The Productivity Plateau: Unraveling the Interplay between Inexperienced Workers, Generative AI, and Stalled Improvement in Modern Manufacturing

Introduction

Manufacturing industries in this country have been stuck in a rut for over a decade. This slowdown, known as the productivity plateau, began in 2010. Since then, these industries have struggled to achieve the growth they once enjoyed. Many managers believe that the latest trends, such as generative AI models, might be the solution to this problem. However, the issue may be more complicated than that.

Is the Problem Inexperienced Employees or Adaptive Capacity: Incomplete and Unevenly Distributed Knowledge?

In modern workplaces, especially in industries where complex tasks and continuous adaptability are necessary, it's vital to recognize that while inexperienced employees are certainly a factor, they are not the sole contributor to the need for adaptive capacity in the operational model. Defining the problem merely as "inexperienced employees" can be dimensionally restrictive, focusing only on personnel and not on the broader challenges of the operational model. This narrow view may be typical of organizations that lack systems thinking and focus on symptoms and solutions one level removed from the true expression of the outcome. The relationship between inexperience and adaptive capacity is systemic, encompassing both individual and organizational components:

The Individual Impact:

Lack of Adaptive Capacity in Inexperienced Employees

Inexperienced employees, by definition, lack the seasoned expertise and refined skill set that comes with time and practice. This inexperience manifests itself in several ways:

• Increased Cognitive Load: Primary tasks require more mental effort, leaving fewer resources for secondary tasks or unexpected challenges. The proliferation of ERP/MES systems intersecting with the frontline may aggravate this problem, depending on deployment, due to the added transactional burden and latency system effects placed on employees.
• More Errors and Slower Completion Times: Inexperience leads to more mistakes and general inefficiency in task completion.
• Potential Safety Concerns: Without the ability to recognize and respond to warning signs quickly, inexperience can lead to safety hazards.

The Systemic Impact:

Lack of Adaptive Capacity as a Systemic Effect

Beyond the individual challenges posed by inexperienced employees, a broader, systemic problem affects the entire operational model of an organization. The lack of adaptive capacity has profound implications:

• Asset Decisions: One of the most critical decisions managers make in the operations model is how and when they buy assets. The method of marginal contribution asset configuration informs the operational model, engagement, and adaptive capacity required. It also affects the efficiency and safety of the operation, especially when the available adaptive capacity in the personnel landscape is not static. This decision-making process must align with organizational needs and strategic goals to nurture an environment conducive to continuous adaptability and innovation.
• Operational Inefficiencies: The slow pace and higher error rates among inexperienced workers can create ripple effects throughout an organization, impacting timelines, quality, and overall productivity.
• Training and Development Challenges: Building adaptive capacity in a less experienced workforce requires investment in training and ongoing support, possibly stretching resources.
• Innovation Stagnation: A workforce with inadequate adaptive capacity might struggle with dynamism, potentially hindering the organization's responsiveness to market changes and technological advancements.
• Cultural Impact: A consistent mismatch between the skills of inexperienced employees and the requirements of their roles can influence morale, collaboration, and overall organizational culture.

Recognizing the multifaceted nature of the issue—where inexperienced employees are but one component of a larger challenge—enables leaders to address root causes rather than just the symptoms. This comprehensive understanding can pave the way for more effective strategies, fostering the evolution of a genuinely adaptive and forward-thinking organization.

The Technology Fix?

The intersection of inexperienced employees and a lack of adaptive capacity presents a complex systemic challenge that reaches far beyond individuals. It's a problem that affects the very operational model of an organization. Strategies must be implemented to enhance training, support growth, and facilitate the transfer of knowledge and skills. Recognizing and addressing this systemic issue is vital for any organization striving to maintain efficiency, innovation, and a robust and responsive operational model. By investing in the development of inexperienced employees, an organization can foster the adaptive capacity needed to thrive in a complex and ever-changing business environment.

Three Stages in Skill Acquisition:

1. Declarative: Knowledge about a task
2. Procedural: Ability to perform the task
3. Automatic: Ability to perform the task fluently

Generative AI Applied to The Productivity Plateau

For many grappling with a decline in productivity improvement, generative AI models are considered as potential solutions. However, there's a mismatch between the tool and the complex problem, highlighting that adaptive capacity requires more than just data-driven insights. The shortcomings of generative AI models in addressing the productivity plateau include:

• Lack of Adaptive Capacity: These models can't emulate the human capacity needed to adapt and respond to intricate scenarios.
• Overreliance on Data and Skillset Limitation of the Technical Organization: They are heavily dependent on the quality and quantity of input data, limiting their flexibility.
• Ignoring the Human Factor: By discounting the essential role of skilled and experienced workers, or assuming, as we have done in the past, that they will always be there, the models may overlook critical aspects of the issue. People will become increasingly more important in the future to get things done at a faster pace.
• Failing to Restore Productivity: Despite the technological allure, generative AI models may not succeed in reviving lost productivity due to the multi-dimensional nature of the restorative challenges.

Insights and Perspectives: Not Enough for Adaptive Capacity

Challenges related to integrating technology and traditional manufacturing skills create barriers in training and development, hinder innovation, affect morale, and lead to a loss of institutional knowledge. At some point, humans will always have to intervene to make it work.

Conclusion

The productivity plateau in manufacturing is a complex and multifaceted issue that began around 2010 and continues to challenge the industry. Relying on generative AI models alone is insufficient to reverse this stall, as they lack the adaptive capacity and human participation needed to navigate intricate challenges. Managers must acknowledge the invaluable asset of experienced employees, whose availability will likely continue diminish in the future. A balanced approach, integrating both technological advances and human expertise, appears to be the most promising path forward. The true winners will be those who transcend generative AI to embrace Automated Reasoning and full Special Purpose Intelligence for the enterprise. In an ever-changing world, clinging to unproven assumptions or over-relying on a single solution could exacerbate the problem. As Arthur Kordon insightfully stated, becoming adept at generative AI is "the end of your beginning." It's time for the manufacturing sector to appreciate the complexities of the issue and adopt a more nuanced and holistic strategy, potentially paving the way to overcome the productivity plateau. Perhaps time has already passed you by if you are only just now exploring generative AI?

Shelley Nandkeolyar Ron Norris Subrata Sen Harirajan Padmanabhan Arthur Kordon Rajib Saha John B. Vicente Jr. PhD Sarath Chandershaker parabole.ai

Herb Dowdy Dr. Joshua Thomason, CPA, MBA

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