Lean Manufacturing is Transformed by AI

Artificial intelligence (AI) allows greater connectivity between people, information, and machines, improving how manufacturers optimize products and processes. Just as manufacturers have benefited from Lean Management principles, AI promises to be the next evolutionary step in productivity advancement.

Already, 92% of senior manufacturing executives believe that ‘Smart Factory’ digital technologies like AI will help them increase their productivity levels and empower staff to work smarter.

But just as AI will improve how workers interface with technologies, advances in AI will make human involvement in many manufacturing processes redundant. In fact, Pragmatic AI — the practical application of artificial intelligence to specific tasks and business processes — eliminates the need for human involvement in advanced cases, where machines’ speed and performance are superior to those of humans.

As manufacturers combine the data collected from connected devices with rapidly advancing Artificial Intelligence to enable ‘smart machines’… these will, in turn, simulate intelligent behavior with little or no human intervention.”

Through self-learning, AI will improve the quality, lead time, and costs associated with product development, changing the face of workforce management entirely. So, where does that leave your people?

As a technology, AI’s principal role is as an agent of continuous optimization — much as continuous improvement and a commitment to change are tenets of a Lean philosophy. That’s why it’s essential that stakeholders incorporate Lean principles as they offset some traditional roles of humans while integrating AI.

Already, the use of AI can reduce producers’ conversion costs by up to 20%, with up to 70% of the cost reduction resulting from higher workforce productivity. Combining AI with Lean will allow manufacturers to create a new company culture, ensuring not only better operations but a more adaptable workflow for employees as they cede certain responsibilities and transition into new ones.

The Collaboration of LEAN and AI to improve manufacturing methodology.

In many cases, Pragmatic AI is narrow in its scope, playing a supportive role to human experts who use its analyses to improve decision-making processes. However it will soon graduate from its ‘assisted intelligence’ role to become autonomous and AI will increasingly make its own decisions on which business leaders can rely.

Since there are already dozens of pragmatic AI applications across manufacturing verticals, it’s transition to autonomy will transform all areas of the enterprise. But Lean principles capture the value of Pragmatic AI as companies improve upon dozens of manual business functions — whether its analyzing IoT sensor data, predicting equipment failure, or optimizing inefficiencies in the supply chain.

Viewing ChatGPT through the Lens of Jidoka

Jidoka is one of the Toyota Production System house’s pillars. It allows machines to work and stop automatically when a defect occurs, freeing up the operator to work multiple machines simultaneously. And because machines stop only when a problem occurs, it enables rapid problem identification and solving.?

ChatGPT may usher in a new era of jidoka. But the relationship it may transform is not between humans and machines but between humans and their minds. The positive consequences include higher productivity, particularly in white-collar jobs, and accelerated learning. But the negative consequence is it may make it hard or impossible to detect problems in human development.

LEAN Management and the transition.

With all this talk about human redundancies, don’t lose sight of the fact that your people still matter — a lot. Companies that want to take a Lean approach to AI must start by aligning expectations across all teams that interact with new AI solutions. With their expectations aligned, they can use Lean processes to tackle the biggest risks associated with AI, i.e. misalignment of goals and disengagement of the workforce, during the early-stage implementation process.

The Lean approach is not linear — it requires iterative data modeling and consistent user feedback as the implementation process unfolds. Sometimes that feedback will require Lean managers to reset their approach to adoption, which is part of the Lean process. This is critical to delivering a successful end result that directly addresses business needs.

After implementation, teams must learn to continue using the Lean AI process not only for future projects. But for day-to-day continuous improvement efforts. Ultimately, Lean AI will be able to design processes by extracting Lean principles as needed, removing companies’ dependencies on human involvement to do so.

That’s why Lean AI requires a significant, coordinated effort across diverse teams — many of which are siloed in organizations — including stakeholders, data scientists, and professionals working on the factory floor. Lean Management principles are essential as these teams integrate Lean AI into their company culture. As we will find, companies that have successfully ‘married’ AI and Lean management have realized substantial benefits in adding value for customers, reducing waste, improving the quality of products and services, and accelerating service delivery.

LEAN AI Vice versa.

Today’s Lean management systems drive the front-line staff experience, converting deep organizational knowledge into practical behavior and business value. Lean management and AI have the potential for innovation as company leaders incorporate staff experience into the development of new roles and technology structures. Even as AI reduces companies’ dependence on human processes, failure to involve staff in AI initiatives may undermine its potential benefits.

Consider Lean Management as it stands today. It is a method that depends on interventions, whereby work is halted to solve a problem or improve a process. Team members observe and analyze before taking action and restarting work. It’s this dedication to constant improvement that has made Lean so successful.

Unlike this ‘start-and-stop’ process which humans must carry out to make Lean improvements, AI can conduct its observations, analyses, and resolutions without slowing or sacrificing production value. What’s more, AI improves the ways in which it resolves problems as time goes on. Manufacturers can apply AI in this way at any scale. This will increasingly become standard as the industry evolves.

AI to improve Lean Manufacturing

AI can assist Lean Manufacturing by providing insights into production processes and identifying waste areas. AI can analyze data from sensors, machines, and other sources to identify patterns and trends. This analysis can identify the root causes of inefficiencies and waste in production. AI can also assist in predicting equipment failures before they occur, enabling maintenance teams to take proactive measures to minimize downtime.

One of the ways AI can be used in Lean Manufacturing is by implementing predictive maintenance. Predictive maintenance uses AI algorithms to predict equipment failures before they occur. Predictive maintenance can reduce downtime, increase lifespan, and improve safety. AI algorithms can analyze equipment data, such as temperature, vibration, and energy consumption, to predict when a machine will fail. Maintenance teams can then schedule maintenance before the machine fails, minimizing downtime and repair costs.

Another way AI can be used in Lean Manufacturing is by optimizing production schedules. AI algorithms can analyze historical production data, customer demand, and inventory levels to optimize production schedules. It can help manufacturers produce products promptly, reduce inventory costs, and improve customer satisfaction. AI can also improve demand forecasting accuracy, enabling manufacturers to make products more efficiently.

AI can also be used to improve quality control in Lean Manufacturing. Its algorithms can analyze production data to identify defects and anomalies. Manufacturers can use this data to identify the root causes of defects and implement corrective actions. This analysis can help manufacturers improve product quality and reduce waste.

Integrating AI in Lean Manufacturing can improve production efficiency by optimizing manufacturing processes. AI can analyze data from machines, sensors, and other sources to identify patterns and trends, predict equipment failures, optimize production schedules, and improve quality control. AI can help manufacturers reduce waste, increase efficiency, and enhance product quality. As AI technology evolves, AI in Lean Manufacturing will become more prevalent and essential to improving production processes.