Challenge: When “fast fashion” collides with “slow logistics.”

The global sneaker market is rapidly entering an era of “small orders, rapid returns” – with orders becoming smaller, styles changing more frequently, and delivery periods becoming shorter. For this shoe factory that produces shoes for multiple international brands, production lines may need to be changed every few days, necessitating frequent adjustments to the material flow paths.

However, the existing logistics methods at the factory were unable to keep up with the pace: the cut-out shoe surfaces and shoe sole materials were manually transported by hydraulic vehicles to the stitching area; the semi-finished products stitched by the stitching machines were then manually transferred to the molding line. Each production line required at least two dedicated laborers for transportation, and with nearly 50 laborers across the entire factory, the cumulative walking distance per day exceeded 1,000 kilometers.

“Hiring laborers has become increasingly difficult. Young people are reluctant to do the job. Moreover, manual delivery often results in errors, omissions, or deliveries arriving just as the production line has stopped wAiting.” the factory production director recalled. “There were seven or eight instances of production halts due to delayed material delivery within a month, resulting in direct losses of over 100,000 yuan.”

Breaking the Mold: Robots That Don’t Require “Site Preparation”

The factory had also investigated traditional AGV (Automatic Guided Vehicle) systems, but the methods of deploying magnetic strips or QR codes posed challenges for management: whenever the production line was changed, the floor markings had to be redone, which was time-consuming and costly. That was until they came across Reeman Dual Laser Fly Boat MAX – a robot that employs dual laser SLAM navigation technology, relying on lidar scans to create a real-time map and locate features in the environment without any reliance on ground markings.

“The deployment process was surprisingly straightforward,” said the automation engineer responsible for implementing the project. “The technicians simply walked the robot around the factory site, and within 20 minutes, they had created a complete map of the entire facility. Subsequently, virtual walls and priority pathways were set up at the pillars and corners of the workshops, and the system was put into trial operation the same day. There was absolutely no impact on production.”

The benefits of this “seamless” deployment are immediate: after adjusting the position of a needlework seam, engineers redelineate the area boundaries on the backend system, and the robot automatically updates its path, all within a span of no more than 10 minutes. In the past, re-placing the magnetic strips would take at least half a day.

Scenario: Workers can “invoke” roBots with a single button press, and the robots are immediately available.

Walking into the shoe factory’s production area, the reporter observed numerous scenes of human-machine collaboration.

In the cutting workshop, workers place the cut-out shoe uppers onto standard cartboards and press the wireless call button next to their workstation. Within less than two minutes, a Fly Boat MAX slowly approaches, lifts the cartboard, and proceeds along the designated route towards the stitching area. Along the way, the robot encounters a worker walking from the opposite direction. It immediately slows down and slightly circles around, before resuming its normal speed.

In the sewing workshop, a quality inspector noticed that some shoe uppers had loose threads that needed to be reworked. She pulled out her phone, opened Reeman’s app, and selected the “Follow Mode.” The robot immediately followed her, playing a notification sound while maintaining a 50-centimeter distance. It accompanied her to the rework station and set down the materials.

“The most practical feature is multi-machine scheduling,” said the foreman of the production line. “Previously, we were concerned that multiple vehicles might collide or encroach on each other’s paths. Now, the backend system automatically calculates the optimal route, and it even ‘yields’ to higher-priority tasks in narrow passages.” Currently, the factory has deployed a total of 16 Fly Boat MAX units. During periods when manual transport of vacuums occurs, such as during lunch and night shifts, these units automatically recharge and then resume continuous transportation, ensuring a 24-hour uninterrupted supply of materials.

The Numbers: The Real-World Accounts Behind Efficiency Gains

Four months after implementation, an assessment was conducted within the factory:

Logistics efficiency: The average transit time from cutting to sewing machine has been reduced from 20 minutes per trip to 7 minutes per trip, a decrease of 65%.

Labor costs: The number of full-time movers was reduced from 48 to 12, with the rest being reassigned to more technically demanding positions. This has resulted in annual savings of approximately 1.7 million yuan in labor expenses.

Stoppage losses: The number of production line stoppages due to material shortages was reduced to zero.

Error Rate: Issues such as “delivering to the wrong workstation” and “model confusion” that have occurred with manual delivery have been completely eliminated, as each robot only initiates operation after scanning and confirming the material and destination.

Space Utilization: The robots operate with precision, eliminating the need for the previously 2-meter-wide “manual pushing main corridor,” freeing approximately 8% of the production area for the setup of new production lines.

The Future: From Logistics Automation to Data-Driven Production

The Fly Boat MAX is not just a transporter; it is also a data node for the digitalization of the factory. The transportation records, time taken, and route congestion uploaded in real-time by each robot serve as the basis for management to optimize the layout of the production lines. For example, backend data showed that there was a high frequency of back-and-forth movement between the cutting and sewing machines. As a result, the factory added a temporary buffer area between the two, further enhancing the turnover efficiency.

“Our next step is to integrate AMR with the MES system,” revealed the head of plant informatization. “Once the MES issues production instructions, the robots automatically distribute the corresponding materials as needed, achieving true ‘materials waiting for people’.”

From manual cart pushing to dual-laser AMR, this shoe factory’s transformation exemplifies a trend: in an environment where labor costs continue to rise and market competition intensifies, “silent, flexible, and intelligent” mobile robots are becoming crucial tools for traditional manufacturing companies to overcome logistical bottlenecks. Reeman’s solution also demonstrates that intelligent transformation does not necessarily require significant upheaval; sometimes, change begins with a robot that “knows the way.”

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