initiatives.
Many companies are adopting the “wait-and-see” approach to implementation of AI in factories to mitigate risks. Companies that move slowly, waiting for technology to mature, often find they cannot catch up with first movers who have built the organizational machinery for leveraging AI. AI models require real-world data to improve. Delaying deployment means delaying the accumulation of data necessary for accuracy, leading to poorer performance later. The rapid evolution of AI means that the “wait-and-see” approach is essentially a decision to fall behind, making the cost of inaction far greater than the risks of early adoption.
