Seven Ways IoT Super-Charges Lean Manufacturing

Accelerated by the COVID-19 pandemic, the use of Internet of Things (IoT) technologies on the factory floor provides engineers with high-value data that can lead to improved productivity and operational efficiency, reduced waste, and better and faster decision-making—the ultimate goals of lean manufacturing.
 
Lean was once mostly a manual process of visual inspection, data collection, and deployment of solutions. Today, however, enabled by the speed and accuracy of IoT, lean goals are much easier and faster to attain. While lean principles do not change, how the data is generated does. Lean removes waste and digital technologies make that process much faster, with fewer steps—often independent of human bias.

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According to consulting firm Bain and Company, compared to a 15 percent reduction in operating costs using traditional lean practices, a combined “digital lean” approach can produce up to a 30 percent savings, with faster payback. Digital tools including artificial intelligence (AI), IoT, automation/robotics, and 5G can optimize lean practices by giving engineers detailed data in real time, allowing them to precisely track production and workflow and optimize lean methods in ways that were not possible before.
 

Seven Ways IoT Enhances Lean

 
IoT devices provide real-time views of the important lean indicators that engineers track. This data allows them to more easily identify and eliminate waste and inefficiency in the value stream to maximize productivity. Thousands of sensors on equipment can identify other sources of waste that were not visible before.

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Below are seven ways that IoT can enhance lean manufacturing by streamlining processes, eliminating bottlenecks, and reducing operational costs.
 

1: Wait time

 
Motion sensors monitor the amount of time a product moves or remains stationary. Depending on the system in operation, a period of no movement could represent a delay that needs to be corrected, thereby optimizing the value stream, a core principle of lean. This same approach can be applied to order fulfillment and supply chain delays.
 

2: Inventory control

 
Sensors can track movement of physical inventory throughout the supply chain and distribution centers 24/7, sending staff real-time alerts regarding delays when needed. This data can be presented through easy-to-read dashboards to improve product flow and eliminate bottlenecks. Lean is a continuous process and AI algorithms optimize product movements by constantly analyzing data and making adjustments.
 

3: Over-production

 
Too much production creates delays elsewhere along the line, wasting time and reducing production volume—two negative results that lean tries to eliminate. Sensors can send a “stop” message in real time to prevent upstream processes from producing excess inventory; production can then be automatically re-started when stock levels return to acceptable levels. This keeps flow smooth and steady—a key lean objective.
 

4: Fewer defects

 
Even when quality assurance protocols are perfect, using staff to complete them manually results in production delays, with some defective products inevitably getting through the inspection process. IoT sensors (both in-line and off-line) and high-precision optical viewing systems can detect variances and make adjustments in real time--eliminating the cause of the variance before it becomes a problem. 
 

5: Predictive maintenance

 
Keeping equipment running smoothly with a minimum of downtime is a prime lean objective. In the past, this required visual inspection of machinery and lubricants. Today, instead of manual inspections, sensors can provide operators with real-time updates on machine performance and identify any variances that can be easily fixed early on, thereby avoiding breakdowns and long stretches of downtime. Working on tuned-up equipment also improves worker safety.

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6: Worker safety

 
Overworked employees are less efficient and make mistakes, slowing down production or hurting themselves in accidents. Optimized workflow is a key principle of lean and losing productivity through worker safety issues makes production less efficient. Data from sensors attached to clothing can be used to adjust workflow patterns to be more efficient (often by eliminating unnecessary steps) and to optimize workloads, thereby ensuring labor is evenly distributed.
 

7: Digital twins can optimize every lean process

 
Digital twins are detailed digital representations of objects as small as a single part to entire production systems, including complex material and machine interactions. In simulations, digital twins can be manipulated to see what happens to a process when different factors are adjusted. This can identify the best combination of variables and settings for optimizing that process and maximizing production. For example, digital twins can quickly map and optimize a value stream or seamlessly integrate multiple, complex interconnected workflows that would be almost impossible to complete manually.
 

Moving Forward

Deploying IoT technologies helps companies achieve their lean efforts more quickly. In most cases, combining lean and IoT will accomplish more than if they were carried out separately. Sensor-enabled tools will also allow for faster iterations and more accurate scale-up. As AI and machine-to-machine communications continue to advance—especially the ability to self-correct in real time—lean objectives will be achieved much more quickly, with less human intervention.

Mark Crawford is a technology journalist based in Corrales, N.M.