Key Points

• Lean is dramatically enhanced by the use of artificial intelligence, allowing organizations to proactively take measures to reduce waste.
• AI allows organizations to supercharge their overall efficiency through the use of real-time data analytics.
• Enhanced and consistent quality through the use of AI-powered tools only enhances the customer experience while reducing defects.

Artificial intelligence has been steadily making headway into businesses around the world over the last few years. While some are using it for the likes of ideation, product design, and analysis, it has other applications. When considering something like Lean, you want to target waste, boost efficiency, and overall make for a more nimble organization.

AI is a great help here, and can leverage the data you’re using in your decision-making to remediate issues. The Lean businesses of the future aren’t sticking with the usual methods of doing things, but are likely to leverage this technology to make predictions rather than react based on historical data. Today, we’ll look at how these apply to your business, what to expect, and how to make the most of your AI implementation with Lean.

Waste Reduction Efforts

Overproduction

There is nothing worse than having finished products sitting in inventory. The way some companies are making AI in Lean work is by using demand forecasting models to analyze sales data, market trends, and other patterns. This helps to optimize production scheduling to better suit demand rather than guessing where customers are going next.

Waiting

Your team gets into a steady flow, and a machine goes down for the count. Now, they’re stuck waiting while technicians work to get it operational. Reducing this waste is a matter of predictive maintenance. Modern technology allows for things like predictive maintenance to reduce downtime, keeping track of performance, and keeping throughput at optimal levels. If you want to make the most of your resources, this is a key way to leverage the technology in your Lean workflow.

Unnecessary Transport

If you haven’t noticed a trend yet, human planning is well and good in Lean. However, when you think about the most efficient routes you can take, optimizing logistics, and so forth, AI is going to handle the task far better than most. Some businesses are making use of fully automated guided vehicles to transport materials within the warehouse. Robots are being utilized to move materials in and out of inventory. This cuts down on fuel consumption, lead times, and the risk of damaging goods in transit.

Over-processing

Finding unnecessary steps in any production workflow can be a clear sign that something is going wrong in a process. Process mining software, in conjunction with artificial intelligence, can quickly reveal redundant steps, highlight areas that can be streamlined, and ultimately map out workflows to an idealized state. Further, teams can use AI and other tools to develop more efficient processes on the whole, embodying simpler principles with less waste produced.

Excess Inventory

Keeping materials on hand can quickly mount, but that isn’t a concern with the right sort of artificial intelligence models. Companies are making use of this technology to optimize their inventory management systems, only ordering materials as necessary. Real-time tracking of inventory and automated inventory systems are only part of what AI enables Lean to accomplish. When you consider that demand forecasting and market analysis play into how AI is managing your inventory, you’re keeping things simple but allowing quite a bit of agility.

Defects

AI can inspect output in real-time, detecting things that the human eye can’t grasp. When looking to reduce defects, you can expect systems to learn and grow to eventually anticipate what sort of quality you’re looking for in a given product. As such, AI isn’t just reducing defects, but it is also performing predictive quality control.

Unnecessary Motion

Ergonomic improvements can take a while to sort out with some workflows. Computer-assisted vision and motion sensors make quick work of trying to figure out where processes are leading to fatigue in your workforce. This has the added benefit of optimizing workstations, allowing for better safety and efficiency while streamlining tasks.

Efficiency and Productivity

Real-Time Data Analytics

Manual data collection can yield some fantastic insights into performance, customer satisfaction, and other key metrics. However, it takes time, and you’re often relying on historical data in the first place. AI takes this a step further and enables organizations to use predictive analytics in Lean. You aren’t just looking for pain points in past production workflows, but seeing where bottlenecks are arising now and how to remediate them. This enables teams to proactively make fixes, rather than reacting when something goes wrong.

Automation

Repetitive tasks might be necessary for any given process, but they can be tedious. AI-powered bots and robots can readily automate repetitive basic tasks, like data entry, report generation, and consistent actions on the factory floor. The notable benefit here is that you save your precious human resources for tasks that are better suited to their needs while cutting down on the chances of human errors.

Quality and Customer Value

Digital Poka-Yoke

You can readily use AI in Lean to conduct Poka-Yoke in real time. This has some notable benefits, as you’re making sure the work is being done correctly every step of the way. As you might imagine, this greatly cuts back on defects and prevents the need for expensive rework.

Artificial intelligence goes beyond the confines of simple visual checks, allowing for complex real-time inspections of items. Compared to the way most organizations conduct Poka-Yoke, you’re getting the ability to see subtle issues before they arise, something that is worth its weight in gold.

Quality Control

AI-powered vision systems are going to identify defects, errors, and other areas of concern far faster than any manual inspector can. You’ll still want to have a human at the end of the line to double-check things, but subtle quality issues are going to become a thing of the past fairly quickly. As a result, you’re increasing your customer satisfaction with consistent and reliable quality in all outputs.

Decision Making

Any machine is going to operate at a higher capacity than a human being can. When it comes to looking at raw data, this translates to handling enormous volumes of information from disparate sources. Combining that data with a bespoke AI model makes for a powerful analytical engine that can help aid your decision-making process in real-time while recognizing complex patterns.

Decision-making also benefits from the simple fact that you can rely on predictive means for quality and demand alike. This cuts down on things that might have resulted in rework in the past, leading to greater overall quality and customer satisfaction.

Other Useful Tools and Concepts

Looking to start the work week off right? You might want to take a look at how you can build a Lean culture that lasts. There is no denying that it takes a special touch to make any methodology work, but learning some practical strategies to stack the deck in your favor is the key to getting Lean working all the time.

Additionally, you might want to take a look at some of the ways digital Kanban boards are enabling remote teams to work. While not a replacement for in-person meetings, digital Kanban boards allow for asynchronous teams to work like they’re in the same office.

Conclusion

Artificial intelligence is changing the way we work, and that extends to methodologies like Lean. That said, you can leverage the technology to supercharge your Lean implementation, resulting in higher quality goods, satisfied customers, and actionable insights you might not have had access to in the past.